mirror of
https://github.com/datawhalechina/llms-from-scratch-cn.git
synced 2026-02-19 17:24:43 +08:00
1173 lines
47 KiB
Python
1173 lines
47 KiB
Python
""" PyTorch ChatGLM model. """
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import logging
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import math
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import sys
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from collections import OrderedDict
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from typing import Optional, Tuple, Union, List
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import torch.nn.functional as F
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import torch.utils.checkpoint
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from torch import nn
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from torch.nn import CrossEntropyLoss, LayerNorm, MSELoss, BCEWithLogitsLoss
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from torch.nn.utils import skip_init
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from configuration_chatglm_full import ChatGLMConfig
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# flags required to enable jit fusion kernels
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if sys.platform != 'darwin':
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torch._C._jit_set_profiling_mode(False)
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torch._C._jit_set_profiling_executor(False)
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torch._C._jit_override_can_fuse_on_cpu(True)
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torch._C._jit_override_can_fuse_on_gpu(True)
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logger = logging.getLogger(__name__)
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class BaseModelOutputWithPast(OrderedDict):
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def __init__(self, last_hidden_state,
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past_key_values,
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hidden_states,
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attentions):
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self.last_hidden_state = last_hidden_state
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self.past_key_values = past_key_values
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self.hidden_states = hidden_states
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self.attentions = attentions
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last_hidden_state: torch.FloatTensor = None
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past_key_values: Optional[Tuple[Tuple[torch.FloatTensor]]] = None
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hidden_states: Optional[Tuple[torch.FloatTensor, ...]] = None
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attentions: Optional[Tuple[torch.FloatTensor, ...]] = None
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class CausalLMOutputWithPast(OrderedDict):
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def __init__(self, loss, logits, past_key_values, hidden_states, attentions):
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self.loss = loss
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self.logits = logits
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self.past_key_values = past_key_values
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self.hidden_states = hidden_states
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self.attentions = attentions
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loss: Optional[torch.FloatTensor] = None
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logits: torch.FloatTensor = None
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past_key_values: Optional[Tuple[Tuple[torch.FloatTensor]]] = None
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hidden_states: Optional[Tuple[torch.FloatTensor, ...]] = None
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attentions: Optional[Tuple[torch.FloatTensor, ...]] = None
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class SequenceClassifierOutputWithPast(OrderedDict):
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def __init__(self, loss, logits, past_key_values, hidden_states, attentions):
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self.loss = loss
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self.logits = logits
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self.past_key_values = past_key_values
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self.hidden_states = hidden_states
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self.attentions = attentions
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loss: Optional[torch.FloatTensor] = None
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logits: torch.FloatTensor = None
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past_key_values: Optional[Tuple[Tuple[torch.FloatTensor]]] = None
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hidden_states: Optional[Tuple[torch.FloatTensor, ...]] = None
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attentions: Optional[Tuple[torch.FloatTensor, ...]] = None
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def default_init(cls, *args, **kwargs):
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return cls(*args, **kwargs)
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class InvalidScoreLogitsProcessor:
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def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor) -> torch.FloatTensor:
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if torch.isnan(scores).any() or torch.isinf(scores).any():
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scores.zero_()
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scores[..., 5] = 5e4
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return scores
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class PrefixEncoder(torch.nn.Module):
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"""
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The torch.nn model to encode the prefix
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Input shape: (batch-size, prefix-length)
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Output shape: (batch-size, prefix-length, 2*layers*hidden)
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"""
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def __init__(self, config: ChatGLMConfig):
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super().__init__()
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self.prefix_projection = config.prefix_projection
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if self.prefix_projection:
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# Use a two-layer MLP to encode the prefix
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kv_size = config.num_layers * config.kv_channels * config.multi_query_group_num * 2
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self.embedding = torch.nn.Embedding(config.pre_seq_len, kv_size)
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self.trans = torch.nn.Sequential(
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torch.nn.Linear(kv_size, config.hidden_size),
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torch.nn.Tanh(),
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torch.nn.Linear(config.hidden_size, kv_size)
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)
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else:
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self.embedding = torch.nn.Embedding(config.pre_seq_len,
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config.num_layers * config.kv_channels * config.multi_query_group_num * 2)
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def forward(self, prefix: torch.Tensor):
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if self.prefix_projection:
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prefix_tokens = self.embedding(prefix)
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past_key_values = self.trans(prefix_tokens)
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else:
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past_key_values = self.embedding(prefix)
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return past_key_values
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def split_tensor_along_last_dim(
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tensor: torch.Tensor,
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num_partitions: int,
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contiguous_split_chunks: bool = False,
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) -> List[torch.Tensor]:
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"""Split a tensor along its last dimension.
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Arguments:
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tensor: input tensor.
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num_partitions: number of partitions to split the tensor
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contiguous_split_chunks: If True, make each chunk contiguous
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in memory.
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Returns:
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A list of Tensors
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"""
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# Get the size and dimension.
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last_dim = tensor.dim() - 1
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last_dim_size = tensor.size()[last_dim] // num_partitions
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# Split.
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tensor_list = torch.split(tensor, last_dim_size, dim=last_dim)
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# Note: torch.split does not create contiguous tensors by default.
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if contiguous_split_chunks:
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return tuple(chunk.contiguous() for chunk in tensor_list)
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return tensor_list
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class RotaryEmbedding(nn.Module):
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def __init__(self, dim, device=None, dtype=None):
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super().__init__()
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inv_freq = 1.0 / (10000 ** (torch.arange(0, dim, 2, device=device).to(dtype=dtype) / dim))
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self.register_buffer("inv_freq", inv_freq)
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self.dim = dim
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def forward_impl(
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self, seq_len: int, n_elem: int, dtype: torch.dtype, device: torch.device, base: int = 10000
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):
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"""Enhanced Transformer with Rotary Position Embedding.
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Derived from: https://github.com/labmlai/annotated_deep_learning_paper_implementations/blob/master/labml_nn/
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transformers/rope/__init__.py. MIT License:
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https://github.com/labmlai/annotated_deep_learning_paper_implementations/blob/master/license.
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"""
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# $\Theta = {\theta_i = 10000^{\frac{2(i-1)}{d}}, i \in [1, 2, ..., \frac{d}{2}]}$
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theta = 1.0 / (base ** (torch.arange(0, n_elem, 2, dtype=torch.float, device=device) / n_elem))
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# Create position indexes `[0, 1, ..., seq_len - 1]`
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seq_idx = torch.arange(seq_len, dtype=torch.float, device=device)
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# Calculate the product of position index and $\theta_i$
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idx_theta = torch.outer(seq_idx, theta).float()
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cache = torch.stack([torch.cos(idx_theta), torch.sin(idx_theta)], dim=-1)
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# this is to mimic the behaviour of complex32, else we will get different results
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if dtype in (torch.float16, torch.bfloat16, torch.int8):
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cache = cache.bfloat16() if dtype == torch.bfloat16 else cache.half()
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return cache
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def forward(self, max_seq_len, offset=0):
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return self.forward_impl(
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max_seq_len, self.dim, dtype=self.inv_freq.dtype, device=self.inv_freq.device
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)
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@torch.jit.script
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def apply_rotary_pos_emb(x: torch.Tensor, rope_cache: torch.Tensor) -> torch.Tensor:
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# x: [sq, b, np, hn]
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sq, b, np, hn = x.size(0), x.size(1), x.size(2), x.size(3)
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rot_dim = rope_cache.shape[-2] * 2
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x, x_pass = x[..., :rot_dim], x[..., rot_dim:]
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# truncate to support variable sizes
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rope_cache = rope_cache[:sq]
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xshaped = x.reshape(sq, -1, np, rot_dim // 2, 2)
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rope_cache = rope_cache.view(sq, -1, 1, xshaped.size(3), 2)
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x_out2 = torch.stack(
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[
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xshaped[..., 0] * rope_cache[..., 0] - xshaped[..., 1] * rope_cache[..., 1],
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xshaped[..., 1] * rope_cache[..., 0] + xshaped[..., 0] * rope_cache[..., 1],
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],
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-1,
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)
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x_out2 = x_out2.flatten(3)
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return torch.cat((x_out2, x_pass), dim=-1)
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class RMSNorm(torch.nn.Module):
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def __init__(self, normalized_shape, eps=1e-5, device=None, dtype=None, **kwargs):
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super().__init__()
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self.weight = torch.nn.Parameter(torch.empty(normalized_shape, device=device, dtype=dtype))
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self.eps = eps
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def forward(self, hidden_states: torch.Tensor):
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input_dtype = hidden_states.dtype
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variance = hidden_states.to(torch.float32).pow(2).mean(-1, keepdim=True)
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hidden_states = hidden_states * torch.rsqrt(variance + self.eps)
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return (self.weight * hidden_states).to(input_dtype)
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class CoreAttention(torch.nn.Module):
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def __init__(self, config: ChatGLMConfig, layer_number):
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super(CoreAttention, self).__init__()
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self.apply_query_key_layer_scaling = config.apply_query_key_layer_scaling
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self.attention_softmax_in_fp32 = config.attention_softmax_in_fp32
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if self.apply_query_key_layer_scaling:
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self.attention_softmax_in_fp32 = True
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self.layer_number = max(1, layer_number)
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projection_size = config.kv_channels * config.num_attention_heads
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# Per attention head and per partition values.
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self.hidden_size_per_partition = projection_size
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self.hidden_size_per_attention_head = projection_size // config.num_attention_heads
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self.num_attention_heads_per_partition = config.num_attention_heads
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coeff = None
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self.norm_factor = math.sqrt(self.hidden_size_per_attention_head)
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if self.apply_query_key_layer_scaling:
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coeff = self.layer_number
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self.norm_factor *= coeff
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self.coeff = coeff
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self.attention_dropout = torch.nn.Dropout(config.attention_dropout)
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def forward(self, query_layer, key_layer, value_layer, attention_mask):
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pytorch_major_version = int(torch.__version__.split('.')[0])
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if pytorch_major_version >= 2:
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query_layer, key_layer, value_layer = [k.permute(1, 2, 0, 3) for k in [query_layer, key_layer, value_layer]]
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if attention_mask is None and query_layer.shape[2] == key_layer.shape[2]:
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context_layer = torch.nn.functional.scaled_dot_product_attention(query_layer, key_layer, value_layer,
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is_causal=True)
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else:
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if attention_mask is not None:
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attention_mask = ~attention_mask
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context_layer = torch.nn.functional.scaled_dot_product_attention(query_layer, key_layer, value_layer,
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attention_mask)
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context_layer = context_layer.permute(2, 0, 1, 3)
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new_context_layer_shape = context_layer.size()[:-2] + (self.hidden_size_per_partition,)
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context_layer = context_layer.reshape(*new_context_layer_shape)
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else:
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# Raw attention scores
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# [b, np, sq, sk]
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output_size = (query_layer.size(1), query_layer.size(2), query_layer.size(0), key_layer.size(0))
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# [sq, b, np, hn] -> [sq, b * np, hn]
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query_layer = query_layer.view(output_size[2], output_size[0] * output_size[1], -1)
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# [sk, b, np, hn] -> [sk, b * np, hn]
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key_layer = key_layer.view(output_size[3], output_size[0] * output_size[1], -1)
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# preallocting input tensor: [b * np, sq, sk]
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matmul_input_buffer = torch.empty(
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output_size[0] * output_size[1], output_size[2], output_size[3], dtype=query_layer.dtype,
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device=query_layer.device
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)
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# Raw attention scores. [b * np, sq, sk]
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matmul_result = torch.baddbmm(
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matmul_input_buffer,
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query_layer.transpose(0, 1), # [b * np, sq, hn]
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key_layer.transpose(0, 1).transpose(1, 2), # [b * np, hn, sk]
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beta=0.0,
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alpha=(1.0 / self.norm_factor),
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)
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# change view to [b, np, sq, sk]
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attention_scores = matmul_result.view(*output_size)
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# ===========================
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# Attention probs and dropout
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# ===========================
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# attention scores and attention mask [b, np, sq, sk]
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if self.attention_softmax_in_fp32:
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attention_scores = attention_scores.float()
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if self.coeff is not None:
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attention_scores = attention_scores * self.coeff
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if attention_mask is None and attention_scores.shape[2] == attention_scores.shape[3]:
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attention_mask = torch.ones(output_size[0], 1, output_size[2], output_size[3],
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device=attention_scores.device, dtype=torch.bool)
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attention_mask.tril_()
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attention_mask = ~attention_mask
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if attention_mask is not None:
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attention_scores = attention_scores.masked_fill(attention_mask, float("-inf"))
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attention_probs = F.softmax(attention_scores, dim=-1)
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attention_probs = attention_probs.type_as(value_layer)
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# This is actually dropping out entire tokens to attend to, which might
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# seem a bit unusual, but is taken from the original Transformer paper.
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attention_probs = self.attention_dropout(attention_probs)
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# =========================
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# Context layer. [sq, b, hp]
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# =========================
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# value_layer -> context layer.
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# [sk, b, np, hn] --> [b, np, sq, hn]
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# context layer shape: [b, np, sq, hn]
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output_size = (value_layer.size(1), value_layer.size(2), query_layer.size(0), value_layer.size(3))
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# change view [sk, b * np, hn]
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value_layer = value_layer.view(value_layer.size(0), output_size[0] * output_size[1], -1)
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# change view [b * np, sq, sk]
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attention_probs = attention_probs.view(output_size[0] * output_size[1], output_size[2], -1)
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# matmul: [b * np, sq, hn]
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context_layer = torch.bmm(attention_probs, value_layer.transpose(0, 1))
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# change view [b, np, sq, hn]
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context_layer = context_layer.view(*output_size)
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# [b, np, sq, hn] --> [sq, b, np, hn]
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context_layer = context_layer.permute(2, 0, 1, 3).contiguous()
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# [sq, b, np, hn] --> [sq, b, hp]
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new_context_layer_shape = context_layer.size()[:-2] + (self.hidden_size_per_partition,)
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context_layer = context_layer.view(*new_context_layer_shape)
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return context_layer
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class SelfAttention(torch.nn.Module):
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"""Parallel self-attention layer abstract class.
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Self-attention layer takes input with size [s, b, h]
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and returns output of the same size.
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"""
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def __init__(self, config: ChatGLMConfig, layer_number, device=None):
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super(SelfAttention, self).__init__()
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self.layer_number = max(1, layer_number)
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self.projection_size = config.kv_channels * config.num_attention_heads
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# Per attention head and per partition values.
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self.hidden_size_per_attention_head = self.projection_size // config.num_attention_heads
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self.num_attention_heads_per_partition = config.num_attention_heads
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self.multi_query_attention = config.multi_query_attention
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self.qkv_hidden_size = 3 * self.projection_size
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if self.multi_query_attention:
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self.num_multi_query_groups_per_partition = config.multi_query_group_num
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self.qkv_hidden_size = (
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self.projection_size + 2 * self.hidden_size_per_attention_head * config.multi_query_group_num
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)
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self.query_key_value = nn.Linear(config.hidden_size, self.qkv_hidden_size,
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bias=config.add_bias_linear or config.add_qkv_bias,
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device=device, **_config_to_kwargs(config)
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)
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self.core_attention = CoreAttention(config, self.layer_number)
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# Output.
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self.dense = nn.Linear(self.projection_size, config.hidden_size, bias=config.add_bias_linear,
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device=device, **_config_to_kwargs(config)
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)
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def _allocate_memory(self, inference_max_sequence_len, batch_size, device=None, dtype=None):
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if self.multi_query_attention:
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num_attention_heads = self.num_multi_query_groups_per_partition
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else:
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num_attention_heads = self.num_attention_heads_per_partition
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return torch.empty(
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inference_max_sequence_len,
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batch_size,
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num_attention_heads,
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self.hidden_size_per_attention_head,
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dtype=dtype,
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device=device,
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)
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def forward(
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self, hidden_states, attention_mask, rotary_pos_emb, kv_cache=None, use_cache=True
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):
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# hidden_states: [sq, b, h]
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# =================================================
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# Pre-allocate memory for key-values for inference.
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# =================================================
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# =====================
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# Query, Key, and Value
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# =====================
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# Attention heads [sq, b, h] --> [sq, b, (np * 3 * hn)]
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mixed_x_layer = self.query_key_value(hidden_states)
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if self.multi_query_attention:
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(query_layer, key_layer, value_layer) = mixed_x_layer.split(
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[
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self.num_attention_heads_per_partition * self.hidden_size_per_attention_head,
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self.num_multi_query_groups_per_partition * self.hidden_size_per_attention_head,
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self.num_multi_query_groups_per_partition * self.hidden_size_per_attention_head,
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],
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dim=-1,
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)
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query_layer = query_layer.view(
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query_layer.size()[:-1] + (self.num_attention_heads_per_partition, self.hidden_size_per_attention_head)
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)
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key_layer = key_layer.view(
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key_layer.size()[:-1] + (self.num_multi_query_groups_per_partition, self.hidden_size_per_attention_head)
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)
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value_layer = value_layer.view(
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value_layer.size()[:-1]
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+ (self.num_multi_query_groups_per_partition, self.hidden_size_per_attention_head)
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)
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else:
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new_tensor_shape = mixed_x_layer.size()[:-1] + \
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(self.num_attention_heads_per_partition,
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3 * self.hidden_size_per_attention_head)
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mixed_x_layer = mixed_x_layer.view(*new_tensor_shape)
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# [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,
|
||
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:
|
||
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
|
||
|
||
|
||
# PreTrainedModel
|
||
class ChatGLMPreTrainedModel(nn.Module):
|
||
def __init__(self, *args, **kwargs):
|
||
self.config = args[0]
|
||
|
||
super().__init__()
|
||
|
||
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 gradient_checkpointing_enable(self, gradient_checkpointing_kwargs=None):
|
||
if not self.supports_gradient_checkpointing:
|
||
raise ValueError(f"{self.__class__.__name__} does not support gradient checkpointing.")
|
||
|
||
|
||
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, 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 set_input_embeddings(self, value):
|
||
self.embedding.word_embeddings = value
|
||
|
||
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.device = device
|
||
self.config = config
|
||
self.quantized = False
|
||
|
||
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]
|
||
hidden_states = transformer_outputs.last_hidden_state
|
||
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 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.configquantization_bit = bits
|
||
|
||
self.transformer.encoder = quantize(self.transformer.encoder, bits, empty_init=empty_init, device=device,
|
||
**kwargs)
|
||
return self
|
||
|
||
|
||
class ChatGLMForSequenceClassification(ChatGLMPreTrainedModel):
|
||
def __init__(self, config: ChatGLMConfig, empty_init=True, device=None, bits=None):
|
||
super().__init__(config)
|
||
|
||
self.num_labels = config.num_labels
|
||
self.transformer = ChatGLMModel(config, empty_init=empty_init, device=device)
|
||
|
||
self.classifier_head = nn.Linear(config.hidden_size, config.num_labels, bias=True, dtype=torch.half)
|
||
if config.classifier_dropout is not None:
|
||
self.dropout = nn.Dropout(config.classifier_dropout)
|
||
else:
|
||
self.dropout = None
|
||
self.config = config
|
||
|
||
if bits:
|
||
self.quantize(bits=bits, empty_init=True)
|
||
|
||
def forward(
|
||
self,
|
||
input_ids: Optional[torch.LongTensor] = None,
|
||
position_ids: Optional[torch.LongTensor] = None,
|
||
attention_mask: Optional[torch.Tensor] = None,
|
||
full_attention_mask: Optional[torch.Tensor] = None,
|
||
past_key_values: Optional[Tuple[Tuple[torch.Tensor, torch.Tensor], ...]] = None,
|
||
inputs_embeds: Optional[torch.LongTensor] = None,
|
||
labels: Optional[torch.LongTensor] = None,
|
||
use_cache: Optional[bool] = None,
|
||
output_hidden_states: Optional[bool] = None,
|
||
return_dict: Optional[bool] = None,
|
||
) -> Union[Tuple[torch.Tensor, ...], SequenceClassifierOutputWithPast]:
|
||
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
|
||
|
||
transformer_outputs = self.transformer(
|
||
input_ids=input_ids,
|
||
position_ids=position_ids,
|
||
attention_mask=attention_mask,
|
||
full_attention_mask=full_attention_mask,
|
||
past_key_values=past_key_values,
|
||
inputs_embeds=inputs_embeds,
|
||
use_cache=use_cache,
|
||
output_hidden_states=output_hidden_states,
|
||
return_dict=return_dict,
|
||
)
|
||
|
||
hidden_states = transformer_outputs[0]
|
||
pooled_hidden_states = hidden_states[-1]
|
||
if self.dropout is not None:
|
||
pooled_hidden_states = self.dropout(pooled_hidden_states)
|
||
logits = self.classifier_head(pooled_hidden_states)
|
||
|
||
loss = None
|
||
if labels is not None:
|
||
if self.config.problem_type is None:
|
||
if self.num_labels == 1:
|
||
self.config.problem_type = "regression"
|
||
elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
|
||
self.config.problem_type = "single_label_classification"
|
||
else:
|
||
self.config.problem_type = "multi_label_classification"
|
||
|
||
if self.config.problem_type == "regression":
|
||
loss_fct = MSELoss()
|
||
if self.num_labels == 1:
|
||
loss = loss_fct(logits.squeeze().float(), labels.squeeze())
|
||
else:
|
||
loss = loss_fct(logits.float(), labels)
|
||
elif self.config.problem_type == "single_label_classification":
|
||
loss_fct = CrossEntropyLoss()
|
||
loss = loss_fct(logits.view(-1, self.num_labels).float(), labels.view(-1))
|
||
elif self.config.problem_type == "multi_label_classification":
|
||
loss_fct = BCEWithLogitsLoss()
|
||
loss = loss_fct(logits.float(), labels.view(-1, self.num_labels))
|
||
|
||
if not return_dict:
|
||
output = (logits,) + transformer_outputs[1:]
|
||
return ((loss,) + output) if loss is not None else output
|
||
|
||
return SequenceClassifierOutputWithPast(
|
||
loss=loss,
|
||
logits=logits,
|
||
past_key_values=transformer_outputs.past_key_values,
|
||
hidden_states=transformer_outputs.hidden_states,
|
||
attentions=transformer_outputs.attentions,
|
||
)
|
||
|
||
|
||
if __name__ == "__main__":
|
||
import torch
|
||
from safetensors.torch import load_model
|
||
from torch.nn.functional import softmax
|
||
|
||
import configuration_chatglm_full as configuration_chatglm
|
||
from tokenization_chatglm import ChatGLMTokenizer
|
||
|
||
|
||
def random_sample(logits, temperature=1.0):
|
||
post_logits = logits[:, -1, :] / temperature
|
||
post_logits = softmax(post_logits, dim=-1)
|
||
|
||
selected_indices = torch.multinomial(post_logits, 1)
|
||
return selected_indices
|
||
|
||
|
||
config = configuration_chatglm.ChatGLMConfig()
|
||
tokenlizer = ChatGLMTokenizer(vocab_file='tokenizer.model')
|
||
|
||
m = ChatGLMForConditionalGeneration(config=config, device='cpu')
|
||
load_model(m, "model-00001-of-00007.safetensors", strict=False)
|
||
load_model(m, "model-00002-of-00007.safetensors", strict=False)
|
||
load_model(m, "model-00003-of-00007.safetensors", strict=False)
|
||
load_model(m, "model-00004-of-00007.safetensors", strict=False)
|
||
load_model(m, "model-00005-of-00007.safetensors", strict=False)
|
||
load_model(m, "model-00006-of-00007.safetensors", strict=False)
|
||
load_model(m, "model-00007-of-00007.safetensors", strict=False)
|
||
print(m)
|
||
m = m.quantize(bits=4, device='cuda')
|
||
m = m.to('cuda')
|
||
m = m.eval()
|
||
|
||
t = tokenlizer(['为什么A股指数跌的不多，但是我亏损比之前都多？'], add_special_tokens=False)
|
||
|
||
input_ids = [[64790, 64792] + t['input_ids'][0] + [64796]]
|
||
input_ids = torch.tensor(input_ids, device='cuda')
|
||
|
||
attention_mask = torch.tensor([[1] * len(input_ids[0])], dtype=torch.long, device='cuda')
|
||
position_ids = torch.tensor([range(len(input_ids[0]))], dtype=torch.long, device='cuda')
|
||
|
||
with torch.no_grad():
|
||
for i in range(100):
|
||
output = m.forward(input_ids=input_ids,
|
||
attention_mask=attention_mask,
|
||
position_ids=position_ids,
|
||
# output_hidden_states=True,
|
||
return_last_logit=False,
|
||
use_cache=False
|
||
)
|
||
|
||
result = random_sample(output.logits, 0.1)
|
||
|
||
if result.item() == 2:
|
||
break
|
||
|
||
# print(tokenlizer.decode(input_ids[0], skip_special_tokens=True), end='\n')
|
||
print(tokenlizer.decode(result[0], skip_special_tokens=False), end='')
|
||
|
||
input_ids = torch.cat([input_ids, result],
|
||
dim=1)
|
||
|
||
attention_mask = torch.cat([attention_mask, torch.ones(1, 1, device='cuda', dtype=torch.long)], dim=1)
|
||
position_ids = torch.cat(
|
||
[position_ids, torch.ones(1, 1, device='cuda', dtype=torch.long) * len(position_ids[0])], dim=1)
|
||
|
||
print('\n' * 5, tokenlizer.decode(input_ids[0], skip_special_tokens=True), end='\n')
|
||
|
||
|
||
|
||
|