1638 lines
59 KiB
Python
1638 lines
59 KiB
Python
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import math
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import sys
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import torch
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import torch.utils.checkpoint
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import torch.nn.functional as F
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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 typing import Optional, Tuple, Union, List, Dict, Any
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import pdb
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from transformers.modeling_outputs import (
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BaseModelOutputWithPast,
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CausalLMOutputWithPast,
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SequenceClassifierOutputWithPast,
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)
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from transformers.modeling_utils import PreTrainedModel
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from transformers.utils import logging, is_torch_npu_available
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from transformers.generation.logits_process import LogitsProcessor
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from transformers.generation.utils import (
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LogitsProcessorList,
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StoppingCriteriaList,
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GenerationConfig,
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ModelOutput,
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)
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from .visual import EVA2CLIPModel
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from .configuration_chatglm import ChatGLMConfig
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try:
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from transformers.utils import (
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is_flash_attn_greater_or_equal_2_10,
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is_flash_attn_2_available,
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)
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if is_flash_attn_2_available():
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from flash_attn import flash_attn_func, flash_attn_varlen_func
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from flash_attn.bert_padding import index_first_axis, pad_input, unpad_input # noqa
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except:
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pass
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if sys.platform != "darwin" and not is_torch_npu_available():
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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.get_logger(__name__)
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LANGUAGE_TOKEN_TYPE = 0
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VISION_TOKEN_TYPE = 1
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_CHECKPOINT_FOR_DOC = "THUDM/ChatGLM"
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_CONFIG_FOR_DOC = "ChatGLMConfig"
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def default_init(cls, *args, **kwargs):
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return cls(*args, **kwargs)
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class InvalidScoreLogitsProcessor(LogitsProcessor):
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def __call__(
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self, input_ids: torch.LongTensor, scores: torch.FloatTensor
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) -> 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[..., 198] = 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 = (
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config.num_layers
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* config.kv_channels
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* config.multi_query_group_num
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* 2
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)
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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(
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config.pre_seq_len,
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config.num_layers
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* config.kv_channels
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* config.multi_query_group_num
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* 2,
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)
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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, rope_ratio=1, original_impl=False, device=None, dtype=None):
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super().__init__()
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inv_freq = 1.0 / (
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10000 ** (torch.arange(0, dim, 2, device=device).to(dtype=dtype) / dim)
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)
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self.register_buffer("inv_freq", inv_freq)
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self.dim = dim
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self.original_impl = original_impl
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self.rope_ratio = rope_ratio
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def impl(self, seq_length: int, dim: int, device: torch.device, dtype: torch.dtype):
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base = 10000 * self.rope_ratio
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inv_freq = 1.0 / (
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base ** (torch.arange(0, dim, 2, device=device, dtype=torch.float32) / dim)
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)
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seq = torch.arange(seq_length, device=inv_freq.device, dtype=torch.float32)
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freqs = torch.outer(seq, inv_freq)
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# first part even vector components, second part odd vector components,
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# 2 * dim in dimension size
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# emb = torch.cat((freqs, freqs), dim=-1)
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# emb = torch.stack((freqs, freqs), dim=-1).to(dtype)
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emb = torch.stack((freqs.cos(), freqs.sin()), dim=-1).to(dtype=dtype)
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return emb
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def forward_impl(
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self,
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seq_len: int,
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n_elem: int,
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dtype: torch.dtype,
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device: torch.device,
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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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base = base * self.rope_ratio
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theta = 1.0 / (
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base
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** (torch.arange(0, n_elem, 2, dtype=torch.float, device=device) / n_elem)
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)
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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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if self.original_impl:
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return self.forward_impl(
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max_seq_len,
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self.dim,
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dtype=self.inv_freq.dtype,
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device=self.inv_freq.device,
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)
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else:
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return self.impl(
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max_seq_len,
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self.dim,
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dtype=self.inv_freq.dtype,
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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: [b, np, sq, hn]
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sq = x.size(1)
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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.chunk(2, -1)
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cos, sin = rope_cache[...,0].unsqueeze(2), rope_cache[...,1].unsqueeze(2)
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x_out2 = torch.concat(
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[
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xshaped[0] * cos - xshaped[1] * sin,
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xshaped[1] * cos + xshaped[0] * sin,
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],
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-1,
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)
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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(
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torch.empty(normalized_shape, device=device, dtype=dtype)
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)
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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 = (
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projection_size // config.num_attention_heads
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)
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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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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(
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query_layer, key_layer, value_layer, is_causal=True
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)
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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(
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query_layer, key_layer, value_layer, attention_mask
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)
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context_layer = context_layer.transpose(1, 2).contiguous()
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new_context_layer_shape = context_layer.size()[:-2] + (
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self.hidden_size_per_partition,
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)
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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 = (
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query_layer.size(0),
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query_layer.size(1),
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query_layer.size(2),
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key_layer.size(2),
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)
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# [b, np, sq, hn] -> [b * np, sq, hn]
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query_layer = query_layer.view(
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output_size[0] * output_size[1], output_size[2], -1
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)
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# [b, np, sk, hn] -> [b * np, sk, hn]
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key_layer = key_layer.view(
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output_size[0] * output_size[1], output_size[3], -1
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)
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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],
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output_size[2],
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output_size[3],
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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, # [b * np, sq, hn]
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key_layer.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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|
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if (
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attention_mask is None
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and attention_scores.shape[2] == attention_scores.shape[3]
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):
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attention_mask = torch.ones(
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output_size[0],
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1,
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|
output_size[2],
|
||
|
|
output_size[3],
|
||
|
|
device=attention_scores.device,
|
||
|
|
dtype=torch.bool,
|
||
|
|
)
|
||
|
|
attention_mask.tril_()
|
||
|
|
attention_mask = ~attention_mask
|
||
|
|
if attention_mask is not None:
|
||
|
|
attention_scores = attention_scores.masked_fill(
|
||
|
|
attention_mask, float("-inf")
|
||
|
|
)
|
||
|
|
attention_probs = F.softmax(attention_scores, dim=-1)
|
||
|
|
attention_probs = attention_probs.type_as(value_layer)
|
||
|
|
|
||
|
|
# This is actually dropping out entire tokens to attend to, which might
|
||
|
|
# seem a bit unusual, but is taken from the original Transformer paper.
|
||
|
|
attention_probs = self.attention_dropout(attention_probs)
|
||
|
|
# =========================
|
||
|
|
# Context layer. [sq, b, hp]
|
||
|
|
# =========================
|
||
|
|
|
||
|
|
# value_layer -> context layer.
|
||
|
|
# [sk, b, np, hn] --> [b, np, sq, hn]
|
||
|
|
|
||
|
|
# context layer shape: [b, np, sq, hn]
|
||
|
|
output_size = (
|
||
|
|
value_layer.size(1),
|
||
|
|
value_layer.size(2),
|
||
|
|
query_layer.size(0),
|
||
|
|
value_layer.size(3),
|
||
|
|
)
|
||
|
|
# change view [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
|
||
|
|
self.original_rope = config.original_rope
|
||
|
|
if self.multi_query_attention:
|
||
|
|
self.num_multi_query_groups_per_partition = config.multi_query_group_num
|
||
|
|
self.qkv_hidden_size = (
|
||
|
|
self.projection_size
|
||
|
|
+ 2 * self.hidden_size_per_attention_head * config.multi_query_group_num
|
||
|
|
)
|
||
|
|
self.query_key_value = nn.Linear(
|
||
|
|
config.hidden_size,
|
||
|
|
self.qkv_hidden_size,
|
||
|
|
bias=config.add_bias_linear or config.add_qkv_bias,
|
||
|
|
device=device,
|
||
|
|
**_config_to_kwargs(config),
|
||
|
|
)
|
||
|
|
|
||
|
|
self.core_attention = CoreAttention(config, self.layer_number)
|
||
|
|
|
||
|
|
# Output.
|
||
|
|
self.dense = nn.Linear(
|
||
|
|
self.projection_size,
|
||
|
|
config.hidden_size,
|
||
|
|
bias=config.add_bias_linear,
|
||
|
|
device=device,
|
||
|
|
**_config_to_kwargs(config),
|
||
|
|
)
|
||
|
|
|
||
|
|
def _allocate_memory(
|
||
|
|
self, inference_max_sequence_len, batch_size, device=None, dtype=None
|
||
|
|
):
|
||
|
|
if self.multi_query_attention:
|
||
|
|
num_attention_heads = self.num_multi_query_groups_per_partition
|
||
|
|
else:
|
||
|
|
num_attention_heads = self.num_attention_heads_per_partition
|
||
|
|
return torch.empty(
|
||
|
|
inference_max_sequence_len,
|
||
|
|
batch_size,
|
||
|
|
num_attention_heads,
|
||
|
|
self.hidden_size_per_attention_head,
|
||
|
|
dtype=dtype,
|
||
|
|
device=device,
|
||
|
|
)
|
||
|
|
|
||
|
|
def forward(
|
||
|
|
self,
|
||
|
|
hidden_states,
|
||
|
|
attention_mask,
|
||
|
|
rotary_pos_emb,
|
||
|
|
kv_cache=None,
|
||
|
|
use_cache=True,
|
||
|
|
):
|
||
|
|
# hidden_states: [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
|
||
|
|
)
|
||
|
|
|
||
|
|
|
||
|
|
|
||
|
|
# 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)
|
||
|
|
|
||
|
|
|
||
|
|
# [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]
|
||
|
|
]
|
||
|
|
|
||
|
|
# 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:
|
||
|
|
kv_cache = (key_layer, value_layer)
|
||
|
|
else:
|
||
|
|
kv_cache = None
|
||
|
|
|
||
|
|
if self.multi_query_attention:
|
||
|
|
key_layer = key_layer.repeat_interleave(self.num_attention_heads_per_partition // self.num_multi_query_groups_per_partition, dim=1)
|
||
|
|
value_layer = value_layer.repeat_interleave(self.num_attention_heads_per_partition // self.num_multi_query_groups_per_partition, dim=1)
|
||
|
|
|
||
|
|
# ==================================
|
||
|
|
# 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
|
||
|
|
|
||
|
|
|
||
|
|
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_embeds, past_key_values, padding_mask=None):
|
||
|
|
batch_size, seq_length, embed_size = input_embeds.shape
|
||
|
|
full_attention_mask = torch.ones(
|
||
|
|
batch_size, seq_length, seq_length, device=input_embeds.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_embeds.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 get_multimodal_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)
|
||
|
|
)
|
||
|
|
|
||
|
|
|
||
|
|
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
|
||
|
|
|
||
|
|
|
||
|
|
def is_empty(images_list: Optional[List[List[torch.Tensor]]]):
|
||
|
|
if images_list is None or len(images_list) == 0:
|
||
|
|
return True
|
||
|
|
for image_list in images_list:
|
||
|
|
if image_list is not None:
|
||
|
|
return False
|
||
|
|
return True
|
||
|
|
|
||
|
|
|
||
|
|
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,
|
||
|
|
)
|
||
|
|
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)
|
||
|
|
|
||
|
|
self.vision = EVA2CLIPModel(config)
|
||
|
|
self.position_ids_skipped = False
|
||
|
|
|
||
|
|
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.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: torch.LongTensor = None,
|
||
|
|
images: torch.Tensor = None,
|
||
|
|
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,
|
||
|
|
) -> Union[Tuple, BaseModelOutputWithPast]:
|
||
|
|
"""take care of image_encode, position_ids and (attention_mask = None is fine)"""
|
||
|
|
# generate mode with past_key_values. the image features are already mapped
|
||
|
|
if past_key_values is None:
|
||
|
|
self.position_ids_skipped = False
|
||
|
|
# not allow for inputs_embeds, because we want to process image feature
|
||
|
|
assert (
|
||
|
|
input_ids is not None and inputs_embeds is None
|
||
|
|
), f"{input_ids} {inputs_embeds}"
|
||
|
|
if not is_empty(images): # multi-modality
|
||
|
|
image_size: int = self.config.vision_config["image_size"]
|
||
|
|
patch_size: int = self.config.vision_config["patch_size"]
|
||
|
|
num_patches = (image_size // patch_size // 2) ** 2
|
||
|
|
assert len(input_ids) == len(images), f"{len(input_ids)} {len(images)}"
|
||
|
|
|
||
|
|
inputs_embeds = self.embedding(input_ids)
|
||
|
|
|
||
|
|
images = images.to(dtype=inputs_embeds.dtype)
|
||
|
|
images_features = self.vision(images)
|
||
|
|
|
||
|
|
if position_ids is None:
|
||
|
|
position_ids = self.get_position_ids(
|
||
|
|
input_ids, device=inputs_embeds.device
|
||
|
|
)
|
||
|
|
new_input_embeds, new_position_ids = [], []
|
||
|
|
|
||
|
|
for i in range(len(input_ids)):
|
||
|
|
input_id = input_ids[i].tolist()
|
||
|
|
boi_token_pos, eoi_token_pos = (
|
||
|
|
input_id.index(self.config.boi_token_id),
|
||
|
|
input_id.index(self.config.eoi_token_id),
|
||
|
|
)
|
||
|
|
assert eoi_token_pos - boi_token_pos == 2
|
||
|
|
new_input_embeds.append(
|
||
|
|
torch.cat(
|
||
|
|
(
|
||
|
|
inputs_embeds[i, :boi_token_pos],
|
||
|
|
images_features[i].to(inputs_embeds.device),
|
||
|
|
inputs_embeds[i, eoi_token_pos + 1 :],
|
||
|
|
)
|
||
|
|
)
|
||
|
|
)
|
||
|
|
new_position_ids.append(
|
||
|
|
torch.arange(
|
||
|
|
0,
|
||
|
|
len(input_id) + num_patches - 1,
|
||
|
|
dtype=position_ids.dtype,
|
||
|
|
device=inputs_embeds.device,
|
||
|
|
)
|
||
|
|
)
|
||
|
|
inputs_embeds = torch.stack(new_input_embeds, dim=0)
|
||
|
|
position_ids = torch.stack(new_position_ids, dim=0)
|
||
|
|
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
|
||
|
|
):
|
||
|
|
if self.training:
|
||
|
|
# https://github.com/THUDM/GLM-4/issues/264
|
||
|
|
new_input_ids, new_attention_mask = [], []
|
||
|
|
for i in range(len(input_ids)):
|
||
|
|
input_id = input_ids[i].tolist()
|
||
|
|
boi_token_pos, eoi_token_pos = (
|
||
|
|
input_id.index(self.config.boi_token_id),
|
||
|
|
input_id.index(self.config.eoi_token_id),
|
||
|
|
)
|
||
|
|
assert eoi_token_pos - boi_token_pos == 2
|
||
|
|
|
||
|
|
new_attention_mask.append(
|
||
|
|
torch.cat(
|
||
|
|
(
|
||
|
|
attention_mask[i, : boi_token_pos + 1],
|
||
|
|
torch.ones(num_patches).to(attention_mask.device),
|
||
|
|
attention_mask[i, eoi_token_pos:],
|
||
|
|
)
|
||
|
|
)
|
||
|
|
)
|
||
|
|
|
||
|
|
new_input_ids.append(
|
||
|
|
torch.cat(
|
||
|
|
(
|
||
|
|
input_ids[i, : boi_token_pos + 1],
|
||
|
|
input_ids[i, -1].repeat(num_patches),
|
||
|
|
input_ids[i, eoi_token_pos:],
|
||
|
|
)
|
||
|
|
)
|
||
|
|
)
|
||
|
|
|
||
|
|
attention_mask = torch.stack(new_attention_mask, dim=0)
|
||
|
|
input_ids = torch.stack(new_input_ids, dim=0)
|
||
|
|
inputs_embeds = self.embedding(input_ids)
|
||
|
|
|
||
|
|
full_attention_mask = self.get_masks(
|
||
|
|
inputs_embeds, past_key_values, padding_mask=attention_mask
|
||
|
|
)
|
||
|
|
|
||
|
|
# Rotary positional embeddings
|
||
|
|
rotary_pos_emb = self.rotary_pos_emb(self.seq_length)
|
||
|
|
if position_ids[0].size()[0] == 1 and not self.position_ids_skipped:
|
||
|
|
self.position_ids_skipped = True
|
||
|
|
position_ids[:, 0] = position_ids[:, 0] + 1600 - 1
|
||
|
|
|
||
|
|
if position_ids is not None:
|
||
|
|
rotary_pos_emb = rotary_pos_emb[position_ids]
|
||
|
|
else:
|
||
|
|
rotary_pos_emb = rotary_pos_emb[None, :seq_length]
|
||
|
|
|
||
|
|
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 _history_to_prompt(history, query):
|
||
|
|
prompt = ""
|
||
|
|
flag = False
|
||
|
|
for i, (old_query, response) in enumerate(history):
|
||
|
|
prompt += (
|
||
|
|
("<|user|>" if flag else "")
|
||
|
|
+ old_query
|
||
|
|
+ "<|assistant|>"
|
||
|
|
+ response
|
||
|
|
+ "<|endoftext|>"
|
||
|
|
)
|
||
|
|
flag = True
|
||
|
|
prompt += "{}{}<|assistant|>".format("<|user|>" if flag else "", query)
|
||
|
|
return prompt
|
||
|
|
|
||
|
|
|
||
|
|
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,
|
||
|
|
) -> Dict[str, Any]:
|
||
|
|
|
||
|
|
# update past_key_values
|
||
|
|
cache_name, cache = self._extract_past_from_model_output(outputs)
|
||
|
|
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,
|
||
|
|
images: Optional[torch.Tensor] = None,
|
||
|
|
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 attention_mask is not None:
|
||
|
|
image_size: int = self.config.vision_config["image_size"]
|
||
|
|
patch_size: int = self.config.vision_config["patch_size"]
|
||
|
|
num_patches = (image_size // patch_size // 2) ** 2
|
||
|
|
new_attention_masks = []
|
||
|
|
|
||
|
|
# if not image, use this default id
|
||
|
|
eoi_token_pos = 6
|
||
|
|
boi_token_pos = 4
|
||
|
|
|
||
|
|
for i in range(len(input_ids)):
|
||
|
|
input_id = input_ids[i].tolist()
|
||
|
|
if not is_empty(images):
|
||
|
|
boi_token_pos, eoi_token_pos = (
|
||
|
|
input_id.index(self.config.boi_token_id),
|
||
|
|
input_id.index(self.config.eoi_token_id),
|
||
|
|
)
|
||
|
|
assert eoi_token_pos - boi_token_pos == 2
|
||
|
|
new_attention_masks.append(
|
||
|
|
torch.cat(
|
||
|
|
(
|
||
|
|
attention_mask[i, : boi_token_pos + 1],
|
||
|
|
attention_mask.new_ones(num_patches),
|
||
|
|
attention_mask[i, eoi_token_pos:],
|
||
|
|
)
|
||
|
|
)
|
||
|
|
)
|
||
|
|
attention_mask = torch.stack(new_attention_masks, dim=0)
|
||
|
|
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,
|
||
|
|
"images": images,
|
||
|
|
"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,
|
||
|
|
images: List[List[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,
|
||
|
|
images=images,
|
||
|
|
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:
|
||
|
|
new_labels = []
|
||
|
|
for i in range(len(input_ids)):
|
||
|
|
input_id = input_ids[i].tolist()
|
||
|
|
boi_token_pos, eoi_token_pos = (
|
||
|
|
input_id.index(self.config.boi_token_id),
|
||
|
|
input_id.index(self.config.eoi_token_id),
|
||
|
|
)
|
||
|
|
assert eoi_token_pos - boi_token_pos == 2
|
||
|
|
|
||
|
|
new_labels.append(
|
||
|
|
torch.cat(
|
||
|
|
(
|
||
|
|
labels[i, : boi_token_pos + 1],
|
||
|
|
torch.tensor([-100])
|
||
|
|
.to(labels.device)
|
||
|
|
.to(labels.dtype)
|
||
|
|
.repeat(1600),
|
||
|
|
labels[i, eoi_token_pos:],
|
||
|
|
)
|
||
|
|
)
|
||
|
|
)
|
||
|
|
|
||
|
|
labels = torch.stack(new_labels, dim=0)
|
||
|
|
lm_logits = lm_logits.to(torch.float32)
|
||
|
|
shift_logits = lm_logits[..., :-1, :].contiguous()
|
||
|
|
shift_labels = labels[..., 1:].contiguous()
|
||
|
|
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=torch.half
|
||
|
|
)
|
||
|
|
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_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,
|
||
|
|
)
|