1395 lines
62 KiB
Python
1395 lines
62 KiB
Python
# coding=utf-8
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# Copyright 2021 The LG AI Research EXAONE Lab.
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# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.
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#
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# This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX
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# and OPT implementations in this library. It has been modified from its
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# original forms to accommodate minor architectural differences compared
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# to GPT-NeoX and OPT used by the Meta AI team that trained the model.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""LG AI Research EXAONE Lab"""
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import math
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from typing import Optional, Tuple, Union
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import torch
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import torch.utils.checkpoint
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from packaging import version
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from torch import nn
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from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
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from transformers.activations import ACT2FN
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from transformers.cache_utils import Cache, DynamicCache, StaticCache
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from transformers.generation import GenerationMixin
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from transformers.modeling_attn_mask_utils import AttentionMaskConverter
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from transformers.modeling_flash_attention_utils import _flash_attention_forward
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from transformers.modeling_outputs import (
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BaseModelOutputWithPast,
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BaseModelOutputWithPastAndCrossAttentions,
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CausalLMOutputWithPast,
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QuestionAnsweringModelOutput,
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SequenceClassifierOutputWithPast,
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)
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from transformers.modeling_rope_utils import ROPE_INIT_FUNCTIONS
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from transformers.modeling_utils import PreTrainedModel
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from transformers.pytorch_utils import ALL_LAYERNORM_LAYERS
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from transformers.utils import (
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add_code_sample_docstrings,
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add_start_docstrings,
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add_start_docstrings_to_model_forward,
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is_flash_attn_2_available,
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logging,
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)
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from .configuration_exaone import ExaoneConfig
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if is_flash_attn_2_available():
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try:
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import flash_attn
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if version.parse(flash_attn.__version__) > version.parse("2.4.2"):
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from flash_attn.ops.triton.layer_norm import rms_norm_fn
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else:
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from flash_attn.ops.triton.layernorm import rms_norm_fn
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except ImportError:
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pass
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logger = logging.get_logger(__name__)
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_CHECKPOINT_FOR_DOC = "exaone"
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_CONFIG_FOR_DOC = "ExaoneConfig"
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EXAONE_PRETRAINED_MODEL_ARCHIVE_LIST = [
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"exaone",
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]
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@torch.jit.script
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def repeat_kv(hidden_states: torch.Tensor, n_rep: int) -> torch.Tensor:
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"""
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This is the equivalent of torch.repeat_interleave(x, dim=1, repeats=n_rep). The hidden states go from (batch,
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num_key_value_heads, seqlen, head_dim) to (batch, num_attention_heads, seqlen, head_dim)
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"""
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batch, num_key_value_heads, slen, head_dim = hidden_states.shape
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if n_rep == 1:
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return hidden_states
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hidden_states = hidden_states[:, :, None, :, :].expand(batch, num_key_value_heads, n_rep, slen, head_dim)
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return hidden_states.reshape(batch, num_key_value_heads * n_rep, slen, head_dim)
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def apply_rotary_pos_emb(q, k, cos, sin, unsqueeze_dim=1):
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"""Applies Rotary Position Embedding to the query and key tensors.
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Args:
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q (`torch.Tensor`): The query tensor.
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k (`torch.Tensor`): The key tensor.
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cos (`torch.Tensor`): The cosine part of the rotary embedding.
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sin (`torch.Tensor`): The sine part of the rotary embedding.
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unsqueeze_dim (`int`, *optional*, defaults to 1):
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The 'unsqueeze_dim' argument specifies the dimension along which to unsqueeze cos[position_ids] and
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sin[position_ids] so that they can be properly broadcasted to the dimensions of q and k. For example, note
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that cos[position_ids] and sin[position_ids] have the shape [batch_size, seq_len, head_dim]. Then, if q and
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k have the shape [batch_size, heads, seq_len, head_dim], then setting unsqueeze_dim=1 makes
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cos[position_ids] and sin[position_ids] broadcastable to the shapes of q and k. Similarly, if q and k have
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the shape [batch_size, seq_len, heads, head_dim], then set unsqueeze_dim=2.
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Returns:
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`tuple(torch.Tensor)` comprising of the query and key tensors rotated using the Rotary Position Embedding.
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"""
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cos = cos.unsqueeze(unsqueeze_dim)
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sin = sin.unsqueeze(unsqueeze_dim)
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q_embed = (q * cos) + (rotate_half(q) * sin)
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k_embed = (k * cos) + (rotate_half(k) * sin)
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return q_embed, k_embed
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def rotate_half(x):
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"""Rotates half the hidden dims of the input."""
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x1 = x[..., : x.shape[-1] // 2]
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x2 = x[..., x.shape[-1] // 2 :]
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return torch.cat((-x2, x1), dim=-1)
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def _prepare_4d_causal_attention_mask_with_cache_position(
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attention_mask: torch.Tensor,
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sequence_length: int,
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target_length: int,
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dtype: torch.dtype,
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device: torch.device,
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min_dtype: float,
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cache_position: torch.Tensor,
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batch_size: int,
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):
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"""
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Creates a causal 4D mask of shape `(batch_size, 1, query_length, key_value_length)` from a 2D mask of shape
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`(batch_size, key_value_length)`, or if the input `attention_mask` is already 4D, do nothing.
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Args:
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attention_mask (`torch.Tensor`):
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A 2D attention mask of shape `(batch_size, key_value_length)` or a 4D attention mask of shape `(batch_size, 1, query_length, key_value_length)`.
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sequence_length (`int`):
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The sequence length being processed.
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target_length (`int`):
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The target length: when generating with static cache, the mask should be as long as the static cache, to account for the 0 padding, the part of the cache that is not filled yet.
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dtype (`torch.dtype`):
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The dtype to use for the 4D attention mask.
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device (`torch.device`):
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The device to plcae the 4D attention mask on.
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min_dtype (`float`):
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The minimum value representable with the dtype `dtype`.
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cache_position (`torch.Tensor`):
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Indices depicting the position of the input sequence tokens in the sequence.
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batch_size (`torch.Tensor`):
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Batch size.
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"""
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if attention_mask is not None and attention_mask.dim() == 4:
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# In this case we assume that the mask comes already in inverted form and requires no inversion or slicing.
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causal_mask = attention_mask
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else:
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causal_mask = torch.full((sequence_length, target_length), fill_value=min_dtype, dtype=dtype, device=device)
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if sequence_length != 1:
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causal_mask = torch.triu(causal_mask, diagonal=1)
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causal_mask *= torch.arange(target_length, device=device) > cache_position.reshape(-1, 1)
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causal_mask = causal_mask[None, None, :, :].expand(batch_size, 1, -1, -1)
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if attention_mask is not None:
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causal_mask = causal_mask.clone() # copy to contiguous memory for in-place edit
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mask_length = attention_mask.shape[-1]
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padding_mask = causal_mask[:, :, :, :mask_length] + attention_mask[:, None, None, :]
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padding_mask = padding_mask == 0
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causal_mask[:, :, :, :mask_length] = causal_mask[:, :, :, :mask_length].masked_fill(
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padding_mask, min_dtype
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)
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return causal_mask
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class ExaoneRMSNorm(torch.nn.Module):
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def __init__(self, hidden_size, eps=1e-6):
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super().__init__()
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self.eps = eps
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self.weight = torch.nn.Parameter(torch.ones(hidden_size))
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def forward(self, hidden_states):
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input_dtype = hidden_states.dtype
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hidden_states = hidden_states.to(torch.float32)
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variance = hidden_states.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 ExaoneTritonRMSNorm(torch.nn.Module):
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def __init__(
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self,
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hidden_size: int = 0,
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eps: float = 1e-5,
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):
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super().__init__()
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self.eps = eps
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self.drop = None
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self.weight = torch.nn.Parameter(torch.empty(hidden_size))
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self.register_parameter("bias", None)
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self.reset_parameters()
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def reset_parameters(self):
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torch.nn.init.ones_(self.weight)
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def forward(self, x, residual=None, prenorm=False, residual_in_fp32=False):
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return rms_norm_fn(
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x,
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self.weight,
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self.bias,
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residual=residual,
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eps=self.eps,
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dropout_p=self.drop.p if self.drop is not None and self.training else 0.0,
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prenorm=prenorm,
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residual_in_fp32=residual_in_fp32,
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)
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ALL_LAYERNORM_LAYERS.append(ExaoneRMSNorm)
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ALL_LAYERNORM_LAYERS.append(ExaoneTritonRMSNorm)
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class ExaoneRotaryEmbedding(nn.Module):
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def __init__(self, config: ExaoneConfig, device=None):
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super().__init__()
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if config.rope_scaling is not None:
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self.rope_type = config.rope_scaling.get("rope_type", config.rope_scaling.get("type"))
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else:
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self.rope_type = "default"
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self.rope_theta = config.rope_theta
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self.max_seq_len = config.max_position_embeddings
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self.original_max_seq_len = config.max_position_embeddings
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self.config = config
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if self.rope_type not in ROPE_INIT_FUNCTIONS:
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raise KeyError(f"The EXAONE model does not support RoPE type: {self.rope_type}")
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self.rope_init_fn = ROPE_INIT_FUNCTIONS[self.rope_type]
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inv_freq, self.attention_scaling = self.rope_init_fn(self.config, device)
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self.register_buffer("inv_freq", inv_freq, persistent=False)
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self.original_inv_freq = self.inv_freq
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def _update_freq(self, position_ids, device):
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"""
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dynamic RoPE layers should recompute `inv_freq` in the following situations:
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1 - growing beyond the cached sequence length (allow scaling)
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2 - the current sequence length is in the original scale (avoid losing precision with small sequences)
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"""
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seq_len = torch.max(position_ids) + 1
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if seq_len > self.max_seq_len: # expand to seq_len
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inv_freq, self.attention_scaling = self.rope_init_fn(self.config, device, seq_len=seq_len)
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self.register_buffer("inv_freq", inv_freq, persistent=False)
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self.max_seq_len = seq_len
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if seq_len < self.original_max_seq_len and self.max_seq_len > self.original_max_seq_len: # reset to original
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self.register_buffer("inv_freq", self.original_inv_freq, persistent=False)
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self.max_seq_len = self.original_max_seq_len
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@torch.no_grad()
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def forward(self, x, position_ids):
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if "dynamic" in self.rope_type:
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self._update_freq(position_ids, device=x.device)
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inv_freq_expanded = self.inv_freq[None, :, None].float().expand(position_ids.shape[0], -1, 1)
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position_ids_expanded = position_ids[:, None, :].float()
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device_type = x.device.type
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device_type = device_type if isinstance(device_type, str) and device_type != "mps" else "cpu"
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with torch.autocast(device_type=device_type, enabled=False):
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freqs = (inv_freq_expanded @ position_ids_expanded).transpose(1, 2)
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emb = torch.cat((freqs, freqs), dim=-1)
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cos, sin = emb.cos(), emb.sin()
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cos, sin = cos * self.attention_scaling, sin * self.attention_scaling
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return cos.to(x.dtype), sin.to(x.dtype)
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class ExaoneSelfAttention(nn.Module):
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def __init__(self, config: ExaoneConfig, layer_idx: Optional[int] = None):
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super().__init__()
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self.config = config
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self.layer_idx = layer_idx
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self.embed_dim = config.hidden_size
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self.num_heads = config.num_attention_heads
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self.head_dim = self.embed_dim // self.num_heads
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self.num_key_value_heads = config.num_key_value_heads
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self.num_key_value_groups = self.num_heads // self.num_key_value_heads
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self.attention_dropout_rate = config.attention_dropout
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if self.head_dim * self.num_heads != self.embed_dim:
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raise ValueError(
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f"embed_dim must be divisible by num_heads (got `embed_dim`: {self.embed_dim} and `num_heads`: {self.num_heads})."
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)
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self.rotary = ExaoneRotaryEmbedding(config)
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self.k_proj = nn.Linear(self.embed_dim, self.num_key_value_heads * self.head_dim, bias=False)
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self.v_proj = nn.Linear(self.embed_dim, self.num_key_value_heads * self.head_dim, bias=False)
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self.q_proj = nn.Linear(self.embed_dim, self.num_heads * self.head_dim, bias=False)
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self.out_proj = nn.Linear(self.embed_dim, self.embed_dim, bias=False)
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def forward(
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self,
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hidden_states: torch.Tensor,
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attention_mask: Optional[torch.Tensor] = None,
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position_ids: Optional[torch.LongTensor] = None,
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past_key_value: Optional[Cache] = None,
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output_attentions: Optional[bool] = False,
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use_cache: Optional[bool] = False,
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cache_position: Optional[torch.LongTensor] = None,
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position_embeddings: Optional[Tuple[torch.Tensor, torch.Tensor]] = None,
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**kwargs,
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) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
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bsz, q_len, _ = hidden_states.size()
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query_states = self.q_proj(hidden_states)
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key_states = self.k_proj(hidden_states)
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value_states = self.v_proj(hidden_states)
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query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
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key_states = key_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
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value_states = value_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
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if position_embeddings is None:
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cos, sin = self.rotary(value_states, position_ids=position_ids)
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else:
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cos, sin = position_embeddings
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query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin)
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if past_key_value is not None:
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# sin and cos are specific to RoPE models; cache_position needed for the static cache
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cache_kwargs = {"sin": sin, "cos": cos, "cache_position": cache_position}
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key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs)
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key_states = repeat_kv(key_states, self.num_key_value_groups)
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value_states = repeat_kv(value_states, self.num_key_value_groups)
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attn_weights = torch.matmul(query_states, key_states.transpose(2, 3)) / math.sqrt(self.head_dim)
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if attention_mask is not None:
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causal_mask = attention_mask[:, :, :, : key_states.shape[-2]]
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attn_weights = attn_weights + causal_mask
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attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query_states.dtype)
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attn_weights = nn.functional.dropout(attn_weights, p=self.attention_dropout_rate, training=self.training)
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attn_output = torch.matmul(attn_weights, value_states)
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if attn_output.size() != (bsz, self.num_heads, q_len, self.head_dim):
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raise ValueError(
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f"Attention outputs should be of size {(bsz, self.num_heads, q_len, self.head_dim)}, but is"
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f" {attn_output.size()}"
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)
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attn_output = attn_output.transpose(1, 2).contiguous()
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attn_output = attn_output.reshape(bsz, q_len, self.embed_dim).contiguous()
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attn_output = self.out_proj(attn_output)
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if not output_attentions:
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attn_weights = None
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return attn_output, attn_weights, past_key_value
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class ExaoneFlashAttention(ExaoneSelfAttention):
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def __init__(self, *args, **kwargs):
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super().__init__(*args, **kwargs)
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def _shape(self, tensor: torch.Tensor, seq_len: int, bsz: int):
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return tensor.view(bsz, seq_len, self.num_heads, self.head_dim).transpose(1, 2).contiguous()
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def forward(
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self,
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hidden_states: torch.Tensor,
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attention_mask: Optional[torch.Tensor] = None,
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position_ids: Optional[torch.LongTensor] = None,
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past_key_value: Optional[Cache] = None,
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output_attentions: Optional[bool] = False,
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use_cache: Optional[bool] = False,
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cache_position: Optional[torch.LongTensor] = None,
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position_embeddings: Optional[Tuple[torch.Tensor, torch.Tensor]] = None,
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**kwargs,
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) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
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if isinstance(past_key_value, StaticCache):
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raise ValueError(
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"`static` cache implementation is not compatible with `attn_implementation==flash_attention_2` "
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"make sure to use `sdpa` in the mean time, and open an issue at https://github.com/huggingface/transformers"
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)
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output_attentions = False
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bsz, q_len, h_size = hidden_states.size()
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query_states = self.q_proj(hidden_states)
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key_states = self.k_proj(hidden_states)
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value_states = self.v_proj(hidden_states)
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query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
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key_states = key_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
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value_states = value_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
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if position_embeddings is None:
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cos, sin = self.rotary(value_states, position_ids=position_ids)
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else:
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cos, sin = position_embeddings
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query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin)
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if past_key_value is not None:
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# sin and cos are specific to RoPE models; cache_position needed for the static cache
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cache_kwargs = {"sin": sin, "cos": cos, "cache_position": cache_position}
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# Only update cache as shape of [bsz, n_head, q_len, head_dim]
|
|
# TODO: need to be fixed when transformers' KV cache layout is changed
|
|
key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs)
|
|
|
|
query_states = query_states.transpose(1, 2)
|
|
key_states = key_states.transpose(1, 2)
|
|
value_states = value_states.transpose(1, 2)
|
|
|
|
# In PEFT, usually we cast the layer norms in float32 for training stability reasons
|
|
# therefore the input hidden states gets silently casted in float32. Hence, we need
|
|
# cast them back in the correct dtype just to be sure everything works as expected.
|
|
input_dtype = query_states.dtype
|
|
if input_dtype == torch.float32:
|
|
if torch.is_autocast_enabled():
|
|
target_dtype = torch.get_autocast_gpu_dtype()
|
|
# Handle the case where the model is quantized
|
|
elif hasattr(self.config, "_pre_quantization_dtype"):
|
|
target_dtype = self.config._pre_quantization_dtype
|
|
else:
|
|
target_dtype = self.q_proj.weight.dtype
|
|
|
|
logger.warning_once(
|
|
f"The input hidden states seems to be silently casted in float32, this might be related to"
|
|
f" the fact you have upcasted embedding or layer norm layers in float32. We will cast back the input in"
|
|
f" {target_dtype}."
|
|
)
|
|
|
|
query_states = query_states.to(target_dtype)
|
|
key_states = key_states.to(target_dtype)
|
|
value_states = value_states.to(target_dtype)
|
|
|
|
dropout_rate = self.attention_dropout_rate if self.training else 0.0
|
|
|
|
attn_output = _flash_attention_forward(
|
|
query_states, key_states, value_states, attention_mask, q_len, dropout=dropout_rate, is_causal=True
|
|
)
|
|
|
|
attn_output = attn_output.reshape(bsz, q_len, self.embed_dim).contiguous()
|
|
attn_output = self.out_proj(attn_output)
|
|
|
|
if not output_attentions:
|
|
attn_weights = None
|
|
|
|
return attn_output, attn_weights, past_key_value
|
|
|
|
|
|
class ExaoneSdpaAttention(ExaoneSelfAttention):
|
|
def __init__(self, *args, **kwargs):
|
|
super().__init__(*args, **kwargs)
|
|
|
|
def forward(
|
|
self,
|
|
hidden_states: torch.Tensor,
|
|
attention_mask: Optional[torch.Tensor] = None,
|
|
position_ids: Optional[torch.LongTensor] = None,
|
|
past_key_value: Optional[Cache] = None,
|
|
output_attentions: Optional[bool] = False,
|
|
use_cache: Optional[bool] = False,
|
|
cache_position: Optional[torch.LongTensor] = None,
|
|
position_embeddings: Optional[Tuple[torch.Tensor, torch.Tensor]] = None,
|
|
**kwargs,
|
|
) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
|
|
if output_attentions:
|
|
logger.warning_once(
|
|
"ExaoneModel is using ExaoneSdpaAttention, but `torch.nn.functional.scaled_dot_product_attention` does not support `output_attentions=True`. Falling back to the manual attention implementation, "
|
|
'but specifying the manual implementation will be required from Transformers version v5.0.0 onwards. This warning can be removed using the argument `attn_implementation="eager"` when loading the model.'
|
|
)
|
|
return super().forward(
|
|
hidden_states=hidden_states,
|
|
attention_mask=attention_mask,
|
|
position_ids=position_ids,
|
|
past_key_value=past_key_value,
|
|
output_attentions=output_attentions,
|
|
use_cache=use_cache,
|
|
cache_position=cache_position,
|
|
position_embeddings=position_embeddings,
|
|
**kwargs,
|
|
)
|
|
|
|
bsz, q_len, _ = hidden_states.size()
|
|
|
|
query_states = self.q_proj(hidden_states)
|
|
key_states = self.k_proj(hidden_states)
|
|
value_states = self.v_proj(hidden_states)
|
|
|
|
query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
|
|
key_states = key_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
|
|
value_states = value_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
|
|
|
|
if position_embeddings is None:
|
|
cos, sin = self.rotary(value_states, position_ids=position_ids)
|
|
else:
|
|
cos, sin = position_embeddings
|
|
query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin)
|
|
|
|
if past_key_value is not None:
|
|
# sin and cos are specific to RoPE models; cache_position needed for the static cache
|
|
cache_kwargs = {"sin": sin, "cos": cos, "cache_position": cache_position}
|
|
key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs)
|
|
|
|
key_states = repeat_kv(key_states, self.num_key_value_groups)
|
|
value_states = repeat_kv(value_states, self.num_key_value_groups)
|
|
|
|
causal_mask = attention_mask
|
|
if attention_mask is not None:
|
|
causal_mask = causal_mask[:, :, :, : key_states.shape[-2]]
|
|
|
|
# SDPA with memory-efficient backend is currently (torch==2.1.2) bugged with non-contiguous inputs with custom attn_mask,
|
|
# Reference: https://github.com/pytorch/pytorch/issues/112577.
|
|
if query_states.device.type == "cuda" and causal_mask is not None:
|
|
query_states = query_states.contiguous()
|
|
key_states = key_states.contiguous()
|
|
value_states = value_states.contiguous()
|
|
|
|
# We dispatch to SDPA's Flash Attention or Efficient kernels via this `is_causal` if statement instead of an inline conditional assignment
|
|
# in SDPA to support both torch.compile's dynamic shapes and full graph options. An inline conditional prevents dynamic shapes from compiling.
|
|
is_causal = True if causal_mask is None and q_len > 1 else False
|
|
|
|
attn_output = torch.nn.functional.scaled_dot_product_attention(
|
|
query_states,
|
|
key_states,
|
|
value_states,
|
|
attn_mask=causal_mask,
|
|
dropout_p=self.attention_dropout_rate if self.training else 0.0,
|
|
is_causal=is_causal,
|
|
)
|
|
|
|
attn_output = attn_output.transpose(1, 2).contiguous()
|
|
attn_output = attn_output.reshape(bsz, q_len, self.embed_dim).contiguous()
|
|
|
|
attn_output = self.out_proj(attn_output)
|
|
|
|
return attn_output, None, past_key_value
|
|
|
|
|
|
class ExaoneAttention(nn.Module):
|
|
def __init__(self, config, layer_id=0):
|
|
super().__init__()
|
|
self.layer_id = layer_id
|
|
if "flash" in config._attn_implementation:
|
|
self.attention = ExaoneFlashAttention(config, self.layer_id)
|
|
elif "sdpa" in config._attn_implementation:
|
|
self.attention = ExaoneSdpaAttention(config, self.layer_id)
|
|
else:
|
|
self.attention = ExaoneSelfAttention(config, self.layer_id)
|
|
|
|
def forward(
|
|
self,
|
|
hidden_states: torch.Tensor,
|
|
attention_mask: Optional[torch.Tensor] = None,
|
|
position_ids: Optional[torch.LongTensor] = None,
|
|
past_key_value: Optional[Cache] = None,
|
|
output_attentions: Optional[bool] = False,
|
|
use_cache: Optional[bool] = False,
|
|
cache_position: Optional[torch.LongTensor] = None,
|
|
position_embeddings: Optional[Tuple[torch.Tensor, torch.Tensor]] = None,
|
|
**kwargs,
|
|
) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
|
|
return self.attention(
|
|
hidden_states=hidden_states,
|
|
attention_mask=attention_mask,
|
|
position_ids=position_ids,
|
|
past_key_value=past_key_value,
|
|
output_attentions=output_attentions,
|
|
use_cache=use_cache,
|
|
cache_position=cache_position,
|
|
position_embeddings=position_embeddings,
|
|
**kwargs,
|
|
)
|
|
|
|
|
|
class ExaoneGatedMLP(nn.Module):
|
|
def __init__(self, intermediate_size, config):
|
|
super().__init__()
|
|
self.config = config
|
|
embed_dim = config.hidden_size
|
|
self.c_fc_0 = nn.Linear(embed_dim, intermediate_size, bias=False)
|
|
self.c_fc_1 = nn.Linear(embed_dim, intermediate_size, bias=False)
|
|
self.c_proj = nn.Linear(intermediate_size, embed_dim, bias=False)
|
|
self.act = ACT2FN[config.activation_function]
|
|
|
|
def forward(self, hidden_states):
|
|
output_proj = self.c_proj(self.act(self.c_fc_0(hidden_states)) * self.c_fc_1(hidden_states))
|
|
return output_proj
|
|
|
|
|
|
class ExaoneBlock(nn.Module):
|
|
def __init__(self, config, layer_id):
|
|
super().__init__()
|
|
self.config = config
|
|
hidden_size = config.hidden_size
|
|
inner_dim = config.intermediate_size if config.intermediate_size is not None else 4 * hidden_size
|
|
self.ln_1 = ExaoneRMSNorm(hidden_size=hidden_size, eps=config.layer_norm_epsilon)
|
|
self.attn = ExaoneAttention(config, layer_id)
|
|
self.ln_2 = ExaoneRMSNorm(hidden_size=hidden_size, eps=config.layer_norm_epsilon)
|
|
self.mlp = ExaoneGatedMLP(inner_dim, config)
|
|
|
|
def forward(
|
|
self,
|
|
hidden_states: torch.Tensor,
|
|
attention_mask: Optional[torch.Tensor] = None,
|
|
position_ids: Optional[torch.LongTensor] = None,
|
|
past_key_value: Optional[Cache] = None,
|
|
output_attentions: Optional[bool] = False,
|
|
use_cache: Optional[bool] = False,
|
|
cache_position: Optional[torch.LongTensor] = None,
|
|
position_embeddings: Optional[Tuple[torch.Tensor, torch.Tensor]] = None,
|
|
**kwargs,
|
|
) -> Tuple[torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]]]:
|
|
residual = hidden_states
|
|
hidden_states = self.ln_1(hidden_states)
|
|
|
|
hidden_states, self_attn_weights, present_key_value = self.attn(
|
|
hidden_states=hidden_states,
|
|
attention_mask=attention_mask,
|
|
position_ids=position_ids,
|
|
past_key_value=past_key_value,
|
|
output_attentions=output_attentions,
|
|
use_cache=use_cache,
|
|
cache_position=cache_position,
|
|
position_embeddings=position_embeddings,
|
|
**kwargs,
|
|
)
|
|
# residual connection
|
|
hidden_states = residual + hidden_states
|
|
|
|
residual = hidden_states
|
|
hidden_states = self.ln_2(hidden_states)
|
|
hidden_states = self.mlp(hidden_states)
|
|
|
|
hidden_states = residual + hidden_states
|
|
|
|
outputs = (hidden_states,)
|
|
|
|
if output_attentions:
|
|
outputs += (self_attn_weights,)
|
|
|
|
if use_cache:
|
|
outputs += (present_key_value,)
|
|
|
|
return outputs
|
|
|
|
|
|
class ExaonePreTrainedModel(PreTrainedModel):
|
|
"""
|
|
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
|
|
models.
|
|
"""
|
|
|
|
config_class = ExaoneConfig
|
|
base_model_prefix = "transformer"
|
|
supports_gradient_checkpointing = True
|
|
_no_split_modules = ["ExaoneBlock"]
|
|
_skip_keys_device_placement = "past_key_values"
|
|
_supports_flash_attn_2 = True
|
|
_supports_sdpa = True
|
|
_supports_cache_class = True
|
|
|
|
def __init__(self, *inputs, **kwargs):
|
|
super().__init__(*inputs, **kwargs)
|
|
|
|
def _init_weights(self, module):
|
|
"""Initialize the weights."""
|
|
if isinstance(module, (nn.Linear,)):
|
|
# Slightly different from the TF version which uses truncated_normal for initialization
|
|
# cf https://github.com/pytorch/pytorch/pull/5617
|
|
module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
|
|
if module.bias is not None:
|
|
module.bias.data.zero_()
|
|
elif isinstance(module, nn.Embedding):
|
|
module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
|
|
if module.padding_idx is not None:
|
|
module.weight.data[module.padding_idx].zero_()
|
|
elif isinstance(module, ExaoneRMSNorm):
|
|
module.weight.data.fill_(1.0)
|
|
|
|
|
|
EXAONE_START_DOCSTRING = r"""
|
|
|
|
This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
|
|
library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
|
|
etc.)
|
|
|
|
This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass.
|
|
Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
|
|
and behavior.
|
|
|
|
Parameters:
|
|
config ([`ExaoneConfig`]): Model configuration class with all the parameters of the model.
|
|
Initializing with a config file does not load the weights associated with the model, only the
|
|
configuration. Check out the `PreTrainedModel.from_pretrained` method to load the model weights.
|
|
"""
|
|
|
|
EXAONE_INPUTS_DOCSTRING = r"""
|
|
Args:
|
|
input_ids (`torch.LongTensor` of shape `(batch_size, input_ids_length)`, *optional*):
|
|
`input_ids_length` = `sequence_length` if `past_key_values` is `None` else
|
|
`past_key_values.get_seq_length()` (`sequence_length` of input past key value states). Indices of input
|
|
sequence tokens in the vocabulary.
|
|
|
|
If `past_key_values` is used, only `input_ids` that do not have their past calculated should be
|
|
passed as `input_ids`.
|
|
|
|
`What are input IDs? <../glossary.html#input-ids>`__
|
|
attention_mask (`torch.FloatTensor` of shape `(batch_size, sequence_length)`, *optional*):
|
|
Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
|
|
|
|
- 1 for tokens that are **not masked**,
|
|
- 0 for tokens that are **masked**.
|
|
|
|
`What are attention masks? <../glossary.html#attention-mask>`__
|
|
position_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
|
|
Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
|
|
config.max_position_embeddings - 1]`.
|
|
|
|
`What are position IDs? <../glossary.html#position-ids>`_
|
|
past_key_values (`Cache`, *optional*):
|
|
Contains precomputed hidden-states (key and values in the attention blocks) as computed by the model (see
|
|
`past_key_values` output below). Can be used to speed up sequential decoding. This typically consists
|
|
in the `past_key_values` returned by the model at a previous stage of decoding, when `use_cache=True` or
|
|
`config.use_cache=True`.
|
|
inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
|
|
Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation.
|
|
This is useful if you want more control over how to convert `input_ids` indices into associated
|
|
vectors than the model's internal embedding lookup matrix.
|
|
|
|
If `past_key_values` is used, optionally only the last `inputs_embeds` have to be input (see
|
|
`past_key_values`).
|
|
use_cache (`bool`, *optional*):
|
|
If set to `True`, `past_key_values` key value states are returned and can be used to speed up
|
|
decoding (see `past_key_values`).
|
|
output_attentions (`bool`, *optional*):
|
|
Whether or not to return the attentions tensors of all attention layers. See ``attentions`` under returned
|
|
tensors for more detail.
|
|
output_hidden_states (`bool`, *optional*):
|
|
Whether or not to return the hidden states of all layers. See ``hidden_states`` under returned tensors for
|
|
more detail.
|
|
return_dict (`bool`, *optional*):
|
|
Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
|
|
cache_position (`torch.LongTensor` of shape `(sequence_length)`, *optional*):
|
|
Indices depicting the position of the input sequence tokens in the sequence. Contrarily to `position_ids`,
|
|
this tensor is not affected by padding. It is used to update the cache in the correct position and to infer
|
|
the complete sequence length.
|
|
"""
|
|
|
|
|
|
@add_start_docstrings(
|
|
"The bare EXAONE Model transformer outputting raw hidden-states without any specific head on top.",
|
|
EXAONE_START_DOCSTRING,
|
|
)
|
|
class ExaoneModel(ExaonePreTrainedModel):
|
|
def __init__(self, config):
|
|
super().__init__(config)
|
|
self.config = config
|
|
self.embed_dim = config.hidden_size
|
|
self.wte = nn.Embedding(config.vocab_size, self.embed_dim, self.config.pad_token_id)
|
|
self.drop = nn.Dropout(float(config.embed_dropout))
|
|
self.h = nn.ModuleList([ExaoneBlock(config, layer_id=i) for i in range(config.num_layers)])
|
|
self.ln_f = ExaoneRMSNorm(hidden_size=self.embed_dim, eps=config.layer_norm_epsilon)
|
|
self.rotary = ExaoneRotaryEmbedding(config)
|
|
self.gradient_checkpointing = False
|
|
# Initialize weights and apply final processing
|
|
self.post_init()
|
|
|
|
def get_input_embeddings(self):
|
|
return self.wte
|
|
|
|
def set_input_embeddings(self, new_embeddings):
|
|
self.wte = new_embeddings
|
|
|
|
@add_start_docstrings_to_model_forward(EXAONE_INPUTS_DOCSTRING)
|
|
@add_code_sample_docstrings(
|
|
checkpoint=_CHECKPOINT_FOR_DOC,
|
|
output_type=BaseModelOutputWithPastAndCrossAttentions,
|
|
config_class=_CONFIG_FOR_DOC,
|
|
)
|
|
def forward(
|
|
self,
|
|
input_ids: Optional[torch.Tensor] = None,
|
|
attention_mask: Optional[torch.Tensor] = None,
|
|
position_ids: Optional[torch.Tensor] = None,
|
|
past_key_values: Optional[Cache] = None,
|
|
inputs_embeds: Optional[torch.Tensor] = None,
|
|
use_cache: Optional[bool] = None,
|
|
output_attentions: Optional[bool] = None,
|
|
output_hidden_states: Optional[bool] = None,
|
|
return_dict: Optional[bool] = None,
|
|
cache_position: Optional[torch.LongTensor] = None,
|
|
) -> Union[Tuple[torch.Tensor], BaseModelOutputWithPast]:
|
|
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
|
|
output_hidden_states = (
|
|
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
|
|
)
|
|
use_cache = use_cache if use_cache is not None else self.config.use_cache
|
|
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
|
|
|
|
if self.gradient_checkpointing and self.training:
|
|
if use_cache:
|
|
logger.warning_once(
|
|
"`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..."
|
|
)
|
|
use_cache = False
|
|
|
|
if input_ids is not None and inputs_embeds is not None:
|
|
raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time")
|
|
elif input_ids is not None:
|
|
batch_size, seq_length = input_ids.shape[:2]
|
|
elif inputs_embeds is not None:
|
|
batch_size, seq_length = inputs_embeds.shape[:2]
|
|
else:
|
|
raise ValueError("You have to specify either input_ids or inputs_embeds")
|
|
|
|
return_legacy_cache = False
|
|
if (
|
|
use_cache and not isinstance(past_key_values, Cache) and not self.training
|
|
): # kept for BC (non `Cache` `past_key_values` inputs)
|
|
return_legacy_cache = True
|
|
past_key_values = DynamicCache.from_legacy_cache(past_key_values)
|
|
logger.warning_once(
|
|
"We detected that you are passing `past_key_values` as a tuple and this is deprecated and will be removed in v4.43. "
|
|
"Please use an appropriate `Cache` class (https://huggingface.co/docs/transformers/v4.41.3/en/internal/generation_utils#transformers.Cache)"
|
|
)
|
|
|
|
if inputs_embeds is None:
|
|
inputs_embeds = self.wte(input_ids)
|
|
|
|
if cache_position is None:
|
|
past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0
|
|
cache_position = torch.arange(
|
|
past_seen_tokens, past_seen_tokens + inputs_embeds.shape[1], device=inputs_embeds.device
|
|
)
|
|
if position_ids is None:
|
|
position_ids = cache_position.unsqueeze(0)
|
|
|
|
causal_mask = self._update_causal_mask(
|
|
attention_mask, inputs_embeds, cache_position, past_key_values, output_attentions
|
|
)
|
|
|
|
hidden_states = inputs_embeds
|
|
hidden_states = self.drop(hidden_states)
|
|
|
|
position_embeddings = self.rotary(hidden_states, position_ids)
|
|
|
|
all_hidden_states = () if output_hidden_states else None
|
|
all_self_attns = () if output_attentions else None
|
|
next_decoder_cache = None
|
|
|
|
for block in self.h:
|
|
if output_hidden_states:
|
|
all_hidden_states = all_hidden_states + (hidden_states,)
|
|
|
|
if self.gradient_checkpointing and self.training:
|
|
outputs = self._gradient_checkpointing_func(
|
|
block.__call__,
|
|
hidden_states,
|
|
causal_mask,
|
|
position_ids,
|
|
past_key_values,
|
|
output_attentions,
|
|
use_cache,
|
|
cache_position,
|
|
position_embeddings,
|
|
)
|
|
else:
|
|
outputs = block(
|
|
hidden_states,
|
|
attention_mask=causal_mask,
|
|
position_ids=position_ids,
|
|
past_key_value=past_key_values,
|
|
output_attentions=output_attentions,
|
|
use_cache=use_cache,
|
|
cache_position=cache_position,
|
|
position_embeddings=position_embeddings,
|
|
)
|
|
|
|
hidden_states = outputs[0]
|
|
if use_cache:
|
|
next_decoder_cache = outputs[2 if output_attentions else 1]
|
|
|
|
if output_attentions:
|
|
all_self_attns += (outputs[1],)
|
|
|
|
hidden_states = self.ln_f(hidden_states)
|
|
# Add last hidden state
|
|
if output_hidden_states:
|
|
all_hidden_states += (hidden_states,)
|
|
|
|
next_cache = None
|
|
if use_cache:
|
|
next_cache = next_decoder_cache.to_legacy_cache() if return_legacy_cache else next_decoder_cache
|
|
if not return_dict:
|
|
return tuple(v for v in [hidden_states, next_cache, all_hidden_states, all_self_attns] if v is not None)
|
|
|
|
return BaseModelOutputWithPast(
|
|
last_hidden_state=hidden_states,
|
|
past_key_values=next_cache,
|
|
hidden_states=all_hidden_states,
|
|
attentions=all_self_attns,
|
|
)
|
|
|
|
def _update_causal_mask(
|
|
self,
|
|
attention_mask: torch.Tensor,
|
|
input_tensor: torch.Tensor,
|
|
cache_position: torch.Tensor,
|
|
past_key_values: Cache,
|
|
output_attentions: bool,
|
|
):
|
|
# TODO: As of torch==2.2.0, the `attention_mask` passed to the model in `generate` is 2D and of dynamic length even when the static
|
|
# KV cache is used. This is an issue for torch.compile which then recaptures cudagraphs at each decode steps due to the dynamic shapes.
|
|
# (`recording cudagraph tree for symint key 13`, etc.), which is VERY slow. A workaround is `@torch.compiler.disable`, but this prevents using
|
|
# `fullgraph=True`. See more context in https://github.com/huggingface/transformers/pull/29114
|
|
|
|
if self.config._attn_implementation == "flash_attention_2":
|
|
if attention_mask is not None and 0.0 in attention_mask:
|
|
return attention_mask
|
|
return None
|
|
|
|
# For SDPA, when possible, we will rely on its `is_causal` argument instead of its `attn_mask` argument, in
|
|
# order to dispatch on Flash Attention 2. This feature is not compatible with static cache, as SDPA will fail
|
|
# to infer the attention mask.
|
|
past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0
|
|
using_static_cache = isinstance(past_key_values, StaticCache)
|
|
|
|
# When output attentions is True, sdpa implementation's forward method calls the eager implementation's forward
|
|
if self.config._attn_implementation == "sdpa" and not using_static_cache and not output_attentions:
|
|
if AttentionMaskConverter._ignore_causal_mask_sdpa(
|
|
attention_mask,
|
|
inputs_embeds=input_tensor,
|
|
past_key_values_length=past_seen_tokens,
|
|
is_training=self.training,
|
|
):
|
|
return None
|
|
|
|
dtype, device = input_tensor.dtype, input_tensor.device
|
|
min_dtype = torch.finfo(dtype).min
|
|
sequence_length = input_tensor.shape[1]
|
|
if using_static_cache:
|
|
target_length = past_key_values.get_max_length()
|
|
else:
|
|
target_length = (
|
|
attention_mask.shape[-1]
|
|
if isinstance(attention_mask, torch.Tensor)
|
|
else past_seen_tokens + sequence_length + 1
|
|
)
|
|
|
|
# In case the provided `attention` mask is 2D, we generate a causal mask here (4D).
|
|
causal_mask = _prepare_4d_causal_attention_mask_with_cache_position(
|
|
attention_mask,
|
|
sequence_length=sequence_length,
|
|
target_length=target_length,
|
|
dtype=dtype,
|
|
device=device,
|
|
min_dtype=min_dtype,
|
|
cache_position=cache_position,
|
|
batch_size=input_tensor.shape[0],
|
|
)
|
|
|
|
if (
|
|
self.config._attn_implementation == "sdpa"
|
|
and attention_mask is not None
|
|
and attention_mask.device.type == "cuda"
|
|
and not output_attentions
|
|
):
|
|
# Attend to all tokens in fully masked rows in the causal_mask, for example the relevant first rows when
|
|
# using left padding. This is required by F.scaled_dot_product_attention memory-efficient attention path.
|
|
# Details: https://github.com/pytorch/pytorch/issues/110213
|
|
causal_mask = AttentionMaskConverter._unmask_unattended(causal_mask, min_dtype)
|
|
|
|
return causal_mask
|
|
|
|
|
|
@add_start_docstrings(
|
|
"""
|
|
The EXAONE Model transformer with a language modeling head on top (linear layer with weights tied to the input
|
|
embeddings).
|
|
""",
|
|
EXAONE_START_DOCSTRING,
|
|
)
|
|
class ExaoneForCausalLM(ExaonePreTrainedModel, GenerationMixin):
|
|
_tied_weights_keys = ["lm_head.weight"]
|
|
|
|
def __init__(self, config):
|
|
super().__init__(config)
|
|
self.transformer = ExaoneModel(config)
|
|
self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
|
|
self.config = config
|
|
# Initialize weights and apply final processing
|
|
self.post_init()
|
|
|
|
def get_output_embeddings(self):
|
|
return self.lm_head
|
|
|
|
def set_output_embeddings(self, new_embeddings):
|
|
self.lm_head = new_embeddings
|
|
|
|
@add_start_docstrings_to_model_forward(EXAONE_INPUTS_DOCSTRING)
|
|
@add_code_sample_docstrings(
|
|
checkpoint=_CHECKPOINT_FOR_DOC,
|
|
output_type=BaseModelOutputWithPast,
|
|
config_class=_CONFIG_FOR_DOC,
|
|
)
|
|
def forward(
|
|
self,
|
|
input_ids: Optional[torch.Tensor] = None,
|
|
attention_mask: Optional[torch.Tensor] = None,
|
|
position_ids: Optional[torch.Tensor] = None,
|
|
past_key_values: Optional[Cache] = 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,
|
|
cache_position: Optional[torch.LongTensor] = None,
|
|
) -> Union[Tuple[torch.Tensor], BaseModelOutputWithPast]:
|
|
r"""
|
|
Args:
|
|
labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
|
|
Labels for language modeling. Note that the labels **are shifted** inside the model, i.e. you can set
|
|
`labels = input_ids` Indices are selected in `[-100, 0, ..., config.vocab_size]` All labels set to `-100`
|
|
are ignored (masked), the loss is only computed for labels in `[0, ..., config.vocab_size]`
|
|
|
|
Example:
|
|
|
|
```python
|
|
>>> from transformers import AutoModelForCausalLM, AutoTokenizer
|
|
|
|
>>> model = AutoModelForCausalLM.from_pretrained("LGAI-EXAONE/EXAONE-3.0-7.8B-Instruct",
|
|
trust_remote_code=True)
|
|
>>> tokenizer = AutoTokenizer.from_pretrained("LGAI-EXAONE/EXAONE-3.0-7.8B-Instruct")
|
|
|
|
>>> prompt = "Explain how wonderful you are"
|
|
>>> messages = [
|
|
{"role": "system", "content": "You are a helpful assistant."},
|
|
{"role": "user", "content": prompt}
|
|
]
|
|
>>> input_ids = tokenizer.apply_chat_template(
|
|
messages,
|
|
tokenize=True,
|
|
add_generation_prompt=True,
|
|
return_tensors="pt"
|
|
)
|
|
|
|
>>> output = model.generate(input_ids, max_new_tokens=128)
|
|
>>> tokenizer.decode(output[0], skip_special_tokens=True)
|
|
"[|system|]You are a helpful assistant.\n[|user|]Explain how wonderful you are\n[|assistant|]Thank you for your kind words! I'm here to assist you with information, answer questions, and help you in any way I can. My goal is to provide accurate, helpful, and timely responses. Whether you need help with a specific task, want to learn something new, or just need someone to talk to, I'm here for you. How can I assist you today?"
|
|
```
|
|
"""
|
|
|
|
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
|
|
output_hidden_states = (
|
|
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
|
|
)
|
|
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
|
|
transformer_outputs = self.transformer(
|
|
input_ids,
|
|
attention_mask=attention_mask,
|
|
past_key_values=past_key_values,
|
|
position_ids=position_ids,
|
|
inputs_embeds=inputs_embeds,
|
|
use_cache=use_cache,
|
|
output_attentions=output_attentions,
|
|
output_hidden_states=output_hidden_states,
|
|
return_dict=return_dict,
|
|
cache_position=cache_position,
|
|
)
|
|
hidden_states = transformer_outputs[0]
|
|
lm_logits = self.lm_head(hidden_states)
|
|
lm_logits = lm_logits.float()
|
|
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()
|
|
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,
|
|
)
|
|
|
|
def prepare_inputs_for_generation(
|
|
self,
|
|
input_ids,
|
|
past_key_values=None,
|
|
attention_mask=None,
|
|
inputs_embeds=None,
|
|
cache_position=None,
|
|
position_ids=None,
|
|
use_cache=True,
|
|
**kwargs,
|
|
):
|
|
# If we have cache: let's slice `input_ids` through `cache_position`, to keep only the unprocessed tokens
|
|
# Exception 1: when passing input_embeds, input_ids may be missing entries
|
|
# Exception 2: some generation methods do special slicing of input_ids, so we don't need to do it here
|
|
if past_key_values is not None:
|
|
if inputs_embeds is not None: # Exception 1
|
|
input_ids = input_ids[:, -cache_position.shape[0] :]
|
|
elif input_ids.shape[1] != cache_position.shape[0]: # Default case (the "else", a no op, is Exception 2)
|
|
input_ids = input_ids[:, cache_position]
|
|
|
|
if attention_mask is not None and position_ids is None:
|
|
# create position_ids on the fly for batch generation
|
|
position_ids = attention_mask.long().cumsum(-1) - 1
|
|
position_ids.masked_fill_(attention_mask == 0, 1)
|
|
if past_key_values:
|
|
position_ids = position_ids[:, -input_ids.shape[1] :]
|
|
|
|
# This `clone` call is needed to avoid recapturing cuda graphs with `torch.compile`'s `mode="reduce-overhead`, as otherwise the input `position_ids` would have various stride during the decoding. Here, simply using `.contiguous()` is not sufficient as in the batch size = 1 case, `position_ids` is already contiguous but with varying stride which retriggers a capture.
|
|
position_ids = position_ids.clone(memory_format=torch.contiguous_format)
|
|
|
|
# if `inputs_embeds` are passed, we only want to use them in the 1st generation step
|
|
if inputs_embeds is not None and cache_position[0] == 0:
|
|
model_inputs = {"inputs_embeds": inputs_embeds, "input_ids": None}
|
|
else:
|
|
model_inputs = {"input_ids": input_ids, "inputs_embeds": None}
|
|
|
|
if isinstance(past_key_values, StaticCache) and attention_mask.ndim == 2:
|
|
if inputs_embeds is not None:
|
|
batch_size, sequence_length, _ = inputs_embeds.shape
|
|
device = inputs_embeds.device
|
|
else:
|
|
batch_size, sequence_length = input_ids.shape
|
|
device = input_ids.device
|
|
|
|
dtype = self.lm_head.weight.dtype
|
|
min_dtype = torch.finfo(dtype).min
|
|
|
|
attention_mask = _prepare_4d_causal_attention_mask_with_cache_position(
|
|
attention_mask,
|
|
sequence_length=sequence_length,
|
|
target_length=past_key_values.get_max_length(),
|
|
dtype=dtype,
|
|
device=device,
|
|
min_dtype=min_dtype,
|
|
cache_position=cache_position,
|
|
batch_size=batch_size,
|
|
)
|
|
|
|
model_inputs.update(
|
|
{
|
|
"position_ids": position_ids,
|
|
"cache_position": cache_position,
|
|
"past_key_values": past_key_values,
|
|
"use_cache": use_cache,
|
|
"attention_mask": attention_mask,
|
|
}
|
|
)
|
|
return model_inputs
|
|
|
|
|
|
@add_start_docstrings(
|
|
"""
|
|
The EXAONE Model transformer with a sequence classification head on top (linear layer).
|
|
|
|
[`ExaoneForSequenceClassification`] uses the last token in order to do the classification, as
|
|
other causal models (e.g. GPT-1) do.
|
|
|
|
Since it does classification on the last token, it requires to know the position of the last token. If a
|
|
`pad_token_id` is defined in the configuration, it finds the last token that is not a padding token in each
|
|
row. If no `pad_token_id` is defined, it simply takes the last value in each row of the batch. Since it cannot
|
|
guess the padding tokens when `inputs_embeds` are passed instead of `input_ids`, it does the same (take
|
|
the last value in each row of the batch).
|
|
""",
|
|
EXAONE_START_DOCSTRING,
|
|
)
|
|
class ExaoneForSequenceClassification(ExaonePreTrainedModel):
|
|
def __init__(self, config):
|
|
super().__init__(config)
|
|
self.num_labels = config.num_labels
|
|
self.transformer = ExaoneModel(config)
|
|
self.score = nn.Linear(config.hidden_size, self.num_labels, bias=False)
|
|
|
|
# Initialize weights and apply final processing
|
|
self.post_init()
|
|
|
|
@add_start_docstrings_to_model_forward(EXAONE_INPUTS_DOCSTRING)
|
|
@add_code_sample_docstrings(
|
|
checkpoint=_CHECKPOINT_FOR_DOC,
|
|
output_type=SequenceClassifierOutputWithPast,
|
|
config_class=_CONFIG_FOR_DOC,
|
|
)
|
|
def forward(
|
|
self,
|
|
input_ids: Optional[torch.Tensor] = None,
|
|
attention_mask: Optional[torch.Tensor] = None,
|
|
position_ids: Optional[torch.Tensor] = None,
|
|
past_key_values: Optional[Cache] = 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,
|
|
) -> Union[Tuple[torch.Tensor], SequenceClassifierOutputWithPast]:
|
|
r"""
|
|
labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
|
|
Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
|
|
config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
|
|
`config.num_labels > 1` a classification loss is computed (Cross-Entropy).
|
|
"""
|
|
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
|
|
|
|
transformer_outputs = self.transformer(
|
|
input_ids,
|
|
attention_mask=attention_mask,
|
|
position_ids=position_ids,
|
|
past_key_values=past_key_values,
|
|
inputs_embeds=inputs_embeds,
|
|
use_cache=use_cache,
|
|
output_attentions=output_attentions,
|
|
output_hidden_states=output_hidden_states,
|
|
return_dict=return_dict,
|
|
)
|
|
hidden_states = transformer_outputs[0]
|
|
logits = self.score(hidden_states)
|
|
|
|
if input_ids is not None:
|
|
batch_size, sequence_length = input_ids.shape[:2]
|
|
else:
|
|
batch_size, sequence_length = inputs_embeds.shape[:2]
|
|
|
|
if self.config.pad_token_id is None and batch_size != 1:
|
|
raise ValueError("Cannot handle batch sizes > 1 if no padding token is defined.")
|
|
if self.config.pad_token_id is None:
|
|
sequence_lengths = -1
|
|
else:
|
|
if input_ids is not None:
|
|
# if no pad token found, use modulo instead of reverse indexing for ONNX compatibility
|
|
sequence_lengths = torch.ne(input_ids, self.config.pad_token_id).sum(-1) - 1
|
|
sequence_lengths = sequence_lengths % input_ids.shape[-1]
|
|
sequence_lengths = sequence_lengths.to(logits.device)
|
|
else:
|
|
sequence_lengths = -1
|
|
logger.warning(
|
|
f"{self.__class__.__name__} will not detect padding tokens in `inputs_embeds`. Results may be "
|
|
"unexpected if using padding tokens in conjunction with `inputs_embeds.`"
|
|
)
|
|
|
|
pooled_logits = logits[torch.arange(batch_size, device=logits.device), sequence_lengths]
|
|
|
|
loss = None
|
|
if labels is not None:
|
|
labels = labels.to(logits.device)
|
|
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(pooled_logits.squeeze(), labels.squeeze())
|
|
else:
|
|
loss = loss_fct(pooled_logits, labels)
|
|
elif self.config.problem_type == "single_label_classification":
|
|
loss_fct = CrossEntropyLoss()
|
|
loss = loss_fct(pooled_logits.view(-1, self.num_labels), labels.view(-1))
|
|
elif self.config.problem_type == "multi_label_classification":
|
|
loss_fct = BCEWithLogitsLoss()
|
|
loss = loss_fct(pooled_logits, labels)
|
|
if not return_dict:
|
|
output = (pooled_logits,) + transformer_outputs[1:]
|
|
return ((loss,) + output) if loss is not None else output
|
|
|
|
return SequenceClassifierOutputWithPast(
|
|
loss=loss,
|
|
logits=pooled_logits,
|
|
past_key_values=transformer_outputs.past_key_values,
|
|
hidden_states=transformer_outputs.hidden_states,
|
|
attentions=transformer_outputs.attentions,
|
|
)
|
|
|
|
|
|
@add_start_docstrings(
|
|
"""
|
|
The EXAONE Model transformer with a span classification head on top for extractive question-answering tasks like
|
|
SQuAD (a linear layers on top of the hidden-states output to compute `span start logits` and `span end logits`).
|
|
""",
|
|
EXAONE_START_DOCSTRING,
|
|
)
|
|
class ExaoneForQuestionAnswering(ExaonePreTrainedModel):
|
|
def __init__(self, config):
|
|
super().__init__(config)
|
|
self.num_labels = config.num_labels
|
|
self.transformer = ExaoneModel(config)
|
|
self.qa_outputs = nn.Linear(config.hidden_size, config.num_labels)
|
|
|
|
# Model parallel
|
|
self.model_parallel = False
|
|
self.device_map = None
|
|
|
|
# Initialize weights and apply final processing
|
|
self.post_init()
|
|
|
|
def forward(
|
|
self,
|
|
input_ids: Optional[torch.LongTensor] = None,
|
|
attention_mask: Optional[torch.FloatTensor] = None,
|
|
position_ids: Optional[torch.LongTensor] = None,
|
|
past_key_values: Optional[Cache] = None,
|
|
inputs_embeds: Optional[torch.FloatTensor] = None,
|
|
start_positions: Optional[torch.LongTensor] = None,
|
|
end_positions: Optional[torch.LongTensor] = None,
|
|
output_attentions: Optional[bool] = None,
|
|
output_hidden_states: Optional[bool] = None,
|
|
return_dict: Optional[bool] = None,
|
|
) -> Union[Tuple[torch.Tensor], QuestionAnsweringModelOutput]:
|
|
r"""
|
|
start_positions (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
|
|
Labels for position (index) of the start of the labelled span for computing the token classification loss.
|
|
Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the
|
|
sequence are not taken into account for computing the loss.
|
|
end_positions (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
|
|
Labels for position (index) of the end of the labelled span for computing the token classification loss.
|
|
Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the
|
|
sequence are not taken into account for computing the loss.
|
|
"""
|
|
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
|
|
|
|
outputs = self.transformer(
|
|
input_ids,
|
|
attention_mask=attention_mask,
|
|
position_ids=position_ids,
|
|
past_key_values=past_key_values,
|
|
inputs_embeds=inputs_embeds,
|
|
output_attentions=output_attentions,
|
|
output_hidden_states=output_hidden_states,
|
|
return_dict=return_dict,
|
|
)
|
|
|
|
sequence_output = outputs[0]
|
|
|
|
logits = self.qa_outputs(sequence_output)
|
|
start_logits, end_logits = logits.split(1, dim=-1)
|
|
start_logits = start_logits.squeeze(-1).contiguous()
|
|
end_logits = end_logits.squeeze(-1).contiguous()
|
|
|
|
total_loss = None
|
|
if start_positions is not None and end_positions is not None:
|
|
# If we are on multi-GPU, split add a dimension
|
|
if len(start_positions.size()) > 1:
|
|
start_positions = start_positions.squeeze(-1).to(start_logits.device)
|
|
if len(end_positions.size()) > 1:
|
|
end_positions = end_positions.squeeze(-1).to(end_logits.device)
|
|
# sometimes the start/end positions are outside our model inputs, we ignore these terms
|
|
ignored_index = start_logits.size(1)
|
|
start_positions = start_positions.clamp(0, ignored_index)
|
|
end_positions = end_positions.clamp(0, ignored_index)
|
|
|
|
loss_fct = CrossEntropyLoss(ignore_index=ignored_index)
|
|
start_loss = loss_fct(start_logits, start_positions)
|
|
end_loss = loss_fct(end_logits, end_positions)
|
|
total_loss = (start_loss + end_loss) / 2
|
|
|
|
if not return_dict:
|
|
output = (start_logits, end_logits) + outputs[2:]
|
|
return ((total_loss,) + output) if total_loss is not None else output
|
|
|
|
return QuestionAnsweringModelOutput(
|
|
loss=total_loss,
|
|
start_logits=start_logits,
|
|
end_logits=end_logits,
|
|
hidden_states=outputs.hidden_states,
|
|
attentions=outputs.attentions,
|
|
)
|