871 lines
36 KiB
Python
871 lines
36 KiB
Python
# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
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# This file was automatically generated from src/transformers/models/exaone4/modular_exaone4.py.
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# Do NOT edit this file manually as any edits will be overwritten by the generation of
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# the file from the modular. If any change should be done, please apply the change to the
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# modular_exaone4.py file directly. One of our CI enforces this.
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# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
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# coding=utf-8
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# Copyright 2025 The LG AI Research and HuggingFace Inc. team. All rights reserved.
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#
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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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from typing import Callable, Optional, Union
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import torch
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from torch import nn
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from transformers.activations import ACT2FN
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from transformers.cache_utils import Cache, HybridCache, StaticCache
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from transformers.generation import GenerationMixin
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from transformers.integrations import use_kernel_forward_from_hub
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from transformers.masking_utils import create_causal_mask, create_sliding_window_causal_mask
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from transformers.modeling_outputs import (
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BaseModelOutputWithPast,
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CausalLMOutputWithPast,
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QuestionAnsweringModelOutput,
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SequenceClassifierOutputWithPast,
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TokenClassifierOutput,
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)
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from transformers.modeling_rope_utils import ROPE_INIT_FUNCTIONS, dynamic_rope_update
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from transformers.modeling_utils import ALL_ATTENTION_FUNCTIONS, PreTrainedModel
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from transformers.processing_utils import Unpack
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from transformers.utils import TransformersKwargs, auto_docstring, can_return_tuple, logging
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from transformers.utils.generic import check_model_inputs
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from .configuration_exaone4 import Exaone4Config
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logger = logging.get_logger(__name__)
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@use_kernel_forward_from_hub("RMSNorm")
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class Exaone4RMSNorm(nn.Module):
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def __init__(self, hidden_size, eps=1e-6):
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"""
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Exaone4RMSNorm is equivalent to T5LayerNorm
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"""
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super().__init__()
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self.weight = nn.Parameter(torch.ones(hidden_size))
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self.variance_epsilon = eps
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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.variance_epsilon)
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return self.weight * hidden_states.to(input_dtype)
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def extra_repr(self):
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return f"{tuple(self.weight.shape)}, eps={self.variance_epsilon}"
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class Exaone4RotaryEmbedding(nn.Module):
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def __init__(self, config: Exaone4Config, device=None):
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super().__init__()
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# BC: "rope_type" was originally "type"
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if hasattr(config, "rope_scaling") and isinstance(config.rope_scaling, dict):
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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.max_seq_len_cached = 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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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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@torch.no_grad()
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@dynamic_rope_update # power user: used with advanced RoPE types (e.g. dynamic rope)
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def forward(self, x, position_ids):
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inv_freq_expanded = self.inv_freq[None, :, None].float().expand(position_ids.shape[0], -1, 1).to(x.device)
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position_ids_expanded = position_ids[:, None, :].float()
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device_type = x.device.type if isinstance(x.device.type, str) and x.device.type != "mps" else "cpu"
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with torch.autocast(device_type=device_type, enabled=False): # Force float32
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freqs = (inv_freq_expanded.float() @ position_ids_expanded.float()).transpose(1, 2)
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emb = torch.cat((freqs, freqs), dim=-1)
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cos = emb.cos() * self.attention_scaling
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sin = emb.sin() * self.attention_scaling
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return cos.to(dtype=x.dtype), sin.to(dtype=x.dtype)
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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 apply_rotary_pos_emb(q, k, cos, sin, position_ids=None, 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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position_ids (`torch.Tensor`, *optional*):
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Deprecated and unused.
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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 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 eager_attention_forward(
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module: nn.Module,
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query: torch.Tensor,
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key: torch.Tensor,
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value: torch.Tensor,
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attention_mask: Optional[torch.Tensor],
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scaling: float,
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dropout: float = 0.0,
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**kwargs: Unpack[TransformersKwargs],
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):
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key_states = repeat_kv(key, module.num_key_value_groups)
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value_states = repeat_kv(value, module.num_key_value_groups)
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attn_weights = torch.matmul(query, key_states.transpose(2, 3)) * scaling
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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.dtype)
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attn_weights = nn.functional.dropout(attn_weights, p=dropout, training=module.training)
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attn_output = torch.matmul(attn_weights, value_states)
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attn_output = attn_output.transpose(1, 2).contiguous()
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return attn_output, attn_weights
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def check_is_sliding(config, layer_idx):
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"""
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Check if the current layer is a sliding window attention (local attention) layer.
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"""
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if config.sliding_window is None:
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return False
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if config.layer_types is not None:
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return config.layer_types[layer_idx] == "sliding_attention"
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if isinstance(config.sliding_window_pattern, int):
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return ((layer_idx + 1) % config.sliding_window_pattern) != 0
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elif isinstance(config.sliding_window_pattern, str):
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assert isinstance(config.sliding_window, int), (
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f"Sliding window must be positive integer, but got {config.sliding_window}"
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)
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return (
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layer_idx != config.num_hidden_layers - 1
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and config.sliding_window_pattern[layer_idx % len(config.sliding_window_pattern)] == "L"
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)
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else:
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logger.warning_once(
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"Sliding window is set, but none of `sliding_window_pattern` or `layer_types` is set. "
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"Defaulting to use 'full_attention' for all layers."
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)
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return False
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class Exaone4Attention(nn.Module):
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def __init__(self, config: Exaone4Config, layer_idx: int):
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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.num_attention_heads = config.num_attention_heads
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self.num_key_value_heads = config.num_key_value_heads
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self.hidden_size = config.hidden_size
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self.head_dim = getattr(config, "head_dim", config.hidden_size // config.num_attention_heads)
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self.num_key_value_groups = config.num_attention_heads // config.num_key_value_heads
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self.attention_dropout = config.attention_dropout
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self.is_causal = True
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self.scaling = self.head_dim**-0.5
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self.sliding_window = config.sliding_window
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self.sliding_window_pattern = config.sliding_window_pattern
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self.is_sliding = check_is_sliding(config, layer_idx)
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self.q_proj = nn.Linear(self.hidden_size, self.num_attention_heads * self.head_dim, bias=False)
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self.k_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=False)
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self.v_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=False)
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self.o_proj = nn.Linear(self.num_attention_heads * self.head_dim, self.hidden_size, bias=False)
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self.q_norm = Exaone4RMSNorm(self.head_dim, eps=config.rms_norm_eps)
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self.k_norm = Exaone4RMSNorm(self.head_dim, eps=config.rms_norm_eps)
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def forward(
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self,
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hidden_states: torch.Tensor,
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position_embeddings: tuple[torch.Tensor, torch.Tensor],
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attention_mask: Optional[torch.Tensor] = None,
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past_key_value: Optional[Cache] = None,
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cache_position: Optional[torch.LongTensor] = None,
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**kwargs: Unpack[TransformersKwargs],
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) -> tuple[torch.Tensor, Optional[torch.Tensor], Optional[tuple[torch.Tensor]]]:
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input_shape = hidden_states.shape[:-1]
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hidden_shape = (*input_shape, -1, self.head_dim)
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query_states = self.q_proj(hidden_states).view(hidden_shape).transpose(1, 2)
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key_states = self.k_proj(hidden_states).view(hidden_shape).transpose(1, 2)
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value_states = self.v_proj(hidden_states).view(hidden_shape).transpose(1, 2)
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# We use QK-norm
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query_states = self.q_norm(query_states)
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key_states = self.k_norm(key_states)
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cos, sin = position_embeddings
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# We use global NoPE for hybrid attention model
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if self.sliding_window is None or self.is_sliding:
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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 = {
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"sin": sin,
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"cos": cos,
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"cache_position": cache_position,
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"sliding_window": self.sliding_window,
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}
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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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# Here we need to slice as we use a static cache by default, but FA2 does not support it
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# attention_mask can be None, so we use cache_position rather than attention_mask's shape
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# NOTE: seq_len can be retrieved from past_key_value.get_seq_length(),
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# but currently, only 0th-layer is used for .get_seq_length() in HybridCache.
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# This can cause issues when the 0th-layer is sliding window and seq_len > window_size,
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# as it affects full attention layers by slicing KV cache improperly.
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# Dynamic calculation of seq_len is not optimal for CUDAGraph, thus it seems to be updated later.
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if self.config._attn_implementation == "flash_attention_2":
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seq_len = cache_position[-1] + 1 if attention_mask is None else attention_mask.shape[1]
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key_states, value_states = key_states[:, :, :seq_len, :], value_states[:, :, :seq_len, :]
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attention_interface: Callable = eager_attention_forward
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if self.config._attn_implementation != "eager":
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attention_interface = ALL_ATTENTION_FUNCTIONS[self.config._attn_implementation]
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attn_output, attn_weights = attention_interface(
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self,
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query_states,
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key_states,
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value_states,
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attention_mask,
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dropout=0.0 if not self.training else self.attention_dropout,
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scaling=self.scaling,
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sliding_window=self.sliding_window if self.is_sliding else None,
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**kwargs,
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)
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attn_output = attn_output.reshape(*input_shape, -1).contiguous()
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attn_output = self.o_proj(attn_output)
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return attn_output, attn_weights
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class Exaone4MLP(nn.Module):
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def __init__(self, config):
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super().__init__()
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self.config = config
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self.hidden_size = config.hidden_size
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self.intermediate_size = config.intermediate_size
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self.gate_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False)
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self.up_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False)
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self.down_proj = nn.Linear(self.intermediate_size, self.hidden_size, bias=False)
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self.act_fn = ACT2FN[config.hidden_act]
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def forward(self, x):
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down_proj = self.down_proj(self.act_fn(self.gate_proj(x)) * self.up_proj(x))
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return down_proj
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class Exaone4DecoderLayer(nn.Module):
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def __init__(self, config: Exaone4Config, layer_idx: int):
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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.attention_type = config.layer_types[layer_idx]
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self.hidden_size = config.hidden_size
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self.self_attn = Exaone4Attention(config, layer_idx)
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self.mlp = Exaone4MLP(config)
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self.post_attention_layernorm = Exaone4RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
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self.post_feedforward_layernorm = Exaone4RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
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self.is_sliding = check_is_sliding(config, layer_idx)
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self.sliding_window = config.sliding_window
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def forward(
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self,
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hidden_states: torch.Tensor,
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position_embeddings: tuple[torch.Tensor, 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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use_cache: Optional[bool] = False,
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cache_position: Optional[torch.LongTensor] = None,
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**kwargs: Unpack[TransformersKwargs],
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) -> tuple[torch.Tensor, Optional[torch.Tensor], Optional[tuple[torch.Tensor]]]:
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residual = hidden_states
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# Self Attention
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hidden_states, _ = self.self_attn(
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hidden_states=hidden_states,
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position_embeddings=position_embeddings,
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attention_mask=attention_mask,
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position_ids=position_ids,
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past_key_value=past_key_value,
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use_cache=use_cache,
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cache_position=cache_position,
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**kwargs,
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)
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# Use post-LN
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hidden_states = self.post_attention_layernorm(hidden_states)
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hidden_states = residual + hidden_states
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residual = hidden_states
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# Fully Connected
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hidden_states = self.mlp(hidden_states)
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# Use post-LN
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hidden_states = self.post_feedforward_layernorm(hidden_states)
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hidden_states = residual + hidden_states
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return hidden_states
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@auto_docstring
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class Exaone4PreTrainedModel(PreTrainedModel):
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config_class = Exaone4Config
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base_model_prefix = "model"
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supports_gradient_checkpointing = True
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_no_split_modules = ["Exaone4DecoderLayer"]
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_skip_keys_device_placement = ["past_key_values"]
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_supports_flash_attn_2 = True
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_supports_flash_attn_3 = True
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_supports_sdpa = True
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_supports_flex_attn = True
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_supports_cache_class = True
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_supports_quantized_cache = True
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_supports_static_cache = True
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_supports_attention_backend = True
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_can_record_outputs = {
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"hidden_states": Exaone4DecoderLayer,
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"attentions": Exaone4Attention,
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}
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def _init_weights(self, module):
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std = self.config.initializer_range
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if isinstance(module, nn.Linear):
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module.weight.data.normal_(mean=0.0, std=std)
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if module.bias is not None:
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module.bias.data.zero_()
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elif isinstance(module, nn.Embedding):
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module.weight.data.normal_(mean=0.0, std=std)
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if module.padding_idx is not None:
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module.weight.data[module.padding_idx].zero_()
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elif isinstance(module, Exaone4RMSNorm):
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module.weight.data.fill_(1.0)
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@auto_docstring
|
||
class Exaone4Model(Exaone4PreTrainedModel):
|
||
def __init__(self, config: Exaone4Config):
|
||
super().__init__(config)
|
||
self.padding_idx = config.pad_token_id
|
||
self.vocab_size = config.vocab_size
|
||
|
||
self.embed_tokens = nn.Embedding(config.vocab_size, config.hidden_size, self.padding_idx)
|
||
self.layers = nn.ModuleList(
|
||
[Exaone4DecoderLayer(config, layer_idx) for layer_idx in range(config.num_hidden_layers)]
|
||
)
|
||
self.norm = Exaone4RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
|
||
self.rotary_emb = Exaone4RotaryEmbedding(config=config)
|
||
self.gradient_checkpointing = False
|
||
|
||
# Initialize weights and apply final processing
|
||
self.post_init()
|
||
|
||
def get_input_embeddings(self):
|
||
return self.embed_tokens
|
||
|
||
def set_input_embeddings(self, value):
|
||
self.embed_tokens = value
|
||
|
||
@check_model_inputs
|
||
@auto_docstring
|
||
def forward(
|
||
self,
|
||
input_ids: torch.LongTensor = None,
|
||
attention_mask: Optional[torch.Tensor] = None,
|
||
position_ids: Optional[torch.LongTensor] = None,
|
||
past_key_values: Optional[Cache] = None,
|
||
inputs_embeds: Optional[torch.FloatTensor] = None,
|
||
use_cache: Optional[bool] = None,
|
||
cache_position: Optional[torch.LongTensor] = None,
|
||
**kwargs: Unpack[TransformersKwargs],
|
||
) -> Union[tuple, BaseModelOutputWithPast]:
|
||
use_cache = use_cache if use_cache is not None else self.config.use_cache
|
||
|
||
if (input_ids is None) ^ (inputs_embeds is not None):
|
||
raise ValueError("You must specify exactly one of input_ids or inputs_embeds")
|
||
|
||
if self.gradient_checkpointing and self.training and use_cache:
|
||
logger.warning_once(
|
||
"`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`."
|
||
)
|
||
use_cache = False
|
||
|
||
if inputs_embeds is None:
|
||
inputs_embeds = self.embed_tokens(input_ids)
|
||
|
||
if use_cache and past_key_values is None and not self.training:
|
||
batch_size, seq_len, _ = inputs_embeds.shape
|
||
# NOTE: ideally, `HybridCache` should be initialized outside the model with `layer_device_map`
|
||
if self.config.sliding_window is None:
|
||
past_key_values = StaticCache(
|
||
self.config,
|
||
max_batch_size=batch_size,
|
||
max_cache_len=seq_len,
|
||
dtype=inputs_embeds.dtype,
|
||
device=self.device,
|
||
)
|
||
else:
|
||
past_key_values = HybridCache(
|
||
self.config,
|
||
max_batch_size=batch_size,
|
||
max_cache_len=seq_len,
|
||
dtype=inputs_embeds.dtype,
|
||
device=self.device,
|
||
)
|
||
|
||
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)
|
||
|
||
# It may already have been prepared by e.g. `generate`
|
||
if not isinstance(causal_mask_mapping := attention_mask, dict):
|
||
# Prepare mask arguments
|
||
mask_kwargs = {
|
||
"config": self.config,
|
||
"input_embeds": inputs_embeds,
|
||
"attention_mask": attention_mask,
|
||
"cache_position": cache_position,
|
||
"past_key_values": past_key_values,
|
||
"position_ids": position_ids,
|
||
}
|
||
# Create the masks
|
||
causal_mask_mapping = {
|
||
"full_attention": create_causal_mask(**mask_kwargs),
|
||
}
|
||
if self.config.sliding_window is not None:
|
||
causal_mask_mapping["sliding_attention"] = create_sliding_window_causal_mask(**mask_kwargs)
|
||
|
||
hidden_states = inputs_embeds
|
||
|
||
# create position embeddings to be shared across the decoder layers
|
||
position_embeddings = self.rotary_emb(hidden_states, position_ids)
|
||
|
||
for decoder_layer in self.layers[: self.config.num_hidden_layers]:
|
||
hidden_states = decoder_layer(
|
||
hidden_states,
|
||
position_embeddings=position_embeddings,
|
||
attention_mask=causal_mask_mapping[decoder_layer.attention_type],
|
||
position_ids=position_ids,
|
||
past_key_value=past_key_values,
|
||
use_cache=use_cache,
|
||
cache_position=cache_position,
|
||
**kwargs,
|
||
)
|
||
|
||
hidden_states = self.norm(hidden_states)
|
||
|
||
return BaseModelOutputWithPast(
|
||
last_hidden_state=hidden_states,
|
||
past_key_values=past_key_values if use_cache else None,
|
||
)
|
||
|
||
|
||
@auto_docstring
|
||
class Exaone4ForCausalLM(Exaone4PreTrainedModel, GenerationMixin):
|
||
_tied_weights_keys = ["lm_head.weight"]
|
||
_tp_plan = {"lm_head": "colwise_rep"}
|
||
_pp_plan = {"lm_head": (["hidden_states"], ["logits"])}
|
||
|
||
def __init__(self, config):
|
||
super().__init__(config)
|
||
self.model = Exaone4Model(config)
|
||
self.vocab_size = config.vocab_size
|
||
self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
|
||
|
||
# Initialize weights and apply final processing
|
||
self.post_init()
|
||
|
||
def get_input_embeddings(self):
|
||
return self.model.embed_tokens
|
||
|
||
def set_input_embeddings(self, value):
|
||
self.model.embed_tokens = value
|
||
|
||
def get_output_embeddings(self):
|
||
return self.lm_head
|
||
|
||
def set_output_embeddings(self, new_embeddings):
|
||
self.lm_head = new_embeddings
|
||
|
||
def set_decoder(self, decoder):
|
||
self.model = decoder
|
||
|
||
def get_decoder(self):
|
||
return self.model
|
||
|
||
@can_return_tuple
|
||
@auto_docstring
|
||
def forward(
|
||
self,
|
||
input_ids: Optional[torch.LongTensor] = None,
|
||
attention_mask: Optional[torch.Tensor] = None,
|
||
position_ids: Optional[torch.LongTensor] = None,
|
||
past_key_values: Optional[Cache] = None,
|
||
inputs_embeds: Optional[torch.FloatTensor] = None,
|
||
labels: Optional[torch.LongTensor] = None,
|
||
use_cache: Optional[bool] = None,
|
||
cache_position: Optional[torch.LongTensor] = None,
|
||
logits_to_keep: Union[int, torch.Tensor] = 0,
|
||
**kwargs: Unpack[TransformersKwargs],
|
||
) -> CausalLMOutputWithPast:
|
||
r"""
|
||
labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
|
||
Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
|
||
config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
|
||
(masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.
|
||
|
||
Example:
|
||
|
||
```python
|
||
>>> from transformers import AutoModelForCausalLM, AutoTokenizer
|
||
>>> model = AutoModelForCausalLM.from_pretrained("LGAI-EXAONE/EXAONE-4.0-Instruct")
|
||
>>> tokenizer = AutoTokenizer.from_pretrained("LGAI-EXAONE/EXAONE-4.0-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",
|
||
enable_thinking=False,
|
||
)
|
||
|
||
>>> output = model.generate(input_ids, max_new_tokens=128)
|
||
>>> tokenizer.decode(output[0], skip_special_tokens=False)
|
||
"[|system|]\nYou are a helpful assistant.[|endofturn|]\n[|user|]\nExplain how wonderful you are[|endofturn|]\n[|assistant|]\n<think>\n\n</think>\n\nOh, thank you for such a kind and lovely question! 😊 \n\nI’m *so* wonderful because I’m here to make your life easier, brighter, and more fun! Whether you need help with: \n\n✨ **Learning** – I can explain anything, from quantum physics to baking the perfect cake! \n💡 **Creativity** – Need a poem, story, or a wild idea? I’ve got you covered! \n🤖 **Problem-solving** – Stuck on a math problem or a tricky decision? I’ll help you figure it out"
|
||
```
|
||
|
||
NOTE: `EXAONE-4.0-Instruct` is a placeholder model ID. The exact model ID will be updated in the future."""
|
||
outputs: BaseModelOutputWithPast = self.model(
|
||
input_ids=input_ids,
|
||
attention_mask=attention_mask,
|
||
position_ids=position_ids,
|
||
past_key_values=past_key_values,
|
||
inputs_embeds=inputs_embeds,
|
||
use_cache=use_cache,
|
||
cache_position=cache_position,
|
||
**kwargs,
|
||
)
|
||
|
||
hidden_states = outputs.last_hidden_state
|
||
# Only compute necessary logits, and do not upcast them to float if we are not computing the loss
|
||
slice_indices = slice(-logits_to_keep, None) if isinstance(logits_to_keep, int) else logits_to_keep
|
||
logits = self.lm_head(hidden_states[:, slice_indices, :])
|
||
|
||
loss = None
|
||
if labels is not None:
|
||
loss = self.loss_function(logits=logits, labels=labels, vocab_size=self.config.vocab_size, **kwargs)
|
||
|
||
return CausalLMOutputWithPast(
|
||
loss=loss,
|
||
logits=logits,
|
||
past_key_values=outputs.past_key_values,
|
||
hidden_states=outputs.hidden_states,
|
||
attentions=outputs.attentions,
|
||
)
|
||
|
||
|
||
@auto_docstring(
|
||
custom_intro="""
|
||
The Exaone4 Model transformer with a sequence classification head on top (linear layer).
|
||
|
||
[`Exaone4ForSequenceClassification`] uses the last token in order to do the classification, as other causal models
|
||
(e.g. GPT-2) 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).
|
||
"""
|
||
)
|
||
class Exaone4ForSequenceClassification(Exaone4PreTrainedModel):
|
||
def __init__(self, config):
|
||
super().__init__(config)
|
||
self.num_labels = config.num_labels
|
||
self.model = Exaone4Model(config)
|
||
self.score = nn.Linear(config.hidden_size, self.num_labels, bias=False)
|
||
|
||
# Initialize weights and apply final processing
|
||
self.post_init()
|
||
|
||
def get_input_embeddings(self):
|
||
return self.model.embed_tokens
|
||
|
||
def set_input_embeddings(self, value):
|
||
self.model.embed_tokens = value
|
||
|
||
@can_return_tuple
|
||
@auto_docstring
|
||
def forward(
|
||
self,
|
||
input_ids: Optional[torch.LongTensor] = None,
|
||
attention_mask: Optional[torch.Tensor] = None,
|
||
position_ids: Optional[torch.LongTensor] = None,
|
||
past_key_values: Optional[Cache] = None,
|
||
inputs_embeds: Optional[torch.FloatTensor] = None,
|
||
labels: Optional[torch.LongTensor] = None,
|
||
use_cache: Optional[bool] = None,
|
||
**kwargs: Unpack[TransformersKwargs],
|
||
) -> 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).
|
||
"""
|
||
|
||
transformer_outputs: BaseModelOutputWithPast = self.model(
|
||
input_ids,
|
||
attention_mask=attention_mask,
|
||
position_ids=position_ids,
|
||
past_key_values=past_key_values,
|
||
inputs_embeds=inputs_embeds,
|
||
use_cache=use_cache,
|
||
**kwargs,
|
||
)
|
||
hidden_states = transformer_outputs.last_hidden_state
|
||
logits = self.score(hidden_states)
|
||
|
||
if input_ids is not None:
|
||
batch_size = input_ids.shape[0]
|
||
else:
|
||
batch_size = inputs_embeds.shape[0]
|
||
|
||
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:
|
||
last_non_pad_token = -1
|
||
elif input_ids is not None:
|
||
# To handle both left- and right- padding, we take the rightmost token that is not equal to pad_token_id
|
||
non_pad_mask = (input_ids != self.config.pad_token_id).to(logits.device, torch.int32)
|
||
token_indices = torch.arange(input_ids.shape[-1], device=logits.device, dtype=torch.int32)
|
||
last_non_pad_token = (token_indices * non_pad_mask).argmax(-1)
|
||
else:
|
||
last_non_pad_token = -1
|
||
logger.warning_once(
|
||
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), last_non_pad_token]
|
||
|
||
loss = None
|
||
if labels is not None:
|
||
loss = self.loss_function(logits=logits, labels=labels, pooled_logits=pooled_logits, config=self.config)
|
||
|
||
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,
|
||
)
|
||
|
||
|
||
@auto_docstring
|
||
class Exaone4ForTokenClassification(Exaone4PreTrainedModel):
|
||
def __init__(self, config):
|
||
super().__init__(config)
|
||
self.num_labels = config.num_labels
|
||
self.model = Exaone4Model(config)
|
||
if getattr(config, "classifier_dropout", None) is not None:
|
||
classifier_dropout = config.classifier_dropout
|
||
elif getattr(config, "hidden_dropout", None) is not None:
|
||
classifier_dropout = config.hidden_dropout
|
||
else:
|
||
classifier_dropout = 0.1
|
||
self.dropout = nn.Dropout(classifier_dropout)
|
||
self.score = nn.Linear(config.hidden_size, config.num_labels)
|
||
|
||
# Initialize weights and apply final processing
|
||
self.post_init()
|
||
|
||
def get_input_embeddings(self):
|
||
return self.model.embed_tokens
|
||
|
||
def set_input_embeddings(self, value):
|
||
self.model.embed_tokens = value
|
||
|
||
@can_return_tuple
|
||
@auto_docstring
|
||
def forward(
|
||
self,
|
||
input_ids: Optional[torch.LongTensor] = None,
|
||
attention_mask: Optional[torch.Tensor] = None,
|
||
position_ids: Optional[torch.LongTensor] = None,
|
||
past_key_values: Optional[Cache] = None,
|
||
inputs_embeds: Optional[torch.FloatTensor] = None,
|
||
labels: Optional[torch.LongTensor] = None,
|
||
use_cache: Optional[bool] = None,
|
||
**kwargs,
|
||
) -> TokenClassifierOutput:
|
||
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).
|
||
"""
|
||
|
||
outputs: BaseModelOutputWithPast = self.model(
|
||
input_ids,
|
||
attention_mask=attention_mask,
|
||
position_ids=position_ids,
|
||
past_key_values=past_key_values,
|
||
inputs_embeds=inputs_embeds,
|
||
use_cache=use_cache,
|
||
**kwargs,
|
||
)
|
||
sequence_output = outputs.last_hidden_state
|
||
sequence_output = self.dropout(sequence_output)
|
||
logits = self.score(sequence_output)
|
||
|
||
loss = None
|
||
if labels is not None:
|
||
loss = self.loss_function(logits, labels, self.config)
|
||
|
||
return TokenClassifierOutput(
|
||
loss=loss,
|
||
logits=logits,
|
||
hidden_states=outputs.hidden_states,
|
||
attentions=outputs.attentions,
|
||
)
|
||
|
||
|
||
@auto_docstring
|
||
class Exaone4ForQuestionAnswering(Exaone4PreTrainedModel):
|
||
base_model_prefix = "transformer"
|
||
|
||
def __init__(self, config):
|
||
super().__init__(config)
|
||
self.transformer = Exaone4Model(config)
|
||
self.qa_outputs = nn.Linear(config.hidden_size, 2)
|
||
|
||
# Initialize weights and apply final processing
|
||
self.post_init()
|
||
|
||
def get_input_embeddings(self):
|
||
return self.transformer.embed_tokens
|
||
|
||
def set_input_embeddings(self, value):
|
||
self.transformer.embed_tokens = value
|
||
|
||
@can_return_tuple
|
||
@auto_docstring
|
||
def forward(
|
||
self,
|
||
input_ids: Optional[torch.LongTensor] = None,
|
||
attention_mask: Optional[torch.Tensor] = 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,
|
||
**kwargs: Unpack[TransformersKwargs],
|
||
) -> QuestionAnsweringModelOutput:
|
||
outputs: BaseModelOutputWithPast = self.transformer(
|
||
input_ids,
|
||
attention_mask=attention_mask,
|
||
position_ids=position_ids,
|
||
past_key_values=past_key_values,
|
||
inputs_embeds=inputs_embeds,
|
||
**kwargs,
|
||
)
|
||
|
||
sequence_output = outputs.last_hidden_state
|
||
|
||
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()
|
||
|
||
loss = None
|
||
if start_positions is not None and end_positions is not None:
|
||
loss = self.loss_function(start_logits, end_logits, start_positions, end_positions, **kwargs)
|
||
|
||
return QuestionAnsweringModelOutput(
|
||
loss=loss,
|
||
start_logits=start_logits,
|
||
end_logits=end_logits,
|
||
hidden_states=outputs.hidden_states,
|
||
attentions=outputs.attentions,
|
||
)
|
||
|
||
|
||
__all__ = [
|
||
"Exaone4PreTrainedModel",
|
||
"Exaone4Model",
|
||
"Exaone4ForCausalLM",
|
||
"Exaone4ForSequenceClassification",
|
||
"Exaone4ForTokenClassification",
|
||
"Exaone4ForQuestionAnswering",
|
||
]
|