780 lines
32 KiB
Python
780 lines
32 KiB
Python
# coding=utf-8
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# Copyright 2018 Salesforce and HuggingFace Inc. team.
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# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.
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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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"""PyTorch CTRL model."""
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from typing import Optional, Union
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import numpy as np
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import torch
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from torch import nn
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from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
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from ...cache_utils import Cache, DynamicCache
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from ...generation import GenerationMixin
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from ...modeling_outputs import BaseModelOutputWithPast, CausalLMOutputWithPast, SequenceClassifierOutput
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from ...modeling_utils import PreTrainedModel
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from ...pytorch_utils import Conv1D, find_pruneable_heads_and_indices, prune_linear_layer
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from ...utils import (
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auto_docstring,
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logging,
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)
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from .configuration_ctrl import CTRLConfig
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logger = logging.get_logger(__name__)
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def angle_defn(pos, i, d_model_size):
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angle_rates = 1 / torch.pow(10000, (2 * (i // 2)) / d_model_size)
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return pos * angle_rates
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def positional_encoding(position, d_model_size, dtype):
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# create the sinusoidal pattern for the positional encoding
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angle_rads = angle_defn(
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torch.arange(position, dtype=torch.int64).to(dtype).unsqueeze(1),
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torch.arange(d_model_size, dtype=torch.int64).to(dtype).unsqueeze(0),
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d_model_size,
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)
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sines = torch.sin(angle_rads[:, 0::2])
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cosines = torch.cos(angle_rads[:, 1::2])
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pos_encoding = torch.cat([sines, cosines], dim=-1)
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return pos_encoding
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def scaled_dot_product_attention(q, k, v, mask, attention_mask=None, head_mask=None):
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# calculate attention
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matmul_qk = torch.matmul(q, k.permute(0, 1, 3, 2))
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dk = k.shape[-1]
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scaled_attention_logits = matmul_qk / np.sqrt(dk)
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if mask is not None:
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nd, ns = scaled_attention_logits.size(-2), scaled_attention_logits.size(-1)
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scaled_attention_logits += mask[ns - nd : ns, :ns] * -1e4
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if attention_mask is not None:
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# Apply the attention mask
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scaled_attention_logits = scaled_attention_logits + attention_mask
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attention_weights = torch.softmax(scaled_attention_logits, dim=-1)
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# Mask heads if we want to
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if head_mask is not None:
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attention_weights = attention_weights * head_mask
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output = torch.matmul(attention_weights, v)
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return output, attention_weights
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class MultiHeadAttention(nn.Module):
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def __init__(self, d_model_size, num_heads, layer_idx=None):
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super().__init__()
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self.num_heads = num_heads
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self.d_model_size = d_model_size
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self.layer_idx = layer_idx
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self.depth = int(d_model_size / self.num_heads)
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self.Wq = nn.Linear(d_model_size, d_model_size)
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self.Wk = nn.Linear(d_model_size, d_model_size)
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self.Wv = nn.Linear(d_model_size, d_model_size)
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self.dense = nn.Linear(d_model_size, d_model_size)
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self.pruned_heads = set()
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def prune_heads(self, heads):
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attention_head_size = self.d_model_size // self.num_heads
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if len(heads) == 0:
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return
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heads, index = find_pruneable_heads_and_indices(heads, self.num_heads, attention_head_size, self.pruned_heads)
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# Prune linear layers
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self.Wq = prune_linear_layer(self.Wq, index)
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self.Wk = prune_linear_layer(self.Wk, index)
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self.Wv = prune_linear_layer(self.Wv, index)
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self.dense = prune_linear_layer(self.dense, index, dim=1)
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# Update hyper params
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self.num_heads = self.num_heads - len(heads)
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self.d_model_size = attention_head_size * self.num_heads
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self.pruned_heads = self.pruned_heads.union(heads)
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def split_into_heads(self, x, batch_size):
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x = x.reshape(batch_size, -1, self.num_heads, self.depth)
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return x.permute([0, 2, 1, 3])
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def forward(
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self,
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v,
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k,
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q,
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mask,
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layer_past=None,
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attention_mask=None,
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head_mask=None,
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use_cache=False,
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output_attentions=False,
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cache_position=None,
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):
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batch_size = q.shape[0]
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q = self.Wq(q)
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k = self.Wk(k)
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v = self.Wv(v)
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q = self.split_into_heads(q, batch_size)
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k = self.split_into_heads(k, batch_size)
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v = self.split_into_heads(v, batch_size)
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if layer_past is not None:
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k, v = layer_past.update(k, v, self.layer_idx, {"cache_position": cache_position})
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output = scaled_dot_product_attention(q, k, v, mask, attention_mask, head_mask)
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scaled_attention = output[0].permute([0, 2, 1, 3])
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attn = output[1]
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original_size_attention = scaled_attention.reshape(batch_size, -1, self.d_model_size)
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output = self.dense(original_size_attention)
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return output, attn
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def point_wise_feed_forward_network(d_model_size, dff):
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return nn.Sequential(nn.Linear(d_model_size, dff), nn.ReLU(), nn.Linear(dff, d_model_size))
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class EncoderLayer(nn.Module):
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def __init__(self, d_model_size, num_heads, dff, rate=0.1, layer_idx=None):
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super().__init__()
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self.multi_head_attention = MultiHeadAttention(d_model_size, num_heads, layer_idx=layer_idx)
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self.ffn = point_wise_feed_forward_network(d_model_size, dff)
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self.layernorm1 = nn.LayerNorm(d_model_size, eps=1e-6)
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self.layernorm2 = nn.LayerNorm(d_model_size, eps=1e-6)
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self.dropout1 = nn.Dropout(rate)
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self.dropout2 = nn.Dropout(rate)
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def forward(
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self,
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x,
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mask,
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layer_past=None,
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attention_mask=None,
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head_mask=None,
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use_cache=False,
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output_attentions=False,
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cache_position=None,
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):
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normed = self.layernorm1(x)
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attn_outputs = self.multi_head_attention(
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normed,
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normed,
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normed,
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mask,
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layer_past=layer_past,
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attention_mask=attention_mask,
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head_mask=head_mask,
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use_cache=use_cache,
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output_attentions=output_attentions,
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cache_position=cache_position,
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)
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attn_output = attn_outputs[0]
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attn_output = self.dropout1(attn_output)
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out1 = x + attn_output
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out2 = self.layernorm2(out1)
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ffn_output = self.ffn(out2)
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ffn_output = self.dropout2(ffn_output)
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out2 = out1 + ffn_output
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outputs = (out2,) + attn_outputs[1:]
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return outputs
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@auto_docstring
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class CTRLPreTrainedModel(PreTrainedModel):
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config: CTRLConfig
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base_model_prefix = "transformer"
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def _init_weights(self, module):
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"""Initialize the weights."""
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if isinstance(module, (nn.Linear, Conv1D)):
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# Slightly different from the TF version which uses truncated_normal for initialization
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# cf https://github.com/pytorch/pytorch/pull/5617
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module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
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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=self.config.initializer_range)
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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, nn.LayerNorm):
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module.bias.data.zero_()
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module.weight.data.fill_(1.0)
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@auto_docstring
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class CTRLModel(CTRLPreTrainedModel):
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def __init__(self, config):
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super().__init__(config)
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self.d_model_size = config.n_embd
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self.num_layers = config.n_layer
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self.pos_encoding = positional_encoding(config.n_positions, self.d_model_size, torch.float)
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self.w = nn.Embedding(config.vocab_size, config.n_embd)
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self.dropout = nn.Dropout(config.embd_pdrop)
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self.h = nn.ModuleList(
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[
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EncoderLayer(config.n_embd, config.n_head, config.dff, config.resid_pdrop, layer_idx=i)
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for i in range(config.n_layer)
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]
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)
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self.layernorm = nn.LayerNorm(config.n_embd, eps=config.layer_norm_epsilon)
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# Initialize weights and apply final processing
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self.post_init()
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def get_input_embeddings(self):
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return self.w
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def set_input_embeddings(self, new_embeddings):
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self.w = new_embeddings
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def _prune_heads(self, heads_to_prune):
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"""
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Prunes heads of the model. heads_to_prune: dict of {layer_num: list of heads to prune in this layer}
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"""
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for layer, heads in heads_to_prune.items():
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self.h[layer].multi_head_attention.prune_heads(heads)
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@auto_docstring
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def forward(
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self,
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input_ids: Optional[torch.LongTensor] = None,
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past_key_values: Optional[tuple[tuple[torch.FloatTensor]]] = None,
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attention_mask: Optional[torch.FloatTensor] = None,
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token_type_ids: Optional[torch.LongTensor] = None,
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position_ids: Optional[torch.LongTensor] = None,
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head_mask: Optional[torch.FloatTensor] = None,
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inputs_embeds: Optional[torch.FloatTensor] = None,
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use_cache: Optional[bool] = None,
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output_attentions: Optional[bool] = None,
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output_hidden_states: Optional[bool] = None,
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return_dict: Optional[bool] = None,
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cache_position: Optional[torch.Tensor] = None,
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**kwargs, # NOOP kwargs, for now
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) -> Union[tuple[torch.Tensor], BaseModelOutputWithPast]:
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r"""
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input_ids (`torch.LongTensor` of shape `(batch_size, input_ids_length)`):
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`input_ids_length` = `sequence_length` if `past_key_values` is `None` else `past_key_values[0].shape[-2]`
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(`sequence_length` of input past key value states). Indices of input sequence tokens in the vocabulary.
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If `past_key_values` is used, only input IDs that do not have their past calculated should be passed as
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`input_ids`.
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Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.__call__`] and
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[`PreTrainedTokenizer.encode`] for details.
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[What are input IDs?](../glossary#input-ids)
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Example:
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```python
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>>> from transformers import AutoTokenizer, CTRLModel
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>>> import torch
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>>> tokenizer = AutoTokenizer.from_pretrained("Salesforce/ctrl")
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>>> model = CTRLModel.from_pretrained("Salesforce/ctrl")
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>>> # CTRL was trained with control codes as the first token
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>>> inputs = tokenizer("Opinion My dog is cute", return_tensors="pt")
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>>> assert inputs["input_ids"][0, 0].item() in tokenizer.control_codes.values()
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>>> outputs = model(**inputs)
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>>> last_hidden_states = outputs.last_hidden_state
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>>> list(last_hidden_states.shape)
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[1, 5, 1280]
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```"""
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output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
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use_cache = use_cache if use_cache is not None else self.config.use_cache
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output_hidden_states = (
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output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
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)
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return_dict = return_dict if return_dict is not None else self.config.use_return_dict
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if input_ids is not None and inputs_embeds is not None:
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raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time")
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elif input_ids is not None:
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self.warn_if_padding_and_no_attention_mask(input_ids, attention_mask)
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input_shape = input_ids.size()
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input_ids = input_ids.view(-1, input_shape[-1])
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batch_size = input_ids.shape[0]
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elif inputs_embeds is not None:
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input_shape = inputs_embeds.size()[:-1]
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batch_size = inputs_embeds.shape[0]
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else:
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raise ValueError("You have to specify either input_ids or inputs_embeds")
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device = input_ids.device if input_ids is not None else inputs_embeds.device
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return_legacy_cache = False
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if use_cache and not isinstance(past_key_values, Cache):
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logger.warning_once(
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"Passing a tuple of `past_key_values` is deprecated and will be removed in Transformers v4.58.0. "
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"You should pass an instance of `DynamicCache` instead, e.g. "
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"`past_key_values=DynamicCache.from_legacy_cache(past_key_values)`."
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)
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return_legacy_cache = True
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past_key_values = DynamicCache.from_legacy_cache(past_key_values)
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past_length = past_key_values.get_seq_length() if past_key_values is not None else 0
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if position_ids is None:
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position_ids = torch.arange(past_length, input_shape[-1] + past_length, dtype=torch.long, device=device)
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position_ids = position_ids.unsqueeze(0)
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# Attention mask.
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if attention_mask is not None:
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if batch_size <= 0:
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raise ValueError("batch_size has to be defined and > 0")
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attention_mask = attention_mask.view(batch_size, -1)
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# We create a 3D attention mask from a 2D tensor mask.
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# Sizes are [batch_size, 1, 1, to_seq_length]
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# So we can broadcast to [batch_size, num_heads, from_seq_length, to_seq_length]
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# this attention mask is more simple than the triangular masking of causal attention
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# used in OpenAI GPT, we just need to prepare the broadcast dimension here.
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attention_mask = attention_mask.unsqueeze(1).unsqueeze(2)
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# Since attention_mask is 1.0 for positions we want to attend and 0.0 for
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# masked positions, this operation will create a tensor which is 0.0 for
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# positions we want to attend and the dtype's smallest value for masked positions.
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# Since we are adding it to the raw scores before the softmax, this is
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# effectively the same as removing these entirely.
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attention_mask = attention_mask.to(dtype=self.dtype) # fp16 compatibility
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attention_mask = (1.0 - attention_mask) * torch.finfo(self.dtype).min
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# Prepare head mask if needed
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head_mask = self.get_head_mask(head_mask, self.config.n_layer)
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if token_type_ids is not None:
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token_type_ids = token_type_ids.view(-1, input_shape[-1])
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token_type_embeds = self.w(token_type_ids)
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token_type_embeds *= np.sqrt(self.d_model_size)
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else:
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token_type_embeds = 0
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if inputs_embeds is None:
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inputs_embeds = self.w(input_ids)
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# inputs_embeds = embedded.unsqueeze(0) if len(input_ids.shape)<2 else embedded
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seq_len = input_shape[-1]
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mask = torch.triu(torch.ones(seq_len + past_length, seq_len + past_length), 1).to(device)
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inputs_embeds *= np.sqrt(self.d_model_size)
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# `self.pos_encoding` won't be sent to the correct device along the model, so we do it manually.
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self.pos_encoding = self.pos_encoding.to(device)
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pos_embeds = self.pos_encoding[position_ids, :]
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hidden_states = inputs_embeds + pos_embeds + token_type_embeds
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hidden_states = self.dropout(hidden_states)
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all_hidden_states = () if output_hidden_states else None
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all_attentions = () if output_attentions else None
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for i, h in enumerate(self.h):
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if output_hidden_states:
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all_hidden_states = all_hidden_states + (hidden_states,)
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outputs = h(
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hidden_states,
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mask,
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layer_past=past_key_values,
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attention_mask=attention_mask,
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head_mask=head_mask[i],
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use_cache=use_cache,
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output_attentions=output_attentions,
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cache_position=cache_position,
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)
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hidden_states = outputs[0]
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if output_attentions:
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all_attentions += (outputs[1],)
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hidden_states = self.layernorm(hidden_states)
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if output_hidden_states:
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all_hidden_states = all_hidden_states + (hidden_states,)
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if return_legacy_cache:
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past_key_values = past_key_values.to_legacy_cache()
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if not return_dict:
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return tuple(
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v for v in [hidden_states, past_key_values, all_hidden_states, all_attentions] if v is not None
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)
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return BaseModelOutputWithPast(
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last_hidden_state=hidden_states,
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past_key_values=past_key_values,
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hidden_states=all_hidden_states,
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attentions=all_attentions,
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)
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@auto_docstring(
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custom_intro="""
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The CTRL Model transformer with a language modeling head on top (linear layer with weights tied to the input
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embeddings).
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"""
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)
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class CTRLLMHeadModel(CTRLPreTrainedModel, GenerationMixin):
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_tied_weights_keys = ["lm_head.weight"]
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def __init__(self, config):
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super().__init__(config)
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self.transformer = CTRLModel(config)
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self.lm_head = nn.Linear(config.n_embd, config.vocab_size, bias=True)
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# Initialize weights and apply final processing
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self.post_init()
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@auto_docstring
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def forward(
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self,
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input_ids: Optional[torch.LongTensor] = None,
|
|
past_key_values: Optional[tuple[tuple[torch.FloatTensor]]] = None,
|
|
attention_mask: Optional[torch.FloatTensor] = None,
|
|
token_type_ids: Optional[torch.LongTensor] = None,
|
|
position_ids: Optional[torch.LongTensor] = None,
|
|
head_mask: Optional[torch.FloatTensor] = None,
|
|
inputs_embeds: Optional[torch.FloatTensor] = None,
|
|
labels: Optional[torch.LongTensor] = None,
|
|
use_cache: Optional[bool] = None,
|
|
output_attentions: Optional[bool] = None,
|
|
output_hidden_states: Optional[bool] = None,
|
|
return_dict: Optional[bool] = None,
|
|
cache_position: Optional[torch.Tensor] = None,
|
|
**kwargs,
|
|
) -> Union[tuple[torch.Tensor], CausalLMOutputWithPast]:
|
|
r"""
|
|
input_ids (`torch.LongTensor` of shape `(batch_size, input_ids_length)`):
|
|
`input_ids_length` = `sequence_length` if `past_key_values` is `None` else `past_key_values[0].shape[-2]`
|
|
(`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`.
|
|
|
|
Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.__call__`] and
|
|
[`PreTrainedTokenizer.encode`] for details.
|
|
|
|
[What are input IDs?](../glossary#input-ids)
|
|
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
|
|
>>> import torch
|
|
>>> from transformers import AutoTokenizer, CTRLLMHeadModel
|
|
|
|
>>> tokenizer = AutoTokenizer.from_pretrained("Salesforce/ctrl")
|
|
>>> model = CTRLLMHeadModel.from_pretrained("Salesforce/ctrl")
|
|
|
|
>>> # CTRL was trained with control codes as the first token
|
|
>>> inputs = tokenizer("Wikipedia The llama is", return_tensors="pt")
|
|
>>> assert inputs["input_ids"][0, 0].item() in tokenizer.control_codes.values()
|
|
|
|
>>> sequence_ids = model.generate(inputs["input_ids"])
|
|
>>> sequences = tokenizer.batch_decode(sequence_ids)
|
|
>>> sequences
|
|
['Wikipedia The llama is a member of the family Bovidae. It is native to the Andes of Peru,']
|
|
|
|
>>> outputs = model(**inputs, labels=inputs["input_ids"])
|
|
>>> round(outputs.loss.item(), 2)
|
|
9.21
|
|
|
|
>>> list(outputs.logits.shape)
|
|
[1, 5, 246534]
|
|
```"""
|
|
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
|
|
|
|
transformer_outputs = self.transformer(
|
|
input_ids,
|
|
past_key_values=past_key_values,
|
|
attention_mask=attention_mask,
|
|
token_type_ids=token_type_ids,
|
|
position_ids=position_ids,
|
|
head_mask=head_mask,
|
|
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)
|
|
|
|
loss = None
|
|
if labels is not None:
|
|
loss = self.loss_function(
|
|
lm_logits,
|
|
labels,
|
|
vocab_size=self.config.vocab_size,
|
|
**kwargs,
|
|
)
|
|
|
|
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, use_cache=None, **kwargs):
|
|
# Overwritten -- inputs_embeds not working properly
|
|
|
|
# only last tokens for inputs_ids if past is defined in kwargs
|
|
if past_key_values is not None:
|
|
past_length = past_key_values.get_seq_length()
|
|
|
|
# Some generation methods already pass only the last input ID
|
|
if input_ids.shape[1] > past_length:
|
|
remove_prefix_length = past_length
|
|
else:
|
|
# Default to old behavior: keep only final ID
|
|
remove_prefix_length = input_ids.shape[1] - 1
|
|
|
|
input_ids = input_ids[:, remove_prefix_length:]
|
|
|
|
return {"input_ids": input_ids, "past_key_values": past_key_values, "use_cache": use_cache}
|
|
|
|
|
|
@auto_docstring(
|
|
custom_intro="""
|
|
The CTRL Model transformer with a sequence classification head on top (linear layer).
|
|
[`CTRLForSequenceClassification`] 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 CTRLForSequenceClassification(CTRLPreTrainedModel):
|
|
def __init__(self, config):
|
|
super().__init__(config)
|
|
self.num_labels = config.num_labels
|
|
self.transformer = CTRLModel(config)
|
|
self.classifier = nn.Linear(config.n_embd, self.num_labels, bias=False)
|
|
|
|
# Initialize weights and apply final processing
|
|
self.post_init()
|
|
|
|
@auto_docstring
|
|
def forward(
|
|
self,
|
|
input_ids: Optional[torch.LongTensor] = None,
|
|
past_key_values: Optional[tuple[tuple[torch.FloatTensor]]] = None,
|
|
attention_mask: Optional[torch.FloatTensor] = None,
|
|
token_type_ids: Optional[torch.LongTensor] = None,
|
|
position_ids: Optional[torch.LongTensor] = None,
|
|
head_mask: Optional[torch.FloatTensor] = None,
|
|
inputs_embeds: Optional[torch.FloatTensor] = None,
|
|
labels: Optional[torch.LongTensor] = None,
|
|
use_cache: Optional[bool] = None,
|
|
output_attentions: Optional[bool] = None,
|
|
output_hidden_states: Optional[bool] = None,
|
|
return_dict: Optional[bool] = None,
|
|
) -> Union[tuple[torch.Tensor], SequenceClassifierOutput]:
|
|
r"""
|
|
input_ids (`torch.LongTensor` of shape `(batch_size, input_ids_length)`):
|
|
`input_ids_length` = `sequence_length` if `past_key_values` is `None` else `past_key_values[0].shape[-2]`
|
|
(`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`.
|
|
|
|
Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.__call__`] and
|
|
[`PreTrainedTokenizer.encode`] for details.
|
|
|
|
[What are input IDs?](../glossary#input-ids)
|
|
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).
|
|
|
|
Example of single-label classification:
|
|
|
|
```python
|
|
>>> import torch
|
|
>>> from transformers import AutoTokenizer, CTRLForSequenceClassification
|
|
|
|
>>> tokenizer = AutoTokenizer.from_pretrained("Salesforce/ctrl")
|
|
>>> model = CTRLForSequenceClassification.from_pretrained("Salesforce/ctrl")
|
|
|
|
>>> # CTRL was trained with control codes as the first token
|
|
>>> inputs = tokenizer("Opinion My dog is cute", return_tensors="pt")
|
|
>>> assert inputs["input_ids"][0, 0].item() in tokenizer.control_codes.values()
|
|
|
|
>>> with torch.no_grad():
|
|
... logits = model(**inputs).logits
|
|
|
|
>>> predicted_class_id = logits.argmax().item()
|
|
>>> model.config.id2label[predicted_class_id]
|
|
'LABEL_0'
|
|
```
|
|
|
|
```python
|
|
>>> import torch
|
|
|
|
>>> torch.manual_seed(42) # doctest: +IGNORE_RESULT
|
|
>>> # To train a model on `num_labels` classes, you can pass `num_labels=num_labels` to `.from_pretrained(...)`
|
|
>>> num_labels = len(model.config.id2label)
|
|
>>> model = CTRLForSequenceClassification.from_pretrained("Salesforce/ctrl", num_labels=num_labels)
|
|
|
|
>>> labels = torch.tensor(1)
|
|
>>> loss = model(**inputs, labels=labels).loss
|
|
>>> round(loss.item(), 2)
|
|
0.93
|
|
```
|
|
|
|
Example of multi-label classification:
|
|
|
|
```python
|
|
>>> import torch
|
|
>>> from transformers import AutoTokenizer, CTRLForSequenceClassification
|
|
|
|
>>> tokenizer = AutoTokenizer.from_pretrained("Salesforce/ctrl")
|
|
>>> model = CTRLForSequenceClassification.from_pretrained(
|
|
... "Salesforce/ctrl", problem_type="multi_label_classification"
|
|
... )
|
|
|
|
>>> # CTRL was trained with control codes as the first token
|
|
>>> inputs = tokenizer("Opinion My dog is cute", return_tensors="pt")
|
|
>>> assert inputs["input_ids"][0, 0].item() in tokenizer.control_codes.values()
|
|
|
|
>>> with torch.no_grad():
|
|
... logits = model(**inputs).logits
|
|
|
|
>>> predicted_class_id = logits.argmax().item()
|
|
>>> model.config.id2label[predicted_class_id]
|
|
'LABEL_0'
|
|
```
|
|
|
|
```python
|
|
>>> # To train a model on `num_labels` classes, you can pass `num_labels=num_labels` to `.from_pretrained(...)`
|
|
>>> num_labels = len(model.config.id2label)
|
|
>>> model = CTRLForSequenceClassification.from_pretrained("Salesforce/ctrl", num_labels=num_labels)
|
|
|
|
>>> num_labels = len(model.config.id2label)
|
|
>>> labels = torch.nn.functional.one_hot(torch.tensor([predicted_class_id]), num_classes=num_labels).to(
|
|
... torch.float
|
|
... )
|
|
>>> loss = model(**inputs, labels=labels).loss
|
|
>>> loss.backward() # doctest: +IGNORE_RESULT
|
|
```"""
|
|
|
|
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
|
|
|
|
transformer_outputs = self.transformer(
|
|
input_ids,
|
|
past_key_values=past_key_values,
|
|
attention_mask=attention_mask,
|
|
token_type_ids=token_type_ids,
|
|
position_ids=position_ids,
|
|
head_mask=head_mask,
|
|
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.classifier(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:
|
|
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:
|
|
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[2:]
|
|
return ((loss,) + output) if loss is not None else output
|
|
|
|
return SequenceClassifierOutput(
|
|
loss=loss,
|
|
logits=pooled_logits,
|
|
hidden_states=transformer_outputs.hidden_states,
|
|
attentions=transformer_outputs.attentions,
|
|
)
|
|
|
|
|
|
__all__ = ["CTRLForSequenceClassification", "CTRLLMHeadModel", "CTRLModel", "CTRLPreTrainedModel"]
|