Reorganize repo (#8580)
* Put models in subfolders * Styling * Fix imports in tests * More fixes in test imports * Sneaky hidden imports * Fix imports in doc files * More sneaky imports * Finish fixing tests * Fix examples * Fix path for copies * More fixes for examples * Fix dummy files * More fixes for example * More model import fixes * Is this why you're unhappy GitHub? * Fix imports in conver command
This commit is contained in:
Sylvain Gugger
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src/transformers/models/ctrl/modeling_ctrl.py
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599
src/transformers/models/ctrl/modeling_ctrl.py
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# 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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import warnings
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import numpy as np
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import torch
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import torch.nn as nn
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from torch.nn import CrossEntropyLoss
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from ...file_utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward
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from ...modeling_outputs import BaseModelOutputWithPast, CausalLMOutputWithPast
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from ...modeling_utils import Conv1D, PreTrainedModel, find_pruneable_heads_and_indices, prune_linear_layer
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from ...utils import logging
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from .configuration_ctrl import CTRLConfig
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logger = logging.get_logger(__name__)
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_CONFIG_FOR_DOC = "CTRLConfig"
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_TOKENIZER_FOR_DOC = "CTRLTokenizer"
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CTRL_PRETRAINED_MODEL_ARCHIVE_LIST = [
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"ctrl"
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# See all CTRL models at https://huggingface.co/models?filter=ctrl
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]
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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=dtype).unsqueeze(1),
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torch.arange(d_model_size, dtype=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(torch.nn.Module):
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def __init__(self, d_model_size, num_heads):
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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.depth = int(d_model_size / self.num_heads)
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self.Wq = torch.nn.Linear(d_model_size, d_model_size)
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self.Wk = torch.nn.Linear(d_model_size, d_model_size)
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self.Wv = torch.nn.Linear(d_model_size, d_model_size)
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self.dense = torch.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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):
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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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past_key, past_value = layer_past[0], layer_past[1]
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k = torch.cat((past_key, k), dim=-2)
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v = torch.cat((past_value, v), dim=-2)
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if use_cache is True:
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present = torch.stack((k, v))
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else:
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present = (None,)
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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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outputs = (output, present)
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if output_attentions:
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outputs = outputs + (attn,)
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return outputs
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def point_wise_feed_forward_network(d_model_size, dff):
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return torch.nn.Sequential(torch.nn.Linear(d_model_size, dff), torch.nn.ReLU(), torch.nn.Linear(dff, d_model_size))
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class EncoderLayer(torch.nn.Module):
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def __init__(self, d_model_size, num_heads, dff, rate=0.1):
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super().__init__()
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self.multi_head_attention = MultiHeadAttention(d_model_size, num_heads)
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self.ffn = point_wise_feed_forward_network(d_model_size, dff)
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self.layernorm1 = torch.nn.LayerNorm(d_model_size, eps=1e-6)
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self.layernorm2 = torch.nn.LayerNorm(d_model_size, eps=1e-6)
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self.dropout1 = torch.nn.Dropout(rate)
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self.dropout2 = torch.nn.Dropout(rate)
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def forward(
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self, x, mask, layer_past=None, attention_mask=None, head_mask=None, use_cache=False, output_attentions=False
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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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)
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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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class CTRLPreTrainedModel(PreTrainedModel):
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"""
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An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
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models.
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"""
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config_class = 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, nn.Embedding, 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 isinstance(module, (nn.Linear, Conv1D)) and module.bias is not None:
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module.bias.data.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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CTRL_START_DOCSTRING = r"""
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This model inherits from :class:`~transformers.PreTrainedModel`. Check the superclass documentation for the generic
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methods the library implements for all its model (such as downloading or saving, resizing the input embeddings,
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pruning heads etc.)
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This model is also a PyTorch `torch.nn.Module <https://pytorch.org/docs/stable/nn.html#torch.nn.Module>`__
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subclass. Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to
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general usage and behavior.
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Parameters:
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config (:class:`~transformers.CTRLConfig`): Model configuration class with all the parameters of the model.
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Initializing with a config file does not load the weights associated with the model, only the
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configuration. Check out the :meth:`~transformers.PreTrainedModel.from_pretrained` method to load the model
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weights.
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"""
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CTRL_INPUTS_DOCSTRING = r"""
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Args:
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input_ids (:obj:`torch.LongTensor` of shape :obj:`(batch_size, sequence_length)`):
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:obj:`input_ids_length` = ``sequence_length`` if :obj:`past_key_values` is ``None`` else
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``past_key_values[0].shape[-2]`` (``sequence_length`` of input past key value states). Indices of input
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sequence tokens in the vocabulary.
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If :obj:`past_key_values` is used, only input IDs that do not have their past calculated should be passed
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as ``input_ids``.
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Indices can be obtained using :class:`~transformers.CTRLTokenizer`. See
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:meth:`transformers.PreTrainedTokenizer.__call__` and :meth:`transformers.PreTrainedTokenizer.encode` for
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details.
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`What are input IDs? <../glossary.html#input-ids>`__
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past_key_values (:obj:`List[torch.FloatTensor]` of length :obj:`config.n_layers`):
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Contains pre-computed hidden-states (key and values in the attention blocks) as computed by the model (see
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:obj:`past_key_values` output below). Can be used to speed up sequential decoding. The ``input_ids`` which
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have their past given to this model should not be passed as input ids as they have already been computed.
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attention_mask (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length)`, `optional`):
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Mask to avoid performing attention on padding token indices. Mask values selected in ``[0, 1]``:
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- 1 for tokens that are **not masked**,
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- 0 for tokens that are **masked**.
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`What are attention masks? <../glossary.html#attention-mask>`__
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token_type_ids (:obj:`torch.LongTensor` of shape :obj:`(batch_size, sequence_length)`, `optional`):
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Segment token indices to indicate first and second portions of the inputs. Indices are selected in ``[0,
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1]``:
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- 0 corresponds to a `sentence A` token,
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- 1 corresponds to a `sentence B` token.
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`What are token type IDs? <../glossary.html#token-type-ids>`_
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position_ids (:obj:`torch.LongTensor` of shape :obj:`(batch_size, sequence_length)`, `optional`):
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Indices of positions of each input sequence tokens in the position embeddings. Selected in the range ``[0,
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config.max_position_embeddings - 1]``.
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`What are position IDs? <../glossary.html#position-ids>`_
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head_mask (:obj:`torch.FloatTensor` of shape :obj:`(num_heads,)` or :obj:`(num_layers, num_heads)`, `optional`):
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Mask to nullify selected heads of the self-attention modules. Mask values selected in ``[0, 1]``:
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- 1 indicates the head is **not masked**,
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- 0 indicates the head is **masked**.
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inputs_embeds (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length, hidden_size)`, `optional`):
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Optionally, instead of passing :obj:`input_ids` you can choose to directly pass an embedded representation.
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This is useful if you want more control over how to convert :obj:`input_ids` indices into associated
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vectors than the model's internal embedding lookup matrix.
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use_cache (:obj:`bool`, `optional`):
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If set to :obj:`True`, :obj:`past_key_values` key value states are returned and can be used to speed up
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decoding (see :obj:`past_key_values`).
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output_attentions (:obj:`bool`, `optional`):
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Whether or not to return the attentions tensors of all attention layers. See ``attentions`` under returned
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tensors for more detail.
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output_hidden_states (:obj:`bool`, `optional`):
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Whether or not to return the hidden states of all layers. See ``hidden_states`` under returned tensors for
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more detail.
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return_dict (:obj:`bool`, `optional`):
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Whether or not to return a :class:`~transformers.file_utils.ModelOutput` instead of a plain tuple.
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"""
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@add_start_docstrings(
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"The bare CTRL Model transformer outputting raw hidden-states without any specific head on top.",
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CTRL_START_DOCSTRING,
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)
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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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[EncoderLayer(config.n_embd, config.n_head, config.dff, config.resid_pdrop) for _ in range(config.n_layer)]
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)
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self.layernorm = nn.LayerNorm(config.n_embd, eps=config.layer_norm_epsilon)
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self.init_weights()
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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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@add_start_docstrings_to_model_forward(CTRL_INPUTS_DOCSTRING)
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@add_code_sample_docstrings(
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tokenizer_class=_TOKENIZER_FOR_DOC,
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checkpoint="ctrl",
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output_type=BaseModelOutputWithPast,
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config_class=_CONFIG_FOR_DOC,
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)
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def forward(
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self,
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input_ids=None,
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past_key_values=None,
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attention_mask=None,
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token_type_ids=None,
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position_ids=None,
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head_mask=None,
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inputs_embeds=None,
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use_cache=None,
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output_attentions=None,
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output_hidden_states=None,
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return_dict=None,
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**kwargs,
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):
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if "past" in kwargs:
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warnings.warn(
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"The `past` argument is deprecated and will be removed in a future version, use `past_key_values` instead.",
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FutureWarning,
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)
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past_key_values = kwargs.pop("past")
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assert kwargs == {}, f"Unexpected keyword arguments: {list(kwargs.keys())}."
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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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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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if past_key_values is None:
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past_length = 0
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past_key_values = [None] * len(self.h)
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else:
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past_length = past_key_values[0][0].size(-2)
|
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if position_ids is None:
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device = input_ids.device if input_ids is not None else inputs_embeds.device
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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).view(-1, input_shape[-1])
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# Attention mask.
|
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if attention_mask is not None:
|
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assert batch_size > 0, "batch_size has to be defined and > 0"
|
||||
attention_mask = attention_mask.view(batch_size, -1)
|
||||
# We create a 3D attention mask from a 2D tensor mask.
|
||||
# Sizes are [batch_size, 1, 1, to_seq_length]
|
||||
# So we can broadcast to [batch_size, num_heads, from_seq_length, to_seq_length]
|
||||
# this attention mask is more simple than the triangular masking of causal attention
|
||||
# used in OpenAI GPT, we just need to prepare the broadcast dimension here.
|
||||
attention_mask = attention_mask.unsqueeze(1).unsqueeze(2)
|
||||
|
||||
# Since attention_mask is 1.0 for positions we want to attend and 0.0 for
|
||||
# masked positions, this operation will create a tensor which is 0.0 for
|
||||
# positions we want to attend and -10000.0 for masked positions.
|
||||
# Since we are adding it to the raw scores before the softmax, this is
|
||||
# effectively the same as removing these entirely.
|
||||
attention_mask = attention_mask.to(dtype=self.dtype) # fp16 compatibility
|
||||
attention_mask = (1.0 - attention_mask) * -10000.0
|
||||
|
||||
# Prepare head mask if needed
|
||||
head_mask = self.get_head_mask(head_mask, self.config.n_layer)
|
||||
|
||||
if token_type_ids is not None:
|
||||
token_type_ids = token_type_ids.view(-1, input_shape[-1])
|
||||
token_type_embeds = self.w(token_type_ids)
|
||||
token_type_embeds *= np.sqrt(self.d_model_size)
|
||||
else:
|
||||
token_type_embeds = 0
|
||||
position_ids = position_ids.view(-1, input_shape[-1])
|
||||
|
||||
if inputs_embeds is None:
|
||||
inputs_embeds = self.w(input_ids)
|
||||
# inputs_embeds = embedded.unsqueeze(0) if len(input_ids.shape)<2 else embedded
|
||||
seq_len = input_shape[-1]
|
||||
mask = torch.triu(torch.ones(seq_len + past_length, seq_len + past_length), 1).to(inputs_embeds.device)
|
||||
|
||||
inputs_embeds *= np.sqrt(self.d_model_size)
|
||||
|
||||
pos_embeds = self.pos_encoding[position_ids, :].to(inputs_embeds.device)
|
||||
|
||||
hidden_states = inputs_embeds + pos_embeds + token_type_embeds
|
||||
|
||||
hidden_states = self.dropout(hidden_states)
|
||||
|
||||
output_shape = input_shape + (inputs_embeds.size(-1),)
|
||||
presents = () if use_cache else None
|
||||
all_hidden_states = () if output_hidden_states else None
|
||||
all_attentions = [] if output_attentions else None
|
||||
for i, (h, layer_past) in enumerate(zip(self.h, past_key_values)):
|
||||
if output_hidden_states:
|
||||
all_hidden_states = all_hidden_states + (hidden_states.view(*output_shape),)
|
||||
outputs = h(
|
||||
hidden_states,
|
||||
mask,
|
||||
layer_past=layer_past,
|
||||
attention_mask=attention_mask,
|
||||
head_mask=head_mask[i],
|
||||
use_cache=use_cache,
|
||||
output_attentions=output_attentions,
|
||||
)
|
||||
hidden_states, present = outputs[:2]
|
||||
if use_cache is True:
|
||||
presents = presents + (present,)
|
||||
|
||||
if output_attentions:
|
||||
all_attentions.append(outputs[2])
|
||||
|
||||
hidden_states = self.layernorm(hidden_states)
|
||||
hidden_states = hidden_states.view(*output_shape)
|
||||
if output_hidden_states:
|
||||
all_hidden_states = all_hidden_states + (hidden_states,)
|
||||
|
||||
if output_attentions:
|
||||
# let the number of heads free (-1) so we can extract attention even after head pruning
|
||||
attention_output_shape = input_shape[:-1] + (-1,) + all_attentions[0].shape[-2:]
|
||||
all_attentions = tuple(t.view(*attention_output_shape) for t in all_attentions)
|
||||
|
||||
if not return_dict:
|
||||
return tuple(v for v in [hidden_states, presents, all_hidden_states, all_attentions] if v is not None)
|
||||
|
||||
return BaseModelOutputWithPast(
|
||||
last_hidden_state=hidden_states,
|
||||
past_key_values=presents,
|
||||
hidden_states=all_hidden_states,
|
||||
attentions=all_attentions,
|
||||
)
|
||||
|
||||
|
||||
@add_start_docstrings(
|
||||
"""
|
||||
The CTRL Model transformer with a language modeling head on top (linear layer with weights tied to the input
|
||||
embeddings).
|
||||
""",
|
||||
CTRL_START_DOCSTRING,
|
||||
)
|
||||
class CTRLLMHeadModel(CTRLPreTrainedModel):
|
||||
def __init__(self, config):
|
||||
super().__init__(config)
|
||||
self.transformer = CTRLModel(config)
|
||||
self.lm_head = nn.Linear(config.n_embd, config.vocab_size, bias=True)
|
||||
|
||||
self.init_weights()
|
||||
|
||||
def get_output_embeddings(self):
|
||||
return self.lm_head
|
||||
|
||||
def prepare_inputs_for_generation(self, input_ids, past=None, use_cache=None, **kwargs):
|
||||
# only last token for inputs_ids if past is defined in kwargs
|
||||
if past:
|
||||
input_ids = input_ids[:, -1].unsqueeze(-1)
|
||||
|
||||
return {"input_ids": input_ids, "past_key_values": past, "use_cache": use_cache}
|
||||
|
||||
@add_start_docstrings_to_model_forward(CTRL_INPUTS_DOCSTRING)
|
||||
@add_code_sample_docstrings(
|
||||
tokenizer_class=_TOKENIZER_FOR_DOC,
|
||||
checkpoint="ctrl",
|
||||
output_type=CausalLMOutputWithPast,
|
||||
config_class=_CONFIG_FOR_DOC,
|
||||
)
|
||||
def forward(
|
||||
self,
|
||||
input_ids=None,
|
||||
past_key_values=None,
|
||||
attention_mask=None,
|
||||
token_type_ids=None,
|
||||
position_ids=None,
|
||||
head_mask=None,
|
||||
inputs_embeds=None,
|
||||
labels=None,
|
||||
use_cache=None,
|
||||
output_attentions=None,
|
||||
output_hidden_states=None,
|
||||
return_dict=None,
|
||||
**kwargs,
|
||||
):
|
||||
r"""
|
||||
labels (:obj:`torch.LongTensor` of shape :obj:`(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]``
|
||||
"""
|
||||
if "past" in kwargs:
|
||||
warnings.warn(
|
||||
"The `past` argument is deprecated and will be removed in a future version, use `past_key_values` instead.",
|
||||
FutureWarning,
|
||||
)
|
||||
past_key_values = kwargs.pop("past")
|
||||
assert kwargs == {}, f"Unexpected keyword arguments: {list(kwargs.keys())}."
|
||||
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]
|
||||
|
||||
lm_logits = self.lm_head(hidden_states)
|
||||
|
||||
loss = None
|
||||
if labels is not None:
|
||||
# Shift so that tokens < n predict n
|
||||
shift_logits = lm_logits[..., :-1, :].contiguous()
|
||||
shift_labels = labels[..., 1:].contiguous()
|
||||
# Flatten the tokens
|
||||
loss_fct = CrossEntropyLoss()
|
||||
loss = loss_fct(shift_logits.view(-1, shift_logits.size(-1)), shift_labels.view(-1))
|
||||
|
||||
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,
|
||||
)
|
||||
Reference in New Issue
Block a user