1787 lines
81 KiB
Python
1787 lines
81 KiB
Python
# coding=utf-8
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# Copyright 2023 The HuggingFace Inc. & Google team. 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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""" Pix2Struct modeling file"""
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import math
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from typing import Dict, List, Optional, Tuple, Union
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import torch
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import torch.utils.checkpoint
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from torch import nn
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from ...activations import ACT2FN
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from ...modeling_outputs import (
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BaseModelOutput,
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BaseModelOutputWithPooling,
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CausalLMOutputWithCrossAttentions,
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Seq2SeqLMOutput,
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Seq2SeqModelOutput,
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)
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from ...modeling_utils import PreTrainedModel
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from ...pytorch_utils import ALL_LAYERNORM_LAYERS
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from ...utils import (
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DUMMY_INPUTS,
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DUMMY_MASK,
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add_start_docstrings,
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add_start_docstrings_to_model_forward,
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is_torch_fx_proxy,
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logging,
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replace_return_docstrings,
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)
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from .configuration_pix2struct import Pix2StructConfig, Pix2StructTextConfig, Pix2StructVisionConfig
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logger = logging.get_logger(__name__)
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# General docstring
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_CONFIG_FOR_DOC = "Pix2StructConfig"
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from ..deprecated._archive_maps import PIX2STRUCT_PRETRAINED_MODEL_ARCHIVE_LIST # noqa: F401, E402
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# Adapted from transformers.models.t5.modeling_t5.T5LayerNorm with T5->Pix2Struct
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class Pix2StructLayerNorm(nn.Module):
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def __init__(self, hidden_size, eps=1e-6):
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"""
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Construct a layernorm module in the T5 style. No bias and no subtraction of mean.
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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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# T5 uses a layer_norm which only scales and doesn't shift, which is also known as Root Mean
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# Square Layer Normalization https://arxiv.org/abs/1910.07467 thus varience is calculated
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# w/o mean and there is no bias. Additionally we want to make sure that the accumulation for
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# half-precision inputs is done in fp32
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variance = hidden_states.to(torch.float32).pow(2).mean(-1, keepdim=True)
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hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon)
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# convert into half-precision if necessary
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if self.weight.dtype in [torch.float16, torch.bfloat16]:
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hidden_states = hidden_states.to(self.weight.dtype)
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return self.weight * hidden_states
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try:
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from apex.normalization import FusedRMSNorm
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Pix2StructLayerNorm = FusedRMSNorm # noqa
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logger.info("Discovered apex.normalization.FusedRMSNorm - will use it instead of Pix2StructLayerNorm")
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except ImportError:
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# using the normal Pix2StructLayerNorm
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pass
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except Exception:
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logger.warning("Discovered apex but it failed to load, falling back to Pix2StructLayerNorm")
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pass
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ALL_LAYERNORM_LAYERS.append(Pix2StructLayerNorm)
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class Pix2StructVisionEmbeddings(nn.Module):
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r"""
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Construct the embeddings from patch. In `Pix2Struct` the input is different from classic Vision-transformer models.
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Here the input is a sequence of `seq_len` flattened patches that also combines padding patches (tokens). Each patch
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is represented by a vector of `hidden_size` values.
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"""
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def __init__(self, config: Pix2StructConfig) -> None:
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super().__init__()
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self.patch_projection = nn.Linear(config.patch_embed_hidden_size, config.hidden_size)
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self.row_embedder = nn.Embedding(config.seq_len, config.hidden_size)
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self.column_embedder = nn.Embedding(config.seq_len, config.hidden_size)
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self.dropout = nn.Dropout(config.dropout_rate)
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def forward(self, flattened_patches: torch.Tensor) -> torch.Tensor:
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# the row and column indices are stored in the first and second position of the flattened_patches
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# flattened_patches: `batch_size`, `seq_len`, `hidden_size` + 2
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row_indices = flattened_patches[:, :, 0].long()
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col_indices = flattened_patches[:, :, 1].long()
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flattened_patches = flattened_patches[:, :, 2:]
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embeddings = self.patch_projection(flattened_patches)
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row_embeddings = self.row_embedder(row_indices)
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col_embeddings = self.column_embedder(col_indices)
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# sum all embeddings together
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embeddings = embeddings + row_embeddings + col_embeddings
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embeddings = self.dropout(embeddings)
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return embeddings
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class Pix2StructVisionAttention(nn.Module):
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def __init__(self, config):
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super().__init__()
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self.hidden_size = config.hidden_size
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self.key_value_proj_dim = config.d_kv
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self.n_heads = config.num_attention_heads
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self.dropout = config.attention_dropout
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self.inner_dim = self.n_heads * self.key_value_proj_dim
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# Mesh TensorFlow initialization to avoid scaling before softmax
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self.query = nn.Linear(self.hidden_size, self.inner_dim, bias=False)
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self.key = nn.Linear(self.hidden_size, self.inner_dim, bias=False)
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self.value = nn.Linear(self.hidden_size, self.inner_dim, bias=False)
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self.output = nn.Linear(self.inner_dim, self.hidden_size, bias=False)
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self.gradient_checkpointing = False
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def forward(
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self,
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hidden_states,
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attention_mask=None,
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position_bias=None,
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layer_head_mask=None,
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output_attentions=False,
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):
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"""
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Self-attention block
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"""
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# Input is (batch_size, seq_length, dim)
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# Mask is (batch_size, key_length) (non-causal) or (batch_size, key_length, key_length)
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# past_key_value[0] is (batch_size, n_heads, q_len - 1, dim_per_head)
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batch_size, seq_length = hidden_states.shape[:2]
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def to_projection_shape(states):
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"""projection"""
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return states.contiguous().view(batch_size, -1, self.n_heads, self.key_value_proj_dim).transpose(1, 2)
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# get query states
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# (batch_size, n_heads, seq_length, dim_per_head)
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query_states = to_projection_shape(self.query(hidden_states))
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# get key/value states
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key_states = to_projection_shape(self.key(hidden_states))
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value_states = to_projection_shape(self.value(hidden_states))
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# compute scores
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# equivalent of torch.einsum("bnqd,bnkd->bnqk", query_states, key_states), compatible with onnx op>9
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scores = torch.matmul(query_states, key_states.transpose(3, 2))
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if position_bias is None:
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position_bias = torch.zeros(
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(1, self.n_heads, seq_length, seq_length), device=scores.device, dtype=scores.dtype
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)
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if self.gradient_checkpointing and self.training:
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position_bias.requires_grad = True
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if attention_mask is None:
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attention_mask = torch.ones((batch_size, seq_length), device=scores.device, dtype=scores.dtype)
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if attention_mask.dim() == 2:
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position_bias = position_bias + attention_mask[:, None, None, :].to(position_bias.device)
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else:
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# (batch_size, n_heads, seq_length, key_length)
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position_bias = position_bias + attention_mask.to(position_bias.device)
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position_bias = 1 - position_bias
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position_bias_masked = position_bias.masked_fill(position_bias == 1, torch.finfo(scores.dtype).min)
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scores += position_bias_masked
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scores = torch.max(scores, torch.tensor(torch.finfo(scores.dtype).min))
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# (batch_size, n_heads, seq_length, key_length)
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attn_weights = nn.functional.softmax(scores, dim=-1, dtype=torch.float32).type_as(scores)
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# (batch_size, n_heads, seq_length, key_length)
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attn_weights = nn.functional.dropout(attn_weights, p=self.dropout, training=self.training)
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# Mask heads if we want to
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if layer_head_mask is not None:
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attn_weights = attn_weights * layer_head_mask
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attn_output = torch.matmul(attn_weights, value_states)
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# (batch_size, seq_length, dim)
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attn_output = attn_output.transpose(1, 2).contiguous().view(batch_size, -1, self.inner_dim)
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attn_output = self.output(attn_output)
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outputs = (attn_output,) + (position_bias,)
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if output_attentions:
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outputs = outputs + (attn_weights,)
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return outputs
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# Copied from transformers.models.t5.modeling_t5.T5DenseGatedActDense with T5DenseGatedActDense->Pix2StructVisionMlp,T5Config->Pix2StructVisionConfig,config.d_model->config.hidden_size,dropout_rate->dropout_rate
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class Pix2StructVisionMlp(nn.Module):
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def __init__(self, config: Pix2StructVisionConfig):
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super().__init__()
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self.wi_0 = nn.Linear(config.hidden_size, config.d_ff, bias=False)
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self.wi_1 = nn.Linear(config.hidden_size, config.d_ff, bias=False)
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self.wo = nn.Linear(config.d_ff, config.hidden_size, bias=False)
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self.dropout = nn.Dropout(config.dropout_rate)
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self.act = ACT2FN[config.dense_act_fn]
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def forward(self, hidden_states):
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hidden_gelu = self.act(self.wi_0(hidden_states))
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hidden_linear = self.wi_1(hidden_states)
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hidden_states = hidden_gelu * hidden_linear
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hidden_states = self.dropout(hidden_states)
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# To make 8bit quantization work for google/flan-t5-xxl, self.wo is kept in float32.
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# See https://github.com/huggingface/transformers/issues/20287
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# we also make sure the weights are not in `int8` in case users will force `_keep_in_fp32_modules` to be `None``
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if (
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isinstance(self.wo.weight, torch.Tensor)
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and hidden_states.dtype != self.wo.weight.dtype
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and self.wo.weight.dtype != torch.int8
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):
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hidden_states = hidden_states.to(self.wo.weight.dtype)
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hidden_states = self.wo(hidden_states)
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return hidden_states
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class Pix2StructVisionLayer(nn.Module):
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def __init__(self, config: Pix2StructConfig) -> None:
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super().__init__()
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self.chunk_size_feed_forward = config.chunk_size_feed_forward
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self.seq_len_dim = 1
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self.attention = Pix2StructVisionAttention(config)
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self.mlp = Pix2StructVisionMlp(config)
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self.pre_mlp_layer_norm = Pix2StructLayerNorm(config.hidden_size, eps=config.layer_norm_eps)
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self.pre_attention_layer_norm = Pix2StructLayerNorm(config.hidden_size, eps=config.layer_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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attention_mask: Optional[torch.Tensor] = None,
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head_mask: Optional[torch.Tensor] = None,
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output_attentions: bool = False,
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) -> Union[Tuple[torch.Tensor, torch.Tensor], Tuple[torch.Tensor]]:
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residual = hidden_states
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# in Pix2StructVision, layernorm is applied before self-attention
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hidden_states = self.pre_attention_layer_norm(hidden_states)
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self_attention_outputs = self.attention(
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hidden_states,
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attention_mask=attention_mask,
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layer_head_mask=head_mask,
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output_attentions=output_attentions,
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)
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attention_output = self_attention_outputs[0]
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outputs = self_attention_outputs[1:] # add self attentions if we output attention weights
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# first residual connection
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hidden_states = attention_output + residual
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# in Pix2StructVision, layernorm is also applied after self-attention
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layer_output = self.pre_mlp_layer_norm(hidden_states)
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layer_output = self.mlp(layer_output) + hidden_states # second residual connection
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outputs = (layer_output,) + outputs
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return outputs
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class Pix2StructVisionEncoder(nn.Module):
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def __init__(self, config: Pix2StructConfig) -> None:
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super().__init__()
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self.config = config
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self.layer = nn.ModuleList([Pix2StructVisionLayer(config) for _ in range(config.num_hidden_layers)])
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self.gradient_checkpointing = False
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def forward(
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self,
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hidden_states: torch.Tensor,
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attention_mask: Optional[torch.Tensor] = None,
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head_mask: Optional[torch.Tensor] = None,
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output_attentions: bool = False,
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output_hidden_states: bool = False,
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return_dict: bool = True,
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) -> Union[tuple, BaseModelOutput]:
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all_hidden_states = () if output_hidden_states else None
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all_self_attentions = () if output_attentions else None
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for i, layer_module in enumerate(self.layer):
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if output_hidden_states:
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all_hidden_states = all_hidden_states + (hidden_states,)
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layer_head_mask = head_mask[i] if head_mask is not None else None
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if self.gradient_checkpointing and self.training:
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layer_outputs = self._gradient_checkpointing_func(
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layer_module.__call__,
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hidden_states,
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attention_mask,
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layer_head_mask,
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output_attentions,
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)
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else:
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layer_outputs = layer_module(hidden_states, attention_mask, layer_head_mask, output_attentions)
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hidden_states = layer_outputs[0]
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if output_attentions:
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all_self_attentions = all_self_attentions + (layer_outputs[1],)
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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 not return_dict:
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return tuple(v for v in [hidden_states, all_hidden_states, all_self_attentions] if v is not None)
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return BaseModelOutput(
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last_hidden_state=hidden_states,
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hidden_states=all_hidden_states,
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attentions=all_self_attentions,
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)
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class Pix2StructPreTrainedModel(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 = Pix2StructConfig
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@property
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def dummy_inputs(self):
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input_ids = torch.tensor(DUMMY_INPUTS)
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input_mask = torch.tensor(DUMMY_MASK)
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dummy_inputs = {
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"decoder_input_ids": input_ids,
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"input_ids": input_ids,
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"decoder_attention_mask": input_mask,
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}
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return dummy_inputs
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def _init_weights(self, module):
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"""Initialize the weights"""
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factor = self.config.initializer_factor # Used for testing weights initialization
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if isinstance(module, Pix2StructLayerNorm):
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module.weight.data.fill_(factor * 1.0)
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elif isinstance(module, Pix2StructTextDenseGatedActDense):
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hidden_size = (
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self.config.text_config.hidden_size
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if isinstance(self.config, Pix2StructConfig)
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else self.config.hidden_size
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)
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d_ff = self.config.text_config.d_ff if isinstance(self.config, Pix2StructConfig) else self.config.d_ff
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module.wi_0.weight.data.normal_(mean=0.0, std=factor * ((hidden_size) ** -0.5))
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if hasattr(module.wi_0, "bias") and module.wi_0.bias is not None:
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module.wi_0.bias.data.zero_()
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module.wi_1.weight.data.normal_(mean=0.0, std=factor * ((hidden_size) ** -0.5))
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if hasattr(module.wi_1, "bias") and module.wi_1.bias is not None:
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module.wi_1.bias.data.zero_()
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module.wo.weight.data.normal_(mean=0.0, std=factor * ((d_ff) ** -0.5))
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if hasattr(module.wo, "bias") and module.wo.bias is not None:
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module.wo.bias.data.zero_()
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elif isinstance(module, Pix2StructTextAttention):
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# Mesh TensorFlow attention initialization to avoid scaling before softmax
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# See https://github.com/tensorflow/mesh/blob/fa19d69eafc9a482aff0b59ddd96b025c0cb207d/mesh_tensorflow/transformer/attention.py#L136
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hidden_size = (
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self.config.text_config.hidden_size
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if isinstance(self.config, Pix2StructConfig)
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else self.config.hidden_size
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)
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key_value_proj_dim = (
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self.config.text_config.d_kv if isinstance(self.config, Pix2StructConfig) else self.config.hidden_size
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)
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n_heads = (
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self.config.text_config.num_heads
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if isinstance(self.config, Pix2StructConfig)
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else self.config.num_heads
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)
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module.query.weight.data.normal_(mean=0.0, std=factor * ((hidden_size * key_value_proj_dim) ** -0.5))
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module.key.weight.data.normal_(mean=0.0, std=factor * (hidden_size**-0.5))
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module.value.weight.data.normal_(mean=0.0, std=factor * (hidden_size**-0.5))
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module.output.weight.data.normal_(mean=0.0, std=factor * ((n_heads * key_value_proj_dim) ** -0.5))
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if module.has_relative_attention_bias:
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module.relative_attention_bias.weight.data.normal_(mean=0.0, std=factor * ((hidden_size) ** -0.5))
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elif isinstance(module, nn.Embedding):
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hidden_size = (
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self.config.text_config.hidden_size
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if isinstance(self.config, Pix2StructConfig)
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else self.config.hidden_size
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)
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module.weight.data.normal_(mean=0.0, std=factor * ((hidden_size) ** -0.5))
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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, Pix2StructTextModel):
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hidden_size = (
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self.config.text_config.hidden_size
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if isinstance(self.config, Pix2StructConfig)
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else self.config.hidden_size
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)
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module.lm_head.weight.data.normal_(mean=0.0, std=factor * ((hidden_size) ** -0.5))
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elif isinstance(module, (nn.Linear, nn.Conv2d)):
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# Upcast the input in `fp32` and cast it back to desired `dtype` to avoid
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# `trunc_normal_cpu` not implemented in `half` issues
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module.weight.data = nn.init.trunc_normal_(
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module.weight.data.to(torch.float32), mean=0.0, std=self.config.initializer_range
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).to(module.weight.dtype)
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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, Pix2StructLayerNorm):
|
|
if module.weight is not None:
|
|
module.weight.data.fill_(1.0)
|
|
elif isinstance(module, nn.Embedding):
|
|
module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
|
|
if module.padding_idx is not None:
|
|
module.weight.data[module.padding_idx].zero_()
|
|
|
|
# Copied from transformers.models.t5.modeling_t5.T5PreTrainedModel._shift_right with T5->Pix2Struct
|
|
def _shift_right(self, input_ids):
|
|
decoder_start_token_id = self.config.decoder_start_token_id
|
|
pad_token_id = self.config.pad_token_id
|
|
|
|
if decoder_start_token_id is None:
|
|
raise ValueError(
|
|
"self.model.config.decoder_start_token_id has to be defined. In Pix2Struct it is usually set to the pad_token_id. "
|
|
"See Pix2Struct docs for more information."
|
|
)
|
|
|
|
# shift inputs to the right
|
|
if is_torch_fx_proxy(input_ids):
|
|
# Item assignment is not supported natively for proxies.
|
|
shifted_input_ids = torch.full(input_ids.shape[:-1] + (1,), decoder_start_token_id)
|
|
shifted_input_ids = torch.cat([shifted_input_ids, input_ids[..., :-1]], dim=-1)
|
|
else:
|
|
shifted_input_ids = input_ids.new_zeros(input_ids.shape)
|
|
shifted_input_ids[..., 1:] = input_ids[..., :-1].clone()
|
|
shifted_input_ids[..., 0] = decoder_start_token_id
|
|
|
|
if pad_token_id is None:
|
|
raise ValueError("self.model.config.pad_token_id has to be defined.")
|
|
# replace possible -100 values in labels by `pad_token_id`
|
|
shifted_input_ids.masked_fill_(shifted_input_ids == -100, pad_token_id)
|
|
|
|
return shifted_input_ids
|
|
|
|
|
|
PIX2STRUCT_VISION_START_DOCSTRING = r"""
|
|
This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. Use it
|
|
as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and
|
|
behavior.
|
|
|
|
Parameters:
|
|
config ([`Pix2StructConfig`]): Model configuration class with all the parameters of the model.
|
|
Initializing with a config file does not load the weights associated with the model, only the
|
|
configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.
|
|
"""
|
|
|
|
PIX2STRUCT_VISION_INPUTS_DOCSTRING = r"""
|
|
Args:
|
|
flattened_patches (`torch.FloatTensor` of shape `(batch_size, sequence_length, num_channels x patch_height x patch_width)`):
|
|
Flattened and padded pixel values. These values can be obtained using [`AutoImageProcessor`]. See
|
|
[`Pix2StructVisionImageProcessor.__call__`] for details. Check the [original
|
|
paper](https://arxiv.org/abs/2210.03347) (figure 5) for more details.
|
|
|
|
attention_mask (`torch.FloatTensor` of shape `(batch_size, sequence_length)`, *optional*):
|
|
Mask to avoid performing attention on padding pixel values. Mask values selected in `[0, 1]`:
|
|
|
|
head_mask (`torch.FloatTensor` of shape `(num_heads,)` or `(num_layers, num_heads)`, *optional*):
|
|
Mask to nullify selected heads of the self-attention modules. Mask values selected in `[0, 1]`:
|
|
|
|
- 1 indicates the head is **not masked**,
|
|
- 0 indicates the head is **masked**.
|
|
|
|
output_attentions (`bool`, *optional*):
|
|
Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
|
|
tensors for more detail.
|
|
output_hidden_states (`bool`, *optional*):
|
|
Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
|
|
more detail.
|
|
return_dict (`bool`, *optional*):
|
|
Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
|
|
"""
|
|
|
|
|
|
@add_start_docstrings(
|
|
"The bare Pix2StructVision Model transformer outputting raw hidden-states without any specific head on top.",
|
|
PIX2STRUCT_VISION_START_DOCSTRING,
|
|
)
|
|
class Pix2StructVisionModel(Pix2StructPreTrainedModel):
|
|
config_class = Pix2StructVisionConfig
|
|
main_input_name = "flattened_patches"
|
|
supports_gradient_checkpointing = True
|
|
_no_split_modules = ["Pix2StructVisionLayer"]
|
|
|
|
def __init__(self, config: Pix2StructConfig):
|
|
super().__init__(config)
|
|
self.config = config
|
|
|
|
self.embeddings = Pix2StructVisionEmbeddings(config)
|
|
self.encoder = Pix2StructVisionEncoder(config)
|
|
|
|
self.layernorm = Pix2StructLayerNorm(config.hidden_size, eps=config.layer_norm_eps)
|
|
|
|
# Initialize weights and apply final processing
|
|
self.post_init()
|
|
|
|
def get_input_embeddings(self):
|
|
return self.embeddings.patch_projection
|
|
|
|
def _prune_heads(self, heads_to_prune: Dict[int, List[int]]) -> None:
|
|
"""
|
|
Prunes heads of the model. heads_to_prune: dict of {layer_num: list of heads to prune in this layer} See base
|
|
class PreTrainedModel
|
|
"""
|
|
for layer, heads in heads_to_prune.items():
|
|
self.encoder.layer[layer].attention.prune_heads(heads)
|
|
|
|
@add_start_docstrings_to_model_forward(PIX2STRUCT_VISION_INPUTS_DOCSTRING)
|
|
@replace_return_docstrings(output_type=BaseModelOutputWithPooling, config_class=_CONFIG_FOR_DOC)
|
|
def forward(
|
|
self,
|
|
flattened_patches: Optional[torch.Tensor] = None,
|
|
attention_mask: Optional[torch.Tensor] = None,
|
|
head_mask: Optional[torch.Tensor] = None,
|
|
output_attentions: Optional[bool] = None,
|
|
output_hidden_states: Optional[bool] = None,
|
|
return_dict: Optional[bool] = None,
|
|
) -> Union[Tuple, BaseModelOutputWithPooling]:
|
|
r"""
|
|
Returns:
|
|
|
|
Example:
|
|
|
|
```python
|
|
>>> import requests
|
|
>>> from PIL import Image
|
|
>>> from transformers import AutoProcessor, Pix2StructVisionModel
|
|
|
|
>>> image_processor = AutoProcessor.from_pretrained("google/pix2struct-textcaps-base")
|
|
>>> model = Pix2StructVisionModel.from_pretrained("google/pix2struct-textcaps-base")
|
|
|
|
>>> url = "https://www.ilankelman.org/stopsigns/australia.jpg"
|
|
>>> image = Image.open(requests.get(url, stream=True).raw)
|
|
|
|
>>> inputs = image_processor(images=image, return_tensors="pt")
|
|
>>> with torch.no_grad():
|
|
... outputs = model(**inputs)
|
|
|
|
>>> last_hidden_states = outputs.last_hidden_state
|
|
>>> list(last_hidden_states.shape)
|
|
[1, 2048, 768]
|
|
```
|
|
"""
|
|
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
|
|
output_hidden_states = (
|
|
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
|
|
)
|
|
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
|
|
|
|
if flattened_patches is None:
|
|
raise ValueError("You have to specify flattened_patches")
|
|
|
|
if attention_mask is None:
|
|
# check where `flattened_patches` is not 0
|
|
attention_mask = (flattened_patches.sum(dim=-1) != 0).float()
|
|
|
|
# Prepare head mask if needed
|
|
# 1.0 in head_mask indicate we keep the head
|
|
# attention_probs has shape bsz x n_heads x N x N
|
|
# input head_mask has shape [num_heads] or [num_hidden_layers x num_heads]
|
|
# and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length]
|
|
head_mask = self.get_head_mask(head_mask, self.config.num_hidden_layers)
|
|
|
|
embedding_output = self.embeddings(flattened_patches)
|
|
|
|
encoder_outputs = self.encoder(
|
|
embedding_output,
|
|
attention_mask=attention_mask,
|
|
head_mask=head_mask,
|
|
output_attentions=output_attentions,
|
|
output_hidden_states=output_hidden_states,
|
|
return_dict=return_dict,
|
|
)
|
|
sequence_output = encoder_outputs[0]
|
|
sequence_output = self.layernorm(sequence_output)
|
|
|
|
if not return_dict:
|
|
head_outputs = (sequence_output,)
|
|
return head_outputs + encoder_outputs[1:]
|
|
|
|
return BaseModelOutput(
|
|
last_hidden_state=sequence_output,
|
|
hidden_states=encoder_outputs.hidden_states,
|
|
attentions=encoder_outputs.attentions,
|
|
)
|
|
|
|
|
|
# Copied from transformers.models.t5.modeling_t5.T5DenseGatedActDense with T5->Pix2StructText,d_model->hidden_size
|
|
class Pix2StructTextDenseGatedActDense(nn.Module):
|
|
def __init__(self, config: Pix2StructTextConfig):
|
|
super().__init__()
|
|
self.wi_0 = nn.Linear(config.hidden_size, config.d_ff, bias=False)
|
|
self.wi_1 = nn.Linear(config.hidden_size, config.d_ff, bias=False)
|
|
self.wo = nn.Linear(config.d_ff, config.hidden_size, bias=False)
|
|
self.dropout = nn.Dropout(config.dropout_rate)
|
|
self.act = ACT2FN[config.dense_act_fn]
|
|
|
|
def forward(self, hidden_states):
|
|
hidden_gelu = self.act(self.wi_0(hidden_states))
|
|
hidden_linear = self.wi_1(hidden_states)
|
|
hidden_states = hidden_gelu * hidden_linear
|
|
hidden_states = self.dropout(hidden_states)
|
|
|
|
# To make 8bit quantization work for google/flan-t5-xxl, self.wo is kept in float32.
|
|
# See https://github.com/huggingface/transformers/issues/20287
|
|
# we also make sure the weights are not in `int8` in case users will force `_keep_in_fp32_modules` to be `None``
|
|
if (
|
|
isinstance(self.wo.weight, torch.Tensor)
|
|
and hidden_states.dtype != self.wo.weight.dtype
|
|
and self.wo.weight.dtype != torch.int8
|
|
):
|
|
hidden_states = hidden_states.to(self.wo.weight.dtype)
|
|
|
|
hidden_states = self.wo(hidden_states)
|
|
return hidden_states
|
|
|
|
|
|
class Pix2StructTextLayerFF(nn.Module):
|
|
def __init__(self, config: Pix2StructTextConfig):
|
|
super().__init__()
|
|
self.DenseReluDense = Pix2StructTextDenseGatedActDense(config)
|
|
|
|
self.layer_norm = Pix2StructLayerNorm(config.hidden_size, eps=config.layer_norm_epsilon)
|
|
self.dropout = nn.Dropout(config.dropout_rate)
|
|
|
|
# Copied from transformers.models.t5.modeling_t5.T5LayerFF.forward
|
|
def forward(self, hidden_states):
|
|
forwarded_states = self.layer_norm(hidden_states)
|
|
forwarded_states = self.DenseReluDense(forwarded_states)
|
|
hidden_states = hidden_states + self.dropout(forwarded_states)
|
|
return hidden_states
|
|
|
|
|
|
class Pix2StructTextAttention(nn.Module):
|
|
def __init__(self, config: Pix2StructTextConfig, has_relative_attention_bias=False):
|
|
super().__init__()
|
|
self.has_relative_attention_bias = has_relative_attention_bias
|
|
self.relative_attention_num_buckets = config.relative_attention_num_buckets
|
|
self.relative_attention_max_distance = config.relative_attention_max_distance
|
|
self.hidden_size = config.hidden_size
|
|
self.key_value_proj_dim = config.d_kv
|
|
self.n_heads = config.num_heads
|
|
self.dropout = config.dropout_rate
|
|
self.inner_dim = self.n_heads * self.key_value_proj_dim
|
|
|
|
# Mesh TensorFlow initialization to avoid scaling before softmax
|
|
self.query = nn.Linear(self.hidden_size, self.hidden_size, bias=False)
|
|
self.key = nn.Linear(self.hidden_size, self.hidden_size, bias=False)
|
|
self.value = nn.Linear(self.hidden_size, self.hidden_size, bias=False)
|
|
self.output = nn.Linear(self.hidden_size, self.hidden_size, bias=False)
|
|
|
|
if self.has_relative_attention_bias:
|
|
self.relative_attention_bias = nn.Embedding(self.relative_attention_num_buckets, self.n_heads)
|
|
self.pruned_heads = set()
|
|
self.gradient_checkpointing = False
|
|
|
|
@staticmethod
|
|
# Copied from transformers.models.t5.modeling_t5.T5Attention._relative_position_bucket
|
|
def _relative_position_bucket(relative_position, bidirectional=True, num_buckets=32, max_distance=128):
|
|
"""
|
|
Adapted from Mesh Tensorflow:
|
|
https://github.com/tensorflow/mesh/blob/0cb87fe07da627bf0b7e60475d59f95ed6b5be3d/mesh_tensorflow/transformer/transformer_layers.py#L593
|
|
|
|
Translate relative position to a bucket number for relative attention. The relative position is defined as
|
|
memory_position - query_position, i.e. the distance in tokens from the attending position to the attended-to
|
|
position. If bidirectional=False, then positive relative positions are invalid. We use smaller buckets for
|
|
small absolute relative_position and larger buckets for larger absolute relative_positions. All relative
|
|
positions >=max_distance map to the same bucket. All relative positions <=-max_distance map to the same bucket.
|
|
This should allow for more graceful generalization to longer sequences than the model has been trained on
|
|
|
|
Args:
|
|
relative_position: an int32 Tensor
|
|
bidirectional: a boolean - whether the attention is bidirectional
|
|
num_buckets: an integer
|
|
max_distance: an integer
|
|
|
|
Returns:
|
|
a Tensor with the same shape as relative_position, containing int32 values in the range [0, num_buckets)
|
|
"""
|
|
relative_buckets = 0
|
|
if bidirectional:
|
|
num_buckets //= 2
|
|
relative_buckets += (relative_position > 0).to(torch.long) * num_buckets
|
|
relative_position = torch.abs(relative_position)
|
|
else:
|
|
relative_position = -torch.min(relative_position, torch.zeros_like(relative_position))
|
|
# now relative_position is in the range [0, inf)
|
|
|
|
# half of the buckets are for exact increments in positions
|
|
max_exact = num_buckets // 2
|
|
is_small = relative_position < max_exact
|
|
|
|
# The other half of the buckets are for logarithmically bigger bins in positions up to max_distance
|
|
relative_position_if_large = max_exact + (
|
|
torch.log(relative_position.float() / max_exact)
|
|
/ math.log(max_distance / max_exact)
|
|
* (num_buckets - max_exact)
|
|
).to(torch.long)
|
|
relative_position_if_large = torch.min(
|
|
relative_position_if_large, torch.full_like(relative_position_if_large, num_buckets - 1)
|
|
)
|
|
|
|
relative_buckets += torch.where(is_small, relative_position, relative_position_if_large)
|
|
return relative_buckets
|
|
|
|
# Adapted from transformers.models.t5.modeling_t5.T5Attention.compute_bias
|
|
def compute_bias(self, query_length, key_length, device=None):
|
|
"""Compute binned relative position bias"""
|
|
if device is None:
|
|
device = self.relative_attention_bias.weight.device
|
|
context_position = torch.arange(query_length, dtype=torch.long, device=device)[:, None]
|
|
memory_position = torch.arange(key_length, dtype=torch.long, device=device)[None, :]
|
|
relative_position = memory_position - context_position # shape (query_length, key_length)
|
|
relative_position_bucket = self._relative_position_bucket(
|
|
relative_position, # shape (query_length, key_length)
|
|
bidirectional=False,
|
|
num_buckets=self.relative_attention_num_buckets,
|
|
max_distance=self.relative_attention_max_distance,
|
|
)
|
|
values = self.relative_attention_bias(relative_position_bucket) # shape (query_length, key_length, num_heads)
|
|
values = values.permute([2, 0, 1]).unsqueeze(0) # shape (1, num_heads, query_length, key_length)
|
|
return values
|
|
|
|
def forward(
|
|
self,
|
|
hidden_states,
|
|
mask=None,
|
|
key_value_states=None,
|
|
position_bias=None,
|
|
past_key_value=None,
|
|
layer_head_mask=None,
|
|
query_length=None,
|
|
use_cache=False,
|
|
output_attentions=False,
|
|
):
|
|
"""
|
|
Self-attention (if key_value_states is None) or attention over source sentence (provided by key_value_states).
|
|
"""
|
|
# Input is (batch_size, seq_length, dim)
|
|
# Mask is (batch_size, key_length) (non-causal) or (batch_size, key_length, key_length)
|
|
# past_key_value[0] is (batch_size, n_heads, q_len - 1, dim_per_head)
|
|
batch_size, seq_length = hidden_states.shape[:2]
|
|
|
|
real_seq_length = seq_length
|
|
|
|
if past_key_value is not None:
|
|
if len(past_key_value) != 2:
|
|
raise ValueError(
|
|
f"past_key_value should have 2 past states: keys and values. Got { len(past_key_value)} past states"
|
|
)
|
|
real_seq_length += past_key_value[0].shape[2] if query_length is None else query_length
|
|
|
|
key_length = real_seq_length if key_value_states is None else key_value_states.shape[1]
|
|
|
|
def to_projection_shape(states):
|
|
"""projection"""
|
|
return states.contiguous().view(batch_size, -1, self.n_heads, self.key_value_proj_dim).transpose(1, 2)
|
|
|
|
def project(hidden_states, proj_layer, key_value_states, past_key_value):
|
|
"""projects hidden states correctly to key/query states"""
|
|
if key_value_states is None:
|
|
# self-attn
|
|
# (batch_size, n_heads, seq_length, dim_per_head)
|
|
hidden_states = to_projection_shape(proj_layer(hidden_states))
|
|
elif past_key_value is None:
|
|
# cross-attn
|
|
# (batch_size, n_heads, seq_length, dim_per_head)
|
|
hidden_states = to_projection_shape(proj_layer(key_value_states))
|
|
|
|
if past_key_value is not None:
|
|
if key_value_states is None:
|
|
# self-attn
|
|
# (batch_size, n_heads, key_length, dim_per_head)
|
|
hidden_states = torch.cat([past_key_value, hidden_states], dim=2)
|
|
elif past_key_value.shape[2] != key_value_states.shape[1]:
|
|
# checking that the `sequence_length` of the `past_key_value` is the same as
|
|
# the provided `key_value_states` to support prefix tuning
|
|
# cross-attn
|
|
# (batch_size, n_heads, seq_length, dim_per_head)
|
|
hidden_states = to_projection_shape(proj_layer(key_value_states))
|
|
else:
|
|
# cross-attn
|
|
hidden_states = past_key_value
|
|
return hidden_states
|
|
|
|
# get query states
|
|
# (batch_size, n_heads, seq_length, dim_per_head)
|
|
query_states = to_projection_shape(self.query(hidden_states))
|
|
|
|
# get key/value states
|
|
key_states = project(
|
|
hidden_states, self.key, key_value_states, past_key_value[0] if past_key_value is not None else None
|
|
)
|
|
value_states = project(
|
|
hidden_states, self.value, key_value_states, past_key_value[1] if past_key_value is not None else None
|
|
)
|
|
|
|
# compute scores
|
|
scores = torch.matmul(
|
|
query_states, key_states.transpose(3, 2)
|
|
) # equivalent of torch.einsum("bnqd,bnkd->bnqk", query_states, key_states), compatible with onnx op>9
|
|
|
|
if position_bias is None:
|
|
if not self.has_relative_attention_bias:
|
|
position_bias = torch.zeros(
|
|
(1, self.n_heads, real_seq_length, key_length), device=scores.device, dtype=scores.dtype
|
|
)
|
|
if self.gradient_checkpointing and self.training:
|
|
position_bias.requires_grad = True
|
|
else:
|
|
position_bias = self.compute_bias(real_seq_length, key_length, device=scores.device)
|
|
|
|
# if key and values are already calculated
|
|
# we want only the last query position bias
|
|
if past_key_value is not None:
|
|
position_bias = position_bias[:, :, -hidden_states.size(1) :, :]
|
|
|
|
if mask is not None:
|
|
position_bias = position_bias + mask # (batch_size, n_heads, seq_length, key_length)
|
|
|
|
if self.pruned_heads:
|
|
mask = torch.ones(position_bias.shape[1])
|
|
mask[list(self.pruned_heads)] = 0
|
|
position_bias_masked = position_bias[:, mask.bool()]
|
|
else:
|
|
position_bias_masked = position_bias
|
|
|
|
scores += position_bias_masked
|
|
# (batch_size, n_heads, seq_length, key_length)
|
|
attn_weights = nn.functional.softmax(scores.float(), dim=-1).type_as(scores)
|
|
|
|
# (batch_size, n_heads, seq_length, key_length)
|
|
attn_weights = nn.functional.dropout(attn_weights, p=self.dropout, training=self.training)
|
|
|
|
# Mask heads if we want to
|
|
if layer_head_mask is not None:
|
|
attn_weights = attn_weights * layer_head_mask
|
|
|
|
attn_output = torch.matmul(attn_weights, value_states)
|
|
# (batch_size, seq_length, dim)
|
|
attn_output = attn_output.transpose(1, 2).contiguous().view(batch_size, -1, self.inner_dim)
|
|
|
|
attn_output = self.output(attn_output)
|
|
|
|
present_key_value_state = (key_states, value_states) if use_cache else None
|
|
outputs = (attn_output,) + (present_key_value_state,) + (position_bias,)
|
|
|
|
if output_attentions:
|
|
outputs = outputs + (attn_weights,)
|
|
return outputs
|
|
|
|
|
|
# Copied from transformers.models.t5.modeling_t5.T5LayerSelfAttention with T5LayerNorm->Pix2StructLayerNorm,T5Attention->Pix2StructTextAttention,self.SelfAttention->self.attention,config.d_model->config.hidden_size
|
|
class Pix2StructTextLayerSelfAttention(nn.Module):
|
|
def __init__(self, config, has_relative_attention_bias=False):
|
|
super().__init__()
|
|
self.attention = Pix2StructTextAttention(config, has_relative_attention_bias=has_relative_attention_bias)
|
|
self.layer_norm = Pix2StructLayerNorm(config.hidden_size, eps=config.layer_norm_epsilon)
|
|
self.dropout = nn.Dropout(config.dropout_rate)
|
|
|
|
def forward(
|
|
self,
|
|
hidden_states,
|
|
attention_mask=None,
|
|
position_bias=None,
|
|
layer_head_mask=None,
|
|
past_key_value=None,
|
|
use_cache=False,
|
|
output_attentions=False,
|
|
):
|
|
normed_hidden_states = self.layer_norm(hidden_states)
|
|
attention_output = self.attention(
|
|
normed_hidden_states,
|
|
mask=attention_mask,
|
|
position_bias=position_bias,
|
|
layer_head_mask=layer_head_mask,
|
|
past_key_value=past_key_value,
|
|
use_cache=use_cache,
|
|
output_attentions=output_attentions,
|
|
)
|
|
hidden_states = hidden_states + self.dropout(attention_output[0])
|
|
outputs = (hidden_states,) + attention_output[1:] # add attentions if we output them
|
|
return outputs
|
|
|
|
|
|
# Copied from transformers.models.t5.modeling_t5.T5LayerCrossAttention with T5LayerNorm->Pix2StructLayerNorm,T5Attention->Pix2StructTextAttention,self.EncDecAttention->self.attention,config.d_model->config.hidden_size
|
|
class Pix2StructTextLayerCrossAttention(nn.Module):
|
|
def __init__(self, config):
|
|
super().__init__()
|
|
self.attention = Pix2StructTextAttention(config, has_relative_attention_bias=False)
|
|
self.layer_norm = Pix2StructLayerNorm(config.hidden_size, eps=config.layer_norm_epsilon)
|
|
self.dropout = nn.Dropout(config.dropout_rate)
|
|
|
|
def forward(
|
|
self,
|
|
hidden_states,
|
|
key_value_states,
|
|
attention_mask=None,
|
|
position_bias=None,
|
|
layer_head_mask=None,
|
|
past_key_value=None,
|
|
use_cache=False,
|
|
query_length=None,
|
|
output_attentions=False,
|
|
):
|
|
normed_hidden_states = self.layer_norm(hidden_states)
|
|
attention_output = self.attention(
|
|
normed_hidden_states,
|
|
mask=attention_mask,
|
|
key_value_states=key_value_states,
|
|
position_bias=position_bias,
|
|
layer_head_mask=layer_head_mask,
|
|
past_key_value=past_key_value,
|
|
use_cache=use_cache,
|
|
query_length=query_length,
|
|
output_attentions=output_attentions,
|
|
)
|
|
layer_output = hidden_states + self.dropout(attention_output[0])
|
|
outputs = (layer_output,) + attention_output[1:] # add attentions if we output them
|
|
return outputs
|
|
|
|
|
|
class Pix2StructTextBlock(nn.Module):
|
|
def __init__(self, config, has_relative_attention_bias=False):
|
|
super().__init__()
|
|
|
|
self.self_attention = Pix2StructTextLayerSelfAttention(
|
|
config, has_relative_attention_bias=has_relative_attention_bias
|
|
)
|
|
|
|
self.encoder_decoder_attention = Pix2StructTextLayerCrossAttention(config)
|
|
|
|
self.mlp = Pix2StructTextLayerFF(config)
|
|
|
|
def forward(
|
|
self,
|
|
hidden_states,
|
|
attention_mask=None,
|
|
position_bias=None,
|
|
encoder_hidden_states=None,
|
|
encoder_attention_mask=None,
|
|
encoder_decoder_position_bias=None,
|
|
layer_head_mask=None,
|
|
cross_attn_layer_head_mask=None,
|
|
past_key_value=None,
|
|
use_cache=False,
|
|
output_attentions=False,
|
|
return_dict=True,
|
|
):
|
|
if past_key_value is not None:
|
|
expected_num_past_key_values = 2 if encoder_hidden_states is None else 4
|
|
|
|
if len(past_key_value) != expected_num_past_key_values:
|
|
raise ValueError(
|
|
f"There should be {expected_num_past_key_values} past states. "
|
|
f"{'2 (past / key) for cross attention. ' if expected_num_past_key_values == 4 else ''}"
|
|
f"Got {len(past_key_value)} past key / value states"
|
|
)
|
|
|
|
self_attn_past_key_value = past_key_value[:2]
|
|
cross_attn_past_key_value = past_key_value[2:]
|
|
else:
|
|
self_attn_past_key_value, cross_attn_past_key_value = None, None
|
|
|
|
self_attention_outputs = self.self_attention(
|
|
hidden_states,
|
|
attention_mask=attention_mask,
|
|
position_bias=position_bias,
|
|
layer_head_mask=layer_head_mask,
|
|
past_key_value=self_attn_past_key_value,
|
|
use_cache=use_cache,
|
|
output_attentions=output_attentions,
|
|
)
|
|
hidden_states, present_key_value_state = self_attention_outputs[:2]
|
|
attention_outputs = self_attention_outputs[2:] # Keep self-attention outputs and relative position weights
|
|
|
|
# clamp inf values to enable fp16 training
|
|
if hidden_states.dtype == torch.float16 and torch.isinf(hidden_states).any():
|
|
clamp_value = torch.finfo(hidden_states.dtype).max - 1000
|
|
hidden_states = torch.clamp(hidden_states, min=-clamp_value, max=clamp_value)
|
|
|
|
do_cross_attention = encoder_hidden_states is not None
|
|
if do_cross_attention:
|
|
# the actual query length is unknown for cross attention
|
|
# if using past key value states. Need to inject it here
|
|
if present_key_value_state is not None:
|
|
query_length = present_key_value_state[0].shape[2]
|
|
else:
|
|
query_length = None
|
|
|
|
cross_attention_outputs = self.encoder_decoder_attention(
|
|
hidden_states,
|
|
key_value_states=encoder_hidden_states,
|
|
attention_mask=encoder_attention_mask,
|
|
position_bias=encoder_decoder_position_bias,
|
|
layer_head_mask=cross_attn_layer_head_mask,
|
|
past_key_value=cross_attn_past_key_value,
|
|
query_length=query_length,
|
|
use_cache=use_cache,
|
|
output_attentions=output_attentions,
|
|
)
|
|
hidden_states = cross_attention_outputs[0]
|
|
|
|
# clamp inf values to enable fp16 training
|
|
if hidden_states.dtype == torch.float16 and torch.isinf(hidden_states).any():
|
|
clamp_value = torch.finfo(hidden_states.dtype).max - 1000
|
|
hidden_states = torch.clamp(hidden_states, min=-clamp_value, max=clamp_value)
|
|
|
|
# Combine self attn and cross attn key value states
|
|
if present_key_value_state is not None:
|
|
present_key_value_state = present_key_value_state + cross_attention_outputs[1]
|
|
|
|
# Keep cross-attention outputs and relative position weights
|
|
attention_outputs = attention_outputs + cross_attention_outputs[2:]
|
|
|
|
# Apply Feed Forward layer
|
|
hidden_states = self.mlp(hidden_states)
|
|
|
|
# clamp inf values to enable fp16 training
|
|
if hidden_states.dtype == torch.float16 and torch.isinf(hidden_states).any():
|
|
clamp_value = torch.finfo(hidden_states.dtype).max - 1000
|
|
hidden_states = torch.clamp(hidden_states, min=-clamp_value, max=clamp_value)
|
|
|
|
outputs = (hidden_states,)
|
|
|
|
if use_cache:
|
|
outputs = outputs + (present_key_value_state,) + attention_outputs
|
|
else:
|
|
outputs = outputs + attention_outputs
|
|
|
|
return outputs
|
|
|
|
|
|
PIX2STRUCT_START_DOCSTRING = r"""
|
|
|
|
The Pix2Struct model was proposed in [Pix2Struct: Screenshot Parsing as Pretraining for Visual Language
|
|
Understanding](https://arxiv.org/abs/2210.03347) by Kenton Lee, Mandar Joshi, Iulia Turc, Hexiang Hu, Fangyu Liu,
|
|
Julian Eisenschlos, Urvashi Khandelwal, Peter Shaw, Ming-Wei Chang, Kristina Toutanova. It's an encoder decoder
|
|
transformer pre-trained in a image-to-text setting.
|
|
|
|
This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
|
|
library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
|
|
etc.)
|
|
|
|
This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass.
|
|
Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
|
|
and behavior.
|
|
|
|
Parameters:
|
|
config (Union[`Pix2StructConfig`, `Pix2StructTextConfig`]):
|
|
Model configuration class with all the parameters of the model. Initializing with a config file does not
|
|
load the weights associated with the model, only the configuration. Check out the
|
|
[`~PreTrainedModel.from_pretrained`] method to load the model weights.
|
|
"""
|
|
|
|
PIX2STRUCT_TEXT_INPUTS_DOCSTRING = r"""
|
|
Args:
|
|
input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
|
|
Indices of input sequence tokens in the vocabulary. Pix2StructText is a model with relative position
|
|
embeddings so you should be able to pad the inputs on both the right and the left.
|
|
|
|
Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
|
|
[`PreTrainedTokenizer.__call__`] for detail.
|
|
|
|
[What are input IDs?](../glossary#input-ids)
|
|
|
|
To know more on how to prepare `input_ids` for pretraining take a look a [Pix2StructText
|
|
Training](./t5#training).
|
|
attention_mask (`torch.FloatTensor` of shape `(batch_size, sequence_length)`, *optional*):
|
|
Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
|
|
|
|
- 1 for tokens that are **not masked**,
|
|
- 0 for tokens that are **masked**.
|
|
|
|
[What are attention masks?](../glossary#attention-mask)
|
|
decoder_input_ids (`torch.LongTensor` of shape `(batch_size, target_sequence_length)`, *optional*):
|
|
Indices of decoder input sequence tokens in the vocabulary.
|
|
|
|
Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
|
|
[`PreTrainedTokenizer.__call__`] for details.
|
|
|
|
[What are decoder input IDs?](../glossary#decoder-input-ids)
|
|
|
|
Pix2StructText uses the `pad_token_id` as the starting token for `decoder_input_ids` generation. If
|
|
`past_key_values` is used, optionally only the last `decoder_input_ids` have to be input (see
|
|
`past_key_values`).
|
|
|
|
To know more on how to prepare `decoder_input_ids` for pretraining take a look at [Pix2StructText
|
|
Training](./t5#training).
|
|
decoder_attention_mask (`torch.BoolTensor` of shape `(batch_size, target_sequence_length)`, *optional*):
|
|
Default behavior: generate a tensor that ignores pad tokens in `decoder_input_ids`. Causal mask will also
|
|
be used by default.
|
|
head_mask (`torch.FloatTensor` of shape `(num_heads,)` or `(num_layers, num_heads)`, *optional*):
|
|
Mask to nullify selected heads of the self-attention modules in the encoder. Mask values selected in `[0,
|
|
1]`:
|
|
|
|
- 1 indicates the head is **not masked**,
|
|
- 0 indicates the head is **masked**.
|
|
|
|
decoder_head_mask (`torch.FloatTensor` of shape `(num_heads,)` or `(num_layers, num_heads)`, *optional*):
|
|
Mask to nullify selected heads of the self-attention modules in the decoder. Mask values selected in `[0,
|
|
1]`:
|
|
|
|
- 1 indicates the head is **not masked**,
|
|
- 0 indicates the head is **masked**.
|
|
|
|
cross_attn_head_mask (`torch.Tensor` of shape `(num_heads,)` or `(num_layers, num_heads)`, *optional*):
|
|
Mask to nullify selected heads of the cross-attention modules in the decoder. Mask values selected in
|
|
`[0, 1]`:
|
|
|
|
- 1 indicates the head is **not masked**,
|
|
- 0 indicates the head is **masked**.
|
|
|
|
encoder_outputs (`tuple(tuple(torch.FloatTensor)`, *optional*):
|
|
Tuple consists of (`last_hidden_state`, `optional`: *hidden_states*, `optional`: *attentions*)
|
|
`last_hidden_state` of shape `(batch_size, sequence_length, hidden_size)` is a sequence of hidden states at
|
|
the output of the last layer of the encoder. Used in the cross-attention of the decoder.
|
|
past_key_values (`tuple(tuple(torch.FloatTensor))` of length `config.n_layers` with each tuple having 4 tensors of shape `(batch_size, num_heads, sequence_length - 1, embed_size_per_head)`):
|
|
Contains precomputed key and value hidden states of the attention layers. Can be used to speed up decoding.
|
|
|
|
If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those that
|
|
don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of all
|
|
`decoder_input_ids` of shape `(batch_size, sequence_length)`.
|
|
inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
|
|
Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This
|
|
is useful if you want more control over how to convert `input_ids` indices into associated vectors than the
|
|
model's internal embedding lookup matrix.
|
|
decoder_inputs_embeds (`torch.FloatTensor` of shape `(batch_size, target_sequence_length, hidden_size)`, *optional*):
|
|
Optionally, instead of passing `decoder_input_ids` you can choose to directly pass an embedded
|
|
representation. If `past_key_values` is used, optionally only the last `decoder_inputs_embeds` have to be
|
|
input (see `past_key_values`). This is useful if you want more control over how to convert
|
|
`decoder_input_ids` indices into associated vectors than the model's internal embedding lookup matrix.
|
|
|
|
If `decoder_input_ids` and `decoder_inputs_embeds` are both unset, `decoder_inputs_embeds` takes the value
|
|
of `inputs_embeds`.
|
|
|
|
use_cache (`bool`, *optional*):
|
|
If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see
|
|
`past_key_values`).
|
|
|
|
output_attentions (`bool`, *optional*):
|
|
Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
|
|
tensors for more detail.
|
|
output_hidden_states (`bool`, *optional*):
|
|
Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
|
|
more detail.
|
|
return_dict (`bool`, *optional*):
|
|
Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
|
|
"""
|
|
|
|
PIX2STRUCT_INPUTS_DOCSTRING = r"""
|
|
Args:
|
|
flattened_patches (`torch.FloatTensor` of shape `(batch_size, seq_length, hidden_size)`):
|
|
Flattened pixel patches. the `hidden_size` is obtained by the following formula: `hidden_size` =
|
|
`num_channels` * `patch_size` * `patch_size`
|
|
|
|
The process of flattening the pixel patches is done by `Pix2StructProcessor`.
|
|
|
|
attention_mask (`torch.FloatTensor` of shape `(batch_size, sequence_length)`, *optional*):
|
|
Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
|
|
|
|
- 1 for tokens that are **not masked**,
|
|
- 0 for tokens that are **masked**.
|
|
|
|
[What are attention masks?](../glossary#attention-mask)
|
|
decoder_input_ids (`torch.LongTensor` of shape `(batch_size, target_sequence_length)`, *optional*):
|
|
Indices of decoder input sequence tokens in the vocabulary.
|
|
|
|
Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
|
|
[`PreTrainedTokenizer.__call__`] for details.
|
|
|
|
[What are decoder input IDs?](../glossary#decoder-input-ids)
|
|
|
|
Pix2StructText uses the `pad_token_id` as the starting token for `decoder_input_ids` generation. If
|
|
`past_key_values` is used, optionally only the last `decoder_input_ids` have to be input (see
|
|
`past_key_values`).
|
|
|
|
To know more on how to prepare `decoder_input_ids` for pretraining take a look at [Pix2StructText
|
|
Training](./t5#training).
|
|
decoder_attention_mask (`torch.BoolTensor` of shape `(batch_size, target_sequence_length)`, *optional*):
|
|
Default behavior: generate a tensor that ignores pad tokens in `decoder_input_ids`. Causal mask will also
|
|
be used by default.
|
|
head_mask (`torch.FloatTensor` of shape `(num_heads,)` or `(num_layers, num_heads)`, *optional*):
|
|
Mask to nullify selected heads of the self-attention modules in the encoder. Mask values selected in `[0,
|
|
1]`:
|
|
|
|
- 1 indicates the head is **not masked**,
|
|
- 0 indicates the head is **masked**.
|
|
|
|
decoder_head_mask (`torch.FloatTensor` of shape `(num_heads,)` or `(num_layers, num_heads)`, *optional*):
|
|
Mask to nullify selected heads of the self-attention modules in the decoder. Mask values selected in `[0,
|
|
1]`:
|
|
|
|
- 1 indicates the head is **not masked**,
|
|
- 0 indicates the head is **masked**.
|
|
|
|
cross_attn_head_mask (`torch.Tensor` of shape `(num_heads,)` or `(num_layers, num_heads)`, *optional*):
|
|
Mask to nullify selected heads of the cross-attention modules in the decoder. Mask values selected in
|
|
`[0, 1]`:
|
|
|
|
- 1 indicates the head is **not masked**,
|
|
- 0 indicates the head is **masked**.
|
|
|
|
encoder_outputs (`tuple(tuple(torch.FloatTensor)`, *optional*):
|
|
Tuple consists of (`last_hidden_state`, `optional`: *hidden_states*, `optional`: *attentions*)
|
|
`last_hidden_state` of shape `(batch_size, sequence_length, hidden_size)` is a sequence of hidden states at
|
|
the output of the last layer of the encoder. Used in the cross-attention of the decoder.
|
|
past_key_values (`tuple(tuple(torch.FloatTensor))` of length `config.n_layers` with each tuple having 4 tensors of shape `(batch_size, num_heads, sequence_length - 1, embed_size_per_head)`):
|
|
Contains precomputed key and value hidden states of the attention layers. Can be used to speed up decoding.
|
|
|
|
If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those that
|
|
don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of all
|
|
`decoder_input_ids` of shape `(batch_size, sequence_length)`.
|
|
decoder_inputs_embeds (`torch.FloatTensor` of shape `(batch_size, target_sequence_length, hidden_size)`, *optional*):
|
|
Optionally, instead of passing `decoder_input_ids` you can choose to directly pass an embedded
|
|
representation. If `past_key_values` is used, optionally only the last `decoder_inputs_embeds` have to be
|
|
input (see `past_key_values`). This is useful if you want more control over how to convert
|
|
`decoder_input_ids` indices into associated vectors than the model's internal embedding lookup matrix.
|
|
|
|
If `decoder_input_ids` and `decoder_inputs_embeds` are both unset, `decoder_inputs_embeds` takes the value
|
|
of `inputs_embeds`.
|
|
labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
|
|
Labels for computing the masked language modeling loss for the decoder.
|
|
|
|
use_cache (`bool`, *optional*):
|
|
If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see
|
|
`past_key_values`).
|
|
|
|
output_attentions (`bool`, *optional*):
|
|
Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
|
|
tensors for more detail.
|
|
output_hidden_states (`bool`, *optional*):
|
|
Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
|
|
more detail.
|
|
return_dict (`bool`, *optional*):
|
|
Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
|
|
"""
|
|
|
|
|
|
@add_start_docstrings(
|
|
"The standalone text decoder of Pix2Struct",
|
|
PIX2STRUCT_START_DOCSTRING,
|
|
)
|
|
class Pix2StructTextModel(Pix2StructPreTrainedModel):
|
|
config_class = Pix2StructTextConfig
|
|
_no_split_modules = ["Pix2StructTextBlock"]
|
|
_tied_weights_keys = ["lm_head.weight"]
|
|
supports_gradient_checkpointing = True
|
|
|
|
def __init__(self, config):
|
|
super().__init__(config)
|
|
self.embed_tokens = nn.Embedding(config.vocab_size, config.hidden_size)
|
|
|
|
self.layer = nn.ModuleList(
|
|
[Pix2StructTextBlock(config, has_relative_attention_bias=bool(i == 0)) for i in range(config.num_layers)]
|
|
)
|
|
self.final_layer_norm = Pix2StructLayerNorm(config.hidden_size, eps=config.layer_norm_epsilon)
|
|
self.dropout = nn.Dropout(config.dropout_rate)
|
|
|
|
self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
|
|
|
|
# Initialize weights and apply final processing
|
|
self.post_init()
|
|
self.gradient_checkpointing = False
|
|
|
|
# Copied from transformers.models.t5.modeling_t5.T5PreTrainedModel._reorder_cache
|
|
def _reorder_cache(self, past_key_values, beam_idx):
|
|
# if decoder past is not included in output
|
|
# speedy decoding is disabled and no need to reorder
|
|
if past_key_values is None:
|
|
logger.warning("You might want to consider setting `use_cache=True` to speed up decoding")
|
|
return past_key_values
|
|
|
|
reordered_decoder_past = ()
|
|
for layer_past_states in past_key_values:
|
|
# get the correct batch idx from layer past batch dim
|
|
# batch dim of `past` is at 2nd position
|
|
reordered_layer_past_states = ()
|
|
for layer_past_state in layer_past_states:
|
|
# need to set correct `past` for each of the four key / value states
|
|
reordered_layer_past_states = reordered_layer_past_states + (
|
|
layer_past_state.index_select(0, beam_idx.to(layer_past_state.device)),
|
|
)
|
|
|
|
if reordered_layer_past_states[0].shape != layer_past_states[0].shape:
|
|
raise ValueError(
|
|
f"reordered_layer_past_states[0] shape {reordered_layer_past_states[0].shape} and layer_past_states[0] shape {layer_past_states[0].shape} mismatched"
|
|
)
|
|
if len(reordered_layer_past_states) != len(layer_past_states):
|
|
raise ValueError(
|
|
f"length of reordered_layer_past_states {len(reordered_layer_past_states)} and length of layer_past_states {len(layer_past_states)} mismatched"
|
|
)
|
|
|
|
reordered_decoder_past = reordered_decoder_past + (reordered_layer_past_states,)
|
|
return reordered_decoder_past
|
|
|
|
def get_input_embeddings(self):
|
|
return self.embed_tokens
|
|
|
|
def set_input_embeddings(self, new_embeddings):
|
|
self.embed_tokens = new_embeddings
|
|
|
|
def get_output_embeddings(self):
|
|
return self.lm_head
|
|
|
|
def set_output_embeddings(self, new_embeddings):
|
|
self.lm_head = new_embeddings
|
|
|
|
@add_start_docstrings_to_model_forward(PIX2STRUCT_TEXT_INPUTS_DOCSTRING)
|
|
@replace_return_docstrings(output_type=CausalLMOutputWithCrossAttentions, config_class=_CONFIG_FOR_DOC)
|
|
def forward(
|
|
self,
|
|
input_ids: Optional[torch.LongTensor] = None,
|
|
attention_mask: Optional[torch.FloatTensor] = None,
|
|
encoder_hidden_states: Optional[torch.FloatTensor] = None,
|
|
encoder_attention_mask: Optional[torch.FloatTensor] = None,
|
|
inputs_embeds: Optional[torch.LongTensor] = None,
|
|
head_mask: Optional[torch.FloatTensor] = None,
|
|
cross_attn_head_mask: Optional[torch.Tensor] = None,
|
|
past_key_values: Optional[Tuple[Tuple[torch.FloatTensor]]] = None,
|
|
use_cache: Optional[bool] = None,
|
|
output_attentions: Optional[bool] = None,
|
|
output_hidden_states: Optional[bool] = None,
|
|
labels: Optional[torch.LongTensor] = None,
|
|
return_dict: Optional[bool] = None,
|
|
**kwargs,
|
|
) -> Union[Tuple[torch.FloatTensor, ...], CausalLMOutputWithCrossAttentions]:
|
|
r"""
|
|
Returns:
|
|
|
|
Example:
|
|
|
|
```python
|
|
>>> from transformers import AutoProcessor, Pix2StructTextModel
|
|
|
|
>>> processor = AutoProcessor.from_pretrained("google/pix2struct-textcaps-base")
|
|
>>> model = Pix2StructTextModel.from_pretrained("google/pix2struct-textcaps-base")
|
|
|
|
>>> inputs = processor(text="Hello, my dog is cute", return_tensors="pt")
|
|
>>> outputs = model(**inputs)
|
|
>>> loss = outputs.loss
|
|
```
|
|
"""
|
|
use_cache = use_cache if use_cache is not None else self.config.use_cache
|
|
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
|
|
output_hidden_states = (
|
|
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
|
|
)
|
|
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
|
|
|
|
if input_ids is not None and inputs_embeds is not None:
|
|
raise ValueError("You cannot specify both decoder_input_ids and decoder_inputs_embeds at the same time")
|
|
elif input_ids is not None:
|
|
input_shape = input_ids.size()
|
|
input_ids = input_ids.view(-1, input_shape[-1])
|
|
elif inputs_embeds is not None:
|
|
input_shape = inputs_embeds.size()[:-1]
|
|
else:
|
|
raise ValueError("You have to specify either decoder_input_ids or decoder_inputs_embeds")
|
|
|
|
if inputs_embeds is None:
|
|
assert self.embed_tokens is not None, "You have to initialize the model with valid token embeddings"
|
|
inputs_embeds = self.embed_tokens(input_ids)
|
|
|
|
batch_size, seq_length = input_shape
|
|
|
|
# required mask seq length can be calculated via length of past
|
|
mask_seq_length = past_key_values[0][0].shape[2] + seq_length if past_key_values is not None else seq_length
|
|
|
|
if attention_mask is None:
|
|
attention_mask = torch.ones(batch_size, mask_seq_length, device=inputs_embeds.device)
|
|
if encoder_attention_mask is None and encoder_hidden_states is not None:
|
|
encoder_seq_length = encoder_hidden_states.shape[1]
|
|
encoder_attention_mask = torch.ones(
|
|
batch_size, encoder_seq_length, device=inputs_embeds.device, dtype=torch.long
|
|
)
|
|
|
|
# initialize past_key_values with `None` if past does not exist
|
|
if past_key_values is None:
|
|
past_key_values = [None] * len(self.layer)
|
|
|
|
# We can provide a self-attention mask of dimensions [batch_size, from_seq_length, to_seq_length]
|
|
# ourselves in which case we just need to make it broadcastable to all heads.
|
|
extended_attention_mask = self.get_extended_attention_mask(attention_mask, input_shape)
|
|
|
|
# If a 2D or 3D attention mask is provided for the cross-attention
|
|
# we need to make broadcastable to [batch_size, num_heads, seq_length, seq_length]
|
|
if encoder_hidden_states is not None:
|
|
encoder_batch_size, encoder_sequence_length, _ = encoder_hidden_states.size()
|
|
encoder_hidden_shape = (encoder_batch_size, encoder_sequence_length)
|
|
if encoder_attention_mask is None:
|
|
encoder_attention_mask = torch.ones(encoder_hidden_shape, device=inputs_embeds.device)
|
|
encoder_extended_attention_mask = self.invert_attention_mask(encoder_attention_mask)
|
|
else:
|
|
encoder_extended_attention_mask = None
|
|
|
|
# Prepare head mask if needed
|
|
head_mask = self.get_head_mask(head_mask, self.config.num_layers)
|
|
cross_attn_head_mask = self.get_head_mask(cross_attn_head_mask, self.config.num_layers)
|
|
present_key_value_states = () if use_cache else None
|
|
all_hidden_states = () if output_hidden_states else None
|
|
all_attentions = () if output_attentions else None
|
|
all_cross_attentions = () if (output_attentions) else None
|
|
position_bias = None
|
|
encoder_decoder_position_bias = None
|
|
|
|
hidden_states = self.dropout(inputs_embeds)
|
|
|
|
for i, (layer_module, past_key_value) in enumerate(zip(self.layer, past_key_values)):
|
|
layer_head_mask = head_mask[i]
|
|
cross_attn_layer_head_mask = cross_attn_head_mask[i]
|
|
if output_hidden_states:
|
|
all_hidden_states = all_hidden_states + (hidden_states,)
|
|
|
|
if self.gradient_checkpointing and self.training:
|
|
if use_cache:
|
|
logger.warning(
|
|
"`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..."
|
|
)
|
|
use_cache = False
|
|
layer_outputs = self._gradient_checkpointing_func(
|
|
layer_module.forward,
|
|
hidden_states,
|
|
extended_attention_mask,
|
|
position_bias,
|
|
encoder_hidden_states,
|
|
encoder_extended_attention_mask,
|
|
encoder_decoder_position_bias,
|
|
layer_head_mask,
|
|
cross_attn_layer_head_mask,
|
|
None, # past_key_value is always None with gradient checkpointing
|
|
use_cache,
|
|
output_attentions,
|
|
)
|
|
else:
|
|
layer_outputs = layer_module(
|
|
hidden_states,
|
|
attention_mask=extended_attention_mask,
|
|
position_bias=position_bias,
|
|
encoder_hidden_states=encoder_hidden_states,
|
|
encoder_attention_mask=encoder_extended_attention_mask,
|
|
encoder_decoder_position_bias=encoder_decoder_position_bias,
|
|
layer_head_mask=layer_head_mask,
|
|
cross_attn_layer_head_mask=cross_attn_layer_head_mask,
|
|
past_key_value=past_key_value,
|
|
use_cache=use_cache,
|
|
output_attentions=output_attentions,
|
|
)
|
|
|
|
# layer_outputs is a tuple with:
|
|
# hidden-states, key-value-states, (self-attention position bias), (self-attention weights), (cross-attention position bias), (cross-attention weights)
|
|
if use_cache is False:
|
|
layer_outputs = layer_outputs[:1] + (None,) + layer_outputs[1:]
|
|
|
|
hidden_states, present_key_value_state = layer_outputs[:2]
|
|
|
|
# We share the position biases between the layers - the first layer store them
|
|
# layer_outputs = hidden-states, key-value-states (self-attention position bias), (self-attention weights),
|
|
# (cross-attention position bias), (cross-attention weights)
|
|
position_bias = layer_outputs[2]
|
|
if encoder_hidden_states is not None:
|
|
encoder_decoder_position_bias = layer_outputs[4 if output_attentions else 3]
|
|
# append next layer key value states
|
|
if use_cache:
|
|
present_key_value_states = present_key_value_states + (present_key_value_state,)
|
|
|
|
if output_attentions:
|
|
all_attentions = all_attentions + (layer_outputs[3],)
|
|
if encoder_hidden_states is not None:
|
|
all_cross_attentions = all_cross_attentions + (layer_outputs[5],)
|
|
|
|
hidden_states = self.final_layer_norm(hidden_states)
|
|
hidden_states = self.dropout(hidden_states)
|
|
|
|
logits = self.lm_head(hidden_states)
|
|
|
|
# Add last layer
|
|
if output_hidden_states:
|
|
all_hidden_states = all_hidden_states + (hidden_states,)
|
|
|
|
loss = None
|
|
if labels is not None:
|
|
# move labels to correct device to enable model parallelism
|
|
labels = labels.to(logits.device)
|
|
loss_fct = nn.CrossEntropyLoss(ignore_index=-100, reduction="mean")
|
|
|
|
loss = loss_fct(logits.contiguous().view(-1, logits.size(-1)), labels.contiguous().view(-1))
|
|
|
|
if not return_dict:
|
|
return tuple(
|
|
v
|
|
for v in [
|
|
loss,
|
|
logits,
|
|
present_key_value_states,
|
|
all_hidden_states,
|
|
all_attentions,
|
|
all_cross_attentions,
|
|
]
|
|
if v is not None
|
|
)
|
|
return CausalLMOutputWithCrossAttentions(
|
|
loss=loss,
|
|
logits=logits,
|
|
past_key_values=present_key_value_states,
|
|
hidden_states=all_hidden_states,
|
|
attentions=all_attentions,
|
|
cross_attentions=all_cross_attentions,
|
|
)
|
|
|
|
|
|
@add_start_docstrings(
|
|
"A conditional generation model with a language modeling head. Can be used for sequence generation tasks.",
|
|
PIX2STRUCT_START_DOCSTRING,
|
|
)
|
|
class Pix2StructForConditionalGeneration(Pix2StructPreTrainedModel):
|
|
config_class = Pix2StructConfig
|
|
main_input_name = "flattened_patches"
|
|
_tied_weights_keys = ["decoder.lm_head.weight"]
|
|
|
|
def __init__(self, config: Pix2StructConfig):
|
|
super().__init__(config)
|
|
|
|
self.encoder = Pix2StructVisionModel(config.vision_config)
|
|
self.decoder = Pix2StructTextModel(config.text_config)
|
|
|
|
self.is_vqa = config.is_vqa
|
|
|
|
# Initialize weights and apply final processing
|
|
self.post_init()
|
|
|
|
def get_input_embeddings(self):
|
|
return self.decoder.get_input_embeddings()
|
|
|
|
def set_input_embeddings(self, new_embeddings):
|
|
self.decoder.set_input_embeddings(new_embeddings)
|
|
|
|
def get_output_embeddings(self) -> nn.Module:
|
|
return self.decoder.get_output_embeddings()
|
|
|
|
def set_output_embeddings(self, new_embeddings):
|
|
self.decoder.set_output_embeddings(new_embeddings)
|
|
|
|
def resize_token_embeddings(self, new_num_tokens: Optional[int] = None) -> nn.Embedding:
|
|
model_embeds = self.decoder.resize_token_embeddings(new_num_tokens)
|
|
|
|
# update vocab size
|
|
self.config.text_config.vocab_size = new_num_tokens
|
|
|
|
return model_embeds
|
|
|
|
def get_decoder(self):
|
|
return self.decoder
|
|
|
|
def get_encoder(self):
|
|
return self.encoder
|
|
|
|
@add_start_docstrings_to_model_forward(PIX2STRUCT_INPUTS_DOCSTRING)
|
|
@replace_return_docstrings(output_type=Seq2SeqModelOutput, config_class=_CONFIG_FOR_DOC)
|
|
def forward(
|
|
self,
|
|
flattened_patches: Optional[torch.FloatTensor] = None,
|
|
attention_mask: Optional[torch.FloatTensor] = None,
|
|
decoder_input_ids: Optional[torch.LongTensor] = None,
|
|
decoder_attention_mask: Optional[torch.BoolTensor] = None,
|
|
head_mask: Optional[torch.FloatTensor] = None,
|
|
decoder_head_mask: Optional[torch.FloatTensor] = None,
|
|
cross_attn_head_mask: Optional[torch.Tensor] = None,
|
|
encoder_outputs: Optional[Tuple[Tuple[torch.FloatTensor]]] = None,
|
|
past_key_values: Optional[Tuple[Tuple[torch.FloatTensor]]] = None,
|
|
labels: Optional[torch.LongTensor] = None,
|
|
decoder_inputs_embeds: Optional[torch.Tensor] = None,
|
|
use_cache: Optional[bool] = None,
|
|
output_attentions: Optional[bool] = None,
|
|
output_hidden_states: Optional[bool] = None,
|
|
return_dict: Optional[bool] = None,
|
|
) -> Union[Tuple[torch.FloatTensor], Seq2SeqModelOutput]:
|
|
r"""
|
|
Returns:
|
|
|
|
Example:
|
|
|
|
Inference:
|
|
|
|
```python
|
|
>>> from PIL import Image
|
|
>>> import requests
|
|
>>> from transformers import AutoProcessor, Pix2StructForConditionalGeneration
|
|
|
|
>>> processor = AutoProcessor.from_pretrained("google/pix2struct-textcaps-base")
|
|
>>> model = Pix2StructForConditionalGeneration.from_pretrained("google/pix2struct-textcaps-base")
|
|
|
|
>>> url = "https://www.ilankelman.org/stopsigns/australia.jpg"
|
|
>>> image = Image.open(requests.get(url, stream=True).raw)
|
|
|
|
>>> inputs = processor(images=image, return_tensors="pt")
|
|
|
|
>>> # autoregressive generation
|
|
>>> generated_ids = model.generate(**inputs, max_new_tokens=50)
|
|
>>> generated_text = processor.batch_decode(generated_ids, skip_special_tokens=True)[0]
|
|
>>> print(generated_text)
|
|
A stop sign is on a street corner.
|
|
|
|
>>> # conditional generation
|
|
>>> text = "A picture of"
|
|
>>> inputs = processor(text=text, images=image, return_tensors="pt", add_special_tokens=False)
|
|
|
|
>>> generated_ids = model.generate(**inputs, max_new_tokens=50)
|
|
>>> generated_text = processor.batch_decode(generated_ids, skip_special_tokens=True)[0]
|
|
>>> print(generated_text)
|
|
A picture of a stop sign with a red stop sign
|
|
```
|
|
|
|
Training:
|
|
|
|
```python
|
|
>>> from PIL import Image
|
|
>>> import requests
|
|
>>> from transformers import AutoProcessor, Pix2StructForConditionalGeneration
|
|
|
|
>>> processor = AutoProcessor.from_pretrained("google/pix2struct-base")
|
|
>>> model = Pix2StructForConditionalGeneration.from_pretrained("google/pix2struct-base")
|
|
|
|
>>> url = "https://www.ilankelman.org/stopsigns/australia.jpg"
|
|
>>> image = Image.open(requests.get(url, stream=True).raw)
|
|
>>> text = "A stop sign is on the street corner."
|
|
|
|
>>> inputs = processor(images=image, return_tensors="pt")
|
|
>>> labels = processor(text=text, return_tensors="pt").input_ids
|
|
|
|
>>> # forward pass
|
|
>>> outputs = model(**inputs, labels=labels)
|
|
>>> loss = outputs.loss
|
|
>>> print(f"{loss.item():.5f}")
|
|
5.94282
|
|
```"""
|
|
use_cache = use_cache if use_cache is not None else self.config.text_config.use_cache
|
|
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
|
|
|
|
# Encode if needed (training, first prediction pass)
|
|
if encoder_outputs is None:
|
|
encoder_outputs = self.encoder(
|
|
flattened_patches=flattened_patches,
|
|
attention_mask=attention_mask,
|
|
head_mask=head_mask,
|
|
output_attentions=output_attentions,
|
|
output_hidden_states=output_hidden_states,
|
|
return_dict=return_dict,
|
|
)
|
|
elif return_dict and not isinstance(encoder_outputs, BaseModelOutput):
|
|
encoder_outputs = BaseModelOutput(
|
|
last_hidden_state=encoder_outputs[0],
|
|
hidden_states=encoder_outputs[1] if len(encoder_outputs) > 1 else None,
|
|
attentions=encoder_outputs[2] if len(encoder_outputs) > 2 else None,
|
|
)
|
|
|
|
hidden_states = encoder_outputs[0]
|
|
|
|
if labels is not None and decoder_input_ids is None and decoder_inputs_embeds is None:
|
|
# get decoder inputs from shifting lm labels to the right
|
|
decoder_input_ids = self._shift_right(labels)
|
|
decoder_attention_mask = (
|
|
decoder_attention_mask
|
|
if decoder_attention_mask is not None
|
|
else decoder_input_ids.ne(self.config.pad_token_id).float()
|
|
)
|
|
# Always attend to the first token
|
|
decoder_attention_mask[:, 0] = 1
|
|
|
|
# Decode
|
|
decoder_outputs = self.decoder(
|
|
input_ids=decoder_input_ids,
|
|
attention_mask=decoder_attention_mask,
|
|
inputs_embeds=decoder_inputs_embeds,
|
|
past_key_values=past_key_values,
|
|
encoder_hidden_states=hidden_states,
|
|
encoder_attention_mask=attention_mask,
|
|
head_mask=decoder_head_mask,
|
|
cross_attn_head_mask=cross_attn_head_mask,
|
|
use_cache=use_cache,
|
|
output_attentions=output_attentions,
|
|
output_hidden_states=output_hidden_states,
|
|
labels=labels,
|
|
return_dict=return_dict,
|
|
)
|
|
|
|
if not return_dict:
|
|
return decoder_outputs + encoder_outputs
|
|
|
|
return Seq2SeqLMOutput(
|
|
loss=decoder_outputs.loss,
|
|
logits=decoder_outputs.logits,
|
|
past_key_values=decoder_outputs.past_key_values,
|
|
decoder_hidden_states=decoder_outputs.hidden_states,
|
|
decoder_attentions=decoder_outputs.attentions,
|
|
cross_attentions=decoder_outputs.cross_attentions,
|
|
encoder_last_hidden_state=encoder_outputs.last_hidden_state,
|
|
encoder_hidden_states=encoder_outputs.hidden_states,
|
|
encoder_attentions=encoder_outputs.attentions,
|
|
)
|
|
|
|
def prepare_inputs_for_generation(
|
|
self,
|
|
input_ids,
|
|
flattened_patches: Optional[torch.FloatTensor] = None,
|
|
attention_mask: Optional[torch.FloatTensor] = None,
|
|
decoder_attention_mask: Optional[torch.BoolTensor] = None,
|
|
past_key_values=None,
|
|
head_mask=None,
|
|
decoder_head_mask=None,
|
|
cross_attn_head_mask=None,
|
|
use_cache=None,
|
|
encoder_outputs=None,
|
|
**kwargs,
|
|
):
|
|
if decoder_attention_mask is None:
|
|
decoder_attention_mask = torch.ones_like(input_ids).to(input_ids.device)
|
|
|
|
# cut decoder_input_ids if past_key_values is used
|
|
if past_key_values is not None:
|
|
past_length = past_key_values[0][0].shape[2]
|
|
|
|
# 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 {
|
|
"flattened_patches": flattened_patches,
|
|
"decoder_input_ids": input_ids,
|
|
"past_key_values": past_key_values,
|
|
"encoder_outputs": encoder_outputs,
|
|
"attention_mask": attention_mask,
|
|
"decoder_attention_mask": decoder_attention_mask,
|
|
"head_mask": head_mask,
|
|
"decoder_head_mask": decoder_head_mask,
|
|
"cross_attn_head_mask": cross_attn_head_mask,
|
|
"use_cache": use_cache,
|
|
}
|