2196 lines
101 KiB
Python
2196 lines
101 KiB
Python
# coding=utf-8
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# Copyright 2022 Google LLC., LongT5 Authors and HuggingFace Inc. team.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""PyTorch LongT5 model."""
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import copy
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import math
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import warnings
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from typing import Any, Optional, Union
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import torch
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from torch import nn
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from torch.nn import CrossEntropyLoss
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from ...activations import ACT2FN
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from ...cache_utils import Cache, DynamicCache, EncoderDecoderCache
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from ...generation import GenerationMixin
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from ...modeling_attn_mask_utils import AttentionMaskConverter
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from ...modeling_layers import GradientCheckpointingLayer
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from ...modeling_outputs import (
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BaseModelOutput,
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BaseModelOutputWithPastAndCrossAttentions,
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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 find_pruneable_heads_and_indices, prune_linear_layer
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from ...utils import (
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DUMMY_INPUTS,
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DUMMY_MASK,
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auto_docstring,
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is_torch_flex_attn_available,
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is_torch_fx_proxy,
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is_torchdynamo_compiling,
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logging,
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)
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from .configuration_longt5 import LongT5Config
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if is_torch_flex_attn_available():
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from torch.nn.attention.flex_attention import BlockMask
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from ...integrations.flex_attention import make_flex_block_causal_mask
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logger = logging.get_logger(__name__)
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# TODO: Update before the merge
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def _pad_to_multiple(x: torch.Tensor, block_len: int, dim: int, pad_value: int = 0) -> torch.Tensor:
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"""Pad a tensor so that a sequence length will be a multiple of `block_len`"""
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pad_len = -x.shape[dim] % block_len
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# Handle cases when an empty input sequence is given
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if not all(x.shape):
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new_shape = list(x.shape)
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new_shape[dim] += pad_len
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return torch.zeros(new_shape, dtype=x.dtype)
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pad = [(0, 0)] * x.ndim
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pad[dim] = (0, pad_len)
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pad = sum(pad[::-1], ())
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x = nn.functional.pad(x, pad=pad, mode="constant", value=pad_value)
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return x
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def _split_into_blocks(x: torch.Tensor, block_len: int, dim: int) -> torch.Tensor:
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"""Split an input tensor into blocks of a given `block_len` along the given `dim`. If the dimension length
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is not a multiple of `block_len`, it will be padded first with selected `pad_value`.
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"""
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# pad tensor to multiple of block_len
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if x.shape[dim] % block_len != 0:
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x = _pad_to_multiple(x, block_len, dim, pad_value=0)
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num_blocks = x.shape[dim] // block_len
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output_shape = x.shape[:dim] + (num_blocks, block_len) + x.shape[(dim + 1) :]
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# If 0 is in output_shape, we cannot apply reshape because of incompatibility with ONNX conversion
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if 0 in output_shape:
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return torch.empty(output_shape, dtype=x.dtype, device=x.device)
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return x.reshape(output_shape)
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def _concatenate_3_blocks(x: torch.Tensor, block_dim: int, sequence_dim: int, pad_value: int = 0) -> torch.Tensor:
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"""Concatenate three consecutive blocks for each input block for local attentiont.
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For more information, see: https://huggingface.co/papers/2112.07916.
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"""
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num_blocks = x.shape[block_dim]
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pad = [(0, 0)] * x.ndim
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pad[block_dim] = (1, 1)
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pad = sum(pad[::-1], ())
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# [batch_size, num_blocks, block_len] -> [batch_size, num_blocks + 2, block_len]
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x = nn.functional.pad(x, pad=pad, mode="constant", value=pad_value)
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blocks_list: list[torch.Tensor] = []
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for i in range(3):
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# We use indexing approach here:
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# https://numpy.org/doc/stable/user/basics.indexing.html#dealing-with-variable-numbers-of-indices-within-programs
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indices = [slice(0, None)] * x.ndim
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indices[block_dim] = slice(i, i + num_blocks)
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indices = tuple(indices)
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blocks_list.append(x[indices])
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# [batch_size, num_blocks, 3 * block_len, ...]
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return torch.cat(blocks_list, dim=sequence_dim)
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def _make_3block_relative_position_ids(block_len: int) -> torch.Tensor:
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"""Makes 3-blocked relative position ids for local attention."""
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position_ids = torch.arange(3 * block_len, dtype=torch.int32)
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center_position_ids = position_ids[block_len:-block_len]
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# [block_len, 3 * block_len]
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relative_position_ids = position_ids.unsqueeze(0) - center_position_ids.unsqueeze(1)
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return relative_position_ids
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def _mask_local_attention_mask(local_attention_mask: torch.Tensor, block_len: int) -> torch.Tensor:
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"""Mask local attention mask to enforce that tokens are not allowed to attend tokens farther than ``local_radius."""
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relative_position_ids = _make_3block_relative_position_ids(block_len)
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locality_mask = torch.abs(relative_position_ids) < block_len
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locality_mask = locality_mask[None, None, :, :]
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locality_mask = locality_mask.to(local_attention_mask.device)
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return torch.logical_and(local_attention_mask, locality_mask)
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def _get_local_attention_mask(attention_mask: torch.Tensor, block_len: int, device: torch.device) -> torch.Tensor:
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"""Prepare attention mask to be applied for a local attention."""
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# [batch_size, num_blocks, block_len]
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_blocked_attention_mask = _split_into_blocks(attention_mask, block_len, dim=1)
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# [batch_size, num_block, 3 * block_len]
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_3blocked_attention_mask = _concatenate_3_blocks(_blocked_attention_mask, block_dim=1, sequence_dim=2)
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_blocked_attention_mask = _blocked_attention_mask.unsqueeze(-1)
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_3blocked_attention_mask = _3blocked_attention_mask.unsqueeze(-2)
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# [batch_size, num_block, block_len, 3 * block_len]
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local_attention_mask = torch.logical_and(_blocked_attention_mask, _3blocked_attention_mask)
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local_attention_mask = _mask_local_attention_mask(local_attention_mask, block_len)
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# [batch_size, 1, num_block, block_len, 3 * block_len]
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return local_attention_mask.unsqueeze(1).to(device)
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def _make_global_fixed_block_ids(
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attention_mask: torch.Tensor, global_block_size: int
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) -> tuple[torch.Tensor, torch.Tensor]:
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"""Obtain the "fixed block" global id corresponding to each input token.
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This implementation is a simplified version of the original Flaxformr implementation adopted from:
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https://github.com/google/flaxformer/blob/main/flaxformer/architectures/longt5/long_attention.py.
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In our scenario, as we use this strategy only for a decoder, orphan tokens, i.e. those tokens which do not make for
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the whole fixed block, are assigned to the preceding block.
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Padding tokens from the original sequence are represented by -1.
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"""
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batch_size, seq_len = attention_mask.shape[:2]
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def handle_orphan_tokens(block_ids: torch.Tensor) -> torch.Tensor:
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block_ends = (torch.arange(seq_len) % global_block_size) == global_block_size - 1
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block_ends = block_ends.to(block_ids.device)
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true_block_ends = torch.logical_and(block_ends, block_ids >= 0)
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full_blocks = true_block_ends.sum(-1).unsqueeze(-1).type(block_ids.dtype) - 1
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block_ids = torch.where(block_ids < full_blocks, block_ids, full_blocks)
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return block_ids
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fixed_block_mask = torch.ones_like(attention_mask, device=attention_mask.device) / global_block_size
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fixed_block_mask = torch.cumsum(fixed_block_mask, axis=1) - fixed_block_mask
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mask = torch.where(attention_mask != 0.0, 1.0, -1000.0).type(attention_mask.dtype)
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global_block_ids = torch.floor(mask + fixed_block_mask - 1.0).type(attention_mask.dtype)
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_global_block_ids_lower_bound = torch.tensor(-1, dtype=global_block_ids.dtype, device=global_block_ids.device)
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global_block_ids = torch.where(
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global_block_ids > _global_block_ids_lower_bound, global_block_ids, _global_block_ids_lower_bound
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)
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# set padding tokens to -1
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global_block_ids = (global_block_ids * attention_mask) + (attention_mask - 1)
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# [batch_size, seq_len]
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global_block_ids = handle_orphan_tokens(global_block_ids)
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num_globals = seq_len // global_block_size
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# [batch_size, seq_len // global_block_size]
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if num_globals > 0:
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_sequence_block_ids_max = torch.max(global_block_ids, dim=-1).values.repeat(num_globals, 1).transpose(0, 1)
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else:
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_sequence_block_ids_max = torch.zeros(
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batch_size, 0, dtype=global_block_ids.dtype, device=global_block_ids.device
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)
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global_segment_ids = torch.cumsum(torch.ones(batch_size, num_globals), dim=-1) - 1
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global_segment_ids = global_segment_ids.to(attention_mask.device)
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global_segment_ids = torch.where(global_segment_ids <= _sequence_block_ids_max, 1, 0)
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return global_block_ids.type(torch.int), global_segment_ids.type(torch.int)
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def _make_side_relative_position_ids(attention_mask: torch.Tensor, global_block_size: int) -> torch.Tensor:
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"""Create the relative position tensor for local -> global attention."""
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block_ids, global_segment_ids = _make_global_fixed_block_ids(attention_mask, global_block_size)
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global_seq_len = global_segment_ids.shape[-1]
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global_positions = torch.arange(global_seq_len, device=block_ids.device)
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side_relative_position = global_positions - block_ids[..., None]
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return side_relative_position.type(torch.int64)
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def _create_global_aggregates(
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hidden_states: torch.Tensor, block_ids: torch.Tensor, global_seq_len: int
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) -> torch.Tensor:
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"""Compute individual block aggregates by summing over individual blocks."""
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# (batch..., seq_len, global_seq_len))
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block_ids = block_ids.where(
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block_ids >= 0, torch.tensor(global_seq_len, dtype=block_ids.dtype, device=block_ids.device)
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)
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one_hot_block_ids = nn.functional.one_hot(block_ids.type(torch.int64), global_seq_len + 1)[:, :, :-1]
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return torch.einsum("...nd,...ng->...gd", hidden_states, one_hot_block_ids.type(hidden_states.dtype))
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# Copied from transformers.models.t5.modeling_t5.T5LayerNorm with T5->LongT5
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class LongT5LayerNorm(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 LongT5 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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# LongT5 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://huggingface.co/papers/1910.07467 thus variance 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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LongT5LayerNorm = FusedRMSNorm # noqa
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logger.info("Discovered apex.normalization.FusedRMSNorm - will use it instead of LongT5LayerNorm")
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except ImportError:
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# using the normal LongT5LayerNorm
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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 LongT5LayerNorm")
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pass
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# Copied from transformers.models.t5.modeling_t5.T5DenseActDense with T5->LongT5
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class LongT5DenseActDense(nn.Module):
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def __init__(self, config: LongT5Config):
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super().__init__()
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self.wi = nn.Linear(config.d_model, config.d_ff, bias=False)
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self.wo = nn.Linear(config.d_ff, config.d_model, 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_states = self.wi(hidden_states)
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hidden_states = self.act(hidden_states)
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hidden_states = self.dropout(hidden_states)
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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 LongT5DenseGatedActDense(nn.Module):
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def __init__(self, config: LongT5Config):
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super().__init__()
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self.wi_0 = nn.Linear(config.d_model, config.d_ff, bias=False)
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self.wi_1 = nn.Linear(config.d_model, config.d_ff, bias=False)
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self.wo = nn.Linear(config.d_ff, config.d_model, 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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hidden_states = self.wo(hidden_states)
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return hidden_states
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# Copied from transformers.models.t5.modeling_t5.T5LayerFF with T5->LongT5
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class LongT5LayerFF(nn.Module):
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def __init__(self, config: LongT5Config):
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super().__init__()
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if config.is_gated_act:
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self.DenseReluDense = LongT5DenseGatedActDense(config)
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else:
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self.DenseReluDense = LongT5DenseActDense(config)
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self.layer_norm = LongT5LayerNorm(config.d_model, eps=config.layer_norm_epsilon)
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self.dropout = nn.Dropout(config.dropout_rate)
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def forward(self, hidden_states):
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forwarded_states = self.layer_norm(hidden_states)
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forwarded_states = self.DenseReluDense(forwarded_states)
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hidden_states = hidden_states + self.dropout(forwarded_states)
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return hidden_states
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# Copied from transformers.models.t5.modeling_t5.T5Attention with T5->LongT5
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class LongT5Attention(nn.Module):
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def __init__(
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self,
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config: LongT5Config,
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has_relative_attention_bias=False,
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layer_idx: Optional[int] = None,
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):
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super().__init__()
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self.is_decoder = config.is_decoder
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self.has_relative_attention_bias = has_relative_attention_bias
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self.relative_attention_num_buckets = config.relative_attention_num_buckets
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self.relative_attention_max_distance = config.relative_attention_max_distance
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self.d_model = config.d_model
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self.key_value_proj_dim = config.d_kv
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self.n_heads = config.num_heads
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self.dropout = config.dropout_rate
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self.inner_dim = self.n_heads * self.key_value_proj_dim
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self.layer_idx = layer_idx
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if layer_idx is None and self.is_decoder:
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logger.warning_once(
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f"Instantiating a decoder {self.__class__.__name__} without passing `layer_idx` is not recommended and "
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"will to errors during the forward call, if caching is used. Please make sure to provide a `layer_idx` "
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"when creating this class."
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)
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# Mesh TensorFlow initialization to avoid scaling before softmax
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self.q = nn.Linear(self.d_model, self.inner_dim, bias=False)
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self.k = nn.Linear(self.d_model, self.inner_dim, bias=False)
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self.v = nn.Linear(self.d_model, self.inner_dim, bias=False)
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self.o = nn.Linear(self.inner_dim, self.d_model, bias=False)
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if self.has_relative_attention_bias:
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self.relative_attention_bias = nn.Embedding(self.relative_attention_num_buckets, self.n_heads)
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self.pruned_heads = set()
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self.gradient_checkpointing = False
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def prune_heads(self, heads):
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if len(heads) == 0:
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return
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heads, index = find_pruneable_heads_and_indices(
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heads, self.n_heads, self.key_value_proj_dim, self.pruned_heads
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)
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# Prune linear layers
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self.q = prune_linear_layer(self.q, index)
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self.k = prune_linear_layer(self.k, index)
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self.v = prune_linear_layer(self.v, index)
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self.o = prune_linear_layer(self.o, index, dim=1)
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# Update hyper params
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self.n_heads = self.n_heads - len(heads)
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self.inner_dim = self.key_value_proj_dim * self.n_heads
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self.pruned_heads = self.pruned_heads.union(heads)
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@staticmethod
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def _relative_position_bucket(relative_position, bidirectional=True, num_buckets=32, max_distance=128):
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"""
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Adapted from Mesh Tensorflow:
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https://github.com/tensorflow/mesh/blob/0cb87fe07da627bf0b7e60475d59f95ed6b5be3d/mesh_tensorflow/transformer/transformer_layers.py#L593
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Translate relative position to a bucket number for relative attention. The relative position is defined as
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memory_position - query_position, i.e. the distance in tokens from the attending position to the attended-to
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position. If bidirectional=False, then positive relative positions are invalid. We use smaller buckets for
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small absolute relative_position and larger buckets for larger absolute relative_positions. All relative
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positions >=max_distance map to the same bucket. All relative positions <=-max_distance map to the same bucket.
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This should allow for more graceful generalization to longer sequences than the model has been trained on
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Args:
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relative_position: an int32 Tensor
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bidirectional: a boolean - whether the attention is bidirectional
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num_buckets: an integer
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max_distance: an integer
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Returns:
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a Tensor with the same shape as relative_position, containing int32 values in the range [0, num_buckets)
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"""
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relative_buckets = 0
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if bidirectional:
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num_buckets //= 2
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relative_buckets += (relative_position > 0).to(torch.long) * num_buckets
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relative_position = torch.abs(relative_position)
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else:
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|
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
|
|
|
|
def compute_bias(self, query_length, key_length, device=None, cache_position=None):
|
|
"""Compute binned relative position bias"""
|
|
if device is None:
|
|
device = self.relative_attention_bias.weight.device
|
|
if cache_position is None:
|
|
context_position = torch.arange(query_length, dtype=torch.long, device=device)[:, None]
|
|
else:
|
|
context_position = cache_position[:, None].to(device)
|
|
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=(not self.is_decoder),
|
|
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,
|
|
cache_position=None,
|
|
):
|
|
"""
|
|
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, 1, 1, key_length) (non-causal encoder) or (batch_size, 1, seq_length, key_length) (causal decoder)
|
|
batch_size, seq_length = hidden_states.shape[:2]
|
|
|
|
# if key_value_states are provided this layer is used as a cross-attention layer for the decoder
|
|
is_cross_attention = key_value_states is not None
|
|
|
|
query_states = self.q(hidden_states)
|
|
query_states = query_states.view(batch_size, -1, self.n_heads, self.key_value_proj_dim).transpose(1, 2)
|
|
|
|
# Check is encoder-decoder model is being used. Otherwise we'll get `DynamicCache`
|
|
if past_key_value is not None and isinstance(past_key_value, EncoderDecoderCache):
|
|
is_updated = past_key_value.is_updated.get(self.layer_idx)
|
|
if is_cross_attention:
|
|
# after the first generated id, we can subsequently re-use all key/value_states from cache
|
|
curr_past_key_value = past_key_value.cross_attention_cache
|
|
else:
|
|
curr_past_key_value = past_key_value.self_attention_cache
|
|
else:
|
|
curr_past_key_value = past_key_value
|
|
|
|
current_states = key_value_states if is_cross_attention else hidden_states
|
|
if is_cross_attention and past_key_value is not None and is_updated:
|
|
# reuse k,v, cross_attentions
|
|
key_states = curr_past_key_value.layers[self.layer_idx].keys
|
|
value_states = curr_past_key_value.layers[self.layer_idx].values
|
|
else:
|
|
key_states = self.k(current_states)
|
|
value_states = self.v(current_states)
|
|
key_states = key_states.view(batch_size, -1, self.n_heads, self.key_value_proj_dim).transpose(1, 2)
|
|
value_states = value_states.view(batch_size, -1, self.n_heads, self.key_value_proj_dim).transpose(1, 2)
|
|
|
|
if past_key_value is not None:
|
|
# save all key/value_states to cache to be re-used for fast auto-regressive generation
|
|
cache_position = cache_position if not is_cross_attention else None
|
|
key_states, value_states = curr_past_key_value.update(
|
|
key_states, value_states, self.layer_idx, {"cache_position": cache_position}
|
|
)
|
|
# set flag that curr layer for cross-attn is already updated so we can re-use in subsequent calls
|
|
if is_cross_attention:
|
|
past_key_value.is_updated[self.layer_idx] = True
|
|
|
|
# compute scores, equivalent of torch.einsum("bnqd,bnkd->bnqk", query_states, key_states), compatible with onnx op>9
|
|
scores = torch.matmul(query_states, key_states.transpose(3, 2))
|
|
|
|
if position_bias is None:
|
|
key_length = key_states.shape[-2]
|
|
# cache position is 0-indexed so we add 1 to get the real length of queries (aka with past)
|
|
real_seq_length = query_length if query_length is not None else cache_position[-1] + 1
|
|
if not self.has_relative_attention_bias:
|
|
position_bias = torch.zeros(
|
|
(1, self.n_heads, 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, cache_position=cache_position
|
|
)
|
|
position_bias = position_bias[:, :, -seq_length:, :]
|
|
|
|
if mask is not None:
|
|
causal_mask = mask[:, :, :, : key_states.shape[-2]]
|
|
position_bias = position_bias + causal_mask
|
|
|
|
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)
|
|
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)
|
|
|
|
attn_output = attn_output.transpose(1, 2).contiguous()
|
|
attn_output = attn_output.view(batch_size, -1, self.inner_dim)
|
|
attn_output = self.o(attn_output)
|
|
|
|
outputs = (attn_output, position_bias)
|
|
|
|
if output_attentions:
|
|
outputs = outputs + (attn_weights,)
|
|
return outputs
|
|
|
|
|
|
class LongT5LocalAttention(nn.Module):
|
|
def __init__(self, config: LongT5Config, has_relative_attention_bias: bool = False) -> None:
|
|
super().__init__()
|
|
self.is_decoder = config.is_decoder
|
|
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.d_model = config.d_model
|
|
self.key_value_proj_dim = config.d_kv
|
|
self.n_heads = config.num_heads
|
|
self.local_radius = config.local_radius
|
|
self.block_len = self.local_radius + 1
|
|
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.q = nn.Linear(self.d_model, self.inner_dim, bias=False)
|
|
self.k = nn.Linear(self.d_model, self.inner_dim, bias=False)
|
|
self.v = nn.Linear(self.d_model, self.inner_dim, bias=False)
|
|
self.o = nn.Linear(self.inner_dim, self.d_model, 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
|
|
|
|
# Copied from transformers.models.t5.modeling_t5.T5Attention.prune_heads
|
|
def prune_heads(self, heads):
|
|
if len(heads) == 0:
|
|
return
|
|
heads, index = find_pruneable_heads_and_indices(
|
|
heads, self.n_heads, self.key_value_proj_dim, self.pruned_heads
|
|
)
|
|
# Prune linear layers
|
|
self.q = prune_linear_layer(self.q, index)
|
|
self.k = prune_linear_layer(self.k, index)
|
|
self.v = prune_linear_layer(self.v, index)
|
|
self.o = prune_linear_layer(self.o, index, dim=1)
|
|
# Update hyper params
|
|
self.n_heads = self.n_heads - len(heads)
|
|
self.inner_dim = self.key_value_proj_dim * self.n_heads
|
|
self.pruned_heads = self.pruned_heads.union(heads)
|
|
|
|
@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
|
|
|
|
def compute_bias(self, block_length: int):
|
|
"""Compute binned relative position bias"""
|
|
target_device = (
|
|
self.relative_attention_bias.weight.device
|
|
if self.relative_attention_bias.weight.device.type != "meta"
|
|
else None
|
|
)
|
|
memory_position = torch.arange(3 * block_length, dtype=torch.long, device=target_device)
|
|
context_position = memory_position[block_length:-block_length]
|
|
|
|
# (block_length, 3 * block_length)
|
|
relative_position = memory_position[None, :] - context_position[:, None]
|
|
relative_position_bucket = self._relative_position_bucket(
|
|
relative_position, # (block_length, 3 * block_length)
|
|
bidirectional=(not self.is_decoder),
|
|
num_buckets=self.relative_attention_num_buckets,
|
|
max_distance=self.relative_attention_max_distance,
|
|
)
|
|
# (block_length, 3 * block_length, num_heads)
|
|
values = self.relative_attention_bias(relative_position_bucket)
|
|
# (1, 1, num_heads, block_length, 3 * block_length)
|
|
values = values.permute([2, 0, 1]).unsqueeze(0).unsqueeze(0)
|
|
return values
|
|
|
|
def forward(
|
|
self,
|
|
hidden_states,
|
|
mask=None,
|
|
position_bias=None,
|
|
layer_head_mask=None,
|
|
output_attentions=False,
|
|
):
|
|
batch_size, seq_length = hidden_states.shape[:2]
|
|
|
|
def shape(states):
|
|
"""projection"""
|
|
return states.view(batch_size, -1, self.n_heads, self.key_value_proj_dim)
|
|
|
|
def unshape(states):
|
|
"""reshape"""
|
|
return states.contiguous().view(batch_size, -1, self.inner_dim)
|
|
|
|
# get query/key/value states -> (batch_size, seq_length, n_heads, dim_per_head)
|
|
query_states = shape(self.q(hidden_states))
|
|
key_states = shape(self.k(hidden_states))
|
|
value_states = shape(self.v(hidden_states))
|
|
|
|
# Split into blocks -> (batch_size, num_blocks, block_len, n_heads, dim_per_head)
|
|
query_states = _split_into_blocks(query_states, self.block_len, dim=1)
|
|
key_states = _split_into_blocks(key_states, self.block_len, dim=1)
|
|
value_states = _split_into_blocks(value_states, self.block_len, dim=1)
|
|
|
|
# Concatenate 3 blocks for keys and values -> (batch_size, num_blocks, 3 * block_len, n_heads, dim_per_head)
|
|
key_states = _concatenate_3_blocks(key_states, block_dim=1, sequence_dim=2)
|
|
value_states = _concatenate_3_blocks(value_states, block_dim=1, sequence_dim=2)
|
|
|
|
# Compute scores
|
|
scores = torch.einsum(
|
|
"...qhd,...khd->...hqk", query_states, key_states
|
|
) # (batch_size, num_block, n_heads, block_len, 3 * block_len)
|
|
|
|
if position_bias is None:
|
|
# position_bias shape: # (1, 1, n_heads, block_len, 3 * block_len)
|
|
if not self.has_relative_attention_bias:
|
|
position_bias = torch.zeros(
|
|
(1, 1, self.n_heads, self.block_len, 3 * self.block_len), device=scores.device, dtype=scores.dtype
|
|
)
|
|
if self.gradient_checkpointing and self.training:
|
|
position_bias.requires_grad = True
|
|
else:
|
|
position_bias = self.compute_bias(self.block_len)
|
|
|
|
if mask is not None:
|
|
# Replace masked positions with -1e10 (according to the original implementation)
|
|
mask = torch.where(mask > 0, 0.0, -1e10)
|
|
# We need to adjust position bias shape to be sum with mask
|
|
position_bias = position_bias + mask.transpose(1, 2)
|
|
|
|
scores += position_bias
|
|
# (batch_size, num_blocks, n_heads, block_len, 3 * block_len)
|
|
attn_weights = nn.functional.softmax(scores.float(), dim=-1).type_as(scores)
|
|
# (batch_size, num_blocks, n_heads, block_len, 3 * block_len)
|
|
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_weights = attn_weights.type(value_states.dtype)
|
|
attn_output = unshape(torch.einsum("...hqk,...khd->...qhd", attn_weights, value_states))
|
|
attn_output = attn_output[:, :seq_length, :]
|
|
attn_output = self.o(attn_output)
|
|
|
|
outputs = (
|
|
attn_output,
|
|
position_bias,
|
|
)
|
|
|
|
if output_attentions:
|
|
outputs = outputs + (attn_weights,)
|
|
return outputs
|
|
|
|
|
|
class LongT5TransientGlobalAttention(nn.Module):
|
|
def __init__(self, config: LongT5Config, has_relative_attention_bias: bool = False) -> None:
|
|
super().__init__()
|
|
self.is_decoder = config.is_decoder
|
|
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.d_model = config.d_model
|
|
self.key_value_proj_dim = config.d_kv
|
|
self.n_heads = config.num_heads
|
|
self.local_radius = config.local_radius
|
|
self.block_len = self.local_radius + 1
|
|
self.global_block_size = config.global_block_size
|
|
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.q = nn.Linear(self.d_model, self.inner_dim, bias=False)
|
|
self.k = nn.Linear(self.d_model, self.inner_dim, bias=False)
|
|
self.v = nn.Linear(self.d_model, self.inner_dim, bias=False)
|
|
self.o = nn.Linear(self.inner_dim, self.d_model, 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()
|
|
|
|
# Relativen attention bias & Layer norm for global attention
|
|
if self.has_relative_attention_bias:
|
|
self.global_relative_attention_bias = nn.Embedding(self.relative_attention_num_buckets, self.n_heads)
|
|
self.global_input_layer_norm = LongT5LayerNorm(config.d_model, eps=config.layer_norm_epsilon)
|
|
|
|
# Copied from transformers.models.t5.modeling_t5.T5Attention.prune_heads
|
|
def prune_heads(self, heads):
|
|
if len(heads) == 0:
|
|
return
|
|
heads, index = find_pruneable_heads_and_indices(
|
|
heads, self.n_heads, self.key_value_proj_dim, self.pruned_heads
|
|
)
|
|
# Prune linear layers
|
|
self.q = prune_linear_layer(self.q, index)
|
|
self.k = prune_linear_layer(self.k, index)
|
|
self.v = prune_linear_layer(self.v, index)
|
|
self.o = prune_linear_layer(self.o, index, dim=1)
|
|
# Update hyper params
|
|
self.n_heads = self.n_heads - len(heads)
|
|
self.inner_dim = self.key_value_proj_dim * self.n_heads
|
|
self.pruned_heads = self.pruned_heads.union(heads)
|
|
|
|
@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
|
|
|
|
def compute_bias(self, block_length: int):
|
|
"""Compute binned relative position bias"""
|
|
target_device = (
|
|
self.relative_attention_bias.weight.device
|
|
if self.relative_attention_bias.weight.device.type != "meta"
|
|
else None
|
|
)
|
|
memory_position = torch.arange(3 * block_length, dtype=torch.long, device=target_device)
|
|
context_position = memory_position[block_length:-block_length]
|
|
|
|
# (block_length, 3 * block_length)
|
|
relative_position = memory_position[None, :] - context_position[:, None]
|
|
relative_position_bucket = self._relative_position_bucket(
|
|
relative_position, # (block_length, 3 * block_length)
|
|
bidirectional=(not self.is_decoder),
|
|
num_buckets=self.relative_attention_num_buckets,
|
|
max_distance=self.relative_attention_max_distance,
|
|
)
|
|
# (block_length, 3 * block_length, num_heads)
|
|
values = self.relative_attention_bias(relative_position_bucket)
|
|
# (1, 1, num_heads, block_length, 3 * block_length)
|
|
values = values.permute([2, 0, 1]).unsqueeze(0).unsqueeze(0)
|
|
return values
|
|
|
|
def compute_side_bias(self, mask: torch.Tensor, global_segment_ids: torch.Tensor) -> torch.Tensor:
|
|
# (batch_size, 1, seq_len, global_seq_len)
|
|
side_attention_mask = torch.eq(mask[..., None], global_segment_ids[:, None, :])[:, None, ...]
|
|
attention_side_bias = torch.where(side_attention_mask > 0, 0.0, -1e10)
|
|
# (batch_size, seq_len, global_seq_len)
|
|
side_relative_position = _make_side_relative_position_ids(mask, self.global_block_size)
|
|
side_relative_position_bucket = self._relative_position_bucket(
|
|
side_relative_position,
|
|
bidirectional=(not self.is_decoder),
|
|
num_buckets=self.relative_attention_num_buckets,
|
|
max_distance=self.relative_attention_max_distance,
|
|
)
|
|
# (batch_size, seq_len, global_seq_len, num_heads)
|
|
side_bias = self.global_relative_attention_bias(side_relative_position_bucket)
|
|
|
|
# (batch_size, num_heads, seq_len, global_seq_len)
|
|
side_bias = side_bias.permute([0, 3, 1, 2])
|
|
# (batch_size, num_heads, seq_len, global_seq_len)
|
|
attention_side_bias = attention_side_bias + side_bias
|
|
return attention_side_bias
|
|
|
|
def forward(
|
|
self,
|
|
hidden_states,
|
|
mask=None,
|
|
position_bias=None,
|
|
layer_head_mask=None,
|
|
output_attentions=False,
|
|
):
|
|
batch_size, seq_length = hidden_states.shape[:2]
|
|
|
|
def shape(states):
|
|
"""projection"""
|
|
return states.view(batch_size, -1, self.n_heads, self.key_value_proj_dim)
|
|
|
|
def unshape(states):
|
|
"""reshape"""
|
|
return states.contiguous().view(batch_size, -1, self.inner_dim)
|
|
|
|
# Prepare components for transient-global attention
|
|
# Obtain block_ids and global_segment_ids
|
|
# global_seq_len := seq_len // self.global_block_size
|
|
# shapes: (batch_size, seq_len) & (batch_size, global_seq_len)
|
|
block_ids, global_segment_ids = _make_global_fixed_block_ids(
|
|
mask if mask is not None else torch.ones(hidden_states.shape[:-1]),
|
|
self.global_block_size,
|
|
)
|
|
# Create global inputs
|
|
_global_seq_len = global_segment_ids.shape[-1]
|
|
global_inputs = _create_global_aggregates(hidden_states, block_ids, _global_seq_len)
|
|
global_inputs = self.global_input_layer_norm(global_inputs)
|
|
|
|
# get query states -> (batch_size, seq_length, n_heads, dim_per_head)
|
|
query_states = shape(self.q(hidden_states))
|
|
key_states = shape(self.k(hidden_states))
|
|
value_states = shape(self.v(hidden_states))
|
|
# Get global/side key/value states shape: (batch_size, global_seq_len, n_heads, dim_per_head)
|
|
side_key_states = shape(self.k(global_inputs))
|
|
side_value_states = shape(self.v(global_inputs))
|
|
|
|
# Split into blocks -> (batch_size, num_blocks, block_len, n_heads, dim_per_head)
|
|
query_states = _split_into_blocks(query_states, self.block_len, dim=1)
|
|
key_states = _split_into_blocks(key_states, self.block_len, dim=1)
|
|
value_states = _split_into_blocks(value_states, self.block_len, dim=1)
|
|
|
|
# Concatenate 3 blocks for keys and values -> (batch_size, num_blocks, 3 * block_len, n_heads, dim_per_head)
|
|
key_states = _concatenate_3_blocks(key_states, block_dim=1, sequence_dim=2)
|
|
value_states = _concatenate_3_blocks(value_states, block_dim=1, sequence_dim=2)
|
|
|
|
# Tile side inputs across local key/value blocks
|
|
# New shape: (batch_size, num_blocks, global_seq_len, n_heads, dim_per_head)
|
|
reps = [1] * (side_key_states.ndim + 1)
|
|
reps[1] = key_states.shape[1]
|
|
side_key_states = side_key_states.unsqueeze(1).repeat(reps)
|
|
side_value_states = side_value_states.unsqueeze(1).repeat(reps)
|
|
|
|
# Concatenate "local" and "side"/"global" key/value states to allow each token to attend global aggregated ones
|
|
# New shape: (batch_size, num_blocks, 3 * block_len + global_seq_len, n_heads, dim_per_head)
|
|
key_states = torch.cat([key_states, side_key_states], dim=2)
|
|
value_states = torch.cat([value_states, side_value_states], dim=2)
|
|
|
|
# Compute scores -> (batch_size, num_block, n_heads, block_len, 3 * block_len + global_seq_len)
|
|
scores = torch.einsum("...qhd,...khd->...hqk", query_states, key_states)
|
|
|
|
if mask is not None:
|
|
# We need to adjust position bias shape to be sum with mask
|
|
local_attention_mask = _get_local_attention_mask(mask, self.block_len, hidden_states.device)
|
|
# Replace masked positions with -10_000 (according to the original implementation)
|
|
local_attention_mask = torch.where(local_attention_mask > 0, 0.0, -1e10)
|
|
else:
|
|
local_attention_mask = None
|
|
|
|
if position_bias is None:
|
|
# position_bias shape: # (1, 1, n_heads, block_len, 3 * block_len)
|
|
if not self.has_relative_attention_bias:
|
|
position_bias = torch.zeros(
|
|
(1, 1, self.n_heads, self.block_len, 3 * self.block_len),
|
|
device=scores.device,
|
|
dtype=scores.dtype,
|
|
)
|
|
if self.gradient_checkpointing and self.training:
|
|
position_bias.requires_grad = True
|
|
else:
|
|
position_bias = self.compute_bias(self.block_len)
|
|
|
|
if local_attention_mask is not None:
|
|
# (batch_size, 1, n_heads, block_len, 3 * block_len)
|
|
position_bias = position_bias + local_attention_mask.transpose(1, 2)
|
|
position_bias = position_bias.type(scores.dtype)
|
|
|
|
# Calculate global/side bias - shape: # (batch_size, num_heads, seq_len, global_seq_len)
|
|
if mask is None:
|
|
mask = torch.ones(batch_size, seq_length)
|
|
# (batch_size, num_heads, seq_len, global_seq_len)
|
|
side_position_bias = self.compute_side_bias(mask, global_segment_ids)
|
|
# (batch_size, num_blocks, num_heads, block_len, global_seq_len)
|
|
side_position_bias = _split_into_blocks(side_position_bias, self.block_len, dim=-2).transpose(1, 2)
|
|
side_position_bias = side_position_bias.type(scores.dtype).to(scores.device)
|
|
# (batch_size, num_blocks, num_heads, block_len, 3 * block_len + global_seq_len)
|
|
position_bias = torch.cat([position_bias, side_position_bias], dim=-1)
|
|
|
|
scores += position_bias
|
|
# (batch_size, num_blocks, n_heads, block_len, 3 * block_len + global_seq_len)
|
|
attn_weights = nn.functional.softmax(scores.float(), dim=-1).type_as(scores)
|
|
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_weights = attn_weights.type(value_states.dtype)
|
|
attn_output = unshape(torch.einsum("...hqk,...khd->...qhd", attn_weights, value_states))
|
|
attn_output = attn_output[:, :seq_length, :]
|
|
attn_output = self.o(attn_output)
|
|
|
|
outputs = (attn_output, position_bias)
|
|
|
|
if output_attentions:
|
|
outputs = outputs + (attn_weights,)
|
|
return outputs
|
|
|
|
|
|
# Copied from transformers.models.t5.modeling_t5.T5LayerSelfAttention with T5->LongT5
|
|
class LongT5LayerSelfAttention(nn.Module):
|
|
def __init__(self, config, has_relative_attention_bias=False, layer_idx: Optional[int] = None):
|
|
super().__init__()
|
|
self.SelfAttention = LongT5Attention(
|
|
config, has_relative_attention_bias=has_relative_attention_bias, layer_idx=layer_idx
|
|
)
|
|
self.layer_norm = LongT5LayerNorm(config.d_model, 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,
|
|
cache_position=None,
|
|
):
|
|
normed_hidden_states = self.layer_norm(hidden_states)
|
|
attention_output = self.SelfAttention(
|
|
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,
|
|
cache_position=cache_position,
|
|
)
|
|
hidden_states = hidden_states + self.dropout(attention_output[0])
|
|
outputs = (hidden_states,) + attention_output[1:] # add attentions if we output them
|
|
return outputs
|
|
|
|
|
|
class LongT5LayerLocalSelfAttention(nn.Module):
|
|
"""Local self attention used in encoder"""
|
|
|
|
def __init__(self, config, has_relative_attention_bias=False, layer_idx: Optional[int] = None):
|
|
super().__init__()
|
|
self.LocalSelfAttention = LongT5LocalAttention(config, has_relative_attention_bias=has_relative_attention_bias)
|
|
self.layer_norm = LongT5LayerNorm(config.d_model, 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,
|
|
output_attentions=False,
|
|
**kwargs: Any, # to accept past_key_value and use_cache kwargs
|
|
):
|
|
normed_hidden_states = self.layer_norm(hidden_states)
|
|
attention_output = self.LocalSelfAttention(
|
|
normed_hidden_states,
|
|
mask=attention_mask,
|
|
position_bias=position_bias,
|
|
layer_head_mask=layer_head_mask,
|
|
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
|
|
|
|
|
|
class LongT5LayerTransientGlobalSelfAttention(nn.Module):
|
|
"""Transient-Global self attention used in encoder"""
|
|
|
|
def __init__(self, config, has_relative_attention_bias=False, layer_idx: Optional[int] = None):
|
|
super().__init__()
|
|
self.TransientGlobalSelfAttention = LongT5TransientGlobalAttention(
|
|
config, has_relative_attention_bias=has_relative_attention_bias
|
|
)
|
|
self.layer_norm = LongT5LayerNorm(config.d_model, 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,
|
|
output_attentions=False,
|
|
**kwargs: Any, # to accept past_key_value and use_cache kwargs
|
|
):
|
|
normed_hidden_states = self.layer_norm(hidden_states)
|
|
attention_output = self.TransientGlobalSelfAttention(
|
|
normed_hidden_states,
|
|
mask=attention_mask,
|
|
position_bias=position_bias,
|
|
layer_head_mask=layer_head_mask,
|
|
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 T5->LongT5
|
|
class LongT5LayerCrossAttention(nn.Module):
|
|
def __init__(self, config, layer_idx: Optional[int] = None):
|
|
super().__init__()
|
|
self.EncDecAttention = LongT5Attention(config, has_relative_attention_bias=False, layer_idx=layer_idx)
|
|
self.layer_norm = LongT5LayerNorm(config.d_model, 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,
|
|
cache_position=None,
|
|
):
|
|
normed_hidden_states = self.layer_norm(hidden_states)
|
|
attention_output = self.EncDecAttention(
|
|
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,
|
|
cache_position=cache_position,
|
|
)
|
|
layer_output = hidden_states + self.dropout(attention_output[0])
|
|
outputs = (layer_output,) + attention_output[1:] # add attentions if we output them
|
|
return outputs
|
|
|
|
|
|
class LongT5Block(GradientCheckpointingLayer):
|
|
def __init__(self, config, has_relative_attention_bias=False, layer_idx: Optional[int] = None):
|
|
super().__init__()
|
|
self.is_decoder = config.is_decoder
|
|
if config.is_decoder:
|
|
attention_layer = LongT5LayerSelfAttention
|
|
elif config.encoder_attention_type == "local":
|
|
attention_layer = LongT5LayerLocalSelfAttention
|
|
elif config.encoder_attention_type == "transient-global":
|
|
attention_layer = LongT5LayerTransientGlobalSelfAttention
|
|
else:
|
|
raise ValueError(
|
|
"For encoder attention mechanism, either `local` or `transient-global` attention type is expected, "
|
|
f"but got {config.encoder_attention_type}."
|
|
)
|
|
self.layer = nn.ModuleList()
|
|
self.layer.append(
|
|
attention_layer(config, has_relative_attention_bias=has_relative_attention_bias, layer_idx=layer_idx)
|
|
)
|
|
if self.is_decoder:
|
|
self.layer.append(LongT5LayerCrossAttention(config, layer_idx=layer_idx))
|
|
|
|
self.layer.append(LongT5LayerFF(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,
|
|
cache_position=None,
|
|
):
|
|
self_attention_outputs = self.layer[0](
|
|
hidden_states,
|
|
attention_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,
|
|
cache_position=cache_position,
|
|
)
|
|
hidden_states = self_attention_outputs[0]
|
|
attention_outputs = self_attention_outputs[1:] # Keep self-attention outputs and relative position weights
|
|
|
|
# clamp inf values to enable fp16 inference - check https://github.com/huggingface/transformers/pull/19229/
|
|
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 = self.is_decoder and encoder_hidden_states is not None
|
|
if do_cross_attention:
|
|
cross_attention_outputs = self.layer[1](
|
|
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=past_key_value,
|
|
query_length=cache_position[-1] + 1,
|
|
use_cache=use_cache,
|
|
output_attentions=output_attentions,
|
|
cache_position=cache_position,
|
|
)
|
|
hidden_states = cross_attention_outputs[0]
|
|
|
|
# clamp inf values to enable fp16 inference - check https://github.com/huggingface/transformers/pull/19229/
|
|
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)
|
|
|
|
# Keep cross-attention outputs and relative position weights
|
|
attention_outputs = attention_outputs + cross_attention_outputs[1:]
|
|
|
|
# Apply Feed Forward layer
|
|
hidden_states = self.layer[-1](hidden_states)
|
|
|
|
# clamp inf values to enable fp16 inference - check https://github.com/huggingface/transformers/pull/19229/
|
|
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)
|
|
|
|
return (
|
|
(hidden_states,) + attention_outputs
|
|
) # hidden-states, (self-attention position bias), (self-attention weights), (cross-attention position bias), (cross-attention weights)
|
|
|
|
|
|
@auto_docstring
|
|
class LongT5PreTrainedModel(PreTrainedModel):
|
|
config: LongT5Config
|
|
base_model_prefix = "transformer"
|
|
supports_gradient_checkpointing = True
|
|
_no_split_modules = ["LongT5Block"]
|
|
|
|
_can_compile_fullgraph = False # TODO: @raushan more involved due to local/global attn
|
|
|
|
@property
|
|
# Copied from transformers.models.t5.modeling_t5.T5PreTrainedModel.dummy_inputs
|
|
def dummy_inputs(self):
|
|
input_ids = torch.tensor(DUMMY_INPUTS)
|
|
input_mask = torch.tensor(DUMMY_MASK)
|
|
dummy_inputs = {
|
|
"decoder_input_ids": input_ids,
|
|
"input_ids": input_ids,
|
|
"decoder_attention_mask": input_mask,
|
|
}
|
|
return dummy_inputs
|
|
|
|
def _init_weights(self, module):
|
|
"""Initialize the weights"""
|
|
factor = self.config.initializer_factor # Used for testing weights initialization
|
|
if isinstance(module, LongT5LayerNorm):
|
|
module.weight.data.fill_(factor * 1.0)
|
|
elif isinstance(module, (LongT5Model, LongT5ForConditionalGeneration, LongT5EncoderModel)):
|
|
# Mesh TensorFlow embeddings initialization
|
|
# See https://github.com/tensorflow/mesh/blob/fa19d69eafc9a482aff0b59ddd96b025c0cb207d/mesh_tensorflow/layers.py#L1624
|
|
module.shared.weight.data.normal_(mean=0.0, std=factor * 1.0)
|
|
if hasattr(module, "lm_head") and not self.config.tie_word_embeddings:
|
|
module.lm_head.weight.data.normal_(mean=0.0, std=factor * 1.0)
|
|
elif isinstance(module, LongT5DenseActDense):
|
|
# Mesh TensorFlow FF initialization
|
|
# See https://github.com/tensorflow/mesh/blob/master/mesh_tensorflow/transformer/transformer_layers.py#L56
|
|
# and https://github.com/tensorflow/mesh/blob/fa19d69eafc9a482aff0b59ddd96b025c0cb207d/mesh_tensorflow/layers.py#L89
|
|
module.wi.weight.data.normal_(mean=0.0, std=factor * ((self.config.d_model) ** -0.5))
|
|
if hasattr(module.wi, "bias") and module.wi.bias is not None:
|
|
module.wi.bias.data.zero_()
|
|
module.wo.weight.data.normal_(mean=0.0, std=factor * ((self.config.d_ff) ** -0.5))
|
|
if hasattr(module.wo, "bias") and module.wo.bias is not None:
|
|
module.wo.bias.data.zero_()
|
|
elif isinstance(module, LongT5DenseGatedActDense):
|
|
module.wi_0.weight.data.normal_(mean=0.0, std=factor * ((self.config.d_model) ** -0.5))
|
|
if hasattr(module.wi_0, "bias") and module.wi_0.bias is not None:
|
|
module.wi_0.bias.data.zero_()
|
|
module.wi_1.weight.data.normal_(mean=0.0, std=factor * ((self.config.d_model) ** -0.5))
|
|
if hasattr(module.wi_1, "bias") and module.wi_1.bias is not None:
|
|
module.wi_1.bias.data.zero_()
|
|
module.wo.weight.data.normal_(mean=0.0, std=factor * ((self.config.d_ff) ** -0.5))
|
|
if hasattr(module.wo, "bias") and module.wo.bias is not None:
|
|
module.wo.bias.data.zero_()
|
|
elif isinstance(module, (LongT5Attention, LongT5LocalAttention, LongT5TransientGlobalAttention)):
|
|
# Mesh TensorFlow attention initialization to avoid scaling before softmax
|
|
# See https://github.com/tensorflow/mesh/blob/fa19d69eafc9a482aff0b59ddd96b025c0cb207d/mesh_tensorflow/transformer/attention.py#L136
|
|
d_model = self.config.d_model
|
|
key_value_proj_dim = self.config.d_kv
|
|
n_heads = self.config.num_heads
|
|
module.q.weight.data.normal_(mean=0.0, std=factor * ((d_model * key_value_proj_dim) ** -0.5))
|
|
module.k.weight.data.normal_(mean=0.0, std=factor * (d_model**-0.5))
|
|
module.v.weight.data.normal_(mean=0.0, std=factor * (d_model**-0.5))
|
|
module.o.weight.data.normal_(mean=0.0, std=factor * ((n_heads * key_value_proj_dim) ** -0.5))
|
|
if module.has_relative_attention_bias:
|
|
module.relative_attention_bias.weight.data.normal_(mean=0.0, std=factor * ((d_model) ** -0.5))
|
|
if isinstance(module, LongT5TransientGlobalAttention):
|
|
module.global_relative_attention_bias.weight.data.normal_(
|
|
mean=0.0, std=factor * ((d_model) ** -0.5)
|
|
)
|
|
|
|
# Copied from transformers.models.t5.modeling_t5.T5PreTrainedModel._shift_right with T5->LongT5
|
|
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 LongT5 it is usually set to the pad_token_id. "
|
|
"See LongT5 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
|
|
|
|
|
|
class LongT5Stack(LongT5PreTrainedModel):
|
|
def __init__(self, config, embed_tokens=None):
|
|
super().__init__(config)
|
|
|
|
self.embed_tokens = nn.Embedding(config.vocab_size, config.d_model)
|
|
if embed_tokens is not None:
|
|
self.embed_tokens.weight = embed_tokens.weight
|
|
self.is_decoder = config.is_decoder
|
|
|
|
self.local_radius = config.local_radius
|
|
self.block_len = self.local_radius + 1
|
|
|
|
self.block = nn.ModuleList(
|
|
[
|
|
LongT5Block(config, has_relative_attention_bias=bool(i == 0), layer_idx=i)
|
|
for i in range(config.num_layers)
|
|
]
|
|
)
|
|
self.final_layer_norm = LongT5LayerNorm(config.d_model, eps=config.layer_norm_epsilon)
|
|
self.dropout = nn.Dropout(config.dropout_rate)
|
|
|
|
self.gradient_checkpointing = False
|
|
|
|
# Initialize weights and apply final processing
|
|
self.post_init()
|
|
|
|
# Copied from transformers.models.t5.modeling_t5.T5Stack.set_input_embeddings
|
|
def set_input_embeddings(self, new_embeddings):
|
|
self.embed_tokens = new_embeddings
|
|
|
|
def forward(
|
|
self,
|
|
input_ids=None,
|
|
attention_mask=None,
|
|
encoder_hidden_states=None,
|
|
encoder_attention_mask=None,
|
|
inputs_embeds=None,
|
|
head_mask=None,
|
|
cross_attn_head_mask=None,
|
|
past_key_values=None,
|
|
use_cache=None,
|
|
output_attentions=None,
|
|
output_hidden_states=None,
|
|
return_dict=None,
|
|
cache_position=None,
|
|
):
|
|
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:
|
|
err_msg_prefix = "decoder_" if self.is_decoder else ""
|
|
raise ValueError(
|
|
f"You cannot specify both {err_msg_prefix}input_ids and {err_msg_prefix}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:
|
|
err_msg_prefix = "decoder_" if self.is_decoder else ""
|
|
raise ValueError(f"You have to specify either {err_msg_prefix}input_ids or {err_msg_prefix}inputs_embeds")
|
|
|
|
if self.gradient_checkpointing and self.training:
|
|
if use_cache:
|
|
logger.warning_once(
|
|
"`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..."
|
|
)
|
|
use_cache = False
|
|
|
|
if 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
|
|
|
|
if self.is_decoder:
|
|
if use_cache and past_key_values is None:
|
|
if self.config.is_encoder_decoder:
|
|
past_key_values = EncoderDecoderCache(DynamicCache(), DynamicCache())
|
|
else:
|
|
past_key_values = DynamicCache()
|
|
elif not self.is_decoder:
|
|
# do not pass cache object down the line for encoder stack
|
|
# it messes indexing later in decoder-stack because cache object is modified in-place
|
|
past_key_values = None
|
|
|
|
past_key_values_length = past_key_values.get_seq_length() if past_key_values is not None else 0
|
|
if cache_position is None:
|
|
cache_position = torch.arange(
|
|
past_key_values_length, past_key_values_length + seq_length, device=inputs_embeds.device
|
|
)
|
|
|
|
if attention_mask is None and not is_torchdynamo_compiling():
|
|
# required mask seq length can be calculated via length of past
|
|
mask_seq_length = past_key_values_length + seq_length
|
|
attention_mask = torch.ones(batch_size, mask_seq_length, device=inputs_embeds.device)
|
|
|
|
if self.is_decoder:
|
|
causal_mask = self._update_causal_mask(
|
|
attention_mask,
|
|
inputs_embeds,
|
|
cache_position,
|
|
past_key_values.self_attention_cache
|
|
if isinstance(past_key_values, EncoderDecoderCache)
|
|
else past_key_values,
|
|
output_attentions,
|
|
)
|
|
# We use local attention in encoder self-attention, otherwise standard self & cross attentions are used
|
|
elif self.config.encoder_attention_type == "local":
|
|
causal_mask = _get_local_attention_mask(attention_mask, self.block_len, inputs_embeds.device)
|
|
else: # we need to use both local attention mask and standard extended mask for transient-global attention
|
|
causal_mask = attention_mask
|
|
|
|
# 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 self.is_decoder and 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)
|
|
all_hidden_states = () if output_hidden_states else None
|
|
all_attentions = () if output_attentions else None
|
|
all_cross_attentions = () if (output_attentions and self.is_decoder) else None
|
|
position_bias = None
|
|
encoder_decoder_position_bias = None
|
|
|
|
hidden_states = self.dropout(inputs_embeds)
|
|
|
|
for i, layer_module in enumerate(self.block):
|
|
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,)
|
|
|
|
layer_outputs = layer_module(
|
|
hidden_states,
|
|
causal_mask,
|
|
position_bias,
|
|
encoder_hidden_states,
|
|
encoder_extended_attention_mask,
|
|
encoder_decoder_position_bias, # as a positional argument for gradient checkpointing
|
|
layer_head_mask=layer_head_mask,
|
|
cross_attn_layer_head_mask=cross_attn_layer_head_mask,
|
|
past_key_value=past_key_values,
|
|
use_cache=use_cache,
|
|
output_attentions=output_attentions,
|
|
return_dict=return_dict,
|
|
cache_position=cache_position,
|
|
)
|
|
|
|
# layer_outputs is a tuple with:
|
|
# hidden-states, (self-attention position bias), (self-attention weights), (cross-attention position bias), (cross-attention weights)
|
|
|
|
hidden_states = layer_outputs[0]
|
|
|
|
# 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[1]
|
|
if self.is_decoder and encoder_hidden_states is not None:
|
|
encoder_decoder_position_bias = layer_outputs[3 if output_attentions else 2]
|
|
|
|
if output_attentions:
|
|
all_attentions = all_attentions + (layer_outputs[2],)
|
|
if self.is_decoder:
|
|
all_cross_attentions = all_cross_attentions + (layer_outputs[4],)
|
|
|
|
hidden_states = self.final_layer_norm(hidden_states)
|
|
hidden_states = self.dropout(hidden_states)
|
|
|
|
# Add last layer
|
|
if output_hidden_states:
|
|
all_hidden_states = all_hidden_states + (hidden_states,)
|
|
|
|
if not return_dict:
|
|
return tuple(
|
|
v
|
|
for v in [
|
|
hidden_states,
|
|
past_key_values,
|
|
all_hidden_states,
|
|
all_attentions,
|
|
all_cross_attentions,
|
|
]
|
|
if v is not None
|
|
)
|
|
return BaseModelOutputWithPastAndCrossAttentions(
|
|
last_hidden_state=hidden_states,
|
|
past_key_values=past_key_values,
|
|
hidden_states=all_hidden_states,
|
|
attentions=all_attentions,
|
|
cross_attentions=all_cross_attentions,
|
|
)
|
|
|
|
# Copied from transformers.models.gptj.modeling_gptj.GPTJModel._update_causal_mask
|
|
def _update_causal_mask(
|
|
self,
|
|
attention_mask: Union[torch.Tensor, "BlockMask"],
|
|
input_tensor: torch.Tensor,
|
|
cache_position: torch.Tensor,
|
|
past_key_values: Cache,
|
|
output_attentions: bool = False,
|
|
):
|
|
if self.config._attn_implementation == "flash_attention_2":
|
|
if attention_mask is not None and (attention_mask == 0.0).any():
|
|
return attention_mask
|
|
return None
|
|
if self.config._attn_implementation == "flex_attention":
|
|
if isinstance(attention_mask, torch.Tensor):
|
|
attention_mask = make_flex_block_causal_mask(attention_mask)
|
|
return attention_mask
|
|
|
|
# For SDPA, when possible, we will rely on its `is_causal` argument instead of its `attn_mask` argument, in
|
|
# order to dispatch on Flash Attention 2. This feature is not compatible with static cache, as SDPA will fail
|
|
# to infer the attention mask.
|
|
past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0
|
|
using_compilable_cache = past_key_values.is_compileable if past_key_values is not None else False
|
|
|
|
# When output attentions is True, sdpa implementation's forward method calls the eager implementation's forward
|
|
if self.config._attn_implementation == "sdpa" and not using_compilable_cache and not output_attentions:
|
|
if AttentionMaskConverter._ignore_causal_mask_sdpa(
|
|
attention_mask,
|
|
inputs_embeds=input_tensor,
|
|
past_key_values_length=past_seen_tokens,
|
|
is_training=self.training,
|
|
):
|
|
return None
|
|
|
|
dtype = input_tensor.dtype
|
|
sequence_length = input_tensor.shape[1]
|
|
if using_compilable_cache:
|
|
target_length = past_key_values.get_max_cache_shape()
|
|
else:
|
|
target_length = (
|
|
attention_mask.shape[-1]
|
|
if isinstance(attention_mask, torch.Tensor)
|
|
else past_seen_tokens + sequence_length + 1
|
|
)
|
|
|
|
# In case the provided `attention` mask is 2D, we generate a causal mask here (4D).
|
|
causal_mask = self._prepare_4d_causal_attention_mask_with_cache_position(
|
|
attention_mask,
|
|
sequence_length=sequence_length,
|
|
target_length=target_length,
|
|
dtype=dtype,
|
|
cache_position=cache_position,
|
|
batch_size=input_tensor.shape[0],
|
|
)
|
|
|
|
if (
|
|
self.config._attn_implementation == "sdpa"
|
|
and attention_mask is not None
|
|
and attention_mask.device.type in ["cuda", "xpu", "npu"]
|
|
and not output_attentions
|
|
):
|
|
# Attend to all tokens in fully masked rows in the causal_mask, for example the relevant first rows when
|
|
# using left padding. This is required by F.scaled_dot_product_attention memory-efficient attention path.
|
|
# Details: https://github.com/pytorch/pytorch/issues/110213
|
|
min_dtype = torch.finfo(dtype).min
|
|
causal_mask = AttentionMaskConverter._unmask_unattended(causal_mask, min_dtype)
|
|
|
|
return causal_mask
|
|
|
|
@staticmethod
|
|
# Copied from transformers.models.gptj.modeling_gptj.GPTJModel._prepare_4d_causal_attention_mask_with_cache_position
|
|
def _prepare_4d_causal_attention_mask_with_cache_position(
|
|
attention_mask: torch.Tensor,
|
|
sequence_length: int,
|
|
target_length: int,
|
|
dtype: torch.dtype,
|
|
cache_position: torch.Tensor,
|
|
batch_size: int,
|
|
**kwargs,
|
|
):
|
|
"""
|
|
Creates a causal 4D mask of shape `(batch_size, 1, query_length, key_value_length)` from a 2D mask of shape
|
|
`(batch_size, key_value_length)`, or if the input `attention_mask` is already 4D, do nothing.
|
|
|
|
Args:
|
|
attention_mask (`torch.Tensor`):
|
|
A 2D attention mask of shape `(batch_size, key_value_length)` or a 4D attention mask of shape
|
|
`(batch_size, 1, query_length, key_value_length)`.
|
|
sequence_length (`int`):
|
|
The sequence length being processed.
|
|
target_length (`int`):
|
|
The target length: when generating with static cache, the mask should be as long as the static cache,
|
|
to account for the 0 padding, the part of the cache that is not filled yet.
|
|
dtype (`torch.dtype`):
|
|
The dtype to use for the 4D attention mask.
|
|
cache_position (`torch.Tensor`):
|
|
Indices depicting the position of the input sequence tokens in the sequence.
|
|
batch_size (`torch.Tensor`):
|
|
Batch size.
|
|
"""
|
|
if attention_mask is not None and attention_mask.dim() == 4:
|
|
# In this case we assume that the mask comes already in inverted form and requires no inversion or slicing.
|
|
causal_mask = attention_mask
|
|
else:
|
|
min_dtype = torch.finfo(dtype).min
|
|
causal_mask = torch.full(
|
|
(sequence_length, target_length), fill_value=min_dtype, dtype=dtype, device=cache_position.device
|
|
)
|
|
if sequence_length != 1:
|
|
causal_mask = torch.triu(causal_mask, diagonal=1)
|
|
causal_mask *= torch.arange(target_length, device=cache_position.device) > cache_position.reshape(-1, 1)
|
|
causal_mask = causal_mask[None, None, :, :].expand(batch_size, 1, -1, -1)
|
|
if attention_mask is not None:
|
|
causal_mask = causal_mask.clone() # copy to contiguous memory for in-place edit
|
|
mask_length = attention_mask.shape[-1]
|
|
padding_mask = causal_mask[:, :, :, :mask_length] + attention_mask[:, None, None, :].to(
|
|
causal_mask.device
|
|
)
|
|
padding_mask = padding_mask == 0
|
|
causal_mask[:, :, :, :mask_length] = causal_mask[:, :, :, :mask_length].masked_fill(
|
|
padding_mask, min_dtype
|
|
)
|
|
|
|
return causal_mask
|
|
|
|
|
|
# Warning message for FutureWarning: head_mask was separated into two input args - head_mask, decoder_head_mask
|
|
__HEAD_MASK_WARNING_MSG = """
|
|
The input argument `head_mask` was split into two arguments `head_mask` and `decoder_head_mask`. Currently,
|
|
`decoder_head_mask` is set to copy `head_mask`, but this feature is deprecated and will be removed in future versions.
|
|
If you do not want to use any `decoder_head_mask` now, please set `decoder_head_mask = torch.ones(num_layers,
|
|
num_heads)`.
|
|
"""
|
|
|
|
|
|
@auto_docstring
|
|
class LongT5Model(LongT5PreTrainedModel):
|
|
_keys_to_ignore_on_load_unexpected = [
|
|
r"decoder.block.0.layer.1.EncDecAttention.relative_attention_bias.weight",
|
|
]
|
|
_tied_weights_keys = ["encoder.embed_tokens.weight", "decoder.embed_tokens.weight"]
|
|
|
|
def __init__(self, config: LongT5Config):
|
|
super().__init__(config)
|
|
self.shared = nn.Embedding(config.vocab_size, config.d_model)
|
|
|
|
encoder_config = copy.deepcopy(config)
|
|
encoder_config.is_decoder = False
|
|
encoder_config.use_cache = False
|
|
encoder_config.tie_encoder_decoder = False
|
|
self.encoder = LongT5Stack(encoder_config, self.shared)
|
|
|
|
decoder_config = copy.deepcopy(config)
|
|
decoder_config.is_decoder = True
|
|
decoder_config.tie_encoder_decoder = False
|
|
decoder_config.num_layers = config.num_decoder_layers
|
|
self.decoder = LongT5Stack(decoder_config, self.shared)
|
|
|
|
# Initialize weights and apply final processing
|
|
self.post_init()
|
|
|
|
def get_input_embeddings(self):
|
|
return self.shared
|
|
|
|
def set_input_embeddings(self, new_embeddings):
|
|
self.shared = new_embeddings
|
|
self.encoder.set_input_embeddings(new_embeddings)
|
|
self.decoder.set_input_embeddings(new_embeddings)
|
|
|
|
def _tie_weights(self):
|
|
if self.config.tie_word_embeddings:
|
|
self._tie_or_clone_weights(self.encoder.embed_tokens, self.shared)
|
|
self._tie_or_clone_weights(self.decoder.embed_tokens, self.shared)
|
|
|
|
def get_encoder(self):
|
|
return self.encoder
|
|
|
|
def get_decoder(self):
|
|
return self.decoder
|
|
|
|
def _prune_heads(self, heads_to_prune):
|
|
"""
|
|
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)
|
|
|
|
@auto_docstring
|
|
def forward(
|
|
self,
|
|
input_ids: Optional[torch.LongTensor] = 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[Cache] = None,
|
|
inputs_embeds: Optional[torch.Tensor] = 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,
|
|
cache_position: Optional[torch.LongTensor] = None,
|
|
) -> Union[tuple[torch.FloatTensor], Seq2SeqModelOutput]:
|
|
r"""
|
|
input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
|
|
Indices of input sequence tokens in the vocabulary. LongT5 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 [LONGT5
|
|
Training](./longt5#training).
|
|
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)
|
|
|
|
LONGT5 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 [LONGT5
|
|
Training](./longt5#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.
|
|
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**.
|
|
|
|
Example:
|
|
|
|
```python
|
|
>>> from transformers import AutoTokenizer, LongT5Model
|
|
|
|
>>> tokenizer = AutoTokenizer.from_pretrained("google/long-t5-local-base")
|
|
>>> model = LongT5Model.from_pretrained("google/long-t5-local-base")
|
|
|
|
>>> # Let's try a very long encoder input.
|
|
>>> input_ids = tokenizer(
|
|
... 100 * "Studies have been shown that owning a dog is good for you", return_tensors="pt"
|
|
... ).input_ids # Batch size 1
|
|
|
|
>>> decoder_input_ids = tokenizer("Studies show that", return_tensors="pt").input_ids # Batch size 1
|
|
|
|
>>> # forward pass
|
|
>>> outputs = model(input_ids=input_ids, decoder_input_ids=decoder_input_ids)
|
|
>>> last_hidden_states = outputs.last_hidden_state
|
|
```"""
|
|
use_cache = use_cache if use_cache is not None else self.config.use_cache
|
|
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
|
|
|
|
# FutureWarning: head_mask was separated into two input args - head_mask, decoder_head_mask
|
|
if head_mask is not None and decoder_head_mask is None:
|
|
if self.config.num_layers == self.config.num_decoder_layers:
|
|
warnings.warn(__HEAD_MASK_WARNING_MSG, FutureWarning)
|
|
decoder_head_mask = head_mask
|
|
|
|
# Encode if needed (training, first prediction pass)
|
|
if encoder_outputs is None:
|
|
encoder_outputs = self.encoder(
|
|
input_ids=input_ids,
|
|
attention_mask=attention_mask,
|
|
inputs_embeds=inputs_embeds,
|
|
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]
|
|
|
|
# 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,
|
|
return_dict=return_dict,
|
|
cache_position=cache_position,
|
|
)
|
|
|
|
if not return_dict:
|
|
return decoder_outputs + encoder_outputs
|
|
|
|
return Seq2SeqModelOutput(
|
|
last_hidden_state=decoder_outputs.last_hidden_state,
|
|
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,
|
|
)
|
|
|
|
|
|
@auto_docstring(
|
|
custom_intro="""
|
|
LONGT5 Model with a `language modeling` head on top.
|
|
"""
|
|
)
|
|
class LongT5ForConditionalGeneration(LongT5PreTrainedModel, GenerationMixin):
|
|
_keys_to_ignore_on_load_unexpected = [
|
|
r"decoder.block.0.layer.1.EncDecAttention.relative_attention_bias.weight",
|
|
]
|
|
_tied_weights_keys = ["encoder.embed_tokens.weight", "decoder.embed_tokens.weight", "lm_head.weight"]
|
|
|
|
def __init__(self, config: LongT5Config):
|
|
super().__init__(config)
|
|
self.model_dim = config.d_model
|
|
|
|
self.shared = nn.Embedding(config.vocab_size, config.d_model)
|
|
|
|
encoder_config = copy.deepcopy(config)
|
|
encoder_config.is_decoder = False
|
|
encoder_config.use_cache = False
|
|
encoder_config.tie_encoder_decoder = False
|
|
self.encoder = LongT5Stack(encoder_config, self.shared)
|
|
|
|
decoder_config = copy.deepcopy(config)
|
|
decoder_config.is_decoder = True
|
|
decoder_config.tie_encoder_decoder = False
|
|
decoder_config.num_layers = config.num_decoder_layers
|
|
self.decoder = LongT5Stack(decoder_config, self.shared)
|
|
|
|
self.lm_head = nn.Linear(config.d_model, config.vocab_size, bias=False)
|
|
|
|
# Initialize weights and apply final processing
|
|
self.post_init()
|
|
|
|
def get_input_embeddings(self):
|
|
return self.shared
|
|
|
|
def set_input_embeddings(self, new_embeddings):
|
|
self.shared = new_embeddings
|
|
self.encoder.set_input_embeddings(new_embeddings)
|
|
self.decoder.set_input_embeddings(new_embeddings)
|
|
|
|
def _tie_weights(self):
|
|
if self.config.tie_word_embeddings:
|
|
self._tie_or_clone_weights(self.encoder.embed_tokens, self.shared)
|
|
self._tie_or_clone_weights(self.decoder.embed_tokens, self.shared)
|
|
|
|
def get_encoder(self):
|
|
return self.encoder
|
|
|
|
def get_decoder(self):
|
|
return self.decoder
|
|
|
|
@auto_docstring
|
|
def forward(
|
|
self,
|
|
input_ids: Optional[torch.LongTensor] = 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.Tensor]]] = None,
|
|
past_key_values: Optional[Cache] = None,
|
|
inputs_embeds: Optional[torch.FloatTensor] = None,
|
|
decoder_inputs_embeds: Optional[torch.FloatTensor] = None,
|
|
labels: Optional[torch.LongTensor] = None,
|
|
use_cache: Optional[bool] = None,
|
|
output_attentions: Optional[bool] = None,
|
|
output_hidden_states: Optional[bool] = None,
|
|
return_dict: Optional[bool] = None,
|
|
cache_position: Optional[torch.LongTensor] = None,
|
|
) -> Union[tuple[torch.FloatTensor], Seq2SeqLMOutput]:
|
|
r"""
|
|
input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
|
|
Indices of input sequence tokens in the vocabulary. LongT5 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 [LONGT5
|
|
Training](./longt5#training).
|
|
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)
|
|
|
|
LONGT5 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 [LONGT5
|
|
Training](./longt5#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.
|
|
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**.
|
|
labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
|
|
Labels for computing the sequence classification/regression loss. Indices should be in `[-100, 0, ...,
|
|
config.vocab_size - 1]`. All labels set to `-100` are ignored (masked), the loss is only computed for
|
|
labels in `[0, ..., config.vocab_size]`
|
|
|
|
Examples:
|
|
|
|
```python
|
|
>>> from transformers import AutoTokenizer, LongT5ForConditionalGeneration
|
|
|
|
>>> tokenizer = AutoTokenizer.from_pretrained("Stancld/longt5-tglobal-large-16384-pubmed-3k_steps")
|
|
>>> model = LongT5ForConditionalGeneration.from_pretrained(
|
|
... "Stancld/longt5-tglobal-large-16384-pubmed-3k_steps"
|
|
... )
|
|
|
|
>>> # Let's try a very long input.
|
|
>>> inputs = tokenizer(100 * "studies have shown that owning a dog is good for you ", return_tensors="pt")
|
|
>>> input_ids = inputs.input_ids
|
|
|
|
>>> outputs = model.generate(input_ids)
|
|
>>> print(tokenizer.decode(outputs[0], skip_special_tokens=True))
|
|
abstractthe aim of this article is to provide an overview of the literature on the role of dog
|
|
```"""
|
|
use_cache = use_cache if use_cache is not None else self.config.use_cache
|
|
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
|
|
|
|
# FutureWarning: head_mask was separated into two input args - head_mask, decoder_head_mask
|
|
if head_mask is not None and decoder_head_mask is None:
|
|
if self.config.num_layers == self.config.num_decoder_layers:
|
|
warnings.warn(__HEAD_MASK_WARNING_MSG, FutureWarning)
|
|
decoder_head_mask = head_mask
|
|
|
|
# Encode if needed (training, first prediction pass)
|
|
if encoder_outputs is None:
|
|
# Convert encoder inputs in embeddings if needed
|
|
encoder_outputs = self.encoder(
|
|
input_ids=input_ids,
|
|
attention_mask=attention_mask,
|
|
inputs_embeds=inputs_embeds,
|
|
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)
|
|
|
|
# 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,
|
|
return_dict=return_dict,
|
|
cache_position=cache_position,
|
|
)
|
|
|
|
sequence_output = decoder_outputs[0]
|
|
|
|
if self.config.tie_word_embeddings:
|
|
# Rescale output before projecting on vocab
|
|
# See https://github.com/tensorflow/mesh/blob/fa19d69eafc9a482aff0b59ddd96b025c0cb207d/mesh_tensorflow/transformer/transformer.py#L586
|
|
sequence_output = sequence_output * (self.model_dim**-0.5)
|
|
|
|
lm_logits = self.lm_head(sequence_output)
|
|
|
|
loss = None
|
|
if labels is not None:
|
|
loss_fct = CrossEntropyLoss(ignore_index=-100)
|
|
|
|
labels = labels.to(lm_logits.device)
|
|
loss = loss_fct(lm_logits.view(-1, lm_logits.size(-1)), labels.view(-1))
|
|
# TODO(thom): Add z_loss https://github.com/tensorflow/mesh/blob/fa19d69eafc9a482aff0b59ddd96b025c0cb207d/mesh_tensorflow/layers.py#L666
|
|
|
|
if not return_dict:
|
|
output = (lm_logits,) + decoder_outputs[1:] + encoder_outputs
|
|
return ((loss,) + output) if loss is not None else output
|
|
|
|
return Seq2SeqLMOutput(
|
|
loss=loss,
|
|
logits=lm_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_decoder_input_ids_from_labels(self, labels: torch.Tensor):
|
|
return self._shift_right(labels)
|
|
|
|
|
|
@auto_docstring
|
|
class LongT5EncoderModel(LongT5PreTrainedModel):
|
|
_tied_weights_keys = ["encoder.embed_tokens.weight"]
|
|
_keys_to_ignore_on_load_unexpected = [r"decoder"]
|
|
|
|
def __init__(self, config: LongT5Config):
|
|
super().__init__(config)
|
|
self.shared = nn.Embedding(config.vocab_size, config.d_model)
|
|
|
|
encoder_config = copy.deepcopy(config)
|
|
encoder_config.use_cache = False
|
|
encoder_config.tie_encoder_decoder = False
|
|
self.encoder = LongT5Stack(encoder_config, self.shared)
|
|
|
|
# Initialize weights and apply final processing
|
|
self.post_init()
|
|
|
|
def get_input_embeddings(self):
|
|
return self.shared
|
|
|
|
def set_input_embeddings(self, new_embeddings):
|
|
self.shared = new_embeddings
|
|
self.encoder.set_input_embeddings(new_embeddings)
|
|
|
|
def _tie_weights(self):
|
|
if self.config.tie_word_embeddings:
|
|
self._tie_or_clone_weights(self.encoder.embed_tokens, self.shared)
|
|
|
|
def get_encoder(self):
|
|
return self.encoder
|
|
|
|
def _prune_heads(self, heads_to_prune):
|
|
"""
|
|
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)
|
|
|
|
@auto_docstring
|
|
def forward(
|
|
self,
|
|
input_ids: Optional[torch.LongTensor] = None,
|
|
attention_mask: Optional[torch.FloatTensor] = None,
|
|
head_mask: Optional[torch.FloatTensor] = None,
|
|
inputs_embeds: Optional[torch.FloatTensor] = None,
|
|
output_attentions: Optional[bool] = None,
|
|
output_hidden_states: Optional[bool] = None,
|
|
return_dict: Optional[bool] = None,
|
|
) -> Union[tuple[torch.FloatTensor], BaseModelOutput]:
|
|
r"""
|
|
input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
|
|
Indices of input sequence tokens in the vocabulary. LongT5 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.
|
|
|
|
To know more on how to prepare `input_ids` for pretraining take a look a [LONGT5
|
|
Training](./longt5#training).
|
|
|
|
Example:
|
|
|
|
```python
|
|
>>> from transformers import AutoTokenizer, LongT5ForConditionalGeneration
|
|
|
|
>>> tokenizer = AutoTokenizer.from_pretrained("google/long-t5-local-base")
|
|
>>> model = LongT5EncoderModel.from_pretrained("google/long-t5-local-base")
|
|
>>> input_ids = tokenizer(
|
|
... 100 * "Studies have been shown that owning a dog is good for you ", return_tensors="pt"
|
|
... ).input_ids # Batch size 1
|
|
>>> outputs = model(input_ids=input_ids)
|
|
>>> last_hidden_states = outputs.last_hidden_state
|
|
```"""
|
|
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
|
|
|
|
encoder_outputs = self.encoder(
|
|
input_ids=input_ids,
|
|
attention_mask=attention_mask,
|
|
inputs_embeds=inputs_embeds,
|
|
head_mask=head_mask,
|
|
output_attentions=output_attentions,
|
|
output_hidden_states=output_hidden_states,
|
|
return_dict=return_dict,
|
|
)
|
|
|
|
return encoder_outputs
|
|
|
|
|
|
__all__ = ["LongT5EncoderModel", "LongT5ForConditionalGeneration", "LongT5Model", "LongT5PreTrainedModel"]
|