1159 lines
49 KiB
Python
1159 lines
49 KiB
Python
#
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# Copyright 2016 The BigDL Authors.
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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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#
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# ===========================================================================
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#
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# This file is adapted from
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# https://huggingface.co/tiiuae/falcon-7b-instruct/blob/c7f670a03d987254220f343c6b026ea0c5147185/modelling_RW.py
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#
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# Apache 2.0 license
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# https://huggingface.co/tiiuae/falcon-7b-instruct#license
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# ===========================================================================
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#
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# The patching on this file refers to https://huggingface.co/tiiuae/falcon-7b/discussions/17
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# port of models described in RW
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# We use the bloom model as a starting point for these model.
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# Please refer to the bloom models for usage instructions.
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import math
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import warnings
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from typing import Optional, Tuple, Union
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import torch
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import torch.utils.checkpoint
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from torch import nn
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from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, LayerNorm, MSELoss
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from torch.nn import functional as F
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from transformers.modeling_outputs import (
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BaseModelOutputWithPastAndCrossAttentions,
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CausalLMOutputWithCrossAttentions,
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QuestionAnsweringModelOutput,
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SequenceClassifierOutputWithPast,
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TokenClassifierOutput,
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)
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from transformers.modeling_utils import PreTrainedModel
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from transformers.utils import logging
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from .configuration_RW import RWConfig
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logger = logging.get_logger(__name__)
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# NOTE(Hesslow): Unfortunately we did not fuse matmul and bias during training, this means that there's one additional quantization to bfloat16 between the operations.
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# In order not to degrade the quality of our HF-port, we keep these characteristics in the final model.
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class Linear(nn.Linear):
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def forward(self, input: torch.Tensor) -> torch.Tensor:
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ret = input @ self.weight.T
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if self.bias is None:
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return ret
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else:
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return ret + self.bias
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from einops import rearrange
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# rotary pos emb helpers (torch.jit.script does not seem to support staticmethod...)
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def rotate_half(x):
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x1, x2 = x[..., : x.shape[-1] // 2], x[..., x.shape[-1] // 2 :]
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return torch.cat((-x2, x1), dim=x1.ndim - 1) # dim=-1 triggers a bug in torch < 1.8.0
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class RotaryEmbedding(torch.nn.Module):
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"""Implementation of RotaryEmbedding from GPT-NeoX.
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This implementation is design to operate on queries and keys that are compatible with
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[batch_size, n_heads_per_partition, seq_len, head_dim] (e.g. MinGPTAttention format).
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"""
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def __init__(
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self,
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head_dim: int,
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base=10000,
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):
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super().__init__()
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inv_freq = 1.0 / (base ** (torch.arange(0, head_dim, 2).float() / head_dim))
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self.register_buffer("inv_freq", inv_freq, persistent=False)
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self.head_dim = head_dim
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self.seq_len_cached = None
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self.batch_size_cached = None
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self.cos_cached: torch.Tensor | None = None
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self.sin_cached: torch.Tensor | None = None
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def cos_sin(
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self,
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seq_len: int,
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device="cuda",
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dtype=torch.bfloat16,
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) -> torch.Tensor:
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if seq_len != self.seq_len_cached:
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self.seq_len_cached = seq_len
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t = torch.arange(seq_len, device=device).type_as(self.inv_freq)
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freqs = torch.einsum("i,j->ij", t, self.inv_freq)
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emb = torch.cat((freqs, freqs), dim=-1).to(device)
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if dtype in [torch.float16, torch.bfloat16]:
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emb = emb.float()
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self.cos_cached = emb.cos()[None, :, :]
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self.sin_cached = emb.sin()[None, :, :]
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self.cos_cached = self.cos_cached.type(dtype)
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self.sin_cached = self.sin_cached.type(dtype)
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return self.cos_cached, self.sin_cached
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# def forward(self, q, k):
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def forward(self, q, k, seq_len):
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# batch, seq_len, head_dim = q.shape
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_,q_len,_ = q.shape
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cos, sin = self.cos_sin(seq_len, q.device, q.dtype)
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cos = cos[:,-q_len:]
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sin = sin[:,-q_len:]
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return (q * cos) + (rotate_half(q) * sin), (k * cos) + (rotate_half(k) * sin)
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def _make_causal_mask(
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input_ids_shape: torch.Size, device: torch.device, past_key_values_length: int
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) -> torch.BoolTensor:
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batch_size, target_length = input_ids_shape
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mask = torch.empty((target_length, target_length + past_key_values_length), dtype=torch.bool, device=device)
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# ONNX doesn't support `torch.Tensor.triu` properly, thus we use this workaround
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seq_ids = torch.arange(target_length, device=device)
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mask[:, past_key_values_length:] = seq_ids[:, None] < seq_ids[None, :]
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if past_key_values_length > 0:
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mask[:, :past_key_values_length] = False
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expanded_mask = mask[None, None, :, :].expand(batch_size, 1, target_length, target_length + past_key_values_length)
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return expanded_mask
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def _expand_mask(mask: torch.Tensor, tgt_length: int) -> torch.BoolTensor:
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batch_size, src_length = mask.shape
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tgt_length = tgt_length if tgt_length is not None else src_length
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expanded_mask = ~(mask[:, None, None, :].to(torch.bool))
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return expanded_mask.expand(batch_size, 1, tgt_length, src_length)
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def build_alibi_tensor(attention_mask: torch.Tensor, num_heads: int, dtype: torch.dtype) -> torch.Tensor:
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batch_size, seq_length = attention_mask.shape
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closest_power_of_2 = 2 ** math.floor(math.log2(num_heads))
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base = torch.tensor(
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2 ** (-(2 ** -(math.log2(closest_power_of_2) - 3))), device=attention_mask.device, dtype=torch.float32
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)
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powers = torch.arange(1, 1 + closest_power_of_2, device=attention_mask.device, dtype=torch.int32)
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slopes = torch.pow(base, powers)
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if closest_power_of_2 != num_heads:
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extra_base = torch.tensor(
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2 ** (-(2 ** -(math.log2(2 * closest_power_of_2) - 3))), device=attention_mask.device, dtype=torch.float32
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)
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num_remaining_heads = min(closest_power_of_2, num_heads - closest_power_of_2)
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extra_powers = torch.arange(1, 1 + 2 * num_remaining_heads, 2, device=attention_mask.device, dtype=torch.int32)
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slopes = torch.cat([slopes, torch.pow(extra_base, extra_powers)], dim=0)
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# Note: alibi will added to the attention bias that will be applied to the query, key product of attention
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# => therefore alibi will have to be of shape (batch_size, num_heads, query_length, key_length)
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# => here we set (batch_size=1, num_heads=num_heads, query_length=1, key_length=max_length)
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# => the query_length dimension will then be broadcasted correctly
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# This is more or less identical to T5's relative position bias:
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# https://github.com/huggingface/transformers/blob/f681437203baa7671de3174b0fa583c349d9d5e1/src/transformers/models/t5/modeling_t5.py#L527
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arange_tensor = ((attention_mask.cumsum(dim=-1) - 1) * attention_mask)[:, None, :]
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alibi = slopes[..., None].bfloat16() * arange_tensor
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return alibi.reshape(batch_size * num_heads, 1, seq_length).to(dtype)
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def dropout_add(x: torch.Tensor, residual: torch.Tensor, prob: float, training: bool) -> torch.Tensor:
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out = F.dropout(x, p=prob, training=training)
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out = residual + out
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return out
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class Attention(nn.Module):
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def __init__(self, config: RWConfig):
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super().__init__()
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self.hidden_size = config.hidden_size
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self.num_heads = config.n_head
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self.head_dim = self.hidden_size // self.num_heads
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self.split_size = self.hidden_size
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self.hidden_dropout = config.hidden_dropout
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if self.head_dim * self.num_heads != self.hidden_size:
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raise ValueError(
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f"`hidden_size` must be divisible by num_heads (got `hidden_size`: {self.hidden_size} and `num_heads`:"
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f" {self.num_heads})."
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)
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self.maybe_rotary = RotaryEmbedding(config.head_dim) if config.rotary else lambda q, k: (q, k)
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# Layer-wise attention scaling
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self.inv_norm_factor = 1.0 / math.sqrt(self.head_dim)
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self.beta = self.inv_norm_factor
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self.query_key_value = Linear(
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self.hidden_size,
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3 * self.hidden_size if not config.multi_query else (self.hidden_size + 2 * self.head_dim),
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bias=config.bias,
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)
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self.multi_query = config.multi_query
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self.dense = Linear(self.hidden_size, self.hidden_size, bias=config.bias)
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self.attention_dropout = nn.Dropout(config.attention_dropout)
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self.num_kv = config.n_head if not self.multi_query else 1
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def _split_heads(self, fused_qkv: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
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"""
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Split the last dimension into (num_heads, head_dim) without making any copies, results share same memory
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storage as `fused_qkv`
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Args:
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fused_qkv (`torch.tensor`, *required*): [batch_size, seq_length, num_heads * 3 * head_dim]
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Returns:
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query: [batch_size, seq_length, num_heads, head_dim] key: [batch_size, seq_length, num_heads, head_dim]
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value: [batch_size, seq_length, num_heads, head_dim]
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"""
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if not self.multi_query:
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batch_size, seq_length, three_times_hidden_size = fused_qkv.shape
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fused_qkv = fused_qkv.view(batch_size, seq_length, self.num_heads, 3, self.head_dim)
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return fused_qkv[..., 0, :], fused_qkv[..., 1, :], fused_qkv[..., 2, :]
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else:
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batch_size, seq_length, three_times_hidden_size = fused_qkv.shape
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fused_qkv = fused_qkv.view(batch_size, seq_length, self.num_heads + 2, self.head_dim)
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return fused_qkv[..., :-2, :], fused_qkv[..., [-2], :], fused_qkv[..., [-1], :]
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def _merge_heads(self, x: torch.Tensor) -> torch.Tensor:
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"""
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Merge heads together over the last dimenstion
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Args:
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x: (`torch.tensor`, *required*): [batch_size * num_heads, seq_length, head_dim]
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Returns:
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torch.tensor: [batch_size, seq_length, num_heads * head_dim]
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"""
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# What we want to achieve is:
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# batch_size * num_heads, seq_length, head_dim -> batch_size, seq_length, num_heads * head_dim
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batch_size_and_num_heads, seq_length, _ = x.shape
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batch_size = batch_size_and_num_heads // self.num_heads
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# First view to decompose the batch size
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# batch_size * num_heads, seq_length, head_dim -> batch_size, num_heads, seq_length, head_dim
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x = x.view(batch_size, self.num_heads, seq_length, self.head_dim)
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# batch_size, num_heads, seq_length, head_dim -> batch_size, seq_length, num_heads, head_dim
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x = x.permute(0, 2, 1, 3)
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# batch_size, seq_length, num_heads, head_dim -> batch_size, seq_length, num_heads * head_dim
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return x.reshape(batch_size, seq_length, self.num_heads * self.head_dim)
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def forward(
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self,
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hidden_states: torch.Tensor,
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alibi: torch.Tensor,
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attention_mask: torch.Tensor,
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layer_past: Optional[Tuple[torch.Tensor, torch.Tensor]] = None,
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head_mask: Optional[torch.Tensor] = None,
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use_cache: bool = False,
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output_attentions: bool = False,
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):
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fused_qkv = self.query_key_value(hidden_states) # [batch_size, seq_length, 3 x hidden_size]
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# 3 x [batch_size, seq_length, num_heads, head_dim]
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(query_layer, key_layer, value_layer) = self._split_heads(fused_qkv)
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batch_size, q_length, _, _ = query_layer.shape
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query_layer = query_layer.transpose(1, 2).reshape(batch_size * self.num_heads, q_length, self.head_dim)
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key_layer = key_layer.transpose(1, 2).reshape(
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batch_size * self.num_kv,
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q_length,
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self.head_dim,
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)
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value_layer = value_layer.transpose(1, 2).reshape(batch_size * self.num_kv, q_length, self.head_dim)
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# query_layer, key_layer = self.maybe_rotary(query_layer, key_layer)
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_, seq_len, _ = query_layer.shape
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if layer_past is not None:
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_, seq_len_past, _ = layer_past[0].shape
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seq_len = seq_len + seq_len_past
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query_layer, key_layer = self.maybe_rotary(query_layer, key_layer, seq_len)
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if layer_past is not None:
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past_key, past_value = layer_past
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# concatenate along seq_length dimension:
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# - key: [batch_size * self.num_heads, head_dim, kv_length]
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# - value: [batch_size * self.num_heads, kv_length, head_dim]
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key_layer = torch.cat((past_key, key_layer), dim=1)
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value_layer = torch.cat((past_value, value_layer), dim=1)
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_, kv_length, _ = key_layer.shape
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if use_cache is True:
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present = (key_layer, value_layer)
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else:
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present = None
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if alibi is None:
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query_layer_ = query_layer.reshape(batch_size, self.num_heads, -1, self.head_dim)
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key_layer_ = key_layer.reshape(batch_size, self.num_kv, -1, self.head_dim)
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value_layer_ = value_layer.reshape(batch_size, self.num_kv, -1, self.head_dim)
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# attn_output = F.scaled_dot_product_attention(
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# query_layer_, key_layer_, value_layer_, None, 0.0, is_causal=True
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# )
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if layer_past is not None:
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L = query_layer_.shape[-2]
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S = key_layer_.shape[-2]
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attn_mask = torch.ones(L, S, dtype=torch.bool, device=query_layer_.device)
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attn_output = F.scaled_dot_product_attention(
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query_layer_, key_layer_, value_layer_, attn_mask, 0.0, is_causal=False
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)
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else:
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attn_output = F.scaled_dot_product_attention(
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query_layer_, key_layer_, value_layer_, None, 0.0, is_causal=True
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)
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x = attn_output.view(batch_size, self.num_heads, q_length, self.head_dim)
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x = x.permute(0, 2, 1, 3)
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attn_output = x.reshape(batch_size, q_length, self.num_heads * self.head_dim)
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output_tensor = self.dense(attn_output)
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outputs = (output_tensor, present)
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assert not output_attentions # not supported.
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return outputs
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else:
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attention_mask_float = (attention_mask * 1.0).masked_fill(attention_mask, -1e9).to(torch.bfloat16)
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matmul_result = query_layer @ key_layer.transpose(-1, -2)
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# change view to [batch_size, num_heads, q_length, kv_length]
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attention_scores = matmul_result.view(batch_size, self.num_heads, q_length, kv_length)
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# cast attention scores to fp32, compute scaled softmax and cast back to initial dtype - [batch_size, num_heads, q_length, kv_length]
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input_dtype = attention_scores.dtype
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# `float16` has a minimum value of -65504.0, whereas `bfloat16` and `float32` have a minimum value of `-3.4e+38`
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if input_dtype == torch.float16 or input_dtype == torch.bfloat16:
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attention_scores = attention_scores.to(torch.float32)
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# attn_weights = torch.masked_fill(attention_scores, attention_mask, torch.finfo(attention_scores.dtype).min)
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attention_probs = F.softmax(
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(attention_scores + alibi) * self.inv_norm_factor + attention_mask_float,
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dim=-1,
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dtype=hidden_states.dtype,
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)
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# [batch_size, num_heads, q_length, kv_length]
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attention_probs = self.attention_dropout(attention_probs)
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if head_mask is not None:
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attention_probs = attention_probs * head_mask
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# change view [batch_size x num_heads, q_length, kv_length]
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attention_probs_reshaped = attention_probs.view(batch_size * self.num_heads, q_length, kv_length)
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# matmul: [batch_size * num_heads, q_length, head_dim]
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context_layer = attention_probs_reshaped @ value_layer
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# change view [batch_size, num_heads, q_length, head_dim]
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context_layer = self._merge_heads(context_layer)
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output_tensor = self.dense(context_layer)
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outputs = (output_tensor, present)
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if output_attentions:
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outputs += (attention_probs,)
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return outputs
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class MLP(nn.Module):
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def __init__(self, config: RWConfig):
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super().__init__()
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hidden_size = config.hidden_size
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self.dense_h_to_4h = Linear(hidden_size, 4 * hidden_size, bias=config.bias)
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self.act = nn.GELU()
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self.dense_4h_to_h = Linear(4 * hidden_size, hidden_size, bias=config.bias)
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self.hidden_dropout = config.hidden_dropout
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def forward(self, x: torch.Tensor) -> torch.Tensor:
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x = self.act(self.dense_h_to_4h(x))
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x = self.dense_4h_to_h(x)
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return x
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class DecoderLayer(nn.Module):
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def __init__(self, config: RWConfig):
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super().__init__()
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hidden_size = config.hidden_size
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self.input_layernorm = LayerNorm(hidden_size, eps=config.layer_norm_epsilon)
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self.num_heads = config.n_head
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self.self_attention = Attention(config)
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if not config.parallel_attn:
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# unused if parallel attn
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self.post_attention_layernorm = LayerNorm(hidden_size, eps=config.layer_norm_epsilon)
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self.mlp = MLP(config)
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|
self.apply_residual_connection_post_layernorm = config.apply_residual_connection_post_layernorm
|
|
self.hidden_dropout = config.hidden_dropout
|
|
|
|
self.config = config
|
|
|
|
def forward(
|
|
self,
|
|
hidden_states: torch.Tensor,
|
|
alibi: torch.Tensor,
|
|
attention_mask: torch.Tensor,
|
|
layer_past: Optional[Tuple[torch.Tensor, torch.Tensor]] = None,
|
|
head_mask: Optional[torch.Tensor] = None,
|
|
use_cache: bool = False,
|
|
output_attentions: bool = False,
|
|
):
|
|
|
|
layernorm_output = self.input_layernorm(hidden_states)
|
|
residual = hidden_states
|
|
|
|
# Self attention.
|
|
attn_outputs = self.self_attention(
|
|
layernorm_output,
|
|
layer_past=layer_past,
|
|
attention_mask=attention_mask,
|
|
alibi=alibi,
|
|
head_mask=head_mask,
|
|
use_cache=use_cache,
|
|
output_attentions=output_attentions,
|
|
)
|
|
|
|
attention_output = attn_outputs[0]
|
|
|
|
if not self.config.parallel_attn:
|
|
residual = dropout_add(attention_output, residual, self.config.attention_dropout, training=self.training)
|
|
layernorm_output = self.post_attention_layernorm(residual)
|
|
|
|
outputs = attn_outputs[1:]
|
|
|
|
# MLP.
|
|
mlp_output = self.mlp(layernorm_output)
|
|
|
|
if self.config.parallel_attn:
|
|
mlp_output += attention_output
|
|
|
|
output = dropout_add(mlp_output, residual, self.config.hidden_dropout, training=self.training)
|
|
|
|
if use_cache:
|
|
outputs = (output,) + outputs
|
|
else:
|
|
outputs = (output,) + outputs[1:]
|
|
|
|
return outputs # hidden_states, present, attentions
|
|
|
|
|
|
class RWPreTrainedModel(PreTrainedModel):
|
|
_keys_to_ignore_on_load_missing = [r"h.*.self_attention.scale_mask_softmax.causal_mask", r"lm_head.weight"]
|
|
"""
|
|
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
|
|
models.
|
|
"""
|
|
|
|
config_class = RWConfig
|
|
base_model_prefix = "transformer"
|
|
supports_gradient_checkpointing = True
|
|
_no_split_modules = ["DecoderLayer"]
|
|
|
|
def __init__(self, *inputs, **kwargs):
|
|
super().__init__(*inputs, **kwargs)
|
|
|
|
def _init_weights(self, module: nn.Module):
|
|
"""Initialize the weights."""
|
|
if isinstance(module, nn.Linear) or isinstance(module, Linear):
|
|
# Slightly different from the TF version which uses truncated_normal for initialization
|
|
# cf https://github.com/pytorch/pytorch/pull/5617
|
|
module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
|
|
if module.bias is not None:
|
|
module.bias.data.zero_()
|
|
elif isinstance(module, nn.Embedding):
|
|
module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
|
|
if module.padding_idx is not None:
|
|
module.weight.data[module.padding_idx].zero_()
|
|
elif isinstance(module, LayerNorm):
|
|
module.bias.data.zero_()
|
|
module.weight.data.fill_(1.0)
|
|
|
|
def _set_gradient_checkpointing(self, module: nn.Module, value: bool = False):
|
|
if isinstance(module, RWModel):
|
|
module.gradient_checkpointing = value
|
|
|
|
@staticmethod
|
|
def _convert_to_standard_cache(
|
|
past_key_value: Tuple[Tuple[torch.Tensor, torch.Tensor]], batch_size: int
|
|
) -> Tuple[Tuple[torch.Tensor, torch.Tensor]]:
|
|
"""
|
|
Standardizes the format of the cache so as to match most implementations, i.e. to tuple(tuple([batch_size,
|
|
num_heads, ...]))
|
|
"""
|
|
batch_size_times_num_heads, head_dim, seq_length = past_key_value[0][0].shape
|
|
num_heads = batch_size_times_num_heads // batch_size
|
|
# key: [batch_size * num_heads, head_dim, seq_length] -> [batch_size, num_heads, head_dim, seq_length]
|
|
# value: [batch_size * num_heads, seq_length, head_dim] -> [batch_size, num_heads, seq_length, head_dim]
|
|
return tuple(
|
|
(
|
|
layer_past[0].view(batch_size, num_heads, head_dim, seq_length),
|
|
layer_past[1].view(batch_size, num_heads, seq_length, head_dim),
|
|
)
|
|
for layer_past in past_key_value
|
|
)
|
|
|
|
@staticmethod
|
|
def _convert_to_rw_cache(
|
|
past_key_value: Tuple[Tuple[torch.Tensor, torch.Tensor]]
|
|
) -> Tuple[Tuple[torch.Tensor, torch.Tensor]]:
|
|
# batch_size, num_heads, head_dim, seq_length = past_key_value[0][0].shape
|
|
batch_size, seq_length, head_dim = past_key_value[0][0].shape
|
|
num_heads = 1
|
|
batch_size_times_num_heads = batch_size * num_heads
|
|
# key: [batch_size, num_heads, head_dim, seq_length] -> [batch_size * num_heads, head_dim, seq_length]
|
|
# value: [batch_size, num_heads, seq_length, head_dim] -> [batch_size * num_heads, seq_length, head_dim]
|
|
return tuple(
|
|
(
|
|
# layer_past[0].view(batch_size_times_num_heads, head_dim, seq_length),
|
|
layer_past[0].view(batch_size_times_num_heads, seq_length, head_dim),
|
|
layer_past[1].view(batch_size_times_num_heads, seq_length, head_dim),
|
|
)
|
|
for layer_past in past_key_value
|
|
)
|
|
|
|
|
|
class RWModel(RWPreTrainedModel):
|
|
def __init__(self, config: RWConfig):
|
|
super().__init__(config)
|
|
|
|
self.embed_dim = config.hidden_size
|
|
self.num_heads = config.n_head
|
|
self.alibi = config.alibi
|
|
|
|
# Embedding + LN Embedding
|
|
self.word_embeddings = nn.Embedding(config.vocab_size, self.embed_dim)
|
|
|
|
# Transformer blocks
|
|
self.h = nn.ModuleList([DecoderLayer(config) for _ in range(config.num_hidden_layers)])
|
|
|
|
# Final Layer Norm
|
|
self.ln_f = LayerNorm(self.embed_dim, eps=config.layer_norm_epsilon)
|
|
|
|
self.gradient_checkpointing = False
|
|
|
|
# Initialize weights and apply final processing
|
|
self.post_init()
|
|
|
|
def get_input_embeddings(self):
|
|
return self.word_embeddings
|
|
|
|
def _prepare_attn_mask(
|
|
self, attention_mask: torch.Tensor, input_shape: Tuple[int, int], past_key_values_length: int
|
|
) -> torch.BoolTensor:
|
|
# create causal mask
|
|
# [batch_size, seq_length] -> [batch_size, 1, tgt_length, src_length]
|
|
combined_attention_mask = None
|
|
device = attention_mask.device
|
|
_, src_length = input_shape
|
|
|
|
if src_length > 1:
|
|
combined_attention_mask = _make_causal_mask(
|
|
input_shape, device=device, past_key_values_length=past_key_values_length
|
|
)
|
|
|
|
# [batch_size, seq_length] -> [batch_size, 1, tgt_length, src_length]
|
|
expanded_attn_mask = _expand_mask(attention_mask, tgt_length=src_length)
|
|
combined_attention_mask = (
|
|
expanded_attn_mask if combined_attention_mask is None else expanded_attn_mask | combined_attention_mask
|
|
)
|
|
|
|
return combined_attention_mask
|
|
|
|
def set_input_embeddings(self, new_embeddings: torch.Tensor):
|
|
self.word_embeddings = new_embeddings
|
|
|
|
def forward(
|
|
self,
|
|
input_ids: Optional[torch.LongTensor] = None,
|
|
past_key_values: Optional[Tuple[Tuple[torch.Tensor, torch.Tensor], ...]] = None,
|
|
attention_mask: Optional[torch.Tensor] = None,
|
|
head_mask: Optional[torch.LongTensor] = None,
|
|
inputs_embeds: 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,
|
|
**deprecated_arguments,
|
|
) -> Union[Tuple[torch.Tensor, ...], BaseModelOutputWithPastAndCrossAttentions]:
|
|
if deprecated_arguments.pop("position_ids", False) is not False:
|
|
# `position_ids` could have been `torch.Tensor` or `None` so defaulting pop to `False` allows to detect if users were passing explicitly `None`
|
|
warnings.warn(
|
|
"`position_ids` have no functionality in BLOOM and will be removed in v5.0.0. You can safely ignore"
|
|
" passing `position_ids`.",
|
|
FutureWarning,
|
|
)
|
|
if len(deprecated_arguments) > 0:
|
|
raise ValueError(f"Got unexpected arguments: {deprecated_arguments}")
|
|
|
|
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
|
|
)
|
|
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
|
|
|
|
if input_ids is not None and inputs_embeds is not None:
|
|
raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time")
|
|
elif input_ids is not None:
|
|
batch_size, seq_length = input_ids.shape
|
|
elif inputs_embeds is not None:
|
|
batch_size, seq_length, _ = inputs_embeds.shape
|
|
else:
|
|
raise ValueError("You have to specify either input_ids or inputs_embeds")
|
|
|
|
if past_key_values is None:
|
|
past_key_values = tuple([None] * len(self.h))
|
|
|
|
# Prepare head mask if needed
|
|
# 1.0 in head_mask indicate we keep the head
|
|
# attention_probs has shape batch_size x num_heads x N x N
|
|
# head_mask has shape n_layer x batch x num_heads x N x N
|
|
head_mask = self.get_head_mask(head_mask, self.config.n_layer)
|
|
|
|
if inputs_embeds is None:
|
|
inputs_embeds = self.word_embeddings(input_ids)
|
|
|
|
hidden_states = inputs_embeds
|
|
|
|
presents = () if use_cache else None
|
|
all_self_attentions = () if output_attentions else None
|
|
all_hidden_states = () if output_hidden_states else None
|
|
|
|
# Compute alibi tensor: check build_alibi_tensor documentation
|
|
seq_length_with_past = seq_length
|
|
past_key_values_length = 0
|
|
if past_key_values[0] is not None:
|
|
past_key_values_length = past_key_values[0][0].shape[2]
|
|
seq_length_with_past = seq_length_with_past + past_key_values_length
|
|
if attention_mask is None:
|
|
attention_mask = torch.ones((batch_size, seq_length_with_past), device=hidden_states.device)
|
|
else:
|
|
attention_mask = attention_mask.to(hidden_states.device)
|
|
|
|
if self.alibi:
|
|
alibi = build_alibi_tensor(attention_mask, self.num_heads, dtype=hidden_states.dtype)
|
|
else:
|
|
alibi = None
|
|
|
|
causal_mask = self._prepare_attn_mask(
|
|
attention_mask,
|
|
input_shape=(batch_size, seq_length),
|
|
past_key_values_length=past_key_values_length,
|
|
)
|
|
|
|
for i, (block, layer_past) in enumerate(zip(self.h, past_key_values)):
|
|
|
|
if output_hidden_states:
|
|
all_hidden_states = all_hidden_states + (hidden_states,)
|
|
|
|
if self.gradient_checkpointing and self.training:
|
|
|
|
if use_cache:
|
|
logger.warning(
|
|
"`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..."
|
|
)
|
|
use_cache = False
|
|
|
|
def create_custom_forward(module):
|
|
def custom_forward(*inputs):
|
|
# None for past_key_value
|
|
return module(*inputs, use_cache=use_cache, output_attentions=output_attentions)
|
|
|
|
return custom_forward
|
|
|
|
outputs = torch.utils.checkpoint.checkpoint(
|
|
create_custom_forward(block),
|
|
hidden_states,
|
|
alibi,
|
|
causal_mask,
|
|
head_mask[i],
|
|
)
|
|
else:
|
|
outputs = block(
|
|
hidden_states,
|
|
layer_past=layer_past,
|
|
attention_mask=causal_mask,
|
|
head_mask=head_mask[i],
|
|
use_cache=use_cache,
|
|
output_attentions=output_attentions,
|
|
alibi=alibi,
|
|
)
|
|
|
|
hidden_states = outputs[0]
|
|
if use_cache is True:
|
|
presents = presents + (outputs[1],)
|
|
|
|
if output_attentions:
|
|
all_self_attentions = all_self_attentions + (outputs[2 if use_cache else 1],)
|
|
|
|
# Add last hidden state
|
|
hidden_states = self.ln_f(hidden_states)
|
|
|
|
if output_hidden_states:
|
|
all_hidden_states = all_hidden_states + (hidden_states,)
|
|
|
|
if not return_dict:
|
|
return tuple(v for v in [hidden_states, presents, all_hidden_states, all_self_attentions] if v is not None)
|
|
|
|
return BaseModelOutputWithPastAndCrossAttentions(
|
|
last_hidden_state=hidden_states,
|
|
past_key_values=presents,
|
|
hidden_states=all_hidden_states,
|
|
attentions=all_self_attentions,
|
|
)
|
|
|
|
|
|
class RWForCausalLM(RWPreTrainedModel):
|
|
_keys_to_ignore_on_load_missing = [r"h.*.self_attention.scale_mask_softmax.causal_mask", r"lm_head.weight"]
|
|
|
|
def __init__(self, config: RWConfig):
|
|
super().__init__(config)
|
|
self.transformer = RWModel(config)
|
|
self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
|
|
|
|
# Initialize weights and apply final processing
|
|
self.post_init()
|
|
|
|
def get_output_embeddings(self):
|
|
return self.lm_head
|
|
|
|
def set_output_embeddings(self, new_embeddings: torch.Tensor):
|
|
self.lm_head = new_embeddings
|
|
|
|
def prepare_inputs_for_generation(
|
|
self,
|
|
input_ids: torch.LongTensor,
|
|
# past: Optional[torch.Tensor] = None,
|
|
past_key_values: Optional[torch.Tensor] = None,
|
|
attention_mask: Optional[torch.Tensor] = None,
|
|
**kwargs,
|
|
) -> dict:
|
|
# only last token for input_ids if past is not None
|
|
# if past:
|
|
if past_key_values:
|
|
input_ids = input_ids[:, -1].unsqueeze(-1)
|
|
|
|
# the cache may be in the stardard format (e.g. in contrastive search), convert to our's format if needed
|
|
# if past[0][0].shape[0] == input_ids.shape[0]:
|
|
# past = self._convert_to_rw_cache(past)
|
|
if past_key_values[0][0].shape[0] == input_ids.shape[0]:
|
|
past_key_values = self._convert_to_rw_cache(past_key_values)
|
|
|
|
return {
|
|
"input_ids": input_ids,
|
|
# "past_key_values": past,
|
|
"past_key_values": past_key_values,
|
|
"use_cache": kwargs.get("use_cache"),
|
|
"attention_mask": attention_mask,
|
|
}
|
|
|
|
def forward(
|
|
self,
|
|
input_ids: Optional[torch.LongTensor] = None,
|
|
past_key_values: Optional[Tuple[Tuple[torch.Tensor, torch.Tensor], ...]] = None,
|
|
attention_mask: Optional[torch.Tensor] = None,
|
|
head_mask: Optional[torch.Tensor] = None,
|
|
inputs_embeds: Optional[torch.Tensor] = None,
|
|
labels: 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,
|
|
**deprecated_arguments,
|
|
) -> Union[Tuple[torch.Tensor], CausalLMOutputWithCrossAttentions]:
|
|
r"""
|
|
labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
|
|
Labels for language modeling. Note that the labels **are shifted** inside the model, i.e. you can set
|
|
`labels = input_ids` Indices are selected in `[-100, 0, ..., config.vocab_size]` All labels set to `-100`
|
|
are ignored (masked), the loss is only computed for labels in `[0, ..., config.vocab_size]`
|
|
"""
|
|
if deprecated_arguments.pop("position_ids", False) is not False:
|
|
# `position_ids` could have been `torch.Tensor` or `None` so defaulting pop to `False` allows to detect if users were passing explicitly `None`
|
|
warnings.warn(
|
|
"`position_ids` have no functionality in BLOOM and will be removed in v5.0.0. You can safely ignore"
|
|
" passing `position_ids`.",
|
|
FutureWarning,
|
|
)
|
|
if len(deprecated_arguments) > 0:
|
|
raise ValueError(f"Got unexpected arguments: {deprecated_arguments}")
|
|
|
|
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
|
|
|
|
transformer_outputs = self.transformer(
|
|
input_ids,
|
|
past_key_values=past_key_values,
|
|
attention_mask=attention_mask,
|
|
head_mask=head_mask,
|
|
inputs_embeds=inputs_embeds,
|
|
use_cache=use_cache,
|
|
output_attentions=output_attentions,
|
|
output_hidden_states=output_hidden_states,
|
|
return_dict=return_dict,
|
|
)
|
|
hidden_states = transformer_outputs[0]
|
|
|
|
lm_logits = self.lm_head(hidden_states)
|
|
|
|
loss = None
|
|
if labels is not None:
|
|
# Shift so that tokens < n predict n
|
|
shift_logits = lm_logits[..., :-1, :].contiguous()
|
|
shift_labels = labels[..., 1:].contiguous()
|
|
batch_size, seq_length, vocab_size = shift_logits.shape
|
|
# Flatten the tokens
|
|
loss_fct = CrossEntropyLoss()
|
|
loss = loss_fct(
|
|
shift_logits.view(batch_size * seq_length, vocab_size), shift_labels.view(batch_size * seq_length)
|
|
)
|
|
|
|
if not return_dict:
|
|
output = (lm_logits,) + transformer_outputs[1:]
|
|
return ((loss,) + output) if loss is not None else output
|
|
|
|
return CausalLMOutputWithCrossAttentions(
|
|
loss=loss,
|
|
logits=lm_logits,
|
|
past_key_values=transformer_outputs.past_key_values,
|
|
hidden_states=transformer_outputs.hidden_states,
|
|
attentions=transformer_outputs.attentions,
|
|
)
|
|
|
|
def _reorder_cache(
|
|
self, past: Tuple[Tuple[torch.Tensor, torch.Tensor], ...], beam_idx: torch.LongTensor
|
|
) -> Tuple[Tuple[torch.Tensor, torch.Tensor], ...]:
|
|
"""
|
|
This function is used to re-order the `past_key_values` cache if [`~PreTrainedModel.beam_search`] or
|
|
[`~PreTrainedModel.beam_sample`] is called. This is required to match `past_key_values` with the correct
|
|
beam_idx at every generation step.
|
|
|
|
Output shares the same memory storage as `past`.
|
|
"""
|
|
standardized_past = self._convert_to_standard_cache(past, batch_size=len(beam_idx))
|
|
|
|
# Get a copy of `beam_idx` on all the devices where we need those indices.
|
|
device_to_beam_idx = {
|
|
past_state.device: beam_idx.to(past_state.device) for layer_past in past for past_state in layer_past
|
|
}
|
|
reordered_past = tuple(
|
|
(
|
|
layer_past[0].index_select(0, device_to_beam_idx[layer_past[0].device]),
|
|
layer_past[1].index_select(0, device_to_beam_idx[layer_past[0].device]),
|
|
)
|
|
for layer_past in standardized_past
|
|
)
|
|
return self._convert_to_rw_cache(reordered_past)
|
|
|
|
|
|
class RWForSequenceClassification(RWPreTrainedModel):
|
|
_keys_to_ignore_on_load_missing = [r"h.*.self_attention.scale_mask_softmax.causal_mask", r"lm_head.weight"]
|
|
|
|
def __init__(self, config: RWConfig):
|
|
super().__init__(config)
|
|
self.num_labels = config.num_labels
|
|
self.transformer = RWModel(config)
|
|
self.score = nn.Linear(config.hidden_size, config.num_labels, bias=False)
|
|
|
|
# Initialize weights and apply final processing
|
|
self.post_init()
|
|
|
|
def forward(
|
|
self,
|
|
input_ids: Optional[torch.LongTensor] = None,
|
|
past_key_values: Optional[Tuple[Tuple[torch.Tensor, torch.Tensor], ...]] = None,
|
|
attention_mask: Optional[torch.Tensor] = None,
|
|
head_mask: Optional[torch.Tensor] = None,
|
|
inputs_embeds: Optional[torch.Tensor] = None,
|
|
labels: 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,
|
|
**deprecated_arguments,
|
|
) -> Union[Tuple[torch.Tensor], SequenceClassifierOutputWithPast]:
|
|
r"""
|
|
labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
|
|
Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
|
|
config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
|
|
`config.num_labels > 1` a classification loss is computed (Cross-Entropy).
|
|
"""
|
|
if deprecated_arguments.pop("position_ids", False) is not False:
|
|
# `position_ids` could have been `torch.Tensor` or `None` so defaulting pop to `False` allows to detect if users were passing explicitly `None`
|
|
warnings.warn(
|
|
"`position_ids` have no functionality in BLOOM and will be removed in v5.0.0. You can safely ignore"
|
|
" passing `position_ids`.",
|
|
FutureWarning,
|
|
)
|
|
if len(deprecated_arguments) > 0:
|
|
raise ValueError(f"Got unexpected arguments: {deprecated_arguments}")
|
|
|
|
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
|
|
|
|
transformer_outputs = self.transformer(
|
|
input_ids,
|
|
past_key_values=past_key_values,
|
|
attention_mask=attention_mask,
|
|
head_mask=head_mask,
|
|
inputs_embeds=inputs_embeds,
|
|
use_cache=use_cache,
|
|
output_attentions=output_attentions,
|
|
output_hidden_states=output_hidden_states,
|
|
return_dict=return_dict,
|
|
)
|
|
|
|
hidden_states = transformer_outputs[0]
|
|
logits = self.score(hidden_states)
|
|
|
|
if input_ids is not None:
|
|
batch_size = input_ids.shape[0]
|
|
else:
|
|
batch_size = inputs_embeds.shape[0]
|
|
|
|
if self.config.pad_token_id is None and batch_size != 1:
|
|
raise ValueError("Cannot handle batch sizes > 1 if no padding token is defined.")
|
|
if self.config.pad_token_id is None:
|
|
sequence_lengths = -1
|
|
else:
|
|
if input_ids is not None:
|
|
sequence_lengths = torch.ne(input_ids, self.config.pad_token_id).sum(dim=-1) - 1
|
|
else:
|
|
sequence_lengths = -1
|
|
logger.warning(
|
|
f"{self.__class__.__name__} will not detect padding tokens in `inputs_embeds`. Results may be "
|
|
"unexpected if using padding tokens in conjunction with `inputs_embeds.`"
|
|
)
|
|
|
|
pooled_logits = logits[torch.arange(batch_size, device=logits.device), sequence_lengths]
|
|
|
|
loss = None
|
|
if labels is not None:
|
|
if self.config.problem_type is None:
|
|
if self.num_labels == 1:
|
|
self.config.problem_type = "regression"
|
|
elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
|
|
self.config.problem_type = "single_label_classification"
|
|
else:
|
|
self.config.problem_type = "multi_label_classification"
|
|
|
|
if self.config.problem_type == "regression":
|
|
loss_fct = MSELoss()
|
|
if self.num_labels == 1:
|
|
loss = loss_fct(pooled_logits.squeeze(), labels.squeeze())
|
|
else:
|
|
loss = loss_fct(pooled_logits, labels)
|
|
elif self.config.problem_type == "single_label_classification":
|
|
loss_fct = CrossEntropyLoss()
|
|
loss = loss_fct(pooled_logits, labels)
|
|
elif self.config.problem_type == "multi_label_classification":
|
|
loss_fct = BCEWithLogitsLoss()
|
|
loss = loss_fct(pooled_logits, labels)
|
|
if not return_dict:
|
|
output = (pooled_logits,) + transformer_outputs[1:]
|
|
return ((loss,) + output) if loss is not None else output
|
|
|
|
return SequenceClassifierOutputWithPast(
|
|
loss=loss,
|
|
logits=pooled_logits,
|
|
past_key_values=transformer_outputs.past_key_values,
|
|
hidden_states=transformer_outputs.hidden_states,
|
|
attentions=transformer_outputs.attentions,
|
|
)
|
|
|
|
|
|
class RWForTokenClassification(RWPreTrainedModel):
|
|
_keys_to_ignore_on_load_missing = [r"h.*.self_attention.scale_mask_softmax.causal_mask", r"lm_head.weight"]
|
|
|
|
def __init__(self, config: RWConfig):
|
|
super().__init__(config)
|
|
self.num_labels = config.num_labels
|
|
|
|
self.transformer = RWModel(config)
|
|
if hasattr(config, "classifier_dropout") and config.classifier_dropout is not None:
|
|
classifier_dropout = config.classifier_dropout
|
|
elif hasattr(config, "hidden_dropout") and config.hidden_dropout is not None:
|
|
classifier_dropout = config.hidden_dropout
|
|
else:
|
|
classifier_dropout = 0.1
|
|
self.dropout = nn.Dropout(classifier_dropout)
|
|
self.classifier = nn.Linear(config.hidden_size, config.num_labels)
|
|
|
|
# Initialize weights and apply final processing
|
|
self.post_init()
|
|
|
|
def forward(
|
|
self,
|
|
input_ids: Optional[torch.LongTensor] = None,
|
|
past_key_values: Optional[Tuple[Tuple[torch.Tensor, torch.Tensor], ...]] = None,
|
|
attention_mask: Optional[torch.Tensor] = None,
|
|
head_mask: Optional[torch.Tensor] = None,
|
|
inputs_embeds: Optional[torch.Tensor] = None,
|
|
labels: 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,
|
|
**deprecated_arguments,
|
|
) -> Union[Tuple[torch.Tensor], TokenClassifierOutput]:
|
|
r"""
|
|
labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
|
|
Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
|
|
config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
|
|
`config.num_labels > 1` a classification loss is computed (Cross-Entropy).
|
|
"""
|
|
if deprecated_arguments.pop("position_ids", False) is not False:
|
|
# `position_ids` could have been `torch.Tensor` or `None` so defaulting pop to `False` allows to detect if users were passing explicitly `None`
|
|
warnings.warn(
|
|
"`position_ids` have no functionality in BLOOM and will be removed in v5.0.0. You can safely ignore"
|
|
" passing `position_ids`.",
|
|
FutureWarning,
|
|
)
|
|
if len(deprecated_arguments) > 0:
|
|
raise ValueError(f"Got unexpected arguments: {deprecated_arguments}")
|
|
|
|
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
|
|
|
|
transformer_outputs = self.transformer(
|
|
input_ids,
|
|
past_key_values=past_key_values,
|
|
attention_mask=attention_mask,
|
|
head_mask=head_mask,
|
|
inputs_embeds=inputs_embeds,
|
|
use_cache=use_cache,
|
|
output_attentions=output_attentions,
|
|
output_hidden_states=output_hidden_states,
|
|
return_dict=return_dict,
|
|
)
|
|
|
|
hidden_states = transformer_outputs[0]
|
|
hidden_states = self.dropout(hidden_states)
|
|
logits = self.classifier(hidden_states)
|
|
|
|
loss = None
|
|
if labels is not None:
|
|
batch_size, seq_length = labels.shape
|
|
loss_fct = CrossEntropyLoss()
|
|
loss = loss_fct(logits.view(batch_size * seq_length, self.num_labels), labels.view(batch_size * seq_length))
|
|
|
|
if not return_dict:
|
|
output = (logits,) + transformer_outputs[2:]
|
|
return ((loss,) + output) if loss is not None else output
|
|
|
|
return TokenClassifierOutput(
|
|
loss=loss,
|
|
logits=logits,
|
|
hidden_states=transformer_outputs.hidden_states,
|
|
attentions=transformer_outputs.attentions,
|
|
)
|
|
|
|
|
|
class RWForQuestionAnswering(RWPreTrainedModel):
|
|
_keys_to_ignore_on_load_missing = [r"h.*.self_attention.scale_mask_softmax.causal_mask", r"lm_head.weight"]
|
|
|
|
def __init__(self, config):
|
|
super().__init__(config)
|
|
self.transformer = RWModel(config)
|
|
self.qa_outputs = nn.Linear(config.hidden_size, 2)
|
|
|
|
# Initialize weights and apply final processing
|
|
self.post_init()
|
|
|
|
def forward(
|
|
self,
|
|
input_ids: Optional[torch.LongTensor] = None,
|
|
attention_mask: Optional[torch.FloatTensor] = None,
|
|
position_ids: Optional[torch.LongTensor] = None,
|
|
head_mask: Optional[torch.FloatTensor] = None,
|
|
inputs_embeds: Optional[torch.FloatTensor] = None,
|
|
start_positions: Optional[torch.LongTensor] = None,
|
|
end_positions: Optional[torch.LongTensor] = None,
|
|
output_attentions: Optional[bool] = None,
|
|
output_hidden_states: Optional[bool] = None,
|
|
return_dict: Optional[bool] = None,
|
|
) -> Union[Tuple, QuestionAnsweringModelOutput]:
|
|
r"""
|
|
start_positions (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
|
|
Labels for position (index) of the start of the labelled span for computing the token classification loss.
|
|
Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the sequence
|
|
are not taken into account for computing the loss.
|
|
end_positions (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
|
|
Labels for position (index) of the end of the labelled span for computing the token classification loss.
|
|
Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the sequence
|
|
are not taken into account for computing the loss.
|
|
"""
|
|
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
|
|
|
|
outputs = self.transformer(
|
|
input_ids,
|
|
attention_mask=attention_mask,
|
|
position_ids=position_ids,
|
|
head_mask=head_mask,
|
|
inputs_embeds=inputs_embeds,
|
|
output_attentions=output_attentions,
|
|
output_hidden_states=output_hidden_states,
|
|
return_dict=return_dict,
|
|
)
|
|
|
|
sequence_output = outputs[0]
|
|
|
|
logits = self.qa_outputs(sequence_output)
|
|
start_logits, end_logits = logits.split(1, dim=-1)
|
|
start_logits = start_logits.squeeze(-1).contiguous()
|
|
end_logits = end_logits.squeeze(-1).contiguous()
|
|
|
|
total_loss = None
|
|
if start_positions is not None and end_positions is not None:
|
|
# If we are on multi-GPU, split add a dimension
|
|
if len(start_positions.size()) > 1:
|
|
start_positions = start_positions.squeeze(-1)
|
|
if len(end_positions.size()) > 1:
|
|
end_positions = end_positions.squeeze(-1)
|
|
# sometimes the start/end positions are outside our model inputs, we ignore these terms
|
|
ignored_index = start_logits.size(1)
|
|
start_positions = start_positions.clamp(0, ignored_index)
|
|
end_positions = end_positions.clamp(0, ignored_index)
|
|
|
|
loss_fct = CrossEntropyLoss(ignore_index=ignored_index)
|
|
start_loss = loss_fct(start_logits, start_positions)
|
|
end_loss = loss_fct(end_logits, end_positions)
|
|
total_loss = (start_loss + end_loss) / 2
|
|
|
|
if not return_dict:
|
|
output = (start_logits, end_logits) + outputs[2:]
|
|
return ((total_loss,) + output) if total_loss is not None else output
|
|
|
|
return QuestionAnsweringModelOutput(
|
|
loss=total_loss,
|
|
start_logits=start_logits,
|
|
end_logits=end_logits,
|
|
hidden_states=outputs.hidden_states,
|
|
attentions=outputs.attentions,
|
|
)
|