OpenBA-V2-Chat / modeling_openba.py
ZetangForward
first commit
3d28019
from typing import Optional, Tuple, Union
import copy
import torch
import torch.nn as nn
import torch.nn.functional as F
from transformers import PreTrainedModel
from transformers.modeling_outputs import (
BaseModelOutputWithPastAndCrossAttentions,
Seq2SeqLMOutput,
BaseModelOutput,
)
from transformers.utils import logging, is_torch_fx_proxy
from .configuration_openba import OpenBAConfig
logger = logging.get_logger(__name__)
# Copied from transformers.models.gptj.modeling_gptj.create_sinusoidal_positions
def create_sinusoidal_positions(num_pos: int, dim: int) -> torch.Tensor:
inv_freq = 1.0 / (10000 ** (torch.arange(0, dim, 2) / dim))
sinusoid_inp = torch.einsum("i , j -> i j", torch.arange(num_pos, dtype=torch.float), inv_freq).float()
return torch.cat((torch.sin(sinusoid_inp), torch.cos(sinusoid_inp)), dim=1)
def rotate_half(x) -> torch.Tensor:
x1, x2 = x[..., : x.shape[-1] // 2], x[..., x.shape[-1] // 2 :]
return torch.cat((-x2, x1), dim=-1)
def apply_rotary_pos_emb(tensor: torch.Tensor, sin: torch.Tensor, cos: torch.Tensor) -> torch.Tensor:
sin = torch.cat((sin, sin), dim=-1).to(tensor.device)[:, :, None, :]
cos = torch.cat((cos, cos), dim=-1).to(tensor.device)[:, :, None, :]
return (tensor * cos) + (rotate_half(tensor) * sin)
class SwiGLUMLP(nn.Module):
def __init__(self, config):
super().__init__()
multiple_of: int = 256 # make SwiGLU hidden layer size multiple of large power of 2
hidden_size = config.hidden_size
# ffn_hidden_size = int(2 * config.ffn_hidden_size / 3)
# ffn_hidden_size = multiple_of * ((ffn_hidden_size + multiple_of - 1) // multiple_of)
ffn_hidden_size=config.ffn_hidden_size
self.ffn_hidden_size = ffn_hidden_size
self.fc_in = nn.Linear(hidden_size, 2 * ffn_hidden_size, bias=config.add_ffn_bias)
self.fc_out = nn.Linear(ffn_hidden_size, hidden_size, bias=config.add_ffn_bias)
def swiglu(x):
x = torch.chunk(x, 2, dim=-1)
return F.silu(x[0]) * x[1]
self.act_func = swiglu
def forward(self, hidden_states: Optional[torch.FloatTensor]) -> torch.FloatTensor:
hidden_states = self.fc_in(hidden_states)
hidden_states = self.act_func(hidden_states)
hidden_states = self.fc_out(hidden_states)
return hidden_states
class OpenBAAttention(nn.Module):
def __init__(self, config, attn_type='self'):
super().__init__()
self.attn_type = attn_type
self.is_decoder = config.is_decoder
self.hidden_size = config.hidden_size
self.num_heads = config.num_heads
self.kv_channels = config.kv_channels
self.proj_size = self.kv_channels * self.num_heads
self.dropout = config.attention_dropout
self.scale_attn = torch.sqrt(torch.tensor(self.kv_channels, dtype=torch.float32))
if self.attn_type == 'self':
self.qkv = nn.Linear(self.hidden_size, 3 * self.proj_size, bias=config.add_qkv_bias)
else:
assert self.attn_type == 'cross'
self.q = nn.Linear(self.hidden_size, self.proj_size, bias=config.add_qkv_bias)
self.kv = nn.Linear(self.hidden_size, 2 * self.proj_size, bias=config.add_qkv_bias)
self.rotary_embedding = create_sinusoidal_positions(
num_pos=config.max_seq_length,
dim=self.kv_channels,
)
self.o = nn.Linear(self.proj_size, self.hidden_size, bias=config.add_qkv_bias)
def forward(
self,
hidden_states: Optional[torch.FloatTensor],
attention_mask: Optional[torch.FloatTensor] = None,
key_value_states: Optional[torch.FloatTensor] = None,
past_key_value: Optional[Tuple[torch.Tensor]] = None,
layer_head_mask: Optional[Tuple[torch.Tensor]] = None,
position_ids:Optional[torch.LongTensor] = None,
use_cache: Optional[bool] = False,
output_attentions: Optional[bool] = False,
):
# input is (batch_size, seq_length, hidden_size)
batch_size, seq_length = hidden_states.shape[:2]
if past_key_value is not None:
if len(past_key_value) != 2:
raise ValueError(
f"past_key_value should have 2 past states: keys and values. Got { len(past_key_value)} past states"
)
if self.rotary_embedding.device != position_ids.device:
self.rotary_embedding = self.rotary_embedding.to(position_ids.device)
if self.attn_type == 'self':
mixed_qkv_states = self.qkv(hidden_states)
new_tensor_shape = mixed_qkv_states.size()[:-1] + (self.num_heads, 3 * self.kv_channels)
mixed_qkv_states = mixed_qkv_states.view(*new_tensor_shape)
query_states, key_states, value_states = torch.chunk(mixed_qkv_states, 3, dim=-1)
# rotary position embedding
sincos = self.rotary_embedding[position_ids]
sin, cos = torch.chunk(sincos, 2, dim=-1)
query_states = apply_rotary_pos_emb(query_states, sin, cos)
key_states = apply_rotary_pos_emb(key_states, sin, cos)
# reshape to (batch_size, num_head, seq_length, kv_channels)
query_states = query_states.transpose(1, 2)
key_states = key_states.transpose(1, 2)
value_states = value_states.transpose(1, 2)
if past_key_value is not None:
past_key_states, past_value_states = past_key_value
key_states = torch.cat([past_key_states, key_states], dim=-2)
value_states = torch.cat([past_value_states, value_states], dim=-2)
else:
assert self.attn_type == 'cross'
query_states = self.q(hidden_states)
new_tensor_shape = query_states.size()[:-1] + (self.num_heads, self.kv_channels)
query_states = query_states.view(*new_tensor_shape)
# reshape to (batch_size, num_head, seq_length, kv_channels)
query_states = query_states.transpose(1, 2)
if past_key_value is None:
mixed_kv_states = self.kv(key_value_states)
new_tensor_shape = mixed_kv_states.size()[:-1] + (self.num_heads, 2 * self.kv_channels)
mixed_kv_states = mixed_kv_states.view(*new_tensor_shape)
key_states, value_states = torch.chunk(mixed_kv_states, 2, dim=-1)
# reshape to (batch_size, num_head, seq_length, kv_channels)
key_states = key_states.transpose(1, 2)
value_states = value_states.transpose(1, 2)
else:
key_states, value_states = past_key_value
# compute attention score
query_states = query_states.to(torch.float32)
key_states = key_states.to(torch.float32)
attn_scores = torch.matmul(query_states, key_states.transpose(-1, -2)) / self.scale_attn
attn_scores = attn_scores.masked_fill_(attention_mask, -10000.0)
attn_weights = F.softmax(attn_scores, dim=-1).type_as(attn_scores)
attn_weights = nn.functional.dropout(attn_weights, p=self.dropout, training=self.training)
attn_weights = attn_weights.to(value_states.dtype)
# 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().view(batch_size, -1, self.proj_size)
attn_output = self.o(attn_output)
present_key_value_state = (key_states, value_states) if (self.is_decoder and use_cache) else None
outputs = (attn_output, present_key_value_state)
if output_attentions:
outputs += (attn_weights,)
return outputs
class OpenBABlock(nn.Module):
def __init__(self, config) -> None:
super().__init__()
self.is_decoder = config.is_decoder
self.dropout = config.hidden_dropout
self.input_layernorm = nn.LayerNorm(config.hidden_size)
self.self_attn = OpenBAAttention(config, attn_type='self')
self.post_attn_layernorm = nn.LayerNorm(config.hidden_size)
if self.is_decoder:
self.inter_attn = OpenBAAttention(config, attn_type='cross')
self.post_inter_attn_layernorm = nn.LayerNorm(config.hidden_size)
self.mlp = SwiGLUMLP(config)
def forward(
self,
hidden_states=None,
attention_mask=None,
position_ids=None,
encoder_hidden_states=None,
encoder_attention_mask=None,
layer_head_mask=None,
cross_attn_layer_head_mask=None,
past_key_value=None,
use_cache=False,
output_attentions=False,
):
if past_key_value is not None:
if not self.is_decoder:
raise ValueError("`past_key_values` is passed to the encoder. Please make sure this is intended.")
expected_num_past_key_values = 2 if encoder_hidden_states is None else 4
if len(past_key_value) != expected_num_past_key_values:
raise ValueError(
f"There should be {expected_num_past_key_values} past states. "
f"{'2 (past / key) for cross attention. ' if expected_num_past_key_values == 4 else ''}"
f"Got {len(past_key_value)} past key / value states"
)
self_attn_past_key_value = past_key_value[:2]
cross_attn_past_key_value = past_key_value[2:]
else:
self_attn_past_key_value, cross_attn_past_key_value = None, None
# Layer norm at the beginning of the transformer layer.
layernorm_output = self.input_layernorm(hidden_states)
# Self attention.
attn_outputs = self.self_attn(
layernorm_output,
attention_mask=attention_mask,
position_ids=position_ids,
layer_head_mask=layer_head_mask,
past_key_value=self_attn_past_key_value,
use_cache=use_cache,
output_attentions=output_attentions,
)
attn_output, present_key_value_state = attn_outputs[:2]
attn_weights = attn_outputs[2:]
residual = hidden_states
# Layer norm post the self attention.
attn_output = nn.functional.dropout(attn_output, p=self.dropout, training=self.training)
layernorm_input = residual + attn_output
layernorm_output = self.post_attn_layernorm(layernorm_input)
if self.is_decoder:
assert encoder_hidden_states is not None
attn_outputs = self.inter_attn(
layernorm_output,
attention_mask=encoder_attention_mask,
key_value_states=encoder_hidden_states,
position_ids=position_ids,
layer_head_mask=cross_attn_layer_head_mask,
past_key_value=cross_attn_past_key_value,
use_cache=use_cache,
output_attentions=output_attentions,
)
attn_output = attn_outputs[0]
attn_output = nn.functional.dropout(attn_output, p=self.dropout, training=self.training)
# residual connection
residual = layernorm_input
layernorm_input = residual + attn_output
layernorm_output = self.post_inter_attn_layernorm(layernorm_input)
# Combine self attn and cross attn key value states
if present_key_value_state is not None:
present_key_value_state += attn_outputs[1]
attn_weights += attn_outputs[2:]
# MLP.
mlp_output = self.mlp(layernorm_output)
mlp_output = nn.functional.dropout(mlp_output, p=self.dropout, training=self.training)
# Second residual connection.
residual = layernorm_input
output = residual + mlp_output
outputs = (output,)
if use_cache:
outputs += (present_key_value_state,) + attn_weights
else:
outputs += attn_weights
return outputs
class OpenBAPreTrainedModel(PreTrainedModel):
config_class = OpenBAConfig
base_model_prefix = "transformer"
_no_split_modules = ["OpenBABlock"]
def _set_gradient_checkpointing(self, module, value=False):
if isinstance(module, (OpenBAAttention, OpenBAStack)):
module.gradient_checkpointing = value
def _init_weights(self, module):
"""Initialize the weights"""
factor = self.config.initializer_factor
if isinstance(module, nn.LayerNorm):
module.weight.data.fill_(1.0)
module.bias.data.zero_()
elif isinstance(module, OpenBAForConditionalGeneration):
module.shared_embedding.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, SwiGLUMLP):
module.fc_in.weight.data.normal_(mean=0.0, std=factor * ((self.config.hidden_size) ** -0.5))
if hasattr(module.fc_in, "bias") and module.fc_in.bias is not None:
module.fc_in.bias.data.zero_()
module.fc_out.weight.data.normal_(mean=0.0, std=factor * ((module.ffn_hidden_size) ** -0.5))
if hasattr(module.fc_out, "bias") and module.fc_out.bias is not None:
module.fc_out.bias.data.zero_()
elif isinstance(module, OpenBAAttention):
hidden_size = self.config.hidden_size
kv_channels = self.config.kv_channels
n_heads = self.config.num_heads
if module.attn_type == 'self':
module.qkv.weight.data[:n_heads * kv_channels].normal_(mean=0.0, std=factor * ((hidden_size * kv_channels) ** -0.5))
module.qkv.weight.data[n_heads * kv_channels:].normal_(mean=0.0, std=factor * (hidden_size ** -0.5))
else:
module.q.weight.data.normal_(mean=0.0, std=factor * ((hidden_size * kv_channels) ** -0.5))
module.kv.weight.data.normal_(mean=0.0, std=factor * (hidden_size ** -0.5))
module.o.weight.data.normal_(mean=0.0, std=factor * ((n_heads * kv_channels) ** -0.5))
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 T5 it is usually set to the pad_token_id."
"See T5 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 OpenBAStack(OpenBAPreTrainedModel):
def __init__(self, config, embed_tokens):
super().__init__(config)
self.embed_tokens = embed_tokens
self.is_decoder = config.is_decoder
self.block = nn.ModuleList(
[OpenBABlock(config) for _ in range(config.num_layers)]
)
self.final_layernorm = nn.LayerNorm(config.hidden_size)
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,
):
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
# get batch size and seq_length
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:
input_shape = input_ids.size()
input_ids = input_ids.view(-1, input_shape[-1])
elif inputs_embeds is not None:
input_shape = inputs_embeds.size()[:-1]
else:
raise ValueError("You have to specify either input_ids or inputs_embeds")
batch_size, seq_length = input_shape
device = input_ids.device if input_ids is not None else inputs_embeds.device
# required mask seq length can be calculated via length of past
if past_key_values is None:
past_length = 0
past_key_values = [None] * len(self.block)
else:
past_length = past_key_values[0][0].size(-2)
cur_length = past_length + seq_length
# position ids
position_ids = torch.arange(past_length, cur_length, dtype=torch.long, device=device)
position_ids = position_ids.unsqueeze(0).view(-1, seq_length)
# Attention mask
if attention_mask is None:
attention_mask = torch.ones(batch_size, seq_length, device=device)
# get extended self-attention mask
if self.is_decoder:
if len(attention_mask.shape) == 2:
seq_ids = torch.arange(seq_length, device=device)
causal_mask = seq_ids[None, None, :].repeat(batch_size, seq_length, 1) <= seq_ids[None, :, None]
causal_mask = causal_mask.to(attention_mask.dtype)
extended_attention_mask = causal_mask[:, None, :, :] * attention_mask[:, None, None, :]
elif len(attention_mask.shape) == 3:
extended_attention_mask = attention_mask[:, None, :, :]
else:
raise ValueError
else:
extended_attention_mask = attention_mask[:, None, None, :]
extended_attention_mask = extended_attention_mask < 0.5
# get extended self-attention mask
# here we replace encoder_decoder_attention_mask with encoder_attention_mask
if self.is_decoder and encoder_hidden_states is not None:
if encoder_attention_mask is None:
encoder_seq_length = encoder_hidden_states.shape[1]
encoder_attention_mask = torch.ones(
batch_size, encoder_seq_length, device=device, dtype=torch.long
)
extended_encoder_attention_mask = encoder_attention_mask[:, None, None, :]
extended_encoder_attention_mask = extended_encoder_attention_mask < 0.5
else:
extended_encoder_attention_mask = None
# input embeddings
if inputs_embeds is None:
inputs_embeds = self.embed_tokens(input_ids)
# Prepare head mask if needed
head_mask = self.get_head_mask(head_mask, self.config.num_layers)
cross_attn_head_mask = self.get_head_mask(cross_attn_head_mask, self.config.num_layers)
present_key_value_states = () if use_cache else None
all_hidden_states = () if output_hidden_states else None
all_attentions = () if output_attentions else None
all_cross_attentions = () if (output_attentions and self.is_decoder) else None
hidden_states = inputs_embeds
for i, (layer_module, past_key_value) in enumerate(zip(self.block, past_key_values)):
layer_head_mask = head_mask[i]
cross_attn_layer_head_mask = cross_attn_head_mask[i]
if output_hidden_states:
all_hidden_states += (hidden_states,)
layer_outputs = layer_module(
hidden_states,
attention_mask=extended_attention_mask,
position_ids=position_ids,
encoder_hidden_states=encoder_hidden_states,
encoder_attention_mask=extended_encoder_attention_mask,
layer_head_mask=layer_head_mask,
cross_attn_layer_head_mask=cross_attn_layer_head_mask,
past_key_value=past_key_value,
use_cache=use_cache,
output_attentions=output_attentions,
)
# layer_outputs is a tuple with:
# hidden-states, key-value-states, (self-attention weights), (cross-attention weights)
if use_cache is False:
layer_outputs = layer_outputs[:1] + (None,) + layer_outputs[1:]
hidden_states, present_key_value_state = layer_outputs[:2]
if use_cache:
present_key_value_states += (present_key_value_state,)
if output_attentions:
all_attentions = all_attentions + (layer_outputs[2],)
if self.is_decoder:
all_cross_attentions = all_cross_attentions + (layer_outputs[3],)
hidden_states = self.final_layernorm(hidden_states)
if output_hidden_states:
all_hidden_states += (hidden_states,)
if not return_dict:
return tuple(
v
for v in [
hidden_states,
present_key_value_states,
all_hidden_states,
all_attentions,
all_cross_attentions,
]
if v is not None
)
return BaseModelOutputWithPastAndCrossAttentions(
last_hidden_state=hidden_states,
past_key_values=present_key_value_states,
hidden_states=all_hidden_states,
attentions=all_attentions,
cross_attentions=all_cross_attentions,
)
class OpenBAForConditionalGeneration(OpenBAPreTrainedModel):
_keys_to_ignore_on_load_missing = [
r"encoder.embed_tokens.weight",
r"decoder.embed_tokens.weight",
]
def __init__(self, config):
super().__init__(config)
self.shared_embedding = nn.Embedding(config.vocab_size, config.hidden_size)
self.hidden_size = config.hidden_size
encoder_config = copy.deepcopy(config)
encoder_config.is_decoder = False
encoder_config.use_cache = False
encoder_config.is_encoder_decoder = False
self.encoder = OpenBAStack(encoder_config, self.shared_embedding)
decoder_config = copy.deepcopy(config)
decoder_config.is_decoder = True
decoder_config.is_encoder_decoder = False
decoder_config.num_layers = config.num_decoder_layers
decoder_config.max_seq_length = config.decoder_max_seq_length
self.decoder = OpenBAStack(decoder_config, self.shared_embedding)
self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=config.add_lm_head_bias)
# Initialize weights and apply final processing
self.post_init()
# Model parallel
self.model_parallel = False
self.device_map = None
def get_input_embeddings(self):
return self.shared_embedding
def set_input_embeddings(self, new_embeddings):
self.shared_embedding = new_embeddings
self.encoder.set_input_embeddings(new_embeddings)
self.decoder.set_input_embeddings(new_embeddings)
def set_output_embeddings(self, new_embeddings):
self.lm_head = new_embeddings
def get_output_embeddings(self):
return self.lm_head
def get_encoder(self):
return self.encoder
def get_decoder(self):
return self.decoder
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[Tuple[Tuple[torch.FloatTensor]]] = None,
inputs_embeds: Optional[torch.Tensor] = None,
decoder_inputs_embeds: Optional[torch.Tensor] = None,
labels: Optional[torch.LongTensor] = None,
use_cache: Optional[bool] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> Union[Tuple[torch.FloatTensor], Seq2SeqLMOutput]:
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
# 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]
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,
)
sequence_output = decoder_outputs[0]
# share embedding and softmax embedding
if self.config.tie_word_embeddings:
# Rescale output before projecting on vocab
sequence_output = sequence_output * (self.hidden_size ** -0.5)
lm_logits = self.lm_head(sequence_output).to(torch.float32)
loss = None
if labels is not None:
loss_fct = nn.CrossEntropyLoss(ignore_index=-100)
# move labels to correct device to enable PP
labels = labels.to(lm_logits.device)
loss = loss_fct(lm_logits.view(-1, lm_logits.size(-1)), labels.view(-1))
loss = loss.to(hidden_states.dtype)
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_inputs_for_generation(
self,
input_ids,
past_key_values=None,
attention_mask=None,
head_mask=None,
decoder_head_mask=None,
decoder_attention_mask=None,
cross_attn_head_mask=None,
use_cache=None,
encoder_outputs=None,
**kwargs,
):
# cut decoder_input_ids if past is used
if past_key_values is not None:
input_ids = input_ids[:, -1:]
return {
"decoder_input_ids": input_ids,
"past_key_values": past_key_values,
"encoder_outputs": encoder_outputs,
"attention_mask": attention_mask,
"head_mask": head_mask,
"decoder_head_mask": decoder_head_mask,
"decoder_attention_mask": decoder_attention_mask,
"cross_attn_head_mask": cross_attn_head_mask,
"use_cache": use_cache,
}
def prepare_decoder_input_ids_from_labels(self, labels: torch.Tensor):
return self._shift_right(labels)
def _reorder_cache(self, past_key_values, beam_idx):
# if decoder past is not included in output
# speedy decoding is disabled and no need to reorder
if past_key_values is None:
logger.warning("You might want to consider setting `use_cache=True` to speed up decoding")
return past_key_values
reordered_decoder_past = ()
for layer_past_states in past_key_values:
# get the correct batch idx from layer past batch dim
# batch dim of `past` is at 2nd position
reordered_layer_past_states = ()
for layer_past_state in layer_past_states:
# need to set correct `past` for each of the four key / value states
reordered_layer_past_states = reordered_layer_past_states + (
layer_past_state.index_select(0, beam_idx.to(layer_past_state.device)),
)
if reordered_layer_past_states[0].shape != layer_past_states[0].shape:
raise ValueError(
f"reordered_layer_past_states[0] shape {reordered_layer_past_states[0].shape} and layer_past_states[0] shape {layer_past_states[0].shape} mismatched"
)
if len(reordered_layer_past_states) != len(layer_past_states):
raise ValueError(
f"length of reordered_layer_past_states {len(reordered_layer_past_states)} and length of layer_past_states {len(layer_past_states)} mismatched"
)
reordered_decoder_past = reordered_decoder_past + (reordered_layer_past_states,)
return reordered_decoder_past