plamo-13b-instruct / modeling_plamo.py
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from typing import Any, Dict, List, NamedTuple, Optional, Tuple, Union
import numpy as np
import torch
from torch import nn
from torch.nn import functional as F
from transformers import PretrainedConfig, PreTrainedModel
from transformers.modeling_outputs import BaseModelOutputWithPast, CausalLMOutputWithPast
class DecoderInput(NamedTuple):
hidden_states: torch.Tensor
position_ids: torch.Tensor
attention_mask: Optional[torch.Tensor] = None
past_key_values: Optional[List[torch.FloatTensor]] = None
output_hidden_states: Optional[bool] = False
output_attentions: Optional[bool] = False
use_cache: Optional[bool] = False
gradient_checkpointing: bool = False
class DecoderOutput(NamedTuple):
hidden_states: torch.Tensor
all_hidden_states: Optional[Tuple[torch.Tensor, ...]]
all_self_attns: Optional[Tuple[torch.Tensor, ...]]
next_decoder_cache: Optional[Tuple[torch.Tensor, ...]]
class PlamoConfig(PretrainedConfig): # type: ignore
model_type: str = "plamo"
def __init__(
self,
vocab_size: int = 32000,
hidden_size: int = 4096,
intermediate_size: int = 13312,
num_hidden_layers: int = 32,
num_attention_heads: int = 32,
num_key_value_heads: Optional[int] = None,
max_position_embeddings: int = 2048,
initializer_range: float = 0.02,
rms_norm_eps: float = 1e-6,
use_cache: bool = True,
tokenizer_class: str = "PlamoTokenizer",
pad_token_id: Optional[int] = None,
bos_token_id: int = 1,
eos_token_id: int = 2,
n_shared_head: int = 8,
tie_word_embeddings: bool = False,
**kwargs: Any,
) -> None:
self.vocab_size = vocab_size
self.max_position_embeddings = max_position_embeddings
self.hidden_size = hidden_size
self.intermediate_size = intermediate_size
self.num_hidden_layers = num_hidden_layers
self.num_attention_heads = num_attention_heads
# for backward compatibility
if num_key_value_heads is None:
num_key_value_heads = num_attention_heads
self.num_key_value_heads = num_key_value_heads
self.initializer_range = initializer_range
self.rms_norm_eps = rms_norm_eps
self.use_cache = use_cache
self.n_shared_head = n_shared_head
super().__init__(
tokenizer_class=tokenizer_class,
pad_token_id=pad_token_id,
bos_token_id=bos_token_id,
eos_token_id=eos_token_id,
tie_word_embeddings=tie_word_embeddings,
**kwargs,
)
# Copied from transformers.models.bart.modeling_bart._make_causal_mask
def _make_causal_mask(
input_ids_shape: Tuple[int, int], dtype: torch.dtype, device: torch.device, past_key_values_length: int = 0
) -> torch.Tensor:
"""
Make causal mask used for bi-directional self-attention.
"""
bsz, tgt_len = input_ids_shape
mask = torch.full((tgt_len, tgt_len), torch.finfo(dtype).min, device=device)
mask_cond = torch.arange(mask.size(-1), device=device)
mask.masked_fill_(mask_cond < (mask_cond + 1).view(mask.size(-1), 1), 0)
mask = mask.to(dtype)
if past_key_values_length > 0:
mask = torch.cat([torch.zeros(tgt_len, past_key_values_length, dtype=dtype, device=device), mask], dim=-1)
return mask[None, None, :, :].expand(bsz, 1, tgt_len, tgt_len + past_key_values_length)
# Copied from transformers.models.bart.modeling_bart._expand_mask
def _expand_mask(mask: torch.Tensor, dtype: torch.dtype, tgt_len: Optional[int] = None) -> torch.Tensor:
"""
Expands attention_mask from `[bsz, seq_len]` to `[bsz, 1, tgt_seq_len, src_seq_len]`.
"""
bsz, src_len = mask.size()
tgt_len = tgt_len if tgt_len is not None else src_len
expanded_mask = mask[:, None, None, :].expand(bsz, 1, tgt_len, src_len).to(dtype)
inverted_mask = 1.0 - expanded_mask
return inverted_mask.masked_fill(inverted_mask.to(torch.bool), torch.finfo(dtype).min) # type: ignore
class RotaryEmbedding(torch.nn.Module):
def __init__(
self, dim: int, max_position_embeddings: int = 2048, base: int = 10000, device: Optional[torch.device] = None
) -> None:
super().__init__()
self.dim = dim
self.max_position_embeddings = max_position_embeddings
self.base = base
inv_freq = 1.0 / (self.base ** (torch.arange(0, self.dim, 2).float().to(device) / self.dim))
self.register_buffer("inv_freq", inv_freq, persistent=False)
# Build here to make `torch.jit.trace` work.
self._set_cos_sin_cache(
seq_len=max_position_embeddings, device=self.inv_freq.device, dtype=torch.get_default_dtype()
)
def _set_cos_sin_cache(self, seq_len: int, device: Any, dtype: Any) -> None:
self.max_seq_len_cached = seq_len
t = torch.arange(self.max_seq_len_cached, device=device, dtype=self.inv_freq.dtype) # type: ignore
freqs = torch.einsum("i,j->ij", t, self.inv_freq)
# Different from paper, but it uses a different permutation in order to obtain the same calculation
emb = torch.cat((freqs, freqs), dim=-1)
self.register_buffer("cos_cached", emb.cos()[None, None, :, :].to(dtype), persistent=False)
self.register_buffer("sin_cached", emb.sin()[None, None, :, :].to(dtype), persistent=False)
def forward(self, x: torch.Tensor, seq_len: int) -> Tuple[torch.Tensor, torch.Tensor]:
# x: [bs, num_attention_heads, seq_len, head_size]
if seq_len > self.max_seq_len_cached:
self._set_cos_sin_cache(seq_len=seq_len, device=x.device, dtype=x.dtype)
return (
self.cos_cached[:, :, :seq_len, ...].to(dtype=x.dtype), # type: ignore
self.sin_cached[:, :, :seq_len, ...].to(dtype=x.dtype), # type: ignore
)
def _rotate_half(x: torch.Tensor) -> torch.Tensor:
"""Rotates half the hidden dims of the input."""
x1 = x[..., : x.shape[-1] // 2]
x2 = x[..., x.shape[-1] // 2 :]
return torch.cat((-x2, x1), dim=-1)
def _rotary_pos_emb(x: torch.Tensor, cos: torch.Tensor, sin: torch.Tensor, position_ids: torch.Tensor) -> torch.Tensor:
# The first two dimensions of cos and sin are always 1, so we can `squeeze` them.
cos = cos.squeeze(1).squeeze(0) # [seq_len, dim]
sin = sin.squeeze(1).squeeze(0) # [seq_len, dim]
cos = cos[position_ids].unsqueeze(1) # [bs, 1, seq_len, dim]
sin = sin[position_ids].unsqueeze(1) # [bs, 1, seq_len, dim]
x_embed = (x * cos) + (_rotate_half(x) * sin)
return x_embed
class RMSNorm(nn.Module):
def __init__(self, hidden_size: int, eps: float = 1e-6) -> None:
super().__init__()
self.weight = nn.Parameter(torch.ones(hidden_size))
self.variance_epsilon = eps
def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
input_dtype = hidden_states.dtype
hidden_states = hidden_states.to(torch.float32)
variance = hidden_states.pow(2).mean(-1, keepdim=True)
hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon)
return self.weight * hidden_states.to(input_dtype)
class Attention(torch.nn.Module):
def __init__(self, config: PlamoConfig) -> None:
super().__init__()
self.config = config
self.hidden_size = config.hidden_size
head_dim = self.hidden_size // config.num_attention_heads
self.max_position_embeddings = config.max_position_embeddings
self.q_num_heads = config.num_attention_heads
self.qk_dim = self.v_dim = head_dim
self.k_num_heads = self.v_num_heads = int(np.ceil(self.q_num_heads / config.n_shared_head))
self.q_proj = nn.Linear(self.hidden_size, self.q_num_heads * self.qk_dim, bias=False)
self.k_proj = nn.Linear(self.hidden_size, self.k_num_heads * self.qk_dim, bias=False)
self.v_proj = nn.Linear(self.hidden_size, self.v_num_heads * self.v_dim, bias=False)
self.o_proj = nn.Linear(self.q_num_heads * self.v_dim, self.hidden_size, bias=False)
self.rotary_emb = RotaryEmbedding(self.qk_dim, max_position_embeddings=self.max_position_embeddings)
def forward(
self,
hidden_states: torch.Tensor,
attention_mask: Optional[torch.Tensor] = None,
position_ids: Optional[torch.Tensor] = None,
past_key_value: Optional[Tuple[torch.Tensor, torch.Tensor]] = None,
output_attentions: bool = False,
use_cache: bool = False,
) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor, torch.Tensor]]]:
bsz, q_len, _ = hidden_states.size()
query_states = self.q_proj(hidden_states).view(bsz, q_len, self.q_num_heads, self.qk_dim).transpose(1, 2)
key_states = self.k_proj(hidden_states).view(bsz, q_len, self.k_num_heads, self.qk_dim).transpose(1, 2)
value_states = self.v_proj(hidden_states).view(bsz, q_len, self.v_num_heads, self.v_dim).transpose(1, 2)
def _expand_kv(t: torch.Tensor, repeat: int, target: int) -> torch.Tensor:
return t.repeat(1, repeat, 1, 1)[:, :target]
# expand shared kv
assert self.k_num_heads == self.v_num_heads
key_states = _expand_kv(key_states, self.config.n_shared_head, self.q_num_heads)
value_states = _expand_kv(value_states, self.config.n_shared_head, self.q_num_heads)
kv_seq_len = key_states.shape[-2]
if past_key_value is not None:
kv_seq_len += past_key_value[0].shape[-2]
cos, sin = self.rotary_emb(value_states, seq_len=kv_seq_len)
assert position_ids is not None
query_states = _rotary_pos_emb(query_states, cos, sin, position_ids)
key_states = _rotary_pos_emb(key_states, cos, sin, position_ids)
# [bsz, nh, t, hd]
if past_key_value is not None:
# reuse k, v, self_attention
key_states = torch.cat([past_key_value[0], key_states], dim=2)
value_states = torch.cat([past_key_value[1], value_states], dim=2)
past_key_value = (key_states, value_states) if use_cache else None
attn_output = F.scaled_dot_product_attention(query_states, key_states, value_states, attn_mask=attention_mask)
attn_output = attn_output.transpose(1, 2)
attn_output = attn_output.reshape(bsz, q_len, self.q_num_heads * self.v_dim)
attn_output = self.o_proj(attn_output)
if not output_attentions:
attn_weights = None
return attn_output, attn_weights, past_key_value
class MLP(nn.Module):
def __init__(self, config: PlamoConfig) -> None:
super().__init__()
self.config = config
self.hidden_size = config.hidden_size
self.intermediate_size = config.intermediate_size
self.gate_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False)
self.up_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False)
self.down_proj = nn.Linear(self.intermediate_size, self.hidden_size, bias=False)
self.act_fn = torch.nn.functional.silu
def forward(self, x: torch.Tensor) -> torch.Tensor:
return self.down_proj(self.act_fn(self.gate_proj(x)) * self.up_proj(x)) # type: ignore
class PlamoDecoderLayer(torch.nn.Module):
def __init__(self, config: PlamoConfig) -> None:
super().__init__()
self.config = config
self.hidden_size = config.hidden_size
self.self_attn = Attention(config)
self.mlp = MLP(config)
self.norm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
def forward(
self,
hidden_states: torch.Tensor,
attention_mask: Optional[torch.Tensor] = None,
position_ids: Optional[torch.LongTensor] = None,
past_key_value: Optional[Tuple[torch.Tensor]] = None,
output_attentions: Optional[bool] = False,
use_cache: Optional[bool] = False,
) -> Tuple[Any, ...]:
# from LlamaDecoder
residual = hidden_states
hidden_states = self.norm(hidden_states)
# Self Attention
hidden_states_sa, self_attn_weights, present_key_value = self.self_attn(
hidden_states=hidden_states,
attention_mask=attention_mask,
position_ids=position_ids,
past_key_value=past_key_value,
output_attentions=output_attentions,
use_cache=use_cache,
)
# Fully Connected
hidden_states_mlp = self.mlp(hidden_states)
# Residual
hidden_states = residual + hidden_states_sa + hidden_states_mlp
outputs: Any = (hidden_states,)
if output_attentions:
outputs += (self_attn_weights,)
if use_cache:
outputs += (present_key_value,)
return outputs # type: ignore
class PlamoDecoder(torch.nn.Module):
def __init__(self, config: PlamoConfig) -> None:
super().__init__()
self.layers = torch.nn.ModuleList([PlamoDecoderLayer(config) for _ in range(config.num_hidden_layers)])
def forward(self, x: DecoderInput) -> DecoderOutput:
all_hidden_states: Optional[Tuple[torch.Tensor, ...]] = () if x.output_hidden_states else None
all_self_attns: Optional[Tuple[torch.Tensor, ...]] = () if x.output_attentions else None
next_decoder_cache: Optional[Tuple[torch.Tensor, ...]] = () if x.use_cache else None
hidden_states = x.hidden_states
for idx, decoder_layer in enumerate(self.layers):
if x.output_hidden_states:
assert all_hidden_states is not None
all_hidden_states += (hidden_states,)
past_key_value = x.past_key_values[idx] if x.past_key_values is not None else None
if self.training and x.gradient_checkpointing:
def create_custom_forward(module): # type: ignore
def custom_forward(*inputs): # type: ignore
# None for past_key_value
return module(*inputs, x.output_attentions, None)
return custom_forward
layer_outputs = torch.utils.checkpoint.checkpoint(
create_custom_forward(decoder_layer), # type: ignore
hidden_states,
x.attention_mask,
x.position_ids,
None,
)
else:
layer_outputs = decoder_layer(
hidden_states,
attention_mask=x.attention_mask,
position_ids=x.position_ids,
past_key_value=past_key_value,
output_attentions=x.output_attentions,
use_cache=x.use_cache,
)
hidden_states = layer_outputs[0]
if x.use_cache:
cache = layer_outputs[2 if x.output_attentions else 1]
assert cache is not None
assert next_decoder_cache is not None
next_decoder_cache += (cache,)
if x.output_attentions:
assert layer_outputs[1] is not None
assert all_self_attns is not None
all_self_attns += (layer_outputs[1],)
return DecoderOutput(hidden_states, all_hidden_states, all_self_attns, next_decoder_cache)
class PlamoPreTrainedModel(PreTrainedModel): # type: ignore
config_class = PlamoConfig
_no_split_modules: List[str]
base_model_prefix = "model"
supports_gradient_checkpointing = True
_no_split_modules = ["PlamoDecoderLayer"]
_skip_keys_device_placement = "past_key_values"
_keys_to_ignore_on_load_unexpected = [r"decoder\.version"]
def _init_weights(self, module: torch.nn.Module) -> None:
std = self.config.initializer_range
if isinstance(module, nn.Linear):
module.weight.data.normal_(mean=0.0, std=std)
if module.bias is not None:
module.bias.data.zero_()
elif isinstance(module, nn.Embedding):
module.weight.data.normal_(mean=0.0, std=std)
if module.padding_idx is not None:
module.weight.data[module.padding_idx].zero_()
def _set_gradient_checkpointing(self, module: torch.nn.Module, value: bool = False) -> None:
module.gradient_checkpointing = value # type: ignore
class PlamoModel(PlamoPreTrainedModel):
def __init__(self, config: PlamoConfig):
super().__init__(config)
self.padding_idx = config.pad_token_id
self.vocab_size = config.vocab_size
self.embed_tokens = nn.Embedding(config.vocab_size, config.hidden_size, self.padding_idx)
self.layers = PlamoDecoder(config) # type: ignore
self.norm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
self.gradient_checkpointing = False
# Initialize weights and apply final processing
self.post_init()
def get_input_embeddings(self) -> torch.nn.Embedding:
return self.embed_tokens
def set_input_embeddings(self, value: torch.nn.Embedding) -> None:
self.embed_tokens = value
# Copied from transformers.models.bart.modeling_bart.BartDecoder._prepare_decoder_attention_mask
def _prepare_decoder_attention_mask(
self,
attention_mask: torch.Tensor,
input_shape: Tuple[int, int],
inputs_embeds: Optional[torch.FloatTensor],
past_key_values_length: int,
) -> Optional[torch.Tensor]:
# create causal mask
# [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
combined_attention_mask: Optional[torch.Tensor] = None
if input_shape[-1] > 1:
assert inputs_embeds is not None
combined_attention_mask = _make_causal_mask(
input_shape,
inputs_embeds.dtype,
device=inputs_embeds.device,
past_key_values_length=past_key_values_length,
)
if attention_mask is not None:
# [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
assert inputs_embeds is not None
expanded_attn_mask = _expand_mask(attention_mask, inputs_embeds.dtype, tgt_len=input_shape[-1]).to(
inputs_embeds.device
)
combined_attention_mask = (
expanded_attn_mask if combined_attention_mask is None else expanded_attn_mask + combined_attention_mask
)
return combined_attention_mask
def forward(
self,
input_ids: Optional[torch.LongTensor] = None,
attention_mask: Optional[torch.Tensor] = None,
position_ids: Optional[torch.Tensor] = None,
past_key_values: Optional[List[torch.FloatTensor]] = None,
inputs_embeds: Optional[torch.FloatTensor] = None,
use_cache: Optional[bool] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> Union[Tuple, BaseModelOutputWithPast]:
assert input_ids is not None
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
# retrieve input_ids and inputs_embeds
if input_ids is not None and inputs_embeds is not None:
raise ValueError("You cannot specify both decoder_input_ids and decoder_inputs_embeds at the same time")
elif input_ids is not None:
batch_size, seq_length = input_ids.shape
else:
raise ValueError("You have to specify either decoder_input_ids or decoder_inputs_embeds")
seq_length_with_past = seq_length
past_key_values_length = 0
if past_key_values 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 position_ids is None:
device = input_ids.device
position_ids = torch.arange(
past_key_values_length, seq_length + past_key_values_length, dtype=torch.long, device=device
)
position_ids = position_ids.unsqueeze(0).view(-1, seq_length)
else:
position_ids = position_ids.view(-1, seq_length).long()
if inputs_embeds is None:
inputs_embeds = self.embed_tokens(input_ids)
# embed positions
if attention_mask is None:
attention_mask = torch.ones(
(batch_size, seq_length_with_past), dtype=torch.bool, device=inputs_embeds.device
)
attention_mask = self._prepare_decoder_attention_mask(
attention_mask, (batch_size, seq_length), inputs_embeds, past_key_values_length
)
hidden_states = inputs_embeds
if self.gradient_checkpointing and self.training:
if use_cache:
use_cache = False
# decoder layers
out = self.layers(
DecoderInput(
hidden_states,
position_ids,
attention_mask,
past_key_values,
output_hidden_states,
output_attentions,
use_cache,
self.gradient_checkpointing,
)
)
assert isinstance(out, DecoderOutput)
hidden_states = out.hidden_states
all_hidden_states = out.all_hidden_states
all_self_attns = out.all_self_attns
next_decoder_cache = out.next_decoder_cache
hidden_states = self.norm(hidden_states)
# add hidden states from the last decoder layer
if output_hidden_states:
assert all_hidden_states is not None
all_hidden_states += (hidden_states,)
next_cache = next_decoder_cache if use_cache else None
if not return_dict:
return tuple(v for v in [hidden_states, next_cache, all_hidden_states, all_self_attns] if v is not None)
return BaseModelOutputWithPast(
last_hidden_state=hidden_states,
past_key_values=next_cache,
hidden_states=all_hidden_states,
attentions=all_self_attns,
)
class PlamoForCausalLM(PlamoPreTrainedModel):
def __init__(self, config: PretrainedConfig) -> None:
super().__init__(config)
self.model = PlamoModel(config)
self.lm_head: torch.nn.Module = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
# Initialize weights and apply final processing
self.post_init()
def get_input_embeddings(self) -> torch.nn.Embedding:
return self.model.embed_tokens
def set_input_embeddings(self, value: torch.nn.Embedding) -> None:
self.model.embed_tokens = value
def get_output_embeddings(self) -> torch.nn.Module:
return self.lm_head
def set_output_embeddings(self, new_embeddings: torch.nn.Module) -> None:
self.lm_head = new_embeddings
def set_decoder(self, decoder: PlamoModel) -> None:
self.model = decoder
def get_decoder(self) -> PlamoModel:
return self.model
def forward( # type: ignore
self,
input_ids: Optional[torch.LongTensor] = None,
attention_mask: Optional[torch.Tensor] = None,
position_ids: Optional[torch.Tensor] = None,
past_key_values: Optional[List[torch.FloatTensor]] = None,
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,
) -> Union[Tuple, CausalLMOutputWithPast]:
r"""
Args:
labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
(masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.
Returns:
Example:
```python
>>> from transformers import AutoTokenizer, LlamaForCausalLM
>>> model = LlamaForCausalLM.from_pretrained(PATH_TO_CONVERTED_WEIGHTS)
>>> tokenizer = AutoTokenizer.from_pretrained(PATH_TO_CONVERTED_TOKENIZER)
>>> prompt = "Hey, are you consciours? Can you talk to me?"
>>> inputs = tokenizer(prompt, return_tensors="pt")
>>> # Generate
>>> generate_ids = model.generate(inputs.input_ids, max_length=30)
>>> tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
"Hey, are you consciours? Can you talk to me?\nI'm not consciours, but I can talk to you."
```"""
assert input_ids is not None
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
# decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)
outputs = self.model(
input_ids=input_ids,
attention_mask=attention_mask,
position_ids=position_ids,
past_key_values=past_key_values,
inputs_embeds=inputs_embeds,
use_cache=use_cache,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
hidden_states = outputs[0]
logits = self.lm_head(hidden_states)
loss = None
if labels is not None:
# Shift so that tokens < n predict n
shift_logits = logits[..., :-1, :].contiguous()
shift_labels = labels[..., 1:].contiguous()
# Flatten the tokens
loss_fct = nn.CrossEntropyLoss()
shift_logits = shift_logits.view(-1, self.config.vocab_size)
shift_labels = shift_labels.view(-1)
# Enable model parallelism
shift_labels = shift_labels.to(shift_logits.device)
loss = loss_fct(shift_logits, shift_labels)
if not return_dict:
output = (logits,) + outputs[1:]
return (loss,) + output if loss is not None else output
return CausalLMOutputWithPast(
loss=loss,
logits=logits,
past_key_values=outputs.past_key_values,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
)
def prepare_inputs_for_generation(
self,
input_ids: torch.Tensor,
past_key_values: Optional[List[torch.FloatTensor]] = None,
attention_mask: Optional[torch.Tensor] = None,
inputs_embeds: Optional[torch.Tensor] = None,
**kwargs: Any,
) -> Dict[str, Any]:
if past_key_values:
input_ids = input_ids[:, -1:]
position_ids = kwargs.get("position_ids", None)
if attention_mask is not None and position_ids is None:
# create position_ids on the fly for batch generation
position_ids = attention_mask.long().cumsum(-1) - 1
position_ids.masked_fill_(attention_mask == 0, 1)
if past_key_values:
position_ids = position_ids[:, -1].unsqueeze(-1)
# if `inputs_embeds` are passed, we only want to use them in the 1st generation step
if inputs_embeds is not None and past_key_values is None:
model_inputs: Dict[str, Any] = {"inputs_embeds": inputs_embeds}
else:
model_inputs = {"input_ids": input_ids}
model_inputs.update(
{
"position_ids": position_ids,
"past_key_values": past_key_values,
"use_cache": kwargs.get("use_cache"),
"attention_mask": attention_mask,
}
)
return model_inputs
@staticmethod
def _reorder_cache(past_key_values: List[torch.FloatTensor], beam_idx: int) -> Tuple[Any, ...]:
reordered_past: Tuple[Any, ...] = ()
for layer_past in past_key_values:
reordered_past += (tuple(past_state.index_select(0, beam_idx) for past_state in layer_past),)
return reordered_past