"""LoRDCoder model class, based on GPT model.
License: Apache-2.0
"""
import math
from typing import List, Optional, Tuple, Union
import torch
import torch.utils.checkpoint
from torch import nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from transformers.activations import ACT2FN
from transformers.modeling_outputs import (
BaseModelOutputWithPastAndCrossAttentions,
CausalLMOutputWithCrossAttentions,
SequenceClassifierOutputWithPast,
TokenClassifierOutput,
)
from transformers.modeling_utils import PreTrainedModel
from .configuration_lordcoder_v0 import LoRDCoderConfig
# Fused kernels
# Use separate functions for each case because conditionals prevent kernel fusion.
@torch.jit.script
def upcast_masked_softmax(
x: torch.Tensor, mask: torch.Tensor, mask_value: torch.Tensor, scale: float, softmax_dtype: torch.dtype
):
input_dtype = x.dtype
x = x.to(softmax_dtype) * scale
x = torch.where(mask, x, mask_value)
x = torch.nn.functional.softmax(x, dim=-1).to(input_dtype)
return x
@torch.jit.script
def upcast_softmax(x: torch.Tensor, scale: float, softmax_dtype: torch.dtype):
input_dtype = x.dtype
x = x.to(softmax_dtype) * scale
x = torch.nn.functional.softmax(x, dim=-1).to(input_dtype)
return x
@torch.jit.script
def masked_softmax(x: torch.Tensor, mask: torch.Tensor, mask_value: torch.Tensor):
x = torch.where(mask, x, mask_value)
x = torch.nn.functional.softmax(x, dim=-1)
return x
class LoRDCoderAttention(nn.Module):
def __init__(self, config, is_cross_attention=False, layer_idx=None):
super().__init__()
self.mask_value = None
self.multi_query = config.multi_query
self.embed_dim = config.hidden_size
self.num_heads = config.num_attention_heads
self.head_dim = self.embed_dim // self.num_heads
self.kv_heads = 1 if self.multi_query else self.num_heads
self.kv_dim = self.kv_heads * self.head_dim
self.split_size = self.embed_dim
if self.head_dim * self.num_heads != self.embed_dim:
raise ValueError(
f"`embed_dim` must be divisible by num_heads (got `embed_dim`: {self.embed_dim} and `num_heads`:"
f" {self.num_heads})."
)
self.scale_attn_weights = config.scale_attn_weights
self.is_cross_attention = is_cross_attention
self.layer_idx = layer_idx
self.attention_softmax_in_fp32 = config.attention_softmax_in_fp32
self.scale_attention_softmax_in_fp32 = (
config.scale_attention_softmax_in_fp32 and config.attention_softmax_in_fp32
)
if self.is_cross_attention:
raise NotImplementedError("Cross Attention not supported.")
if self.multi_query:
raise NotImplementedError("Multi-Query Attention not supported for cross_attention")
self.c_attn = nn.Linear(self.embed_dim, 2 * self.embed_dim)
self.q_attn = nn.Linear(self.embed_dim, self.embed_dim)
else:
self.c_attn = nn.Linear(self.embed_dim, self.embed_dim + 2 * self.kv_dim)
self.c_proj = nn.Linear(self.embed_dim, self.embed_dim)
self.attn_dropout = nn.Dropout(config.attn_pdrop)
self.resid_dropout = nn.Dropout(config.resid_pdrop)
def _get_mask_value(self, device, dtype):
# torch.where expects a tensor. We use a cache to avoid recreating it every time.
if self.mask_value is None or self.mask_value.dtype != dtype or self.mask_value.device != device:
self.mask_value = torch.full([], torch.finfo(dtype).min, dtype=dtype, device=device)
return self.mask_value
def _attn(self, query, key, value, attention_mask=None, head_mask=None):
dtype = query.dtype
softmax_dtype = torch.float32 if self.attention_softmax_in_fp32 else dtype
upcast = dtype != softmax_dtype
unscale = self.layer_idx + 1 if self.scale_attention_softmax_in_fp32 and upcast else 1
scale_factor = unscale**-1
if self.scale_attn_weights:
scale_factor /= self.head_dim**0.5
# MQA models: (batch_size, query_length, num_heads * head_dim)
# MHA models: (batch_size, num_heads, query_length, head_dim)
query_shape = query.shape
batch_size = query_shape[0]
key_length = key.size(-1)
if self.multi_query:
# (batch_size, query_length, num_heads, head_dim) x (batch_size, head_dim, key_length)
# -> (batch_size, query_length, num_heads, key_length)
query_length = query_shape[1]
attn_shape = (batch_size, query_length, self.num_heads, key_length)
attn_view = (batch_size, query_length * self.num_heads, key_length)
# No copy needed for MQA 2, or when layer_past is provided.
query = query.reshape(batch_size, query_length * self.num_heads, self.head_dim)
else:
# (batch_size, num_heads, query_length, head_dim) x (batch_size, num_heads, head_dim, key_length)
# -> (batch_size, num_heads, query_length, key_length)
query_length = query_shape[2]
attn_shape = (batch_size, self.num_heads, query_length, key_length)
attn_view = (batch_size * self.num_heads, query_length, key_length)
# Always copies
query = query.reshape(batch_size * self.num_heads, query_length, self.head_dim)
# No copy when layer_past is provided.
key = key.reshape(batch_size * self.num_heads, self.head_dim, key_length)
attn_weights = torch.empty(attn_view, device=query.device, dtype=query.dtype)
if query.device.type == "cpu":
# This is needed because of a bug in pytorch https://github.com/pytorch/pytorch/issues/80588.
# The bug was fixed in https://github.com/pytorch/pytorch/pull/96086,
# but the fix has not been released as of pytorch version 2.0.0.
attn_weights = torch.zeros_like(attn_weights)
beta = 1
else:
beta = 0
attn_weights = torch.baddbmm(attn_weights, query, key, beta=beta, alpha=scale_factor).view(attn_shape)
if upcast:
# Use a fused kernel to prevent a large overhead from casting and scaling.
# Sub-optimal when the key length is not a multiple of 8.
if attention_mask is None:
attn_weights = upcast_softmax(attn_weights, unscale, softmax_dtype)
else:
mask_value = self._get_mask_value(attn_weights.device, softmax_dtype)
# print(attn_weights.device, attention_mask.device, mask_value.device, unscale.device, softmax_dtype)
attn_weights = upcast_masked_softmax(attn_weights, attention_mask, mask_value, unscale, softmax_dtype)
else:
if attention_mask is not None:
mask_value = self._get_mask_value(attn_weights.device, softmax_dtype)
# The fused kernel is very slow when the key length is not a multiple of 8, so we skip fusion.
attn_weights = torch.where(attention_mask, attn_weights, mask_value)
attn_weights = torch.nn.functional.softmax(attn_weights, dim=-1)
attn_weights = self.attn_dropout(attn_weights)
# Mask heads if we want to
if head_mask is not None:
if self.multi_query:
head_mask = head_mask.transpose(1, 2)
attn_weights = attn_weights * head_mask
if self.multi_query:
attn_output = torch.bmm(attn_weights.view(attn_view), value).view(query_shape)
else:
attn_output = torch.matmul(attn_weights, value)
return attn_output, attn_weights
def forward(
self,
hidden_states: torch.Tensor,
layer_past: Optional[torch.Tensor] = None,
attention_mask: Optional[torch.Tensor] = None,
head_mask: Optional[torch.Tensor] = None,
encoder_hidden_states: Optional[torch.Tensor] = None,
encoder_attention_mask: Optional[torch.Tensor] = None,
use_cache: Optional[bool] = False,
output_attentions: Optional[bool] = False,
) -> Union[
Tuple[torch.Tensor, Optional[torch.Tensor]],
Tuple[torch.Tensor, Optional[torch.Tensor], Tuple[torch.Tensor, ...]],
]:
if encoder_hidden_states is not None:
if not hasattr(self, "q_attn") or not self.is_cross_attention:
raise ValueError(
"If class is used as cross attention, the weights `q_attn` have to be defined. "
"Please make sure to instantiate class with `LoRDCoderAttention(..., is_cross_attention=True)`."
)
query = self.q_attn(hidden_states)
key_value = self.c_attn(encoder_hidden_states)
attention_mask = encoder_attention_mask
elif self.multi_query:
query, key_value = self.c_attn(hidden_states).split((self.embed_dim, 2 * self.kv_dim), dim=2)
else:
# Note: We split as (self.num_heads, 3, self.head_dim) instead of (3, self.num_heads, self.head_dim),
# i.e., the memory layout is not the same as GPT2.
# This makes the concatenation with past_key_value more efficient.
query, key_value = (
self.c_attn(hidden_states)
.view(*hidden_states.shape[:2], self.num_heads, 3 * self.head_dim)
.transpose(1, 2)
.split((self.head_dim, 2 * self.head_dim), dim=3)
)
if layer_past is not None:
key_value = torch.cat((layer_past, key_value), dim=-2)
present = key_value if use_cache else None
key, value = key_value.split((self.head_dim, self.head_dim), dim=-1)
attn_output, attn_weights = self._attn(query, key.transpose(-1, -2), value, attention_mask, head_mask)
if not self.multi_query:
attn_output = attn_output.transpose(1, 2).reshape(hidden_states.shape)
attn_output = self.c_proj(attn_output)
attn_output = self.resid_dropout(attn_output)
outputs = (attn_output, present)
if output_attentions:
if self.multi_query:
# Transpose to return weights in the usual format (batch_size, num_heads, query_length, key_length)
attn_weights = attn_weights.transpose(1, 2)
outputs += (attn_weights,)
return outputs # a, present, (attentions)
class LoRDCoderMLP(nn.Module):
def __init__(self, intermediate_size, config):
super().__init__()
embed_dim = config.hidden_size
self.gate_dim = config.gate_dim
self.c_fc = torch.nn.Linear(in_features=embed_dim, out_features=intermediate_size, bias=True)
self.c_gate = torch.nn.Linear(in_features=intermediate_size, out_features=self.gate_dim, bias=True)
self.c_proj = torch.nn.Linear(in_features=self.gate_dim, out_features=embed_dim, bias=True)
self.act = ACT2FN[config.activation_function]
self.dropout = nn.Dropout(config.resid_pdrop)
def forward(self, hidden_states: Optional[Tuple[torch.FloatTensor]]) -> torch.FloatTensor:
hidden_states = self.c_fc(hidden_states)
hidden_states = self.c_gate(self.act(hidden_states))
hidden_states = self.c_proj(hidden_states)
hidden_states = self.dropout(hidden_states)
return hidden_states
class LoRDCoderBlock(nn.Module):
def __init__(self, config, layer_idx=None):
super().__init__()
hidden_size = config.hidden_size
self.inner_dim = config.n_inner if config.n_inner is not None else 4 * hidden_size
self.ln_1 = nn.LayerNorm(hidden_size, eps=config.layer_norm_epsilon)
self.attn = LoRDCoderAttention(config, layer_idx=layer_idx)
self.ln_2 = nn.LayerNorm(hidden_size, eps=config.layer_norm_epsilon)
if config.add_cross_attention:
if config.multi_query:
raise NotImplementedError("Cross-attention not implemented for MQA")
self.crossattention = LoRDCoderAttention(config, is_cross_attention=True, layer_idx=layer_idx)
self.ln_cross_attn = nn.LayerNorm(hidden_size, eps=config.layer_norm_epsilon)
self.mlp = LoRDCoderMLP(self.inner_dim, config)
def forward(
self,
hidden_states: Optional[Tuple[torch.Tensor]],
layer_past: Optional[torch.Tensor] = None,
attention_mask: Optional[torch.Tensor] = None,
head_mask: Optional[torch.Tensor] = None,
encoder_hidden_states: Optional[torch.Tensor] = None,
encoder_attention_mask: Optional[torch.Tensor] = None,
use_cache: Optional[bool] = False,
output_attentions: Optional[bool] = False,
) -> Union[
Tuple[torch.Tensor], Tuple[torch.Tensor, torch.Tensor], Tuple[torch.Tensor, torch.Tensor, torch.Tensor]
]:
residual = hidden_states
hidden_states = self.ln_1(hidden_states)
attn_outputs = self.attn(
hidden_states,
layer_past=layer_past,
attention_mask=attention_mask,
head_mask=head_mask,
use_cache=use_cache,
output_attentions=output_attentions,
)
attn_output = attn_outputs[0] # output_attn: a, present, (attentions)
outputs = attn_outputs[1:]
# residual connection
hidden_states = attn_output + residual
if encoder_hidden_states is not None:
# add one self-attention block for cross-attention
if not hasattr(self, "crossattention"):
raise ValueError(
f"If `encoder_hidden_states` are passed, {self} has to be instantiated with "
"cross-attention layers by setting `config.add_cross_attention=True`"
)
residual = hidden_states
hidden_states = self.ln_cross_attn(hidden_states)
cross_attn_outputs = self.crossattention(
hidden_states,
attention_mask=attention_mask,
head_mask=head_mask,
encoder_hidden_states=encoder_hidden_states,
encoder_attention_mask=encoder_attention_mask,
output_attentions=output_attentions,
)
attn_output = cross_attn_outputs[0]
# residual connection
hidden_states = residual + attn_output
outputs = outputs + cross_attn_outputs[2:] # add cross attentions if we output attention weights
residual = hidden_states
hidden_states = self.ln_2(hidden_states)
feed_forward_hidden_states = self.mlp(hidden_states)
# residual connection
hidden_states = residual + feed_forward_hidden_states
if use_cache:
outputs = (hidden_states,) + outputs
else:
outputs = (hidden_states,) + outputs[1:]
return outputs # hidden_states, present, (attentions, cross_attentions)
class LoRDCoderPreTrainedModel(PreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.
"""
config_class = LoRDCoderConfig
base_model_prefix = "transformer"
supports_gradient_checkpointing = True
_no_split_modules = ["LoRDCoderBlock"]
_skip_keys_device_placement = "past_key_values"
def __init__(self, *inputs, **kwargs):
super().__init__(*inputs, **kwargs)
def _init_weights(self, module):
"""Initialize the weights."""
if isinstance(module, LoRDCoderMLP):
# Reinitialize selected weights subject to the OpenAI GPT-2 Paper Scheme:
# > A modified initialization which accounts for the accumulation on the residual path with model depth. Scale
# > the weights of residual layers at initialization by a factor of 1/√N where N is the # of residual layers.
# > -- GPT-2 :: https://openai.com/blog/better-language-models/
#
# Reference (Megatron-LM): https://github.com/NVIDIA/Megatron-LM/blob/main/megatron/model/gpt_model.py
module.c_proj.weight.data.normal_(
mean=0.0, std=(self.config.initializer_range / math.sqrt(2 * self.config.n_layer))
)
module.c_proj._is_hf_initialized = True
elif isinstance(module, LoRDCoderAttention):
# Reinitialize selected weights subject to the OpenAI GPT-2 Paper Scheme:
# > A modified initialization which accounts for the accumulation on the residual path with model depth. Scale
# > the weights of residual layers at initialization by a factor of 1/√N where N is the # of residual layers.
# > -- GPT-2 :: https://openai.com/blog/better-language-models/
#
# Reference (Megatron-LM): https://github.com/NVIDIA/Megatron-LM/blob/main/megatron/model/gpt_model.py
module.c_proj.weight.data.normal_(
mean=0.0, std=(self.config.initializer_range / math.sqrt(2 * self.config.n_layer))
)
module.c_proj.weight.data.normal_(
mean=0.0, std=(self.config.initializer_range / math.sqrt(2 * self.config.n_layer))
)
module.c_proj._is_hf_initialized = True
elif isinstance(module, nn.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, nn.LayerNorm):
module.bias.data.zero_()
module.weight.data.fill_(1.0)
def _set_gradient_checkpointing(self, module, value=False):
if isinstance(module, LoRDCoderModel):
module.gradient_checkpointing = value
class LoRDCoderModel(LoRDCoderPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.multi_query = config.multi_query
self.embed_dim = config.hidden_size
self.wte = nn.Embedding(config.vocab_size, self.embed_dim)
self.wpe = nn.Embedding(config.max_position_embeddings, self.embed_dim)
self.drop = nn.Dropout(config.embd_pdrop)
self.h = nn.ModuleList([LoRDCoderBlock(config, layer_idx=i) for i in range(config.num_hidden_layers)])
self.ln_f = nn.LayerNorm(self.embed_dim, eps=config.layer_norm_epsilon)
max_positions = config.max_position_embeddings
self.register_buffer(
"bias", torch.tril(torch.ones((max_positions, max_positions), dtype=torch.bool)), persistent=False
)
self.gradient_checkpointing = False
# Initialize weights and apply final processing
self.post_init()
def get_input_embeddings(self):
return self.wte
def set_input_embeddings(self, new_embeddings):
self.wte = new_embeddings
def warn_if_padding_and_no_attention_mask(self, input_ids, attention_mask):
"""
Shows a one-time warning if the input_ids appear to contain padding and no attention mask was given.
"""
if (attention_mask is not None) or (self.config.pad_token_id is None):
return
# Check only the first and last input IDs to reduce overhead.
if self.config.pad_token_id in input_ids[:, [-1, 0]]:
warn_string = (
"We strongly recommend passing in an `attention_mask` since your input_ids may be padded. See "
"https://huggingface.co/docs/transformers/troubleshooting"
"#incorrect-output-when-padding-tokens-arent-masked."
)
# If the pad token is equal to either BOS, EOS, or SEP, we do not know whether the user should use an
# attention_mask or not. In this case, we should still show a warning because this is a rare case.
if (
(self.config.bos_token_id is not None and self.config.bos_token_id == self.config.pad_token_id)
or (self.config.eos_token_id is not None and self.config.eos_token_id == self.config.pad_token_id)
or (self.config.sep_token_id is not None and self.config.sep_token_id == self.config.pad_token_id)
):
warn_string += (
f"\nYou may ignore this warning if your `pad_token_id` ({self.config.pad_token_id}) is identical "
f"to the `bos_token_id` ({self.config.bos_token_id}), `eos_token_id` ({self.config.eos_token_id}), "
f"or the `sep_token_id` ({self.config.sep_token_id}), and your input is not padded."
)
print("Warning:", warn_string)
def forward(
self,
input_ids: Optional[torch.Tensor] = None,
past_key_values: Optional[List[torch.Tensor]] = None,
attention_mask: Optional[torch.Tensor] = None,
token_type_ids: Optional[torch.Tensor] = None,
position_ids: Optional[torch.Tensor] = None,
head_mask: Optional[torch.Tensor] = None,
inputs_embeds: Optional[torch.Tensor] = None,
encoder_hidden_states: Optional[torch.Tensor] = None,
encoder_attention_mask: Optional[torch.Tensor] = None,
use_cache: Optional[bool] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> Union[Tuple, BaseModelOutputWithPastAndCrossAttentions]:
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:
self.warn_if_padding_and_no_attention_mask(input_ids, attention_mask)
input_shape = input_ids.size()
input_ids = input_ids.view(-1, input_shape[-1])
batch_size = input_ids.shape[0]
elif inputs_embeds is not None:
input_shape = inputs_embeds.size()[:-1]
batch_size = inputs_embeds.shape[0]
else:
raise ValueError("You have to specify either input_ids or inputs_embeds")
if batch_size <= 0:
raise ValueError("batch_size has to be defined and > 0")
device = input_ids.device if input_ids is not None else inputs_embeds.device
if token_type_ids is not None:
token_type_ids = token_type_ids.view(-1, input_shape[-1])
if position_ids is not None:
position_ids = position_ids.view(-1, input_shape[-1])
if past_key_values is None:
past_length = 0
past_key_values = tuple([None] * len(self.h))
else:
past_length = past_key_values[0].size(-2)
if attention_mask is not None and len(attention_mask.shape) == 2 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_length > 0:
position_ids = position_ids[:, past_length : input_shape[-1] + past_length :]
elif position_ids is None:
position_ids = torch.arange(past_length, input_shape[-1] + past_length, dtype=torch.long, device=device)
position_ids = position_ids.unsqueeze(0).view(-1, input_shape[-1])
# Self-attention mask.
query_length = input_shape[-1]
key_length = past_length + query_length
self_attention_mask = self.bias[None, key_length - query_length : key_length, :key_length]
if attention_mask is not None:
self_attention_mask = self_attention_mask * attention_mask.view(batch_size, 1, -1).to(
dtype=torch.bool, device=self_attention_mask.device
)
# MQA models: (batch_size, query_length, n_heads, key_length)
# MHA models: (batch_size, n_heads, query_length, key_length)
attention_mask = self_attention_mask.unsqueeze(2 if self.multi_query else 1)
# If a 2D or 3D attention mask is provided for the cross-attention
# we need to make broadcastable to [batch_size, num_heads, seq_length, seq_length]
if (
self.config.add_cross_attention
and encoder_hidden_states is not None
and encoder_attention_mask is not None
):
if encoder_attention_mask.dim() == 2:
encoder_attention_mask.unsqueeze(1)
assert encoder_attention_mask.dim() == 3
encoder_attention_mask = encoder_attention_mask.bool().unsqueeze(2 if self.multi_query else 1)
else:
encoder_attention_mask = None
# Prepare head mask if needed
# 1.0 in head_mask indicate we keep the head
# attention_probs has shape bsz x n_heads x N x N
# head_mask has shape n_layer x batch x n_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.wte(input_ids)
position_embeds = self.wpe(position_ids)
hidden_states = inputs_embeds + position_embeds
if token_type_ids is not None:
token_type_embeds = self.wte(token_type_ids)
hidden_states = hidden_states + token_type_embeds
hidden_states = self.drop(hidden_states)
output_shape = input_shape + (hidden_states.size(-1),)
presents = [] if use_cache else None
all_self_attentions = () if output_attentions else None
all_cross_attentions = () if output_attentions and self.config.add_cross_attention else None
all_hidden_states = () if output_hidden_states else None
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:
def create_custom_forward(module):
def custom_forward(*inputs):
# None for past_key_value
return module(*inputs, use_cache, output_attentions)
return custom_forward
outputs = torch.utils.checkpoint.checkpoint(
create_custom_forward(block),
hidden_states,
None,
attention_mask,
head_mask[i],
encoder_hidden_states,
encoder_attention_mask,
)
else:
outputs = block(
hidden_states,
layer_past=layer_past,
attention_mask=attention_mask,
head_mask=head_mask[i],
encoder_hidden_states=encoder_hidden_states,
encoder_attention_mask=encoder_attention_mask,
use_cache=use_cache,
output_attentions=output_attentions,
)
hidden_states = outputs[0]
if use_cache:
presents.append(outputs[1])
if output_attentions:
all_self_attentions = all_self_attentions + (outputs[2 if use_cache else 1],)
if self.config.add_cross_attention:
all_cross_attentions = all_cross_attentions + (outputs[3 if use_cache else 2],)
hidden_states = self.ln_f(hidden_states)
hidden_states = hidden_states.view(output_shape)
# Add last hidden state
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, all_cross_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,
cross_attentions=all_cross_attentions,
)
class LoRDCoderForCausalLM(LoRDCoderPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.transformer = LoRDCoderModel(config)
self.lm_head = lambda x: torch.matmul(x, self.transformer.wte.weight.T)
# 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):
raise NotImplementedError("Cannot resize the embeddings of LoRDCoderForCausalLM.")
def prepare_inputs_for_generation(self, input_ids, past_key_values=None, inputs_embeds=None, **kwargs):
token_type_ids = kwargs.get("token_type_ids", None)
# only last token for inputs_ids if past is defined in kwargs
if past_key_values:
input_ids = input_ids[:, -1].unsqueeze(-1)
if token_type_ids is not None:
token_type_ids = token_type_ids[:, -1].unsqueeze(-1)
attention_mask = kwargs.get("attention_mask", None)
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)
else:
position_ids = None
# 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 = {"inputs_embeds": inputs_embeds}
else:
model_inputs = {"input_ids": input_ids}
model_inputs.update(
{
"past_key_values": past_key_values,
"use_cache": kwargs.get("use_cache"),
"position_ids": position_ids,
"attention_mask": attention_mask,
"token_type_ids": token_type_ids,
}
)
return model_inputs
def forward(
self,
input_ids: Optional[torch.Tensor] = None,
past_key_values: Optional[Tuple[Tuple[torch.Tensor]]] = None,
attention_mask: Optional[torch.Tensor] = None,
token_type_ids: Optional[torch.Tensor] = None,
position_ids: Optional[torch.Tensor] = None,
head_mask: Optional[torch.Tensor] = None,
inputs_embeds: Optional[torch.Tensor] = None,
encoder_hidden_states: Optional[torch.Tensor] = None,
encoder_attention_mask: 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,
) -> Union[Tuple, CausalLMOutputWithCrossAttentions]:
r"""
labels (`torch.Tensor` 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]`
"""
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,
token_type_ids=token_type_ids,
position_ids=position_ids,
head_mask=head_mask,
inputs_embeds=inputs_embeds,
encoder_hidden_states=encoder_hidden_states,
encoder_attention_mask=encoder_attention_mask,
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().to(shift_logits.device)
# Flatten the tokens
loss_fct = CrossEntropyLoss()
loss = loss_fct(shift_logits.view(-1, shift_logits.size(-1)), shift_labels.view(-1))
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,
cross_attentions=transformer_outputs.cross_attentions,
)
@staticmethod
def _reorder_cache(
past_key_values: Tuple[Tuple[torch.Tensor]], beam_idx: torch.Tensor
) -> Tuple[Tuple[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.
"""
return tuple(layer_past.index_select(0, beam_idx.to(layer_past.device)) for layer_past in past_key_values)