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import math
from dataclasses import dataclass
from typing import Optional, Tuple
import torch
import torch.utils.checkpoint
from torch import nn
from transformers.activations import ACT2FN
from transformers.pytorch_utils import Conv1D
from transformers.utils import (
ModelOutput,
logging,
)
from transformers.models.gpt2.modeling_gpt2 import GPT2Model, GPT2PreTrainedModel
from .configuration_backpack_gpt2 import BackpackGPT2Config
logger = logging.get_logger(__name__)
### Backpack-Specific
class BackpackGPT2PreTrainedModel(GPT2PreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.
"""
_keys_to_ignore_on_load_missing = [r"attn.masked_bias", r"attn.bias"]
config_class = BackpackGPT2Config
base_model_prefix = "backpack"
is_parallelizable = True
supports_gradient_checkpointing = False
_no_split_modules = ["GPT2Block", "BackpackNoMixBlock"]
def __init__(self, *inputs, **kwargs):
super().__init__(*inputs, **kwargs)
class BackpackMLP(nn.Module):
def __init__(self, embed_dim, intermediate_dim, out_dim, config):
super().__init__()
self.c_fc = Conv1D(intermediate_dim, embed_dim)
self.c_proj = Conv1D(out_dim, intermediate_dim)
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.act(hidden_states)
hidden_states = self.c_proj(hidden_states)
hidden_states = self.dropout(hidden_states)
return hidden_states
class BackpackNoMixBlock(nn.Module):
def __init__(self, config):
super().__init__()
self.ln_1 = nn.LayerNorm(config.n_embd, eps=config.layer_norm_epsilon)
self.ln_2 = nn.LayerNorm(config.n_embd, eps=config.layer_norm_epsilon)
self.mlp = BackpackMLP(config.n_embd, config.n_embd*4, config.n_embd, config)
self.resid_dropout1 = nn.Dropout(config.resid_pdrop)
self.resid_dropout2 = nn.Dropout(config.resid_pdrop)
def forward(self, hidden_states, residual):
residual = self.resid_dropout1(hidden_states) + residual
hidden_states = self.ln_1(residual)
mlp_out = self.mlp(hidden_states)
residual = self.resid_dropout2(mlp_out) + residual
hidden_states = self.ln_2(residual)
return hidden_states
class BackpackSenseNetwork(nn.Module):
def __init__(self, config, num_senses, device=None, dtype=None):
super().__init__()
self.num_senses = num_senses
#self.embeddings = embeddings
self.n_embd = config.n_embd
self.dropout = nn.Dropout(config.embd_pdrop)
self.block = BackpackNoMixBlock(config)
self.ln = nn.LayerNorm(self.n_embd, eps=config.layer_norm_epsilon)
self.final_mlp = BackpackMLP(
embed_dim=config.n_embd,
intermediate_dim=config.sense_intermediate_scale*config.n_embd,
out_dim=config.n_embd*config.num_senses,
config=config,
)
def forward(self, input_embeds):
residual = self.dropout(input_embeds)
hidden_states = self.ln(residual)
hidden_states = self.block(hidden_states, residual)
senses = self.final_mlp(hidden_states)
bs, s, nvd = senses.shape
return senses.reshape(bs, s, self.num_senses, self.n_embd).transpose(1,2) # (bs, nv, s, d)
class BackpackWeightNetwork(nn.Module):
def __init__(self, num_senses, embed_dim):
super().__init__()
self.n_embd = embed_dim
self.num_senses = num_senses
self.c_attn = nn.Linear(embed_dim, 2*embed_dim)
self.softmax_scale = None
def forward(self, encoded):
b, s, d = encoded.shape
encoded = self.c_attn(encoded) # (b, s, 2*d)
encoded = encoded.reshape(b, s, 2, self.num_senses, d // self.num_senses) #(b, s, 2, nv, d//nv)
batch_size, seqlen = encoded.shape[0], encoded.shape[1]
# compute scores & mask
q, k = encoded.unbind(dim=2)
softmax_scale = self.softmax_scale or 1.0 / math.sqrt(q.shape[-1])
scores = torch.einsum('bthd,bshd->bhts', q, k * softmax_scale)
causal_mask = torch.triu(torch.full((seqlen, seqlen), -10000.0, device=scores.device), 1)
scores = scores + causal_mask.to(dtype=scores.dtype)
return torch.softmax(scores, dim=-1, dtype=q.dtype)
@dataclass
class BackpackGPT2BaseModelOutput(ModelOutput):
hidden_states: torch.FloatTensor = None
contextualization: torch.FloatTensor = None
class BackpackGPT2Model(BackpackGPT2PreTrainedModel):
_keys_to_ignore_on_load_missing = [r".*attn.masked_bias", r".*attn.bias"]
def __init__(self, config):
super().__init__(config)
self.embed_dim = config.n_embd
self.num_senses = config.num_senses
self.gpt2_model = GPT2Model(config)
self.sense_network = BackpackSenseNetwork(config, self.num_senses, self.gpt2_model.wte)
self.word_embeddings = self.gpt2_model.wte
self.position_embeddings = self.gpt2_model.wpe
self.sense_weight_net = BackpackWeightNetwork(self.num_senses, self.embed_dim)
# Model parallel
self.model_parallel = False
self.device_map = None
self.gradient_checkpointing = False
def get_num_senses(self):
return self.num_senses
def get_word_embeddings(self):
return self.word_embeddings
def get_sense_network(self):
return self.sense_network
def forward(self, input_ids, position_ids, **kwargs):
# Compute senses
sense_input_embeds = self.word_embeddings(input_ids)
senses = self.sense_network(sense_input_embeds) # (bs, nv, s, d)
# Compute contextualization weights
contextl_hidden_states = self.gpt2_model(input_ids, position_ids=position_ids).last_hidden_state # (bs, s, d)
contextualization = self.sense_weight_net(contextl_hidden_states) # (bs, nv, s, s)
# Compute resulting outputs
hidden_states = torch.sum(contextualization @ senses, dim=1) # (bs, nv, s, d) -> (bs, s, d)
return BackpackGPT2BaseModelOutput(
hidden_states=hidden_states,
contextualization=contextualization,
)
def run_with_custom_contextualization(self, input_ids, contextualization):
# Compute senses
sense_input_embeds = self.word_embeddings(input_ids)
senses = self.sense_network(sense_input_embeds) # (bs, nv, s, d)
# Compute resulting outputs
hidden_states = torch.sum(contextualization @ senses, dim=1) # (bs, nv, s, d) -> (bs, s, d)
return BackpackGPT2BaseModelOutput(
hidden_states=hidden_states,
contextualization=contextualization,
)
@dataclass
class BackpackGPT2LMHeadModelOutput(ModelOutput):
logits: torch.FloatTensor = None
contextualization: torch.FloatTensor = None
class BackpackGPT2LMHeadModel(BackpackGPT2PreTrainedModel):
_keys_to_ignore_on_load_missing = [r".*attn.masked_bias", r".*attn.bias"]
def __init__(self, config):
super().__init__(config)
self.backpack = BackpackGPT2Model(config)
self.lm_head = nn.Linear(config.n_embd, config.vocab_size, bias=False)
# Model parallel
self.model_parallel = False
self.device_map = None
self.tie_weights()
def tie_weights(self):
self.lm_head.weight = self.backpack.word_embeddings.weight # also tied with the underlying underlying transf
def get_lm_head(self):
return self.lm_head
def prepare_inputs_for_generation(self, input_ids, past_key_values=None, inputs_embeds=None, attention_mask=None, **kwargs):
# prepare_inputs_for_generation needs to be overwritten to support generation
# this is inspired from the one in GPT2LMHeadModel: https://github.com/huggingface/transformers/blob/d533465150532b0c5de167b574e59f64c68b1154/src/transformers/models/gpt2/modeling_gpt2.py#L1007C4-L1007C4
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, position_ids=None, **kwargs):
outputs = self.backpack(input_ids, position_ids=position_ids)
hidden_states, contextualization = outputs.hidden_states, outputs.contextualization
lm_logits = self.lm_head(hidden_states) # (bs, s, V)
return BackpackGPT2LMHeadModelOutput(
logits=lm_logits,
contextualization=contextualization,
)
def run_with_custom_contextualization(self, input_ids, contextualization):
outputs = self.backpack.run_with_custom_contextualization(input_ids, contextualization)
hidden_states, contextualization = outputs.hidden_states, outputs.contextualization
lm_logits = self.lm_head(hidden_states)
return BackpackGPT2LMHeadModelOutput(
logits=lm_logits,
contextualization=contextualization,
)
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