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import copy
from typing import Optional, List, Union, Tuple
from transformers import MBartForCausalLM, MBartConfig
from torch import nn
from transformers.activations import ACT2FN
from transformers.modeling_outputs import CausalLMOutputWithCrossAttentions, BaseModelOutputWithPastAndCrossAttentions
from transformers.models.mbart.modeling_mbart import MBartPreTrainedModel, MBartDecoder
from .config import MBartMoEConfig
import torch
import math
class MBartLearnedPositionalEmbedding(nn.Embedding):
"""
This module learns positional embeddings up to a fixed maximum size.
"""
def __init__(self, num_embeddings: int, embedding_dim: int):
# MBart is set up so that if padding_idx is specified then offset the embedding ids by 2
# and adjust num_embeddings appropriately. Other models don't have this hack
self.offset = 2
super().__init__(num_embeddings + self.offset, embedding_dim)
def forward(self, input_ids: torch.Tensor, past_key_values_length: int = 0):
"""`input_ids' shape is expected to be [bsz x seqlen]."""
bsz, seq_len = input_ids.shape[:2]
positions = torch.arange(
past_key_values_length, past_key_values_length + seq_len, dtype=torch.long, device=self.weight.device
).expand(bsz, -1)
return super().forward(positions + self.offset)
class MBartExpertMLP(nn.Module):
def __init__(self, config: MBartConfig, is_lg=False, is_xl=False):
super().__init__()
self.ffn_dim = config.d_expert
if is_lg:
self.ffn_dim = config.d_expert_lg
if is_xl:
self.ffn_dim = config.d_expert_xl
self.hidden_dim = config.d_model
self.w1 = nn.Linear(self.hidden_dim, self.ffn_dim, bias=False)
self.w2 = nn.Linear(self.ffn_dim, self.hidden_dim, bias=False)
self.w3 = nn.Linear(self.hidden_dim, self.ffn_dim, bias=False)
self.dropout = nn.Dropout(config.activation_dropout)
self.act_fn = ACT2FN[config.activation_function]
def forward(self, hidden_states):
current_hidden_states = self.act_fn(self.w1(hidden_states)) * self.w3(hidden_states)
current_hidden_states = self.w2(current_hidden_states)
return current_hidden_states
class MBartExpertLayer(nn.Module):
# From mixtral, with modifications
def __init__(self, config):
super().__init__()
self.dropout = nn.Dropout(config.activation_dropout)
self.hidden_dim = config.d_model
self.lg_lang_codes = sorted(config.lg_langs.values()) if hasattr(config, "lg_langs") else []
self.xl_lang_codes = sorted(config.xl_langs.values()) if hasattr(config, "xl_langs") else []
self.lang_codes = sorted(config.langs.values())
self.num_experts = len(self.lang_codes)
self.experts = nn.ModuleDict({str(lang): MBartExpertMLP(config, is_lg=(lang in self.lg_lang_codes), is_xl=(lang in self.xl_lang_codes)) for lang in self.lang_codes})
def forward(self, hidden_states: torch.Tensor, langs: torch.LongTensor) -> torch.Tensor:
batch_size, sequence_length, hidden_dim = hidden_states.shape
final_hidden_states = torch.zeros(
(batch_size, sequence_length, hidden_dim), dtype=hidden_states.dtype, device=hidden_states.device
)
# Weight experts based on how many languages in the input
routing_weights = 1 / ((langs > 3).sum(axis=-1))
# Set weights to 1 if zero experts activated
routing_weights[torch.isinf(routing_weights)] = 1
unique_langs = langs.unique(dim=None, sorted=True)
unique_langs = unique_langs[unique_langs > 3] # Remove start token
# Loop over all available experts in the model and perform the computation on each expert
for expert_lang in unique_langs:
# Check which samples match with this expert
lang_match = (langs == expert_lang).any(dim=-1)
idx = torch.nonzero(lang_match, as_tuple=True)[0]
if idx.shape[0] == 0:
continue
expert_layer = self.experts[str(expert_lang.item())]
current_state = hidden_states[idx]
current_hidden_states = expert_layer(current_state.view(-1, hidden_dim))
current_hidden_states = current_hidden_states.view(-1, sequence_length, hidden_dim)
# Weight by number of languages in the input
selected_routing_weights = routing_weights[idx].view(-1, 1, 1)
current_hidden_states *= selected_routing_weights
final_hidden_states.index_add_(0, idx, current_hidden_states)
return final_hidden_states
class MBartGQAttention(nn.Module):
def __init__(
self,
embed_dim: int,
num_heads: int,
num_kv_heads: int,
dropout: float = 0.0,
is_decoder: bool = False,
bias: bool = True,
is_causal: bool = False,
config: Optional[MBartConfig] = None,
):
super().__init__()
self.embed_dim = embed_dim
self.num_heads = num_heads
self.num_kv_heads = num_kv_heads
self.num_kv_groups = self.num_heads // self.num_kv_heads
self.dropout = dropout
self.head_dim = embed_dim // num_heads
self.config = config
self.scaling = self.head_dim**-0.5
self.is_decoder = is_decoder
self.is_causal = is_causal
self.k_proj = nn.Linear(embed_dim, self.num_kv_heads * self.head_dim, bias=bias)
self.v_proj = nn.Linear(embed_dim, self.num_kv_heads * self.head_dim, bias=bias)
self.q_proj = nn.Linear(embed_dim, embed_dim, bias=bias)
self.out_proj = nn.Linear(embed_dim, embed_dim, bias=bias)
def _shape(self, tensor: torch.Tensor, seq_len: int, bsz: int):
return tensor.view(bsz, seq_len, self.num_heads, self.head_dim).transpose(1, 2).contiguous()
def _shape_key_value(self, tensor: torch.Tensor, seq_len: int, bsz: int):
return tensor.view(bsz, seq_len, self.num_kv_heads, self.head_dim).transpose(1, 2).contiguous()
def forward(
self,
hidden_states: torch.Tensor,
key_value_states: Optional[torch.Tensor] = None,
past_key_value: Optional[Tuple[torch.Tensor]] = None,
is_prefill: Optional[bool] = False,
attention_mask: Optional[torch.Tensor] = None,
) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
"""Input shape: Batch x Time x Channel"""
# if key_value_states are provided this layer is used as a cross-attention layer
# for the decoder
is_cross_attention = key_value_states is not None
bsz, tgt_len, _ = hidden_states.size()
# get query proj
query_states = self.q_proj(hidden_states) * self.scaling
# get key, value proj
# `past_key_value[0].shape[2] == key_value_states.shape[1]`
# is checking that the `sequence_length` of the `past_key_value` is the same as
# the provided `key_value_states` to support prefix tuning
if is_cross_attention:
if is_prefill:
# cross_attentions
key_states = self._shape_key_value(self.k_proj(key_value_states), -1, bsz)
value_states = self._shape_key_value(self.v_proj(key_value_states), -1, bsz)
past_key_value = torch.cat([key_states.unsqueeze(0), value_states.unsqueeze(0)], dim=0)
else:
# reuse k,v, cross_attentions
key_states = past_key_value[0]
value_states = past_key_value[1]
past_key_value = None
# Self-attention
else:
if is_prefill:
# initial prompt
key_states = self._shape_key_value(self.k_proj(hidden_states), -1, bsz)
value_states = self._shape_key_value(self.v_proj(hidden_states), -1, bsz)
past_key_value = torch.cat([key_states[:, :, -tgt_len:].unsqueeze(0), value_states[:, :, -tgt_len:].unsqueeze(0)], dim=0)
else:
# reuse k, v, self_attention
key_states = self._shape_key_value(self.k_proj(hidden_states), -1, bsz)
value_states = self._shape_key_value(self.v_proj(hidden_states), -1, bsz)
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 = torch.cat([key_states[:, :, -tgt_len:].unsqueeze(0), value_states[:, :, -tgt_len:].unsqueeze(0)], dim=0)
proj_shape = (bsz * self.num_heads, -1, self.head_dim)
query_states = self._shape(query_states, tgt_len, bsz).view(*proj_shape)
# Expand kv heads, then match query shape
key_states = key_states.repeat_interleave(self.num_kv_groups, dim=1).reshape(*proj_shape)
value_states = value_states.repeat_interleave(self.num_kv_groups, dim=1).reshape(*proj_shape)
src_len = key_states.size(1)
attn_weights = torch.bmm(query_states, key_states.transpose(1, 2))
if not is_cross_attention:
attn_weights = attn_weights.view(bsz, self.num_heads, tgt_len, src_len) + attention_mask
attn_weights = attn_weights.view(bsz * self.num_heads, tgt_len, src_len)
attn_weights = nn.functional.softmax(attn_weights, dim=-1)
attn_output = torch.bmm(attn_weights, value_states).view(bsz, self.num_heads, tgt_len, self.head_dim).transpose(1,2)
# Use the `embed_dim` from the config (stored in the class) rather than `hidden_state` because `attn_output` can be
# partitioned across GPUs when using tensor-parallelism.
attn_output = attn_output.reshape(bsz, tgt_len, self.embed_dim)
attn_output = self.out_proj(attn_output)
return attn_output, past_key_value
class MBartMoEDecoderLayer(nn.Module):
def __init__(self, config: MBartConfig, has_moe=False):
super().__init__()
self.embed_dim = config.d_model
self.self_attn = MBartGQAttention(
embed_dim=self.embed_dim,
num_heads=config.decoder_attention_heads,
num_kv_heads=config.kv_heads,
dropout=config.attention_dropout,
is_decoder=True,
is_causal=True,
config=config,
)
self.dropout = config.dropout
self.activation_fn = ACT2FN[config.activation_function]
self.activation_dropout = config.activation_dropout
self.self_attn_layer_norm = nn.LayerNorm(self.embed_dim)
self.encoder_attn = MBartGQAttention(
self.embed_dim,
config.decoder_attention_heads,
num_kv_heads=config.kv_heads,
dropout=config.attention_dropout,
is_decoder=True,
config=config,
)
self.encoder_attn_layer_norm = nn.LayerNorm(self.embed_dim)
self.has_moe = has_moe
if has_moe:
self.moe = MBartExpertLayer(config)
else:
self.fc1 = nn.Linear(self.embed_dim, config.decoder_ffn_dim)
self.fc2 = nn.Linear(config.decoder_ffn_dim, self.embed_dim)
self.final_layer_norm = nn.LayerNorm(self.embed_dim)
def forward(
self,
hidden_states: torch.Tensor,
attention_mask: Optional[torch.Tensor] = None,
langs: Optional[torch.LongTensor] = None,
self_kv_cache: Optional[torch.Tensor] = None,
cross_kv_cache: Optional[torch.Tensor] = None,
is_prefill: Optional[bool] = False,
encoder_hidden_states: Optional[torch.Tensor] = None,
encoder_attention_mask: Optional[torch.Tensor] = None,
use_cache: Optional[bool] = True,
) -> torch.Tensor:
residual = hidden_states
hidden_states = self.self_attn_layer_norm(hidden_states)
# Self Attention
# decoder uni-directional self-attention cached key/values tuple is at positions 1,2
# add present self-attn cache to positions 1,2 of present_key_value tuple
hidden_states, present_key_value = self.self_attn(
hidden_states=hidden_states,
past_key_value=self_kv_cache,
is_prefill=is_prefill,
attention_mask=attention_mask,
)
hidden_states = residual + hidden_states
# Cross-Attention Block
if encoder_hidden_states is not None:
residual = hidden_states
hidden_states = self.encoder_attn_layer_norm(hidden_states)
# cross_attn cached key/values tuple is at positions 3,4 of present_key_value tuple
hidden_states, cross_attn_present_key_value = self.encoder_attn(
hidden_states=hidden_states,
key_value_states=encoder_hidden_states,
is_prefill=is_prefill,
attention_mask=encoder_attention_mask,
past_key_value=cross_kv_cache,
)
hidden_states = residual + hidden_states
# add cross-attn to positions 3,4 of present_key_value tuple
present_key_value = (present_key_value, cross_attn_present_key_value)
# Fully Connected
residual = hidden_states
hidden_states = self.final_layer_norm(hidden_states)
if self.has_moe:
hidden_states = self.moe(hidden_states, langs)
else:
hidden_states = self.activation_fn(self.fc1(hidden_states))
hidden_states = self.fc2(hidden_states)
hidden_states = residual + hidden_states
outputs = (hidden_states,)
if use_cache:
outputs += (present_key_value,)
return outputs
class MBartMoEDecoder(MBartDecoder):
def __init__(self, config: MBartConfig, embed_tokens: Optional[nn.Embedding] = None):
MBartPreTrainedModel.__init__(self, config)
self.dropout = config.dropout
self.layerdrop = config.decoder_layerdrop
self.padding_idx = config.pad_token_id
self.max_target_positions = config.max_position_embeddings
self.embed_scale = math.sqrt(config.d_model) if config.scale_embedding else 1.0
self.embed_tokens = nn.Embedding(config.vocab_size, config.d_model, self.padding_idx)
if embed_tokens is not None:
self.embed_tokens.weight = embed_tokens.weight
self.embed_positions = MBartLearnedPositionalEmbedding(
config.max_position_embeddings,
config.d_model,
)
# Language-specific MoE goes at second and second-to-last layer
self.layers = nn.ModuleList([MBartMoEDecoderLayer(config, has_moe=(i in config.moe_layers) and config.use_moe) for i in range(config.decoder_layers)])
self.layernorm_embedding = nn.LayerNorm(config.d_model)
self.layer_norm = nn.LayerNorm(config.d_model)
self.gradient_checkpointing = False
# Initialize weights and apply final processing
self.post_init()
def forward(
self,
input_ids: torch.LongTensor = None,
attention_mask: Optional[torch.Tensor] = None,
self_kv_cache: Optional[torch.Tensor] = None,
cross_kv_cache: Optional[torch.Tensor] = None,
past_token_count: Optional[int] = None,
langs: Optional[torch.LongTensor] = None,
encoder_hidden_states: Optional[torch.FloatTensor] = None,
) -> Union[Tuple, BaseModelOutputWithPastAndCrossAttentions]:
use_cache = True
return_dict = True
input = input_ids
input_shape = input.size()
input_ids = input_ids.view(-1, input_shape[-1])
# past_key_values_length
past_key_values_length = past_token_count if self_kv_cache is not None else 0
inputs_embeds = self.embed_tokens(input_ids) * self.embed_scale
# embed positions
positions = self.embed_positions(input, past_key_values_length)
hidden_states = inputs_embeds + positions
hidden_states = self.layernorm_embedding(hidden_states)
# decoder layers
all_hidden_states = None
all_self_attns = None
all_cross_attentions = None
next_decoder_cache = () if use_cache else None
for idx, decoder_layer in enumerate(self.layers):
is_prefill = past_token_count == 0
layer_self_kv_cache = self_kv_cache[idx] if self_kv_cache is not None else None
layer_cross_kv_cache = cross_kv_cache[idx] if cross_kv_cache is not None else None
layer_outputs = decoder_layer(
hidden_states,
attention_mask=attention_mask,
langs=langs,
self_kv_cache=layer_self_kv_cache,
cross_kv_cache=layer_cross_kv_cache,
is_prefill=is_prefill,
encoder_hidden_states=encoder_hidden_states,
encoder_attention_mask=None,
use_cache=use_cache,
)
hidden_states = layer_outputs[0]
if use_cache:
next_decoder_cache += (layer_outputs[1],)
hidden_states = self.layer_norm(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, all_cross_attentions]
if v is not None
)
return BaseModelOutputWithPastAndCrossAttentions(
last_hidden_state=hidden_states,
past_key_values=next_cache,
hidden_states=all_hidden_states,
attentions=all_self_attns,
cross_attentions=all_cross_attentions,
)
class MBartMoEDecoderWrapper(MBartPreTrainedModel):
"""
This wrapper class is a helper class to correctly load pretrained checkpoints when the causal language model is
used in combination with the [`EncoderDecoderModel`] framework.
"""
def __init__(self, config):
super().__init__(config)
self.decoder = MBartMoEDecoder(config)
def forward(self, *args, **kwargs):
return self.decoder(*args, **kwargs)
class MBartMoE(MBartForCausalLM):
config_class = MBartMoEConfig
_tied_weights_keys = ["lm_head.weight"]
def __init__(self, config, **kwargs):
config = copy.deepcopy(config)
config.is_decoder = True
config.is_encoder_decoder = False
MBartPreTrainedModel.__init__(self, config)
self.model = MBartMoEDecoderWrapper(config)
self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
# Initialize weights and apply final processing
self.post_init()
def forward(
self,
input_ids: torch.LongTensor = None,
attention_mask: Optional[torch.Tensor] = None,
self_kv_cache: Optional[torch.FloatTensor] = None,
cross_kv_cache: Optional[torch.FloatTensor] = None,
past_token_count: Optional[int] = None,
langs: Optional[torch.LongTensor] = None,
encoder_hidden_states: Optional[torch.FloatTensor] = None,
encoder_attention_mask: Optional[torch.FloatTensor] = None,
head_mask: Optional[torch.Tensor] = None,
cross_attn_head_mask: Optional[torch.Tensor] = None,
past_key_values: Optional[Tuple[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,
**kwargs
) -> Union[Tuple, CausalLMOutputWithCrossAttentions]:
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.decoder(
input_ids=input_ids,
attention_mask=attention_mask,
self_kv_cache=self_kv_cache,
cross_kv_cache=cross_kv_cache,
past_token_count=past_token_count,
langs=langs,
encoder_hidden_states=encoder_hidden_states,
)
logits = self.lm_head(outputs[0])
if not return_dict:
output = (logits,) + outputs[1:]
return output
return CausalLMOutputWithCrossAttentions(
loss=None,
logits=logits,
past_key_values=outputs.past_key_values,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
cross_attentions=outputs.cross_attentions,
)
def prune_moe_experts(self, keep_keys: List[int]):
# Remove experts not specified in keep_keys
str_keep_keys = [str(key) for key in keep_keys]
for layer in self.model.decoder.layers:
if not layer.has_moe:
continue
lang_keys = list(layer.moe.experts.keys())
for lang in lang_keys:
if lang not in str_keep_keys:
layer.moe.experts.pop(lang)
layer.lang_codes = keep_keys
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