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# coding=utf-8
# Copyright 2022 EleutherAI and the HuggingFace Inc. team. All rights reserved.
#
# This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX
# and OPT implementations in this library. It has been modified from its
# original forms to accommodate minor architectural differences compared
# to GPT-NeoX and OPT used by the Meta AI team that trained the model.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""
Thin wrappers and replacement classes for LlamaForCausalLM
- Simple sharding across multiple GPUs; will be slow but good for quality evals
- May need to update for Llama 405B
"""
from typing import Optional, Tuple, List, Union
import warnings
import torch
import torch.nn as nn
import torch.nn.functional as F
from transformers.models.llama.modeling_llama import (
LlamaModel, LlamaForCausalLM, LLAMA_INPUTS_DOCSTRING,
)
from transformers.modeling_outputs import BaseModelOutputWithPast, CausalLMOutputWithPast
from transformers.cache_utils import Cache, DynamicCache
from transformers.utils import (
add_start_docstrings_to_model_forward, logging,
)
from .convert_model import get_attention_cache
logger = logging.get_logger(__name__)
# Modified from transformers.models.llama.modeling_llama.LlamaModel (v4.43)
class ShardedLolcatsLlamaModel(LlamaModel):
"""
Wrapper for Llama or Mistral-like base model
Modified from transformers.models.llama.modeling_llama.LlamaModel
-> Only difference is using KV state for past_key_values instead of cache
"""
def __init__(self, *args: any, **kwargs: any):
super().__init__(*args, **kwargs)
self.layerwise_cpu = False
@add_start_docstrings_to_model_forward(LLAMA_INPUTS_DOCSTRING)
def forward(
self,
input_ids: torch.LongTensor = None,
attention_mask: Optional[torch.Tensor] = None,
position_ids: Optional[torch.LongTensor] = None,
past_key_values: Optional[Union[Cache, 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,
cache_position: Optional[torch.LongTensor] = None,
) -> Union[Tuple, BaseModelOutputWithPast]:
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 None) ^ (inputs_embeds is not None):
raise ValueError(
"You cannot specify both input_ids and inputs_embeds at the same time, and must specify either one"
)
if self.gradient_checkpointing and self.training and use_cache:
logger.warning_once(
"`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`."
)
use_cache = False
batch_size, seq_length = input_ids.shape
if inputs_embeds is None:
inputs_embeds = self.embed_tokens(input_ids)
return_legacy_cache = False
if use_cache:
if past_key_values is None or isinstance(past_key_values, DynamicCache): # Determine and setup our KV cache or state
attention_type = getattr(self.layers[0].self_attn, 'attention_type', None)
past_key_values = get_attention_cache(attention_type, past_key_values)
else:
past_key_values.get_usable_length(seq_length)
if cache_position is None:
past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0
cache_position = torch.arange(
past_seen_tokens, past_seen_tokens + inputs_embeds.shape[1], device=inputs_embeds.device
)
if position_ids is None:
position_ids = cache_position.unsqueeze(0)
causal_mask = self._update_causal_mask(
attention_mask, inputs_embeds, cache_position, past_key_values, output_attentions
)
hidden_states = inputs_embeds
# create position embeddings to be shared across the decoder layers
# - ignored for linearized models
position_embeddings = None
# position_embeddings = self.rotary_emb(hidden_states, position_ids.to(hidden_states.device))
# decoder layers
all_hidden_states = () if output_hidden_states else None
all_self_attns = () if output_attentions else None
next_decoder_cache = None
for decoder_layer in self.layers:
# Move output to right device
device = decoder_layer.self_attn.q_proj.weight.device
hidden_states = hidden_states.to(device)
position_ids = position_ids.to(device)
if attention_mask is not None:
attention_mask = attention_mask.to(device)
if output_hidden_states:
all_hidden_states += (hidden_states,)
if getattr(decoder_layer.self_attn, 'converted', False):
if self.gradient_checkpointing and self.training:
layer_outputs = self._gradient_checkpointing_func(
decoder_layer.__call__,
hidden_states,
causal_mask,
position_ids,
past_key_values,
output_attentions,
use_cache,
cache_position,
position_embeddings,
)
else:
layer_outputs = decoder_layer(
hidden_states,
attention_mask=causal_mask,
position_ids=position_ids,
past_key_value=past_key_values,
output_attentions=output_attentions,
use_cache=use_cache,
cache_position=cache_position,
position_embeddings=position_embeddings,
)
else:
with torch.no_grad():
layer_outputs = decoder_layer(
hidden_states,
attention_mask=attention_mask,
position_ids=position_ids,
past_key_value=past_key_values,
output_attentions=output_attentions,
use_cache=use_cache,
cache_position=cache_position,
position_embeddings=position_embeddings,
)
hidden_states = layer_outputs[0]
if use_cache:
next_decoder_cache = layer_outputs[2 if output_attentions else 1]
if output_attentions:
all_self_attns += (layer_outputs[1],)
hidden_states = self.norm(hidden_states.to(self.norm.weight.device))
# add hidden states from the last decoder layer
if output_hidden_states:
all_hidden_states += (hidden_states,)
next_cache = next_decoder_cache if use_cache else None
if return_legacy_cache:
next_cache = next_cache.to_legacy_cache()
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 ShardedLolcatsLlamaForCausalLM(LlamaForCausalLM):
"""
Wrapper for Llama-like autoregressive language model
"""
def __init__(self, config):
# Adapt config to LlamaConfig
if getattr(config, 'attention_bias', None) is None:
config.attention_bias = False
if getattr(config, 'rope_scaling', None) is None:
config.rope_scaling = None
if getattr(config, 'pretraining_tp', None) is None:
config.pretraining_tp = 1
super().__init__(config)
self.model = ShardedLolcatsLlamaModel(config)
self.vocab_size = config.vocab_size
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, *args: any, labels: Optional[torch.LongTensor] = None, **kwargs: any):
# decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)
outputs = self.model(*args, **kwargs)
hidden_states = outputs[0]
if getattr(self.model.layers[0].self_attn, 'train_attention', False):
logits = None
else: # regular training
if self.config.pretraining_tp > 1:
lm_head_slices = self.lm_head.weight.split(self.vocab_size // self.config.pretraining_tp, dim=0)
logits = [F.linear(hidden_states, lm_head_slices[i])
for i in range(self.config.pretraining_tp)]
logits = torch.cat(logits, dim=-1)
else:
logits = self.lm_head(hidden_states)
logits = logits.float()
return CausalLMOutputWithPast(
logits=logits,
past_key_values=outputs.past_key_values,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
)
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