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a47e733 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 | # Copyright 2023 Haotian Liu
#
# 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.
from typing import List, Optional, Tuple, Union
import torch
import torch.nn as nn
from torch.nn import CrossEntropyLoss
from transformers import AutoConfig, AutoModelForCausalLM
from .modeling_phi.modeling_phi import PhiModel, PhiForCausalLM, CausalLMHead, CausalLMLoss
from .modeling_phi.configuration_phi import PhiConfig
from transformers.modeling_outputs import CausalLMOutputWithPast
from ChatUniVi.model.arch import MetaModel, ChatUniViMetaForCausalLM
class ChatUniViConfig(PhiConfig):
model_type = "ChatUniViPhi2"
class ChatUniViPhiModel(MetaModel, PhiModel):
config_class = ChatUniViConfig
def __init__(self, config: PhiConfig):
super(ChatUniViPhiModel, self).__init__(config)
class ChatUniViPhiForCausalLM(PhiForCausalLM, ChatUniViMetaForCausalLM):
config_class = ChatUniViConfig
supports_gradient_checkpointing = True
def __init__(self, config):
super(PhiForCausalLM, self).__init__(config)
self.config = config
self.transformer = ChatUniViPhiModel(config)
self.lm_head = CausalLMHead(config)
self.loss = CausalLMLoss()
self.post_init()
def get_model(self):
return self.transformer
def _set_gradient_checkpointing(self, module, value=False):
module.gradient_checkpointing = value
def forward(
self,
input_ids: torch.LongTensor = None,
attention_mask: 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,
images: Optional[torch.FloatTensor] = None,
return_dict: Optional[bool] = None,
) -> Union[Tuple, CausalLMOutputWithPast]:
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
input_ids, attention_mask, past_key_values, inputs_embeds, labels = self.prepare_inputs_labels_for_multimodal(input_ids, attention_mask, past_key_values, labels, images)
# decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)
outputs = self.transformer(
input_ids=input_ids,
attention_mask=attention_mask,
past_key_values=past_key_values,
inputs_embeds=inputs_embeds,
)
hidden_states = outputs
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 = CrossEntropyLoss()
shift_logits = shift_logits.view(-1, self.config.vocab_size)
shift_labels = shift_labels.view(-1)
# Enable model/pipeline parallelism
shift_labels = shift_labels.to(shift_logits.device)
try:
loss = loss_fct(shift_logits, shift_labels)
except:
loss = torch.nn.Parameter(torch.zeros(1), requires_grad=True)
if not return_dict:
output = (logits,) + outputs
return (loss,) + output if loss is not None else output
return CausalLMOutputWithPast(
loss=loss,
logits=logits,
hidden_states=outputs,
)
def prepare_inputs_for_generation(
self, input_ids, past_key_values=None, attention_mask=None, inputs_embeds=None, **kwargs
):
if past_key_values:
input_ids = input_ids[:, -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 = {"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"),
"attention_mask": attention_mask,
"images": kwargs.get("images", None),
}
)
return model_inputs
AutoConfig.register("ChatUniViPhi2", ChatUniViConfig)
AutoModelForCausalLM.register(ChatUniViConfig, ChatUniViPhiForCausalLM)
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