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import torch
import torch.nn as nn
from gptq import GPTQ
import argparse
from utils import find_layers, DEV, set_seed, get_wikitext2, get_ptb, get_c4, get_ptb_new, get_c4_new, get_loaders
import quant
import transformers
from transformers import AutoTokenizer
from transformers.models.llama.modeling_llama import LlamaModel, LlamaConfig
from transformers.modeling_outputs import BaseModelOutputWithPast
from typing import List, Optional, Tuple, Union
from accelerate import cpu_offload_with_hook, load_checkpoint_in_model
class Offload_LlamaModel(LlamaModel):
def __init__(self, config: LlamaConfig):
super().__init__(config)
def cpu_offload(self, preload):
hook = None
for cpu_offloaded_model in self.layers[preload:]:
_, hook = cpu_offload_with_hook(cpu_offloaded_model, DEV, prev_module_hook=hook)
def forward(
self,
input_ids: torch.LongTensor = None,
attention_mask: Optional[torch.Tensor] = None,
position_ids: Optional[torch.LongTensor] = None,
past_key_values: Optional[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,
) -> Union[Tuple, BaseModelOutputWithPast]:
r"""
Args:
input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you
provide it.
Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
[`PreTrainedTokenizer.__call__`] for details.
[What are input IDs?](../glossary#input-ids)
attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
- 1 for tokens that are **not masked**,
- 0 for tokens that are **masked**.
[What are attention masks?](../glossary#attention-mask)
position_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
Indices of positions of each input sequence tokens in the position embeddings. Selected in the range
`[0, config.n_positions - 1]`.
[What are position IDs?](../glossary#position-ids)
past_key_values (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`):
Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of
shape `(batch_size, num_heads, sequence_length, embed_size_per_head)`) and 2 additional tensors of
Contains pre-computed hidden-states (key and values in the self-attention blocks and in the
cross-attention blocks) that can be used (see `past_key_values` input) to speed up sequential decoding.
If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those
that don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of
all `decoder_input_ids` of shape `(batch_size, sequence_length)`.
use_cache (`bool`, *optional*):
If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding
(see `past_key_values`).
inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation.
This is useful if you want more control over how to convert `input_ids` indices into associated vectors
than the model's internal embedding lookup matrix.
output_attentions (`bool`, *optional*):
Whether or not to return the attentions tensors of all attention layers. See `attentions` under
returned tensors for more detail.
output_hidden_states (`bool`, *optional*):
Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors
for more detail.
return_dict (`bool`, *optional*):
Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
"""
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
# retrieve input_ids and inputs_embeds
if input_ids is not None and inputs_embeds is not None:
raise ValueError("You cannot specify both decoder_input_ids and decoder_inputs_embeds at the same time")
elif input_ids is not None:
batch_size, seq_length = input_ids.shape
elif inputs_embeds is not None:
batch_size, seq_length, _ = inputs_embeds.shape
else:
raise ValueError("You have to specify either decoder_input_ids or decoder_inputs_embeds")
seq_length_with_past = seq_length
past_key_values_length = 0
if past_key_values is not None:
past_key_values_length = past_key_values[0][0].shape[2]
seq_length_with_past = seq_length_with_past + past_key_values_length
if position_ids is None:
device = input_ids.device if input_ids is not None else inputs_embeds.device
position_ids = torch.arange(past_key_values_length, seq_length + past_key_values_length, dtype=torch.long, device=device)
position_ids = position_ids.unsqueeze(0).view(-1, seq_length)
else:
position_ids = position_ids.view(-1, seq_length).long()
if inputs_embeds is None:
inputs_embeds = self.embed_tokens(input_ids)
# embed positions
if attention_mask is None:
attention_mask = torch.ones((batch_size, seq_length_with_past), dtype=torch.bool, device=inputs_embeds.device)
attention_mask = self._prepare_decoder_attention_mask(attention_mask, (batch_size, seq_length), inputs_embeds, past_key_values_length)
hidden_states = inputs_embeds
if self.gradient_checkpointing and self.training:
if use_cache:
logger.warning_once("`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`...")
use_cache = False
# decoder layers
all_hidden_states = () if output_hidden_states else None
all_self_attns = () if output_attentions else None
next_decoder_cache = () if use_cache else None
for idx in range(len(self.layers)):
decoder_layer = self.layers[idx]
if output_hidden_states:
all_hidden_states += (hidden_states, )
past_key_value = past_key_values[idx] if past_key_values is not None else None
if self.gradient_checkpointing and self.training:
def create_custom_forward(module):
def custom_forward(*inputs):
# None for past_key_value
return module(*inputs, output_attentions, None)
return custom_forward
layer_outputs = torch.utils.checkpoint.checkpoint(
create_custom_forward(decoder_layer),
hidden_states,
attention_mask,
position_ids,
None,
)
else:
layer_outputs = decoder_layer(
hidden_states,
attention_mask=attention_mask,
position_ids=position_ids,
past_key_value=past_key_value,
output_attentions=output_attentions,
use_cache=use_cache,
)
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)
# 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 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,
)
def load_quant(model, checkpoint, wbits, groupsize, pre_layer, fused_mlp=True, warmup_autotune=True):
transformers.models.llama.modeling_llama.LlamaModel = Offload_LlamaModel
from transformers import LlamaConfig, LlamaForCausalLM
config = LlamaConfig.from_pretrained(model)
def noop(*args, **kwargs):
pass
torch.nn.init.kaiming_uniform_ = noop
torch.nn.init.uniform_ = noop
torch.nn.init.normal_ = noop
torch.set_default_dtype(torch.half)
transformers.modeling_utils._init_weights = False
torch.set_default_dtype(torch.half)
model = LlamaForCausalLM(config)
torch.set_default_dtype(torch.float)
model = model.eval()
layers = find_layers(model)
for name in ['lm_head']:
if name in layers:
del layers[name]
quant.make_quant_linear(model, layers, wbits, groupsize)
print('Loading model ...')
load_checkpoint_in_model(model, checkpoint, dtype='float16')
model.seqlen = 2048
if eval:
quant.make_quant_attn(model)
quant.make_quant_norm(model)
if fused_mlp:
quant.make_fused_mlp(model)
if warmup_autotune:
quant.autotune_warmup_linear(model)
if fused_mlp:
quant.autotune_warmup_fused(model)
for i in range(pre_layer):
model.model.layers[i].to(DEV)
model.model.embed_tokens.to(DEV)
model.model.norm.to(DEV)
model.lm_head.to(DEV)
model.model.cpu_offload(pre_layer)
print('Done.')
return model
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('model', type=str, help='llama model to load')
parser.add_argument('--wbits', type=int, default=4, choices=[2, 3, 4, 8], help='#bits to use for quantization')
parser.add_argument('--groupsize', type=int, default=-1, help='Groupsize to use for quantization; default uses full row.')
parser.add_argument('--load', type=str, default='', help='Load quantized model.')
parser.add_argument('--text', type=str, help='input text')
parser.add_argument('--min_length', type=int, default=10, help='The minimum length of the sequence to be generated.')
parser.add_argument('--max_length', type=int, default=50, help='The maximum length of the sequence to be generated.')
parser.add_argument('--top_p',
type=float,
default=0.95,
help='If set to float < 1, only the smallest set of most probable tokens with probabilities that add up to top_p or higher are kept for generation.')
parser.add_argument('--temperature', type=float, default=0.8, help='The value used to module the next token probabilities.')
parser.add_argument('--pre_layer', type=int, default=50, help='The number of layers to preload')
args = parser.parse_args()
if type(args.load) is not str:
args.load = args.load.as_posix()
model = load_quant(args.model, args.load, args.wbits, args.groupsize, args.pre_layer)
tokenizer = AutoTokenizer.from_pretrained(args.model, use_fast=False)
input_ids = tokenizer.encode(args.text, return_tensors="pt").to(DEV)
with torch.no_grad():
generated_ids = model.generate(
input_ids,
do_sample=True,
min_length=args.min_length,
max_length=args.max_length,
top_p=args.top_p,
temperature=args.temperature,
)
print(tokenizer.decode([el.item() for el in generated_ids[0]]))
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