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Implement arrmansa's low VRAM patch

This commit is contained in:
Gnome Ann 2021-08-20 10:25:03 -04:00
parent f12e3576a8
commit b1c13f832a
2 changed files with 610 additions and 28 deletions
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107
aiserver.py
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@ -13,12 +13,15 @@ import json
import requests
import html
import argparse
import sys
import gc
# KoboldAI
import fileops
import gensettings
from utils import debounce
import utils
import breakmodel
#==================================================================#
# Variables & Storage
@ -100,6 +103,8 @@ class vars:
saveow = False # Whether or not overwrite confirm has been displayed
genseqs = [] # Temporary storage for generated sequences
useprompt = True # Whether to send the full prompt with every submit action
breakmodel = False # For GPU users, whether to use both system RAM and VRAM to conserve VRAM while offering speedup compared to CPU-only
bmsupported = False # Whether the breakmodel option is supported (GPT-Neo/GPT-J only, currently)
acregex_ai = re.compile(r'\n* *>(.|\n)*') # Pattern for matching adventure actions from the AI so we can remove them
acregex_ui = re.compile(r'^ *(>.*)$', re.MULTILINE) # Pattern for matching actions in the HTML-escaped story so we can apply colouring, etc (make sure to encase part to format in parentheses)
actionmode = 1
@ -160,6 +165,8 @@ parser.add_argument("--remote", action='store_true', help="Optimizes KoboldAI fo
parser.add_argument("--model", help="Specify the Model Type to skip the Menu")
parser.add_argument("--path", help="Specify the Path for local models (For model NeoCustom or GPT2Custom)")
parser.add_argument("--cpu", action='store_true', help="By default unattended launches are on the GPU use this option to force CPU usage.")
parser.add_argument("--breakmodel", action='store_true', help="For models that support GPU-CPU hybrid generation, use this feature instead of GPU or CPU generation")
parser.add_argument("--breakmodel_layers", type=int, help="Specify the number of layers to commit to system RAM if --breakmodel is used")
args = parser.parse_args()
vars.model = args.model;
@ -184,6 +191,7 @@ if(not vars.model in ["InferKit", "Colab", "OAI", "ReadOnly"]):
import torch
print("{0}Looking for GPU support...{1}".format(colors.PURPLE, colors.END), end="")
vars.hascuda = torch.cuda.is_available()
vars.bmsupported = vars.model in ("EleutherAI/gpt-neo-1.3B", "EleutherAI/gpt-neo-2.7B", "NeoCustom")
if(vars.hascuda):
print("{0}FOUND!{1}".format(colors.GREEN, colors.END))
else:
@ -193,10 +201,20 @@ if(not vars.model in ["InferKit", "Colab", "OAI", "ReadOnly"]):
if(vars.hascuda):
genselected = True
vars.usegpu = True
vars.breakmodel = False
if(args.cpu):
vars.usegpu = False
vars.breakmodel = False
if(args.breakmodel):
vars.usegpu = False
vars.breakmodel = True
elif(vars.hascuda):
print("{0}Use GPU or CPU for generation?: (Default GPU){1}\n".format(colors.CYAN, colors.END))
if(vars.bmsupported):
print(colors.YELLOW + "You're using a model that supports GPU-CPU hybrid generation!\nCurrently only GPT-Neo models and GPT-J-6B support this feature.")
print("{0}Use GPU or CPU for generation?: (Default GPU){1}".format(colors.CYAN, colors.END))
if(vars.bmsupported):
print(f" 1 - GPU\n 2 - CPU\n 3 - Both (slower than GPU-only but uses less VRAM)\n")
else:
print(" 1 - GPU\n 2 - CPU\n")
genselected = False
@ -204,12 +222,19 @@ if(not vars.model in ["InferKit", "Colab", "OAI", "ReadOnly"]):
while(genselected == False):
genselect = input("Mode> ")
if(genselect == ""):
vars.breakmodel = False
vars.usegpu = True
genselected = True
elif(genselect.isnumeric() and int(genselect) == 1):
vars.breakmodel = False
vars.usegpu = True
genselected = True
elif(genselect.isnumeric() and int(genselect) == 2):
vars.breakmodel = False
vars.usegpu = False
genselected = True
elif(vars.bmsupported and genselect.isnumeric() and int(genselect) == 3):
vars.breakmodel = True
vars.usegpu = False
genselected = True
else:
@ -343,15 +368,45 @@ print("{0}OK!{1}".format(colors.GREEN, colors.END))
if(not vars.model in ["InferKit", "Colab", "OAI", "ReadOnly"]):
if(not vars.noai):
print("{0}Initializing transformers, please wait...{1}".format(colors.PURPLE, colors.END))
from transformers import pipeline, GPT2Tokenizer, GPT2LMHeadModel, GPTNeoForCausalLM
from transformers import pipeline, GPT2Tokenizer, GPT2LMHeadModel, GPTNeoForCausalLM, GPTNeoModel, AutoModel
# If custom GPT Neo model was chosen
if(vars.model == "NeoCustom"):
model = GPTNeoForCausalLM.from_pretrained(vars.custmodpth)
tokenizer = GPT2Tokenizer.from_pretrained(vars.custmodpth)
# Is CUDA available? If so, use GPU, otherwise fall back to CPU
if(vars.hascuda and vars.usegpu):
if(vars.hascuda):
if(vars.usegpu):
generator = pipeline('text-generation', model=model, tokenizer=tokenizer, device=0)
elif(vars.breakmodel): # Use both RAM and VRAM (breakmodel)
n_layers = model.config.num_layers
breakmodel.total_blocks = n_layers
model.half().to('cpu')
gc.collect()
model.lm_head.to(breakmodel.gpu_device)
model.transformer.wte.to(breakmodel.gpu_device)
model.transformer.ln_f.to(breakmodel.gpu_device)
gc.collect()
if(args.breakmodel):
breakmodel.ram_blocks = max(0, min(n_layers, args.breakmodel))
else:
print(colors.CYAN + "\nHow many layers would you like to put into system RAM?")
print("The more of them you put into system RAM, the slower it will run,")
print("but it will require less VRAM")
print("(roughly proportional to number of layers).")
print(f"This model has{colors.YELLOW} {n_layers} {colors.CYAN}layers.{colors.END}\n")
while(True):
layerselect = input("# of layers> ")
if(layerselect.isnumeric() and 0 <= int(layerselect) <= n_layers):
breakmodel.ram_blocks = int(layerselect)
break
else:
print(f"{colors.RED}Please enter an integer between 0 and {n_layers}.{colors.END}")
print(f"{colors.PURPLE}Will commit{colors.YELLOW} {breakmodel.ram_blocks} {colors.PURPLE}of{colors.YELLOW} {n_layers} {colors.PURPLE}layers to system RAM.{colors.END}")
GPTNeoModel.forward = breakmodel.new_forward
generator = model.generate
else:
generator = pipeline('text-generation', model=model, tokenizer=tokenizer)
else:
generator = pipeline('text-generation', model=model, tokenizer=tokenizer)
# If custom GPT2 model was chosen
@ -367,8 +422,39 @@ if(not vars.model in ["InferKit", "Colab", "OAI", "ReadOnly"]):
else:
# Is CUDA available? If so, use GPU, otherwise fall back to CPU
tokenizer = GPT2Tokenizer.from_pretrained(vars.model)
if(vars.hascuda and vars.usegpu):
if(vars.hascuda):
if(vars.usegpu):
generator = pipeline('text-generation', model=vars.model, device=0)
elif(vars.breakmodel): # Use both RAM and VRAM (breakmodel)
model = AutoModel.from_pretrained(vars.model)
n_layers = model.config.num_layers
breakmodel.total_blocks = n_layers
model.half().to('cpu')
gc.collect()
model.lm_head.to(breakmodel.gpu_device)
model.transformer.wte.to(breakmodel.gpu_device)
model.transformer.ln_f.to(breakmodel.gpu_device)
gc.collect()
if(args.breakmodel):
breakmodel.ram_blocks = max(0, min(n_layers, args.breakmodel))
else:
print(colors.CYAN + "\nHow many layers would you like to put into system RAM?")
print("The more of them you put into system RAM, the slower it will run,")
print("but it will require less VRAM")
print("(roughly proportional to number of layers).")
print(f"This model has{colors.YELLOW} {n_layers} {colors.CYAN}layers.{colors.END}\n")
while(True):
layerselect = input("# of layers> ")
if(layerselect.isnumeric() and 0 <= int(layerselect) <= n_layers):
breakmodel.ram_blocks = int(layerselect)
break
else:
print(f"{colors.RED}Please enter an integer between 0 and {n_layers}.{colors.END}")
print(f"{colors.PURPLE}Will commit{colors.YELLOW} {breakmodel.ram_blocks} {colors.PURPLE}of{colors.YELLOW} {n_layers} {colors.PURPLE}layers to system RAM.{colors.END}")
GPTNeoModel.forward = breakmodel.new_forward
generator = model.generate
else:
generator = pipeline('text-generation', model=vars.model)
else:
generator = pipeline('text-generation', model=vars.model)
@ -984,7 +1070,8 @@ def generate(txt, min, max):
vars.lastctx = txt
# Clear CUDA cache if using GPU
if(vars.hascuda and vars.usegpu):
if(vars.hascuda and (vars.usegpu or vars.breakmodel)):
gc.collect()
torch.cuda.empty_cache()
# Submit input text to generator
@ -993,8 +1080,16 @@ def generate(txt, min, max):
top_k = vars.top_k if vars.top_k > 0 else None
tfs = vars.tfs if vars.tfs > 0.0 else None
# generator() only accepts a torch tensor of tokens (long datatype) as
# its first argument if we're using breakmodel, otherwise a string
# is fine
if(vars.hascuda and vars.breakmodel):
gen_in = tokenizer.encode(txt, return_tensors="pt", truncation=True).long().to(breakmodel.gpu_device)
else:
gen_in = txt
genout = generator(
txt,
gen_in,
do_sample=True,
min_length=min,
max_length=max,

487
breakmodel.py Normal file
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@ -0,0 +1,487 @@
'''
This is a MODIFIED version of arrmansa's low VRAM patch.
https://github.com/arrmansa/Basic-UI-for-GPT-J-6B-with-low-vram/blob/main/GPT-J-6B-Low-Vram-UI.ipynb
Copyright 2021 arrmansa
Copyright 2021 finetuneanon
Copyright 2018 The Hugging Face team
Released under the Apache License 2.0
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'''
import torch
import copy
import gc
from transformers.modeling_outputs import BaseModelOutputWithPast
from transformers.utils import logging
logger = logging.get_logger(__name__)
class MaxSharedRamBlocksException(Exception):
def __init__(self, i: int):
self.corrected_max_shared_ram_blocks = i
super().__init__('max_shared_ram_blocks is set too high, please set it to '+str(i))
breakmodel = True
gpu_device = 'cuda'
total_blocks = 24
ram_blocks = 7
max_shared_ram_blocks = None
def new_forward(
self,
input_ids=None,
past_key_values=None,
attention_mask=None,
token_type_ids=None,
position_ids=None,
head_mask=None,
inputs_embeds=None,
use_cache=None,
output_attentions=None,
output_hidden_states=None,
return_dict=None,
embs=None,
):
global max_shared_ram_blocks
if breakmodel:
if max_shared_ram_blocks is None:
max_shared_ram_blocks = total_blocks
if not hasattr(self, 'extrastorage'):
setattr(self,"extrastorage",{})
torch.cuda.empty_cache()
for i in range(ram_blocks,len(self.h)):
self.h[i].to(gpu_device)
for i in range(ram_blocks):
self.h[i].to("cpu")
self.extrastorage[i] = copy.deepcopy(self.h[i])
smalltensor = torch.tensor(0).to(gpu_device)
for param1 in self.h[i].parameters():
param1.data = smalltensor
self.h[i].to(gpu_device)
for i in range(len(self.h)):
for param in self.h[i].parameters():
param.requires_grad = False
param.data = param.data.detach()
gc.collect()
torch.cuda.empty_cache()
for i in range(ram_blocks):
for param in self.extrastorage[i].parameters():
param.requires_grad = False
if i < max_shared_ram_blocks:
try:
param.data = param.data.detach().pin_memory()
except:
raise MaxSharedRamBlocksException(i)
else:
param.data = param.data.detach()
gc.collect()
torch.cuda.empty_cache()
for param1,param2 in zip(self.h[0].parameters(),self.extrastorage[0].parameters()):
param1.data = param2.data.to(gpu_device, non_blocking=False).detach()
for param1,param2 in zip(self.h[ram_blocks-1].parameters(),self.extrastorage[ram_blocks-1].parameters()):
param1.data = param2.data.to(gpu_device, non_blocking=False).detach()
#END MODEL BREAK EDITS
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 not None and inputs_embeds is not None:
raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time")
elif input_ids is not None:
input_shape = input_ids.size()
input_ids = input_ids.view(-1, input_shape[-1])
batch_size = input_ids.shape[0]
elif inputs_embeds is not None:
input_shape = inputs_embeds.size()[:-1]
batch_size = inputs_embeds.shape[0]
else:
raise ValueError("You have to specify either input_ids or inputs_embeds")
device = input_ids.device if input_ids is not None else inputs_embeds.device
if token_type_ids is not None:
token_type_ids = token_type_ids.view(-1, input_shape[-1])
if position_ids is not None:
position_ids = position_ids.view(-1, input_shape[-1])
if past_key_values is None:
past_length = 0
past_key_values = tuple([None] * len(self.h))
else:
past_length = past_key_values[0][0].size(-2)
device = input_ids.device if input_ids is not None else inputs_embeds.device
if position_ids is None:
position_ids = torch.arange(past_length, input_shape[-1] + past_length, dtype=torch.long, device=device)
position_ids = position_ids.unsqueeze(0).view(-1, input_shape[-1])
# Attention mask.
if attention_mask is not None:
assert batch_size > 0, "batch_size has to be defined and > 0"
global_attention_mask = attention_mask.view(batch_size, -1)
# We create a 3D attention mask from a 2D tensor mask.
# Sizes are [batch_size, 1, 1, to_seq_length]
# So we can broadcast to [batch_size, num_heads, from_seq_length, to_seq_length]
# this attention mask is more simple than the triangular masking of causal attention
# used in OpenAI GPT, we just need to prepare the broadcast dimension here.
global_attention_mask = global_attention_mask[:, None, None, :]
# Since global_attention_mask is 1.0 for positions we want to attend and 0.0 for
# masked positions, this operation will create a tensor which is 0.0 for
# positions we want to attend and -10000.0 for masked positions.
# Since we are adding it to the raw scores before the softmax, this is
# effectively the same as removing these entirely.
global_attention_mask = global_attention_mask.to(dtype=self.dtype) # fp16 compatibility
global_attention_mask = (1.0 - global_attention_mask) * -10000.0
else:
global_attention_mask = None
# Local causal attention mask
batch_size, seq_length = input_shape
full_seq_length = seq_length + past_length
# Prepare head mask if needed
# 1.0 in head_mask indicate we keep the head
# attention_probs has shape bsz x num_heads x N x N
# head_mask has shape n_layer x batch x num_heads x N x N
head_mask = self.get_head_mask(head_mask, self.config.num_layers)
if inputs_embeds is None:
inputs_embeds = self.wte(input_ids)
if embs is not None and not (use_cache is not None and use_cache and past_key_values is not None and len(past_key_values) > 0 and past_key_values[0] is not None):
offset = 0
for pos, emb in embs:
pos += offset
if len(emb.shape) == 2:
emb = emb.repeat(input_shape[0], 1, 1)
inputs_embeds[:, pos:pos+emb.shape[1]] = emb
offset += emb.shape[1]
if self.rotary:
hidden_states = inputs_embeds
else:
position_embeds = self.wpe(position_ids)
hidden_states = inputs_embeds + position_embeds
if token_type_ids is not None:
token_type_embeds = self.wte(token_type_ids)
hidden_states = hidden_states + token_type_embeds
hidden_states = self.drop(hidden_states)
output_shape = input_shape + (hidden_states.size(-1),)
presents = () if use_cache else None
all_self_attentions = () if output_attentions else None
all_hidden_states = () if output_hidden_states else None
if breakmodel:
copystream = torch.cuda.Stream(device=0,priority = -1)
for i, (block, layer_past) in enumerate(zip(self.h, past_key_values)):
if breakmodel:
if i in range(ram_blocks):
index1 = (i+1)%ram_blocks
for param1,param2 in zip(self.h[index1].parameters(),self.h[(i-1)%ram_blocks].parameters()):
param1.data = param2.data
for param1,param2 in zip(self.h[index1].parameters(),self.extrastorage[index1].parameters()):
with torch.cuda.stream(copystream):
torch.cuda.comm.broadcast(param2.data,out = [param1.data])
attn_type = self.config.attention_layers[i]
attn_mask = global_attention_mask
if output_hidden_states:
all_hidden_states = all_hidden_states + (hidden_states.cpu(),)
if getattr(self.config, "gradient_checkpointing", False) and self.training:
if use_cache:
logger.warning(
"`use_cache=True` is incompatible with `config.gradient_checkpointing=True`. Setting "
"`use_cache=False`..."
)
use_cache = False
def create_custom_forward(module):
def custom_forward(*inputs):
# None for past_key_value
return module(*inputs, use_cache, output_attentions)
return custom_forward
outputs = torch.utils.checkpoint.checkpoint(
create_custom_forward(block),
hidden_states,
None,
attn_mask,
head_mask[i],
)
else:
outputs = block(
hidden_states,
layer_past=layer_past,
attention_mask=attn_mask,
head_mask=head_mask[i],
use_cache=use_cache,
output_attentions=output_attentions,
)
hidden_states = outputs[0]
if use_cache is True:
presents = presents + (outputs[1],)
if output_attentions:
all_self_attentions = all_self_attentions + (outputs[2 if use_cache else 1],)
if breakmodel:
if i in range(ram_blocks):
torch.cuda.synchronize()
torch.cuda.empty_cache()
if breakmodel:
del copystream
torch.cuda.empty_cache()
hidden_states = self.ln_f(hidden_states)
hidden_states = hidden_states.view(*output_shape)
# Add last hidden state
if output_hidden_states:
all_hidden_states = all_hidden_states + (hidden_states,)
if not return_dict:
return tuple(v for v in [hidden_states, presents, all_hidden_states, all_self_attentions] if v is not None)
return BaseModelOutputWithPast(
last_hidden_state=hidden_states,
past_key_values=presents,
hidden_states=all_hidden_states,
attentions=all_self_attentions,
)