KoboldAI-Client/breakmodel.py

480 lines
22 KiB
Python

'''
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
The ORIGINAL version of the patch is released under the Apache License 2.0
Copyright 2021 arrmansa
Copyright 2021 finetuneanon
Copyright 2018 The Hugging Face team
Apache License
Version 2.0, January 2004
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'''
import torch
import torch.cuda.comm
import copy
import gc
import sys
import itertools
import bisect
from transformers.modeling_outputs import BaseModelOutputWithPast
from transformers.utils import logging
logger = logging.get_logger(__name__)
breakmodel = True
gpu_blocks = []
primary_device = 0
def move_hidden_layers(transformer):
assert len(gpu_blocks) <= torch.cuda.device_count()
assert sum(gpu_blocks) <= len(transformer.h)
ram_blocks = len(transformer.h) - sum(gpu_blocks)
transformer.extrastorage = {}
torch.cuda.empty_cache()
able_to_pin_layers = True
for i in range(ram_blocks):
transformer.h[i].to("cpu")
transformer.extrastorage[i] = copy.deepcopy(transformer.h[i])
smalltensor = torch.tensor(0).to(primary_device)
for param1 in transformer.h[i].parameters():
param1.data = smalltensor
transformer.h[i].to(primary_device)
for param in transformer.extrastorage[i].parameters():
param.requires_grad = False
param.data = param.data.detach()
if able_to_pin_layers:
try:
param.data = param.data.pin_memory()
except:
able_to_pin_layers = False
print(f"WARNING: You only have enough shared GPU memory for {i} out of {ram_blocks} CPU layers. Expect suboptimal speed.", file=sys.stderr)
gc.collect()
torch.cuda.empty_cache()
if ram_blocks:
for param1,param2 in zip(transformer.h[0].parameters(),transformer.extrastorage[0].parameters()):
param1.data = param2.data.to(primary_device, non_blocking=False).detach()
for param1,param2 in zip(transformer.h[ram_blocks-1].parameters(),transformer.extrastorage[ram_blocks-1].parameters()):
param1.data = param2.data.to(primary_device, non_blocking=False).detach()
i = ram_blocks
for j in range(len(gpu_blocks)):
for _ in range(gpu_blocks[j]):
transformer.h[i].to(j)
i += 1
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,
):
assert len(gpu_blocks) <= torch.cuda.device_count()
assert sum(gpu_blocks) <= len(self.h)
ram_blocks = len(self.h) - sum(gpu_blocks)
cumulative_gpu_blocks = tuple(itertools.accumulate(gpu_blocks))
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 = primary_device if breakmodel else 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"
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.
attention_mask = attention_mask[:, None, None, :]
# Since 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.
attention_mask = attention_mask.to(dtype=self.dtype) # fp16 compatibility
attention_mask = (1.0 - attention_mask) * -10000.0
# 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, getattr(self.config, "num_layers", None) or self.config.n_layer)
if inputs_embeds is None:
if breakmodel:
input_ids = input_ids.to(primary_device)
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 getattr(self, "wpe", None) is None:
hidden_states = inputs_embeds
else:
if breakmodel:
position_ids = position_ids.to(primary_device)
position_embeds = self.wpe(position_ids)
if breakmodel:
position_embeds = position_embeds.to(primary_device)
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 and ram_blocks:
copystream = torch.cuda.Stream(device=primary_device, 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])
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 gradient checkpointing. 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,
attention_mask,
head_mask[i],
)
else:
if breakmodel:
device = primary_device if i < ram_blocks else bisect.bisect_right(cumulative_gpu_blocks, i - ram_blocks)
outputs = block(
hidden_states.to(device) if breakmodel and hidden_states is not None else hidden_states,
layer_past=tuple(v.to(device) for v in layer_past if v is not None) if breakmodel and layer_past is not None and i >= ram_blocks and len(layer_past) and layer_past[0].device.index != device else layer_past,
attention_mask=attention_mask.to(device) if breakmodel and attention_mask is not None else attention_mask,
head_mask=head_mask[i].to(device) if breakmodel and head_mask[i] is not None else 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:
if ram_blocks:
del copystream
torch.cuda.empty_cache()
hidden_states = hidden_states.to(primary_device)
hidden_states = self.ln_f(hidden_states)
if breakmodel:
hidden_states = hidden_states.to(primary_device)
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,
)