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Gnome Ann
2021-09-23 20:57:18 -04:00
parent 72669e0489
commit 4d9eab3785
2 changed files with 36 additions and 76 deletions
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18
aiserver.py
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@ -413,12 +413,12 @@ if(not vars.model in ["InferKit", "Colab", "OAI", "ReadOnly"]):
breakmodel.total_blocks = n_layers breakmodel.total_blocks = n_layers
model.half().to('cpu') model.half().to('cpu')
gc.collect() gc.collect()
model.transformer.wte.to(breakmodel.gpu_device) model.transformer.wte.to(breakmodel.embedding_device)
model.transformer.ln_f.to(breakmodel.gpu_device) model.transformer.ln_f.to(breakmodel.layernormfinal_device)
if(hasattr(model, 'lm_head')): if(hasattr(model, 'lm_head')):
model.lm_head.to(breakmodel.gpu_device) model.lm_head.to(breakmodel.embedding_device)
if(not hasattr(model.config, 'rotary') or not model.config.rotary): if(not hasattr(model.config, 'rotary') or not model.config.rotary):
model.transformer.wpe.to(breakmodel.gpu_device) model.transformer.wpe.to(breakmodel.positional_device)
gc.collect() gc.collect()
if(args.breakmodel_layers is not None): if(args.breakmodel_layers is not None):
breakmodel.ram_blocks = max(0, min(n_layers, args.breakmodel_layers)) breakmodel.ram_blocks = max(0, min(n_layers, args.breakmodel_layers))
@ -465,12 +465,12 @@ if(not vars.model in ["InferKit", "Colab", "OAI", "ReadOnly"]):
breakmodel.total_blocks = n_layers breakmodel.total_blocks = n_layers
model.half().to('cpu') model.half().to('cpu')
gc.collect() gc.collect()
model.transformer.wte.to(breakmodel.gpu_device) model.transformer.wte.to(breakmodel.embedding_device)
model.transformer.ln_f.to(breakmodel.gpu_device) model.transformer.ln_f.to(breakmodel.layernormfinal_device)
if(hasattr(model, 'lm_head')): if(hasattr(model, 'lm_head')):
model.lm_head.to(breakmodel.gpu_device) model.lm_head.to(breakmodel.embedding_device)
if(not hasattr(model.config, 'rotary') or not model.config.rotary): if(not hasattr(model.config, 'rotary') or not model.config.rotary):
model.transformer.wpe.to(breakmodel.gpu_device) model.transformer.wpe.to(breakmodel.positional_device)
gc.collect() gc.collect()
if(args.breakmodel_layers is not None): if(args.breakmodel_layers is not None):
breakmodel.ram_blocks = max(0, min(n_layers, args.breakmodel_layers)) breakmodel.ram_blocks = max(0, min(n_layers, args.breakmodel_layers))
@ -1229,7 +1229,7 @@ def generate(txt, min, max):
# its first argument if we're using breakmodel, otherwise a string # its first argument if we're using breakmodel, otherwise a string
# is fine # is fine
if(vars.hascuda and vars.breakmodel): if(vars.hascuda and vars.breakmodel):
gen_in = tokenizer.encode(txt, return_tensors="pt", truncation=True).long().to(breakmodel.gpu_device) gen_in = tokenizer.encode(txt, return_tensors="pt", truncation=True).long().to(breakmodel.embedding_device)
else: else:
gen_in = txt gen_in = txt

94
breakmodel.py
View File

@ -229,11 +229,17 @@ class MaxSharedRamBlocksException(Exception):
breakmodel = True breakmodel = True
gpu_device = 'cuda' devices = ['cpu', 'cuda']
total_blocks = 24 total_blocks = 24
ram_blocks = 7 ram_blocks = 7
max_shared_ram_blocks = None max_shared_ram_blocks = None
# I highly suggest these all be set to the same device unless you really know what you're doing!
# (They can all be set to any CPU or GPU device, except layernormfinal_device which can only be a GPU device)
embedding_device = devices[1] # Dealing with text embedding is computationally expensive, I suggest you set this to your fastest device
positional_device = devices[1] # Only used for GPT-Neo (not used for GPT-J)
layernormfinal_device = devices[1] # This setting is unique in that this MUST be set to a GPU device, this cannot be set to 'cpu'
def new_forward( def new_forward(
self, self,
@ -260,44 +266,13 @@ def new_forward(
setattr(self,"extrastorage",{}) setattr(self,"extrastorage",{})
torch.cuda.empty_cache() torch.cuda.empty_cache()
for i in range(ram_blocks):
self.h[i].to(devices[0])
for i in range(ram_blocks,len(self.h)): for i in range(ram_blocks,len(self.h)):
self.h[i].to(gpu_device) self.h[i].to(devices[1])
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()
if ram_blocks:
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_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
output_hidden_states = ( output_hidden_states = (
@ -331,7 +306,7 @@ def new_forward(
else: else:
past_length = past_key_values[0][0].size(-2) past_length = past_key_values[0][0].size(-2)
device = input_ids.device if input_ids is not None else inputs_embeds.device device = positional_device if breakmodel else input_ids.device if input_ids is not None else inputs_embeds.device
if position_ids is None: if position_ids is None:
position_ids = torch.arange(past_length, input_shape[-1] + past_length, dtype=torch.long, device=device) 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]) position_ids = position_ids.unsqueeze(0).view(-1, input_shape[-1])
@ -368,6 +343,8 @@ def new_forward(
head_mask = self.get_head_mask(head_mask, self.config.num_layers) head_mask = self.get_head_mask(head_mask, self.config.num_layers)
if inputs_embeds is None: if inputs_embeds is None:
if breakmodel:
input_ids = input_ids.to(embedding_device)
inputs_embeds = self.wte(input_ids) 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): 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):
@ -382,7 +359,11 @@ def new_forward(
if hasattr(self, 'rotary') and self.rotary: if hasattr(self, 'rotary') and self.rotary:
hidden_states = inputs_embeds hidden_states = inputs_embeds
else: else:
if breakmodel:
position_ids = position_ids.to(positional_device)
position_embeds = self.wpe(position_ids) position_embeds = self.wpe(position_ids)
if breakmodel:
position_embeds = position_embeds.to(embedding_device)
hidden_states = inputs_embeds + position_embeds hidden_states = inputs_embeds + position_embeds
if token_type_ids is not None: if token_type_ids is not None:
@ -396,23 +377,8 @@ def new_forward(
presents = () if use_cache else None presents = () if use_cache else None
all_self_attentions = () if output_attentions else None all_self_attentions = () if output_attentions else None
all_hidden_states = () if output_hidden_states 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)): 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_type = self.config.attention_layers[i]
attn_mask = global_attention_mask attn_mask = global_attention_mask
@ -443,11 +409,13 @@ def new_forward(
head_mask[i], head_mask[i],
) )
else: else:
if breakmodel:
device = devices[0] if i < ram_blocks else devices[1]
outputs = block( outputs = block(
hidden_states, hidden_states.to(device) if breakmodel and hidden_states is not None else hidden_states,
layer_past=layer_past, layer_past=tuple(v.to(device) for v in layer_past if v is not None) if breakmodel and layer_past is not None else layer_past,
attention_mask=attn_mask, attention_mask=attn_mask.to(device) if breakmodel and attn_mask is not None else attn_mask,
head_mask=head_mask[i], head_mask=head_mask[i].to(device) if breakmodel and head_mask[i] is not None else head_mask[i],
use_cache=use_cache, use_cache=use_cache,
output_attentions=output_attentions, output_attentions=output_attentions,
) )
@ -460,18 +428,11 @@ def new_forward(
all_self_attentions = all_self_attentions + (outputs[2 if use_cache else 1],) 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 breakmodel:
del copystream hidden_states = hidden_states.to(layernormfinal_device)
torch.cuda.empty_cache()
hidden_states = self.ln_f(hidden_states) hidden_states = self.ln_f(hidden_states)
if breakmodel:
hidden_states = hidden_states.to(embedding_device)
hidden_states = hidden_states.view(*output_shape) hidden_states = hidden_states.view(*output_shape)
# Add last hidden state # Add last hidden state
@ -480,7 +441,6 @@ def new_forward(
if not return_dict: 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 tuple(v for v in [hidden_states, presents, all_hidden_states, all_self_attentions] if v is not None)
return BaseModelOutputWithPast( return BaseModelOutputWithPast(
last_hidden_state=hidden_states, last_hidden_state=hidden_states,
past_key_values=presents, past_key_values=presents,