diff --git a/aiserver.py b/aiserver.py index 92d33f8f..46ec25d5 100644 --- a/aiserver.py +++ b/aiserver.py @@ -178,6 +178,88 @@ def getmodelname(): modelname = vars.model return modelname +#==================================================================# +# Breakmodel configuration functions +#==================================================================# +def device_list(n_layers, primary=None, selected=None): + device_count = torch.cuda.device_count() + if(device_count < 2): + primary = None + gpu_blocks = breakmodel.gpu_blocks + (device_count - len(breakmodel.gpu_blocks))*[0] + print(f"{colors.YELLOW} DEVICE ID | LAYERS | DEVICE NAME{colors.END}") + for i in range(device_count): + name = torch.cuda.get_device_name(i) + if(len(name) > 47): + name = "..." + name[-44:] + row_color = colors.END + sep_color = colors.YELLOW + print(f"{row_color}{colors.YELLOW + '->' + row_color if i == selected else ' '} {'(primary)' if i == primary else ' '*9} {i:3} {sep_color}|{row_color} {gpu_blocks[i]:3} {sep_color}|{row_color} {name}{colors.END}") + row_color = colors.END + sep_color = colors.YELLOW + print(f"{row_color} {' '*9} N/A {sep_color}|{row_color} {n_layers:3} {sep_color}|{row_color} (CPU){colors.END}") + +def device_config(model): + global breakmodel, generator + import breakmodel + n_layers = model.config.num_layers + model.half().to('cpu') + gc.collect() + if(args.breakmodel_layers is not None): + breakmodel.gpu_blocks = [n_layers - max(0, min(n_layers, args.breakmodel_layers))] + else: + device_count = torch.cuda.device_count() + if(device_count > 1): + print(colors.CYAN + "\nPlease select one of your GPUs to be your primary GPU.") + print("VRAM usage in your primary GPU will be higher than for your other ones.") + print("It is recommended you make your fastest GPU your primary GPU.") + device_list(n_layers) + while(True): + primaryselect = input("device ID> ") + if(primaryselect.isnumeric() and 0 <= int(primaryselect) < device_count): + breakmodel.primary_device = int(primaryselect) + else: + print(f"{colors.RED}Please enter an integer between 0 and {device_count-1}.{colors.END}") + else: + breakmodel.primary_device = 0 + + print(colors.PURPLE + "\nIf you don't have enough VRAM to run the model on a single GPU") + print("you can split the model between your CPU and your GPU(s), or between") + print("multiple GPUs if you have more than one.") + print("By putting more 'layers' on a GPU or CPU, more computations will be") + print("done on that device and more VRAM or RAM will be required on that device") + print("(roughly proportional to number of layers).") + print("It should be noted that GPUs are orders of magnitude faster than the CPU.") + print(f"This model has{colors.YELLOW} {n_layers} {colors.PURPLE}layers.{colors.END}\n") + + for i in range(device_count): + device_list(n_layers, primary=breakmodel.primary_device, selected=i) + print(f"{colors.CYAN}\nHow many of the remaining{colors.YELLOW} {n_layers} {colors.CYAN}layers would you like to put into device {i}?\nYou can also enter -1 to allocate all remaining layers to this device.{colors.END}\n") + while(True): + layerselect = input("# of layers> ") + if((layerselect.isnumeric() or layerselect.strip() == '-1') and -1 <= int(layerselect) <= n_layers): + layerselect = int(layerselect) + layerselect = n_layers if layerselect == -1 else layerselect + breakmodel.gpu_blocks.append(layerselect) + n_layers -= layerselect + break + else: + print(f"{colors.RED}Please enter an integer between -1 and {n_layers}.{colors.END}") + if(n_layers == 0): + break + + print(colors.PURPLE + "\nFinal device configuration:") + device_list(n_layers) + + model.transformer.wte.to(breakmodel.primary_device) + model.transformer.ln_f.to(breakmodel.primary_device) + if(hasattr(model, 'lm_head')): + model.lm_head.to(breakmodel.primary_device) + if(not hasattr(model.config, 'rotary') or not model.config.rotary): + model.transformer.wpe.to(breakmodel.primary_device) + gc.collect() + GPTNeoModel.forward = breakmodel.new_forward + generator = model.generate + #==================================================================# # Startup #==================================================================# @@ -414,36 +496,7 @@ if(not vars.model in ["InferKit", "Colab", "OAI", "ReadOnly"]): if(vars.usegpu): generator = pipeline('text-generation', model=model, tokenizer=tokenizer, device=0) elif(vars.breakmodel): # Use both RAM and VRAM (breakmodel) - import breakmodel - n_layers = model.config.num_layers - breakmodel.total_blocks = n_layers - model.half().to('cpu') - gc.collect() - model.transformer.wte.to(breakmodel.embedding_device) - model.transformer.ln_f.to(breakmodel.layernormfinal_device) - if(hasattr(model, 'lm_head')): - model.lm_head.to(breakmodel.embedding_device) - if(not hasattr(model.config, 'rotary') or not model.config.rotary): - model.transformer.wpe.to(breakmodel.positional_device) - gc.collect() - if(args.breakmodel_layers is not None): - breakmodel.ram_blocks = max(0, min(n_layers, args.breakmodel_layers)) - 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 + device_config(model) else: generator = pipeline('text-generation', model=model, tokenizer=tokenizer) else: @@ -465,37 +518,8 @@ if(not vars.model in ["InferKit", "Colab", "OAI", "ReadOnly"]): if(vars.usegpu): generator = pipeline('text-generation', model=vars.model, device=0) elif(vars.breakmodel): # Use both RAM and VRAM (breakmodel) - import breakmodel model = AutoModelForCausalLM.from_pretrained(vars.model) - n_layers = model.config.num_layers - breakmodel.total_blocks = n_layers - model.half().to('cpu') - gc.collect() - model.transformer.wte.to(breakmodel.embedding_device) - model.transformer.ln_f.to(breakmodel.layernormfinal_device) - if(hasattr(model, 'lm_head')): - model.lm_head.to(breakmodel.embedding_device) - if(not hasattr(model.config, 'rotary') or not model.config.rotary): - model.transformer.wpe.to(breakmodel.positional_device) - gc.collect() - if(args.breakmodel_layers is not None): - breakmodel.ram_blocks = max(0, min(n_layers, args.breakmodel_layers)) - 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 + device_config(model) else: generator = pipeline('text-generation', model=vars.model) else: @@ -1245,7 +1269,7 @@ def generate(txt, min, max): # 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.embedding_device) + gen_in = tokenizer.encode(txt, return_tensors="pt", truncation=True).long().to(breakmodel.primary_device) else: gen_in = txt diff --git a/breakmodel.py b/breakmodel.py index 1c5ae3fd..905768a3 100644 --- a/breakmodel.py +++ b/breakmodel.py @@ -215,6 +215,8 @@ import torch import torch.cuda.comm import copy import gc +import itertools +import bisect from transformers.modeling_outputs import BaseModelOutputWithPast @@ -222,23 +224,9 @@ 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 -devices = ['cpu', 'cuda'] -total_blocks = 24 -ram_blocks = 7 -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' +gpu_blocks = [] +primary_device = 0 def new_forward( @@ -256,21 +244,41 @@ def new_forward( return_dict=None, embs=None, ): - global max_shared_ram_blocks + 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)) 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): - self.h[i].to(devices[0]) + self.h[i].to("cpu") + self.extrastorage[i] = copy.deepcopy(self.h[i]) + smalltensor = torch.tensor(0).to(primary_device) + for param1 in self.h[i].parameters(): + param1.data = smalltensor + self.h[i].to(primary_device) + for param in self.extrastorage[i].parameters(): + param.requires_grad = False + param.data = param.data.detach().pin_memory() + gc.collect() + torch.cuda.empty_cache() - for i in range(ram_blocks,len(self.h)): - self.h[i].to(devices[1]) + if ram_blocks: + for param1,param2 in zip(self.h[0].parameters(),self.extrastorage[0].parameters()): + param1.data = param2.data.to(primary_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(primary_device, non_blocking=False).detach() + + i = ram_blocks + for j in range(len(gpu_blocks)): + for _ in range(gpu_blocks[j]): + self.h[i].to(j) + i += 1 @@ -306,7 +314,7 @@ def new_forward( else: past_length = past_key_values[0][0].size(-2) - device = positional_device if breakmodel else input_ids.device if input_ids is not None else inputs_embeds.device + 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]) @@ -344,7 +352,7 @@ def new_forward( if inputs_embeds is None: if breakmodel: - input_ids = input_ids.to(embedding_device) + 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): @@ -360,10 +368,10 @@ def new_forward( hidden_states = inputs_embeds else: if breakmodel: - position_ids = position_ids.to(positional_device) + position_ids = position_ids.to(primary_device) position_embeds = self.wpe(position_ids) if breakmodel: - position_embeds = position_embeds.to(embedding_device) + position_embeds = position_embeds.to(primary_device) hidden_states = inputs_embeds + position_embeds if token_type_ids is not None: @@ -377,8 +385,21 @@ def new_forward( 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=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 @@ -410,7 +431,7 @@ def new_forward( ) else: if breakmodel: - device = devices[0] if i < ram_blocks else devices[1] + 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 else layer_past, @@ -428,11 +449,19 @@ def new_forward( 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: - hidden_states = hidden_states.to(layernormfinal_device) + 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(embedding_device) + hidden_states = hidden_states.to(primary_device) hidden_states = hidden_states.view(*output_shape) # Add last hidden state