Implement arrmansa's low VRAM patch

aiserver.py

@@ -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:
@@ -193,23 +201,40 @@ 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
-    elif(vars.hascuda):
+            vars.breakmodel = False
-        print("{0}Use GPU or CPU for generation?:  (Default GPU){1}\n".format(colors.CYAN, colors.END))
+        if(args.breakmodel):
-        print("    1 - GPU\n    2 - CPU\n")
+            vars.usegpu = False
+            vars.breakmodel = True
+    elif(vars.hascuda):    
+        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
 
     if(vars.hascuda):
         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):
-                generator = pipeline('text-generation', model=model, tokenizer=tokenizer, device=0)
+                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):
-                generator = pipeline('text-generation', model=vars.model, device=0)
+                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)
         
@@ -480,42 +566,42 @@ def get_message(msg):
     elif(msg['cmd'] == 'settemp'):
         vars.temp = float(msg['data'])
         emit('from_server', {'cmd': 'setlabeltemp', 'data': msg['data']}, broadcast=True)
-        settingschanged()
+        settingschanged()
         refresh_settings()
     elif(msg['cmd'] == 'settopp'):
         vars.top_p = float(msg['data'])
         emit('from_server', {'cmd': 'setlabeltopp', 'data': msg['data']}, broadcast=True)
-        settingschanged()
+        settingschanged()
         refresh_settings()
     elif(msg['cmd'] == 'settopk'):
         vars.top_k = int(msg['data'])
         emit('from_server', {'cmd': 'setlabeltopk', 'data': msg['data']}, broadcast=True)
-        settingschanged()
+        settingschanged()
         refresh_settings()
     elif(msg['cmd'] == 'settfs'):
         vars.tfs = float(msg['data'])
         emit('from_server', {'cmd': 'setlabeltfs', 'data': msg['data']}, broadcast=True)
-        settingschanged()
+        settingschanged()
         refresh_settings()
     elif(msg['cmd'] == 'setreppen'):
         vars.rep_pen = float(msg['data'])
         emit('from_server', {'cmd': 'setlabelreppen', 'data': msg['data']}, broadcast=True)
-        settingschanged()
+        settingschanged()
         refresh_settings()
     elif(msg['cmd'] == 'setoutput'):
         vars.genamt = int(msg['data'])
         emit('from_server', {'cmd': 'setlabeloutput', 'data': msg['data']}, broadcast=True)
-        settingschanged()
+        settingschanged()
         refresh_settings()
     elif(msg['cmd'] == 'settknmax'):
         vars.max_length = int(msg['data'])
         emit('from_server', {'cmd': 'setlabeltknmax', 'data': msg['data']}, broadcast=True)
-        settingschanged()
+        settingschanged()
         refresh_settings()
     elif(msg['cmd'] == 'setikgen'):
         vars.ikgen = int(msg['data'])
         emit('from_server', {'cmd': 'setlabelikgen', 'data': msg['data']}, broadcast=True)
-        settingschanged()
+        settingschanged()
         refresh_settings()
     # Author's Note field update
     elif(msg['cmd'] == 'anote'):
@@ -524,28 +610,28 @@ def get_message(msg):
     elif(msg['cmd'] == 'anotedepth'):
         vars.andepth = int(msg['data'])
         emit('from_server', {'cmd': 'setlabelanotedepth', 'data': msg['data']}, broadcast=True)
-        settingschanged()
+        settingschanged()
         refresh_settings()
     # Format - Trim incomplete sentences
     elif(msg['cmd'] == 'frmttriminc'):
         if('frmttriminc' in vars.formatoptns):
             vars.formatoptns["frmttriminc"] = msg['data']
-        settingschanged()
+        settingschanged()
         refresh_settings()
     elif(msg['cmd'] == 'frmtrmblln'):
         if('frmtrmblln' in vars.formatoptns):
             vars.formatoptns["frmtrmblln"] = msg['data']
-        settingschanged()
+        settingschanged()
         refresh_settings()
     elif(msg['cmd'] == 'frmtrmspch'):
         if('frmtrmspch' in vars.formatoptns):
             vars.formatoptns["frmtrmspch"] = msg['data']
-        settingschanged()
+        settingschanged()
         refresh_settings()
     elif(msg['cmd'] == 'frmtadsnsp'):
         if('frmtadsnsp' in vars.formatoptns):
             vars.formatoptns["frmtadsnsp"] = msg['data']
-        settingschanged()
+        settingschanged()
         refresh_settings()
     elif(msg['cmd'] == 'importselect'):
         vars.importnum = int(msg["data"].replace("import", ""))
@@ -589,20 +675,20 @@ def get_message(msg):
     elif(msg['cmd'] == 'setnumseq'):
         vars.numseqs = int(msg['data'])
         emit('from_server', {'cmd': 'setlabelnumseq', 'data': msg['data']})
-        settingschanged()
+        settingschanged()
         refresh_settings()
     elif(msg['cmd'] == 'setwidepth'):
         vars.widepth = int(msg['data'])
         emit('from_server', {'cmd': 'setlabelwidepth', 'data': msg['data']})
-        settingschanged()
+        settingschanged()
         refresh_settings()
     elif(msg['cmd'] == 'setuseprompt'):
         vars.useprompt = msg['data']
-        settingschanged()
+        settingschanged()
         refresh_settings()
     elif(msg['cmd'] == 'setadventure'):
         vars.adventure = msg['data']
-        settingschanged()
+        settingschanged()
         refresh_settings()
         refresh_story()
     elif(msg['cmd'] == 'importwi'):
@@ -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
@@ -992,9 +1079,17 @@ def generate(txt, min, max):
         top_p = vars.top_p if vars.top_p > 0.0 else None
         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,
@@ -1965,4 +2060,4 @@ if __name__ == "__main__":
     else:
         import webbrowser
         webbrowser.open_new('http://localhost:5000')
-        socketio.run(app)
+        socketio.run(app)

breakmodel.py

@@ -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,
+    )