KoboldAI-Client/breakmodel.py

'''
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 torch.cuda.comm
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 hasattr(self, 'rotary') and 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,
    )