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
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


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'''


import torch
from torch import nn
import torch.cuda.comm
import copy
import gc
import sys
import itertools
import bisect
import random
from typing import Optional

from transformers.modeling_outputs import BaseModelOutputWithPast, BaseModelOutputWithPastAndCrossAttentions

from transformers.utils import logging
logger = logging.get_logger(__name__)


breakmodel = True
gpu_blocks = []
primary_device = 0


# Copied from transformers.models.bart.modeling_bart._expand_mask
def _expand_mask(mask: torch.Tensor, dtype: torch.dtype, tgt_len: Optional[int] = None):
    """
    Expands attention_mask from `[bsz, seq_len]` to `[bsz, 1, tgt_seq_len, src_seq_len]`.
    """
    bsz, src_len = mask.size()
    tgt_len = tgt_len if tgt_len is not None else src_len

    expanded_mask = mask[:, None, None, :].expand(bsz, 1, tgt_len, src_len).to(dtype)

    inverted_mask = 1.0 - expanded_mask

    return inverted_mask.masked_fill(inverted_mask.bool(), torch.finfo(dtype).min)


def move_hidden_layers(transformer, h=None):
    if h is None:
        h = transformer.h

    assert len(gpu_blocks) <= torch.cuda.device_count()
    assert sum(gpu_blocks) <= len(h)
    ram_blocks = len(h) - sum(gpu_blocks)

    transformer.extrastorage = {}
    torch.cuda.empty_cache()
    
    able_to_pin_layers = True
    for i in range(ram_blocks):
        h[i].to("cpu")
        transformer.extrastorage[i] = copy.deepcopy(h[i])
        smalltensor = torch.tensor(0).to(primary_device)
        for param1 in h[i].parameters():
            param1.data = smalltensor
        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(h[0].parameters(),transformer.extrastorage[0].parameters()):
            param1.data = param2.data.to(primary_device, non_blocking=False).detach()

        for param1,param2 in zip(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]):
            h[i].to(j)
            i += 1


def new_forward_neo(
    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,
    )


def new_forward_xglm(
    self,
    input_ids=None,
    attention_mask=None,
    encoder_hidden_states=None,
    encoder_attention_mask=None,
    head_mask=None,
    cross_attn_head_mask=None,
    past_key_values=None,
    inputs_embeds=None,
    use_cache=None,
    output_attentions=None,
    output_hidden_states=None,
    return_dict=None,
):
    assert len(gpu_blocks) <= torch.cuda.device_count()
    assert sum(gpu_blocks) <= len(self.layers)
    ram_blocks = len(self.layers) - 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

    # retrieve input_ids and inputs_embeds
    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])
    elif inputs_embeds is not None:
        input_shape = inputs_embeds.size()[:-1]
    else:
        raise ValueError("You have to specify either input_ids or inputs_embeds")

    # past_key_values_length
    past_key_values_length = past_key_values[0][0].shape[2] if past_key_values is not None else 0

    if inputs_embeds is None:
        if breakmodel:
            input_ids = input_ids.to(primary_device)
        inputs_embeds = self.embed_tokens(input_ids) * self.embed_scale

    attention_mask = self._prepare_decoder_attention_mask(
        attention_mask, input_shape, inputs_embeds, past_key_values_length
    )

    # expand encoder attention mask
    if encoder_hidden_states is not None and encoder_attention_mask is not None:
        # [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
        encoder_attention_mask = _expand_mask(encoder_attention_mask, inputs_embeds.dtype, tgt_len=input_shape[-1])

    # embed positions
    if breakmodel:
        inputs_embeds = inputs_embeds.to(primary_device)
    positions = self.embed_positions(input_ids, inputs_embeds, past_key_values_length)
    if breakmodel:
        positions = positions.to(primary_device)

    hidden_states = inputs_embeds + positions

    hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training)

    # decoder layers
    all_hidden_states = () if output_hidden_states else None
    all_self_attns = () if output_attentions else None
    all_cross_attentions = () if (output_attentions and encoder_hidden_states is not None) else None
    next_decoder_cache = () if use_cache else None

    if breakmodel and ram_blocks:
        copystream = torch.cuda.Stream(device=primary_device, priority=-1)

    # check if head_mask/cross_attn_head_mask has a correct number of layers specified if desired
    for attn_mask, mask_name in zip([head_mask, cross_attn_head_mask], ["head_mask", "cross_attn_head_mask"]):
        if attn_mask is not None:
            assert attn_mask.size()[0] == (
                len(self.layers)
            ), f"The `{mask_name}` should be specified for {len(self.layers)} layers, but it is for {head_mask.size()[0]}."
    for idx, decoder_layer in enumerate(self.layers):
        i = idx
        if breakmodel:
            if i in range(ram_blocks):
                index1 = (i+1)%ram_blocks
                for param1,param2 in zip(self.layers[index1].parameters(),self.layers[(i-1)%ram_blocks].parameters()):
                    param1.data = param2.data
                for param1,param2 in zip(self.layers[index1].parameters(),self.extrastorage[index1].parameters()):
                    with torch.cuda.stream(copystream):
                        torch.cuda.comm.broadcast(param2.data,out = [param1.data])

        # add LayerDrop (see https://arxiv.org/abs/1909.11556 for description)
        if output_hidden_states:
            all_hidden_states += (hidden_states,)
        dropout_probability = random.uniform(0, 1)
        if self.training and (dropout_probability < self.layerdrop):
            continue

        past_key_value = past_key_values[idx] if past_key_values is not None else None

        if self.gradient_checkpointing 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, output_attentions, use_cache)

                return custom_forward

            layer_outputs = torch.utils.checkpoint.checkpoint(
                create_custom_forward(decoder_layer),
                hidden_states,
                attention_mask,
                encoder_hidden_states,
                encoder_attention_mask,
                head_mask[idx] if head_mask is not None else None,
                cross_attn_head_mask[idx] if cross_attn_head_mask is not None else None,
                None,
            )
        else:
            if breakmodel:
                device = primary_device if i < ram_blocks else bisect.bisect_right(cumulative_gpu_blocks, i - ram_blocks)
            layer_outputs = decoder_layer(
                hidden_states.to(device) if breakmodel and hidden_states is not None else hidden_states,
                attention_mask=attention_mask.to(device) if breakmodel and attention_mask is not None else attention_mask,
                encoder_hidden_states=encoder_hidden_states.to(device) if encoder_hidden_states is not None else None,
                encoder_attention_mask=encoder_attention_mask.to(device) if encoder_attention_mask is not None else None,
                layer_head_mask=((head_mask[idx].to(device) if head_mask[idx] is not None else None) if head_mask is not None else None),
                cross_attn_layer_head_mask=(
                    (cross_attn_head_mask[idx].to(device) if cross_attn_head_mask[idx] is not None else None) if cross_attn_head_mask is not None else None
                ),
                past_key_value=tuple(v.to(device) for v in past_key_value if v is not None) if breakmodel and past_key_value is not None and i >= ram_blocks and len(past_key_value) and past_key_value[0].device.index != device else past_key_value,
                output_attentions=output_attentions,
                use_cache=use_cache,
            )
        hidden_states = layer_outputs[0]

        if use_cache:
            next_decoder_cache += (layer_outputs[3 if output_attentions else 1],)

        if output_attentions:
            all_self_attns += (layer_outputs[1],)

            if encoder_hidden_states is not None:
                all_cross_attentions += (layer_outputs[2],)
        
        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.layer_norm(hidden_states)
    if breakmodel:
        hidden_states = hidden_states.to(primary_device)

    # add hidden states from the last decoder layer
    if output_hidden_states:
        all_hidden_states += (hidden_states,)

    next_cache = next_decoder_cache if use_cache else None
    if not return_dict:
        return tuple(
            v
            for v in [hidden_states, next_cache, all_hidden_states, all_self_attns, all_cross_attentions]
            if v is not None
        )
    return BaseModelOutputWithPastAndCrossAttentions(
        last_hidden_state=hidden_states,
        past_key_values=next_cache,
        hidden_states=all_hidden_states,
        attentions=all_self_attns,
        cross_attentions=all_cross_attentions,
    )