Imagen-POP-Music-Medley-Diffusion-Transformer

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-#===================================================================================================================
-#
-# X Trasformer Module
-#
-# Partial x-transformers code With useful modifications
-#
-# Version 1.0
-#
-# Original source code courtesy of lucidrains
-# https://github.com/lucidrains/x-transformers
-#
-# Original source code retrieved on 10/10/2023
-#
-# Project Los Angeles
-# Tegridy Code 2023
-#===================================================================================================================
-# Critical dependencies
-#
-# !pip install torch
-# !pip install einops
-#===================================================================================================================
-from functools import partial
-from typing import Optional, Tuple
-import torch
-from torch import nn, einsum, Tensor
-import torch.nn.functional as F
-# from torch.nn.attention import SDPBackend, sdpa_kernel
-from collections import namedtuple
-from functools import wraps
-from packaging import version
-from dataclasses import dataclass
-from einops import rearrange, repeat
-# constants
-EfficientAttentionConfig = namedtuple('EfficientAttentionConfig', ['enable_flash', 'enable_math', 'enable_mem_efficient'])
-@dataclass
-class Intermediates:
-    qk_similarities: Optional[Tensor] = None
-    pre_softmax_attn: Optional[Tensor] = None
-    post_softmax_attn: Optional[Tensor] = None
-    cached_kv: Optional[Tuple[Tensor, Tensor]] = None
-    def to_tuple(self):
-        return (self.qk_similarities, self.pre_softmax_attn, self.post_softmax_attn)
-# helpers
-def exists(val):
-    return val is not None
-def default(val, d):
-    return val if exists(val) else d
-def compact(arr):
-    return [*filter(exists, arr)]
-def once(fn):
-    called = False
-    @wraps(fn)
-    def inner(x):
-        nonlocal called
-        if called:
-            return
-        called = True
-        return fn(x)
-    return inner
-print_once = once(print)
-# functions for creating causal mask
-# need a special one for onnx cpu (no support for .triu)
-def create_causal_mask(i, j, device):
-    return torch.ones((i, j), device = device, dtype = torch.bool).triu(j - i + 1)
-def onnx_create_causal_mask(i, j, device):
-    r = torch.arange(i, device = device)
-    causal_mask = rearrange(r, 'i -> i 1') < rearrange(r, 'j -> 1 j')
-    causal_mask = F.pad(causal_mask, (j - i, 0), value = False)
-    return causal_mask
-# main class
-class Attend(nn.Module):
-    def __init__(
-        self,
-        *,
-        dropout = 0.,
-        causal = False,
-        heads = None,
-        talking_heads = False,
-        sparse_topk = None,
-        scale = None,
-        qk_norm = False,
-        flash = False,
-        add_zero_kv = False,
-        onnxable = False
-    ):
-        super().__init__()
-        self.scale = scale
-        self.qk_norm = qk_norm
-        self.causal = causal
-        self.create_causal_mask = onnx_create_causal_mask if onnxable else create_causal_mask
-        self.attn_fn = partial(F.softmax, dtype = torch.float32) if not qk_norm else F.softmax
-        self.dropout = dropout
-        self.attn_dropout = nn.Dropout(dropout)
-        # talking heads
-        assert not (flash and talking_heads), 'talking heads not compatible with flash attention'
-        self.talking_heads = talking_heads
-        if talking_heads:
-            self.pre_softmax_talking_heads = nn.Conv2d(heads, heads, 1, bias = False)
-            self.post_softmax_talking_heads = nn.Conv2d(heads, heads, 1, bias = False)
-        # sparse topk
-        assert not (flash and sparse_topk), 'sparse topk not compatible with flash attention'
-        self.sparse_topk = sparse_topk
-        # add a key / value token composed of zeros
-        # in case this helps controlling outliers, proposed by https://www.evanmiller.org/attention-is-off-by-one.html
-        self.add_zero_kv = add_zero_kv
-        # flash attention
-        self.flash = flash
-        assert not (flash and version.parse(torch.__version__) < version.parse('2.0.0')), 'in order to use flash attention, you must be using pytorch 2.0 or above'
-        # determine efficient attention configs for cuda and cpu
-        self.cpu_config = EfficientAttentionConfig(True, True, True)
-        self.cuda_config = None
-        if not torch.cuda.is_available() or not flash:
-            return
-        device_properties = torch.cuda.get_device_properties(torch.device('cuda'))
-        major, minor = device_properties.major, device_properties.minor
-        if (major, minor) == (8, 0):
-            print_once('A100 GPU detected, using flash attention if input tensor is on cuda')
-            self.cuda_config = EfficientAttentionConfig(True, False, False)
-        elif (major, minor) == (9, 0):
-            print_once('H100 GPU detected, using flash attention')
-            self.cuda_config = EfficientAttentionConfig(True, False, False)
-        else:
-            print_once('Non-A100 GPU detected, using math or mem efficient attention if input tensor is on cuda')
-            self.cuda_config = EfficientAttentionConfig(False, True, True)
-    def flash_attn(
-        self,
-        q, k, v,
-        mask = None,
-        attn_bias = None
-    ):
-        batch, heads, q_len, _, k_len, is_cuda, device = *q.shape, k.shape[-2], q.is_cuda, q.device
-        # Recommended for multi-query single-key-value attention by Tri Dao
-        # kv shape torch.Size([1, 512, 64]) -> torch.Size([1, 8, 512, 64])
-        if k.ndim == 3:
-            k = rearrange(k, 'b ... -> b 1 ...').expand_as(q)
-        if v.ndim == 3:
-            v = rearrange(v, 'b ... -> b 1 ...').expand_as(q)
-        # handle scale - by default they scale by dim_head ** -0.5, but need to take care if using cosine sim attention
-        if self.qk_norm:
-            default_scale = q.shape[-1] ** -0.5
-            q = q * (self.scale / default_scale)
-        # Check if mask exists and expand to compatible shape
-        # The mask is B L, so it would have to be expanded to B H N L
-        causal = self.causal
-        # in the case of kv caching with one token (q_len == 1), just turn off causal masking
-        # in speculative decoding, this may go up to 5-6, so right aligned causal mask will be needed there
-        if q_len == 1 and causal:
-            causal = False
-        # expand key padding mask
-        if exists(mask):
-            assert mask.ndim == 4
-            mask = mask.expand(batch, heads, q_len, k_len)
-        # handle kv cache - this should be bypassable in updated flash attention 2
-        if k_len > q_len and causal:
-            causal_mask = self.create_causal_mask(q_len, k_len, device = device)
-            if not exists(mask):
-                mask = ~causal_mask
-            else:
-                mask = mask & ~causal_mask
-            causal = False
-        # manually handle causal mask, if another mask was given
-        row_is_entirely_masked = None
-        if exists(mask) and causal:
-            causal_mask = self.create_causal_mask(q_len, k_len, device = device)
-            mask = mask & ~causal_mask
-            # protect against an entire row being masked out
-            row_is_entirely_masked = ~mask.any(dim = -1)
-            mask[..., 0] = mask[..., 0] | row_is_entirely_masked
-            causal = False
-        # handle alibi positional bias
-        # convert from bool to float
-        if exists(attn_bias):
-            attn_bias = rearrange(attn_bias, 'h i j -> 1 h i j').expand(batch, heads, -1, -1)
-            # if mask given, the mask would already contain the causal mask from above logic
-            # otherwise, if no mask given but still causal, mask out alibi positional bias to a large negative number
-            mask_value = -torch.finfo(q.dtype).max
-            if exists(mask):
-                attn_bias = attn_bias.masked_fill(~mask, mask_value // 2)
-            elif causal:
-                causal_mask = self.create_causal_mask(q_len, k_len, device = device)
-                attn_bias = attn_bias.masked_fill(causal_mask, mask_value // 2)
-                causal = False
-            # scaled_dot_product_attention handles attn_mask either as bool or additive bias
-            # make it an additive bias here
-            mask = attn_bias
-        # Check if there is a compatible device for flash attention
-        config = self.cuda_config if is_cuda else self.cpu_config
-        # pytorch 2.0 flash attn: q, k, v, mask, dropout, causal, softmax_scale
-        # Legacy code...
-        with torch.backends.cuda.sdp_kernel(enable_math=True, enable_mem_efficient=True):
-        # New SDP kernel code...
-        # with sdpa_kernel(SDPBackend.FLASH_ATTENTION):
-            out = F.scaled_dot_product_attention(
-                q, k, v,
-                attn_mask = mask,
-                dropout_p = self.dropout if self.training else 0.,
-                is_causal = causal
-            )
-        # for a row that is entirely masked out, should zero out the output of that row token
-        if exists(row_is_entirely_masked):
-            out = out.masked_fill(row_is_entirely_masked[..., None], 0.)
-        return out, Intermediates()
-    def forward(
-        self,
-        q, k, v,
-        mask = None,
-        attn_bias = None,
-        prev_attn = None
-    ):
-        """
-        einstein notation
-        b - batch
-        h - heads
-        n, i, j - sequence length (base sequence length, source, target)
-        d - feature dimension
-        """
-        n, heads, kv_heads, device = q.shape[-2], q.shape[1], k.shape[1], q.device
-        scale = default(self.scale, q.shape[-1] ** -0.5)
-        causal = self.causal
-        # handle kv cached decoding
-        if n == 1 and causal:
-            causal = False
-        # handle grouped multi-query attention
-        if kv_heads == 1:
-            k, v = map(lambda t: rearrange(t, 'b 1 n d -> b n d'), (k, v))
-        elif kv_heads < heads:
-            k, v = map(lambda t: repeat(t, 'b kvh n d -> b (r kvh) n d', r = heads // kv_heads), (k, v))
-        # handle zero kv, as means for allowing network to attend to nothing
-        if self.add_zero_kv:
-            k, v = map(lambda t: F.pad(t, (0, 0, 1, 0), value = 0.), (k, v))
-            if exists(mask):
-                mask = F.pad(mask, (1, 0), value = True)
-            if exists(attn_bias):
-                attn_bias = F.pad(attn_bias, (1, 0), value = 0.)
-        if self.flash:
-            assert not exists(prev_attn), 'residual attention not compatible with flash attention'
-            return self.flash_attn(q, k, v, mask = mask, attn_bias = attn_bias)
-        kv_einsum_eq = 'b j d' if k.ndim == 3 else 'b h j d'
-        dots = einsum(f'b h i d, {kv_einsum_eq} -> b h i j', q, k) * scale
-        if exists(prev_attn):
-            dots = dots + prev_attn
-        qk_similarities = dots.clone()
-        if self.talking_heads:
-            dots = self.pre_softmax_talking_heads(dots)
-        if exists(attn_bias):
-            dots = dots + attn_bias
-        i, j, dtype = *dots.shape[-2:], dots.dtype
-        mask_value = -torch.finfo(dots.dtype).max
-        if exists(self.sparse_topk) and self.sparse_topk < j:
-            top_values, _ = dots.topk(self.sparse_topk, dim = -1)
-            sparse_topk_mask = dots < top_values[..., -1:]
-            mask = (mask & sparse_topk_mask) if exists(mask) else sparse_topk_mask
-        if exists(mask):
-            dots = dots.masked_fill(~mask, mask_value)
-        if causal:
-            causal_mask = self.create_causal_mask(i, j, device = device)
-            dots = dots.masked_fill(causal_mask, mask_value)
-        pre_softmax_attn = dots.clone()
-        attn = self.attn_fn(dots, dim = -1)
-        attn = attn.type(dtype)
-        post_softmax_attn = attn.clone()
-        attn = self.attn_dropout(attn)
-        if self.talking_heads:
-            attn = self.post_softmax_talking_heads(attn)
-        out = einsum(f'b h i j, {kv_einsum_eq} -> b h i d', attn, v)
-        intermediates = Intermediates(
-            qk_similarities = qk_similarities,
-            pre_softmax_attn = pre_softmax_attn,
-            post_softmax_attn = post_softmax_attn
-        )
-        return out, intermediates
-#===================================================================================================================
-from math import ceil, log
-from typing import Optional, Union, Tuple, Callable
-import torch
-from torch import nn, Tensor
-from torch.nn import Module
-import torch.nn.functional as F
-from einops import rearrange, pack, unpack
-def exists(val):
-    return val is not None
-def default(val, d):
-    return val if exists(val) else d
-def identity(t, *args, **kwargs):
-    return t
-def cast_tuple(t, length = 1):
-    return t if isinstance(t, tuple) else (t,) * length
-def eval_decorator(fn):
-    def inner(self, *args, **kwargs):
-        was_training = self.training
-        self.eval()
-        out = fn(self, *args, **kwargs)
-        self.train(was_training)
-        return out
-    return inner
-# for variable lengthed prefixes
-def align_right(t, lens, pad_id = 0):
-    batch, seq_len, device, dtype = *t.shape, t.device, t.dtype
-    assert lens.ndim == 1 and lens.shape[0] == batch
-    assert lens.amax() <= seq_len
-    pad_lens = seq_len - lens
-    max_pad_len = pad_lens.amax()
-    batch_arange = torch.arange(batch, device = device, dtype = torch.long)[..., None]
-    prompt_len_arange = torch.arange(seq_len, device = device, dtype = torch.long)
-    t = F.pad(t, (max_pad_len, 0), value = 0)
-    offset = max_pad_len - pad_lens
-    aligned = t[batch_arange, prompt_len_arange + offset[..., None]]
-    return aligned
-# nucleus
-def top_p(logits, thres = 0.9):
-    sorted_logits, sorted_indices = torch.sort(logits, descending = True)
-    cum_probs = torch.cumsum(F.softmax(sorted_logits, dim = -1), dim = -1)
-    sorted_indices_to_remove = cum_probs > thres
-    sorted_indices_to_remove = F.pad(sorted_indices_to_remove, (1, -1), value = False)
-    sorted_logits[sorted_indices_to_remove] = float('-inf')
-    return sorted_logits.scatter(1, sorted_indices, sorted_logits)
-# topk
-def top_k(logits, frac_num_tokens = 0.1, k = None):
-    num_tokens = logits.shape[-1]
-    k = default(k, ceil(frac_num_tokens * num_tokens))
-    k = min(k, num_tokens)
-    val, ind = torch.topk(logits, k)
-    probs = torch.full_like(logits, float('-inf'))
-    probs.scatter_(1, ind, val)
-    return probs
-# top_a
-def top_a(logits, min_p_pow = 2.0, min_p_ratio = 0.02):
-    probs = F.softmax(logits, dim = -1)
-    max_probs = torch.amax(probs, dim = -1, keepdim = True)
-    limit = torch.pow(max_probs, min_p_pow) * min_p_ratio
-    return torch.where(probs < limit, float('-inf'), logits)
-# contrastive decoding function
-def contrastive_decode_fn(
-    expert_logits,
-    amateur_logits,
-    alpha = 0.1,
-    beta = 0.5
-):
-    """
-    Appendix A Algorithm 2
-    https://arxiv.org/abs/2309.09117
-    """
-    cutoff = log(alpha) + expert_logits.amax(dim = -1, keepdim = True)
-    diffs = (1 + beta) * expert_logits - beta * amateur_logits
-    contrastive_decode_logits = diffs.masked_fill(expert_logits < cutoff, -torch.finfo(expert_logits.dtype).max)
-    return contrastive_decode_logits
-# autoregressive wrapper class
-class AutoregressiveWrapper(Module):
-    def __init__(
-        self,
-        net,
-        ignore_index = -100,
-        pad_value = 0,
-        mask_prob = 0.,
-        add_attn_z_loss = False
-    ):
-        super().__init__()
-        self.pad_value = pad_value
-        self.ignore_index = ignore_index
-        self.net = net
-        self.max_seq_len = net.max_seq_len
-        # paper shows masking (MLM) in conjunction with autoregressive decoder-only training leads to big improvements https://arxiv.org/abs/2210.13432
-        assert mask_prob < 1.
-        self.mask_prob = mask_prob
-        # whether to add router z-loss
-        self.add_attn_z_loss = add_attn_z_loss
-    @torch.no_grad()
-    @eval_decorator
-    def generate(
-        self,
-        prompts,
-        seq_len,
-        eos_token = None,
-        temperature = 1.,
-        prompt_lens: Optional[Tensor] = None,
-        filter_logits_fn: Callable = top_k,
-        restrict_to_max_seq_len = True,
-        amateur_model: Optional[Union[Module, Tuple[Module]]] = None,
-        filter_kwargs: dict = dict(),
-        contrastive_decode_kwargs: Union[dict, Tuple[dict]] = dict(
-            beta = 0.5,
-            alpha = 0.1
-        ),
-        cache_kv = True,
-        verbose=True,
-        return_prime=False,
-        **kwargs
-    ):
-        max_seq_len, device = self.max_seq_len, prompts.device
-        prompts, ps = pack([prompts], '* n')
-        b, t = prompts.shape
-        # handle variable lengthed prompts (prefixes)
-        seq_start_pos = None
-        if exists(prompt_lens):
-            prompts = align_right(prompts, prompt_lens, pad_id = self.pad_value)
-            seq_start_pos = t - prompt_lens
-        # output from which sampled tokens appended to
-        out = prompts
-        if verbose:
-          print("Generating sequence of max length:", seq_len)
-        # kv caches
-        cache = None
-        # if doing contrastive decoding, turn off filter automatically
-        if exists(amateur_model):
-            amateur_model = cast_tuple(amateur_model)
-            contrastive_decode_kwargs = cast_tuple(contrastive_decode_kwargs)
-            assert len(amateur_model) == len(contrastive_decode_kwargs)
-            amateur_caches = [None] * len(amateur_model)
-            filter_logits_fn = identity
-            for i, module in enumerate(amateur_model):
-                if isinstance(module, AutoregressiveWrapper):
-                    amateur_model[i] = module.net
-                module.eval()
-        # sampling up to seq_len
-        for sl in range(seq_len):
-            if restrict_to_max_seq_len:
-                x = out[:, -max_seq_len:]
-                if exists(cache):
-                    for inter in cache.attn_intermediates:
-                        inter.cached_kv = [t[..., -(max_seq_len - 1):, :] for t in inter.cached_kv]
-            logits, new_cache = self.net(
-                x,
-                return_intermediates = True,
-                cache = cache,
-                seq_start_pos = seq_start_pos,
-                **kwargs
-            )
-            if cache_kv and self.net.can_cache_kv:
-                cache = new_cache
-            logits = logits[:, -1]
-            # handle contrastive decoding, Li et al.
-            # https://arxiv.org/abs/2210.15097
-            if exists(amateur_model):
-                for i, (amateur, amateur_cache, amateur_contrastive_decode_kwargs) in enumerate(zip(amateur_model, amateur_caches, contrastive_decode_kwargs)):
-                    amateur_logits, next_amateur_cache = amateur(
-                        x,
-                        return_intermediates = True,
-                        cache = amateur_cache,
-                        seq_start_pos = seq_start_pos,
-                        **kwargs
-                    )
-                    amateur_logits = amateur_logits[:, -1]
-                    assert amateur_logits.shape == logits.shape, 'logits dimension are not the same between amateur and expert model'
-                    logits = contrastive_decode_fn(logits, amateur_logits, **amateur_contrastive_decode_kwargs)
-                    if cache_kv and amateur.can_cache_kv:
-                        amateur_caches[i] = next_amateur_cache
-            # filter by top_k, top_p (nucleus), top_a, or custom
-            filtered_logits = filter_logits_fn(logits, **filter_kwargs)
-            probs = F.softmax(filtered_logits / temperature, dim=-1)
-            sample = torch.multinomial(probs, 1)
-            out = torch.cat((out, sample), dim=-1)
-            if verbose:
-              if sl % 32 == 0:
-                print(sl, '/', seq_len)
-            if exists(eos_token):
-                is_eos_tokens = (out == eos_token)
-                if is_eos_tokens.any(dim = -1).all():
-                    # mask out everything after the eos tokens
-                    shifted_is_eos_tokens = F.pad(is_eos_tokens, (1, -1))
-                    mask = shifted_is_eos_tokens.float().cumsum(dim = -1) >= 1
-                    out = out.masked_fill(mask, self.pad_value)
-                    if verbose:
-                      print('Model called the end of sequence at:', sl, '/', seq_len)
-                    break
-        if return_prime:
-          return out[:, :]
-        else:
-          return out[:, t:]
-        # out, = unpack(out, ps, '* n')
-        # return out
-    def compute_accuracy(self, logits, labels):
-        out = torch.argmax(logits, dim=-1)
-        out = out.flatten()
-        labels = labels.flatten()
-        mask = (labels != self.ignore_index) # can also be self.pad_value (your choice)
-        out = out[mask]
-        labels = labels[mask]
-        num_right = (out == labels)
-        num_right = torch.sum(num_right).type(torch.float32)
-        acc = num_right / len(labels)
-        return acc
-    def forward(self, x, **kwargs):
-        seq, ignore_index, add_attn_z_loss = x.shape[1], self.ignore_index, self.add_attn_z_loss
-        inp, target = x[:, :-1], x[:, 1:]
-        inp = torch.where(inp == ignore_index, self.pad_value, inp)
-        if self.mask_prob > 0.:
-            rand = torch.randn(inp.shape, device = x.device)
-            rand[:, 0] = -torch.finfo(rand.dtype).max # first token should not be masked out
-            num_mask = min(int(seq * self.mask_prob), seq - 1)
-            indices = rand.topk(num_mask, dim = -1).indices
-            mask = ~torch.zeros_like(inp).scatter(1, indices, 1.).bool()
-            kwargs.update(self_attn_kv_mask = mask)
-        logits, cache = self.net(
-            inp,
-            return_intermediates = True,
-            return_attn_z_loss = add_attn_z_loss,
-            **kwargs
-        )
-        acc = self.compute_accuracy(logits, target)
-        loss = F.cross_entropy(
-            rearrange(logits, 'b n c -> b c n'),
-            target,
-            ignore_index = ignore_index
-        )
-        if add_attn_z_loss:
-            loss = loss + cache.attn_z_loss
-        return loss, acc
-#===============================================================================
-import math
-from random import random
-import torch
-from torch import nn, einsum, Tensor
-import torch.nn.functional as F
-from functools import partial, wraps
-from inspect import isfunction
-from collections import namedtuple
-from dataclasses import dataclass
-from typing import List, Callable, Optional
-from einops import rearrange, repeat, reduce, pack, unpack
-from einops.layers.torch import Rearrange
-# constants
-DEFAULT_DIM_HEAD = 64
-@dataclass
-class LayerIntermediates:
-    hiddens: Optional[List[Tensor]] = None
-    attn_intermediates: Optional[List[Intermediates]] = None
-    layer_hiddens: Optional[List[Tensor]] = None
-    attn_z_loss: Optional[Tensor] = None
-    mems: Optional[Tensor] = None
-# helpers
-def exists(val):
-    return val is not None
-def default(val, d):
-    if exists(val):
-        return val
-    return d() if isfunction(d) else d
-def cast_tuple(val, depth):
-    return val if isinstance(val, tuple) else (val,) * depth
-def divisible_by(num, den):
-    return (num % den) == 0
-def maybe(fn):
-    @wraps(fn)
-    def inner(x, *args, **kwargs):
-        if not exists(x):
-            return x
-        return fn(x, *args, **kwargs)
-    return inner
-class always():
-    def __init__(self, val):
-        self.val = val
-    def __call__(self, *args, **kwargs):
-        return self.val
-class not_equals():
-    def __init__(self, val):
-        self.val = val
-    def __call__(self, x, *args, **kwargs):
-        return x != self.val
-class equals():
-    def __init__(self, val):
-        self.val = val
-    def __call__(self, x, *args, **kwargs):
-        return x == self.val
-def Sequential(*modules):
-    return nn.Sequential(*filter(exists, modules))
-# tensor helpers
-def max_neg_value(tensor):
-    return -torch.finfo(tensor.dtype).max
-def l2norm(t, groups = 1):
-    t = rearrange(t, '... (g d) -> ... g d', g = groups)
-    t = F.normalize(t, p = 2, dim = -1)
-    return rearrange(t, '... g d -> ... (g d)')
-def pad_at_dim(t, pad, dim = -1, value = 0.):
-    dims_from_right = (- dim - 1) if dim < 0 else (t.ndim - dim - 1)
-    zeros = ((0, 0) * dims_from_right)
-    return F.pad(t, (*zeros, *pad), value = value)
-def or_reduce(masks):
-    head, *body = masks
-    for rest in body:
-        head = head | rest
-    return head
-# auxiliary loss helpers
-def calc_z_loss(
-    pre_softmax_attns: List[Tensor],
-    mask = None,
-    weight = 1.
-):
-    # the same loss applied to the mixture of experts router logits in https://arxiv.org/abs/2202.08906
-    # in the paper, in a tiny footnote, they mention using it on attention logits with stabilizing effects
-    # also used in PaLM as one of the measures
-    lse = 0.
-    for attn in pre_softmax_attns:
-        lse = lse + attn.logsumexp(dim = -1)
-    loss = torch.square(lse)
-    loss = reduce(loss, 'b h n -> b n', 'sum')
-    if not exists(mask):
-        return loss.mean() * weight
-    loss = loss[mask].sum() / mask.sum().clamp(min = 1e-5)
-    return loss * weight
-# init helpers
-def init_zero_(layer):
-    nn.init.constant_(layer.weight, 0.)
-    if exists(layer.bias):
-        nn.init.constant_(layer.bias, 0.)
-# keyword argument helpers
-def pick_and_pop(keys, d):
-    values = list(map(lambda key: d.pop(key), keys))
-    return dict(zip(keys, values))
-def group_dict_by_key(cond, d):
-    return_val = [dict(),dict()]
-    for key in d.keys():
-        match = bool(cond(key))
-        ind = int(not match)
-        return_val[ind][key] = d[key]
-    return (*return_val,)
-def string_begins_with(prefix, str):
-    return str.startswith(prefix)
-def group_by_key_prefix(prefix, d):
-    return group_dict_by_key(partial(string_begins_with, prefix), d)
-def groupby_prefix_and_trim(prefix, d):
-    kwargs_with_prefix, kwargs = group_dict_by_key(partial(string_begins_with, prefix), d)
-    kwargs_without_prefix = dict(map(lambda x: (x[0][len(prefix):], x[1]), tuple(kwargs_with_prefix.items())))
-    return kwargs_without_prefix, kwargs
-# structured dropout, more effective than traditional attention dropouts
-def dropout_seq(seq, mask, dropout):
-    b, n, *_, device = *seq.shape, seq.device
-    logits = torch.randn(b, n, device = device)
-    if exists(mask):
-        mask_value = max_neg_value(logits)
-        logits = logits.masked_fill(~mask, mask_value)
-    keep_prob = 1. - dropout
-    num_keep = max(1,  int(keep_prob * n))
-    keep_indices = logits.topk(num_keep, dim = 1).indices
-    batch_indices = torch.arange(b, device = device)
-    batch_indices = rearrange(batch_indices, 'b -> b 1')
-    seq = seq[batch_indices, keep_indices]
-    if exists(mask):
-        seq_counts = mask.sum(dim = -1)
-        seq_keep_counts = torch.ceil(seq_counts * keep_prob).int()
-        keep_mask = torch.arange(num_keep, device = device) < rearrange(seq_keep_counts, 'b -> b 1')
-        mask = mask[batch_indices, keep_indices] & keep_mask
-    return seq, mask
-# activations
-class ReluSquared(nn.Module):
-    def forward(self, x):
-        return F.relu(x) ** 2
-# embedding
-class TokenEmbedding(nn.Module):
-    def __init__(self, dim, num_tokens, l2norm_embed = False):
-        super().__init__()
-        self.l2norm_embed = l2norm_embed
-        self.emb = nn.Embedding(num_tokens, dim)
-    def forward(self, x):
-        token_emb = self.emb(x)
-        return l2norm(token_emb) if self.l2norm_embed else token_emb
-# positional embeddings
-class AbsolutePositionalEmbedding(nn.Module):
-    def __init__(self, dim, max_seq_len, l2norm_embed = False):
-        super().__init__()
-        self.scale = dim ** -0.5 if not l2norm_embed else 1.
-        self.max_seq_len = max_seq_len
-        self.l2norm_embed = l2norm_embed
-        self.emb = nn.Embedding(max_seq_len, dim)
-    def forward(self, x, pos = None, seq_start_pos = None):
-        seq_len, device = x.shape[1], x.device
-        assert seq_len <= self.max_seq_len, f'you are passing in a sequence length of {seq_len} but your absolute positional embedding has a max sequence length of {self.max_seq_len}'
-        if not exists(pos):
-            pos = torch.arange(seq_len, device = device)
-        if exists(seq_start_pos):
-            pos = (pos - seq_start_pos[..., None]).clamp(min = 0)
-        pos_emb = self.emb(pos)
-        pos_emb = pos_emb * self.scale
-        return l2norm(pos_emb) if self.l2norm_embed else pos_emb
-class ScaledSinusoidalEmbedding(nn.Module):
-    def __init__(self, dim, theta = 10000):
-        super().__init__()
-        assert divisible_by(dim, 2)
-        self.scale = nn.Parameter(torch.ones(1) * dim ** -0.5)
-        half_dim = dim // 2
-        freq_seq = torch.arange(half_dim).float() / half_dim
-        inv_freq = theta ** -freq_seq
-        self.register_buffer('inv_freq', inv_freq, persistent = False)
-    def forward(self, x, pos = None, seq_start_pos = None):
-        seq_len, device = x.shape[1], x.device
-        if not exists(pos):
-            pos = torch.arange(seq_len, device = device)
-        if exists(seq_start_pos):
-            pos = pos - seq_start_pos[..., None]
-        emb = einsum('i, j -> i j', pos, self.inv_freq)
-        emb = torch.cat((emb.sin(), emb.cos()), dim = -1)
-        return emb * self.scale
-class RelativePositionBias(nn.Module):
-    def __init__(self, scale, causal = False, num_buckets = 32, max_distance = 128, heads = 8):
-        super().__init__()
-        self.scale = scale
-        self.causal = causal
-        self.num_buckets = num_buckets
-        self.max_distance = max_distance
-        self.relative_attention_bias = nn.Embedding(num_buckets, heads)
-    @staticmethod
-    def _relative_position_bucket(relative_position, causal = True, num_buckets = 32, max_distance = 128):
-        ret = 0
-        n = -relative_position
-        if not causal:
-            num_buckets //= 2
-            ret += (n < 0).long() * num_buckets
-            n = torch.abs(n)
-        else:
-            n = torch.max(n, torch.zeros_like(n))
-        max_exact = num_buckets // 2
-        is_small = n < max_exact
-        val_if_large = max_exact + (
-            torch.log(n.float() / max_exact) / math.log(max_distance / max_exact) * (num_buckets - max_exact)
-        ).long()
-        val_if_large = torch.min(val_if_large, torch.full_like(val_if_large, num_buckets - 1))
-        ret += torch.where(is_small, n, val_if_large)
-        return ret
-    @property
-    def device(self):
-        return next(self.parameters()).device
-    def forward(self, i, j):
-        device = self.device
-        q_pos = torch.arange(j - i, j, dtype = torch.long, device = device)
-        k_pos = torch.arange(j, dtype = torch.long, device = device)
-        rel_pos = k_pos[None, :] - q_pos[:, None]
-        rp_bucket = self._relative_position_bucket(rel_pos, causal = self.causal, num_buckets = self.num_buckets, max_distance = self.max_distance)
-        values = self.relative_attention_bias(rp_bucket)
-        bias = rearrange(values, 'i j h -> h i j')
-        return bias * self.scale
-class DynamicPositionBias(nn.Module):
-    def __init__(self, dim, *, heads, depth, log_distance = False, norm = False):
-        super().__init__()
-        assert depth >= 1, 'depth for dynamic position bias MLP must be greater or equal to 1'
-        self.log_distance = log_distance
-        self.mlp = nn.ModuleList([])
-        self.mlp.append(Sequential(
-            nn.Linear(1, dim),
-            nn.LayerNorm(dim) if norm else None,
-            nn.SiLU()
-        ))
-        for _ in range(depth - 1):
-            self.mlp.append(Sequential(
-                nn.Linear(dim, dim),
-                nn.LayerNorm(dim) if norm else None,
-                nn.SiLU()
-            ))
-        self.mlp.append(nn.Linear(dim, heads))
-    @property
-    def device(self):
-        return next(self.parameters()).device
-    def forward(self, i, j):
-        assert i == j
-        n, device = j, self.device
-        # get the (n x n) matrix of distances
-        seq_arange = torch.arange(n, device = device)
-        context_arange = torch.arange(n, device = device)
-        indices = rearrange(seq_arange, 'i -> i 1') - rearrange(context_arange, 'j -> 1 j')
-        indices += (n - 1)
-        # input to continuous positions MLP
-        pos = torch.arange(-n + 1, n, device = device).float()
-        pos = rearrange(pos, '... -> ... 1')
-        if self.log_distance:
-            pos = torch.sign(pos) * torch.log(pos.abs() + 1)  # log of distance is sign(rel_pos) * log(abs(rel_pos) + 1)
-        for layer in self.mlp:
-            pos = layer(pos)
-        # get position biases
-        bias = pos[indices]
-        bias = rearrange(bias, 'i j h -> h i j')
-        return bias
-class AlibiPositionalBias(nn.Module):
-    def __init__(self, heads, total_heads, **kwargs):
-        super().__init__()
-        self.heads = heads
-        self.total_heads = total_heads
-        slopes = Tensor(self._get_slopes(heads))
-        slopes = rearrange(slopes, 'h -> h 1 1')
-        self.register_buffer('slopes', slopes, persistent = False)
-        self.register_buffer('bias', None, persistent = False)
-    def get_bias(self, i, j, device):
-        i_arange = torch.arange(j - i, j, device = device)
-        j_arange = torch.arange(j, device = device)
-        bias = -torch.abs(rearrange(j_arange, 'j -> 1 1 j') - rearrange(i_arange, 'i -> 1 i 1'))
-        return bias
-    @staticmethod
-    def _get_slopes(heads):
-        def get_slopes_power_of_2(n):
-            start = (2**(-2**-(math.log2(n)-3)))
-            ratio = start
-            return [start*ratio**i for i in range(n)]
-        if math.log2(heads).is_integer():
-            return get_slopes_power_of_2(heads)
-        closest_power_of_2 = 2 ** math.floor(math.log2(heads))
-        return get_slopes_power_of_2(closest_power_of_2) + get_slopes_power_of_2(2 * closest_power_of_2)[0::2][:heads-closest_power_of_2]
-    @property
-    def device(self):
-        return next(self.buffers()).device
-    def forward(self, i, j):
-        h, device = self.total_heads, self.device
-        if exists(self.bias) and self.bias.shape[-1] >= j and self.bias.shape[-2] >= i:
-            return self.bias[..., -i:, -j:]
-        bias = self.get_bias(i, j, device)
-        bias = bias * self.slopes
-        num_heads_unalibied = h - bias.shape[0]
-        bias = pad_at_dim(bias, (0, num_heads_unalibied), dim = 0)
-        self.register_buffer('bias', bias, persistent = False)
-        return self.bias
-class RotaryEmbedding(nn.Module):
-    def __init__(
-        self,
-        dim,
-        use_xpos = False,
-        scale_base = 512,
-        interpolation_factor = 1.,
-        base = 10000,
-        base_rescale_factor = 1.
-    ):
-        super().__init__()
-        # proposed by reddit user bloc97, to rescale rotary embeddings to longer sequence length without fine-tuning
-        # has some connection to NTK literature
-        # https://www.reddit.com/r/LocalLLaMA/comments/14lz7j5/ntkaware_scaled_rope_allows_llama_models_to_have/
-        base *= base_rescale_factor ** (dim / (dim - 2))
-        inv_freq = 1. / (base ** (torch.arange(0, dim, 2).float() / dim))
-        self.register_buffer('inv_freq', inv_freq)
-        assert interpolation_factor >= 1.
-        self.interpolation_factor = interpolation_factor
-        if not use_xpos:
-            self.register_buffer('scale', None)
-            return
-        scale = (torch.arange(0, dim, 2) + 0.4 * dim) / (1.4 * dim)
-        self.scale_base = scale_base
-        self.register_buffer('scale', scale)
-    def forward(self, seq_len):
-        device = self.inv_freq.device
-        t = torch.arange(seq_len, device = device).type_as(self.inv_freq)
-        t = t / self.interpolation_factor
-        freqs = torch.einsum('i , j -> i j', t, self.inv_freq)
-        freqs = torch.cat((freqs, freqs), dim = -1)
-        if not exists(self.scale):
-            return freqs, 1.
-        power = (torch.arange(seq_len, device = device) - (seq_len // 2)) / self.scale_base
-        scale = self.scale ** rearrange(power, 'n -> n 1')
-        scale = torch.cat((scale, scale), dim = -1)
-        return freqs, scale
-def rotate_half(x):
-    x = rearrange(x, '... (j d) -> ... j d', j = 2)
-    x1, x2 = x.unbind(dim = -2)
-    return torch.cat((-x2, x1), dim = -1)
-def apply_rotary_pos_emb(t, freqs, scale = 1):
-    rot_dim, seq_len = freqs.shape[-1], t.shape[-2]
-    freqs = freqs[-seq_len:, :]
-    if t.ndim == 4 and freqs.ndim == 3:
-        freqs = rearrange(freqs, 'b n d -> b 1 n d')
-    # partial rotary embeddings, Wang et al. GPT-J
-    t, t_unrotated = t[..., :rot_dim], t[..., rot_dim:]
-    t = (t * freqs.cos() * scale) + (rotate_half(t) * freqs.sin() * scale)
-    return torch.cat((t, t_unrotated), dim = -1)
-# norms
-class Scale(nn.Module):
-    def __init__(self, value, fn):
-        super().__init__()
-        self.value = value
-        self.fn = fn
-    def forward(self, x, **kwargs):
-        out = self.fn(x, **kwargs)
-        scale_fn = lambda t: t * self.value
-        if not isinstance(out, tuple):
-            return scale_fn(out)
-        return (scale_fn(out[0]), *out[1:])
-class ScaleNorm(nn.Module):
-    def __init__(self, dim, eps = 1e-5):
-        super().__init__()
-        self.eps = eps
-        self.g = nn.Parameter(torch.ones(1) * (dim ** -0.5))
-    def forward(self, x):
-        norm = torch.norm(x, dim = -1, keepdim = True)
-        return x / norm.clamp(min = self.eps) * self.g
-class RMSNorm(nn.Module):
-    def __init__(self, dim):
-        super().__init__()
-        self.scale = dim ** 0.5
-        self.g = nn.Parameter(torch.ones(dim))
-    def forward(self, x):
-        return F.normalize(x, dim = -1) * self.scale * self.g
-class SimpleRMSNorm(nn.Module):
-    def __init__(self, dim):
-        super().__init__()
-        self.scale = dim ** 0.5
-    def forward(self, x):
-        return F.normalize(x, dim = -1) * self.scale
-# residual and residual gates
-class Residual(nn.Module):
-    def __init__(self, dim, scale_residual = False, scale_residual_constant = 1.):
-        super().__init__()
-        self.residual_scale = nn.Parameter(torch.ones(dim)) if scale_residual else None
-        self.scale_residual_constant = scale_residual_constant
-    def forward(self, x, residual):
-        if exists(self.residual_scale):
-            residual = residual * self.residual_scale
-        if self.scale_residual_constant != 1:
-            residual = residual * self.scale_residual_constant
-        return x + residual
-class GRUGating(nn.Module):
-    def __init__(self, dim, scale_residual = False, **kwargs):
-        super().__init__()
-        self.gru = nn.GRUCell(dim, dim)
-        self.residual_scale = nn.Parameter(torch.ones(dim)) if scale_residual else None
-    def forward(self, x, residual):
-        if exists(self.residual_scale):
-            residual = residual * self.residual_scale
-        gated_output = self.gru(
-            rearrange(x, 'b n d -> (b n) d'),
-            rearrange(residual, 'b n d -> (b n) d')
-        )
-        return gated_output.reshape_as(x)
-# token shifting
-def shift(t, amount, mask = None):
-    if amount == 0:
-        return t
-    else:
-        amount = min(amount, t.shape[1])
-    if exists(mask):
-        t = t.masked_fill(~mask[..., None], 0.)
-    return pad_at_dim(t, (amount, -amount), dim = - 2, value = 0.)
-class ShiftTokens(nn.Module):
-    def __init__(self, shifts, fn):
-        super().__init__()
-        self.fn = fn
-        self.shifts = tuple(shifts)
-    def forward(self, x, **kwargs):
-        mask = kwargs.get('mask', None)
-        shifts = self.shifts
-        segments = len(shifts)
-        feats_per_shift = x.shape[-1] // segments
-        splitted = x.split(feats_per_shift, dim = -1)
-        segments_to_shift, rest = splitted[:segments], splitted[segments:]
-        segments_to_shift = list(map(lambda args: shift(*args, mask = mask), zip(segments_to_shift, shifts)))
-        x = torch.cat((*segments_to_shift, *rest), dim = -1)
-        return self.fn(x, **kwargs)
-# feedforward
-class GLU(nn.Module):
-    def __init__(
-        self,
-        dim_in,
-        dim_out,
-        activation: Callable,
-        mult_bias = False
-    ):
-        super().__init__()
-        self.act = activation
-        self.proj = nn.Linear(dim_in, dim_out * 2)
-        self.mult_bias = nn.Parameter(torch.ones(dim_out)) if mult_bias else 1.
-    def forward(self, x):
-        x, gate = self.proj(x).chunk(2, dim = -1)
-        return x * self.act(gate) * self.mult_bias
-class FeedForward(nn.Module):
-    def __init__(
-        self,
-        dim,
-        dim_out = None,
-        mult = 4,
-        glu = False,
-        glu_mult_bias = False,
-        swish = False,
-        relu_squared = False,
-        post_act_ln = False,
-        dropout = 0.,
-        no_bias = False,
-        zero_init_output = False
-    ):
-        super().__init__()
-        inner_dim = int(dim * mult)
-        dim_out = default(dim_out, dim)
-        if relu_squared:
-            activation = ReluSquared()
-        elif swish:
-            activation = nn.SiLU()
-        else:
-            activation = nn.GELU()
-        if glu:
-            project_in = GLU(dim, inner_dim, activation, mult_bias = glu_mult_bias)
-        else:
-            project_in = nn.Sequential(
-                nn.Linear(dim, inner_dim, bias = not no_bias),
-                activation
-            )
-        self.ff = Sequential(
-            project_in,
-            nn.LayerNorm(inner_dim) if post_act_ln else None,
-            nn.Dropout(dropout),
-            nn.Linear(inner_dim, dim_out, bias = not no_bias)
-        )
-        # init last linear layer to 0
-        if zero_init_output:
-            init_zero_(self.ff[-1])
-    def forward(self, x):
-        return self.ff(x)
-# attention. it is all we need
-class Attention(nn.Module):
-    def __init__(
-        self,
-        dim,
-        dim_head = DEFAULT_DIM_HEAD,
-        heads = 8,
-        causal = False,
-        flash = False,
-        talking_heads = False,
-        head_scale = False,
-        sparse_topk = None,
-        num_mem_kv = 0,
-        dropout = 0.,
-        on_attn = False,
-        gate_value_heads = False,
-        gate_values = False,
-        zero_init_output = False,
-        max_attend_past = None,
-        qk_norm = False,
-        qk_norm_groups = 1,
-        qk_norm_scale = 10,
-        qk_norm_dim_scale = False,
-        one_kv_head = False,
-        kv_heads = None,
-        shared_kv = False,
-        value_dim_head = None,
-        tensor_product = False,      # https://arxiv.org/abs/2208.06061
-        add_zero_kv = False,         # same as add_zero_attn in pytorch
-        rotary_embed_values = False,
-        onnxable = False
-    ):
-        super().__init__()
-        self.scale = dim_head ** -0.5
-        self.heads = heads
-        self.causal = causal
-        self.max_attend_past = max_attend_past
-        assert not (exists(kv_heads) and one_kv_head), 'either attn_one_kv_head is set to True (in which case kv_heads is set to 1), or attn_kv_heads is set, but not both'
-        value_dim_head = default(value_dim_head, dim_head)
-        kv_heads = default(kv_heads, heads)
-        kv_heads = 1 if one_kv_head else kv_heads
-        assert divisible_by(heads, kv_heads)
-        self.kv_heads = kv_heads
-        q_dim = dim_head * heads
-        k_dim = dim_head * kv_heads
-        v_dim = value_dim_head * kv_heads
-        out_dim = value_dim_head * heads
-        self.to_q = nn.Linear(dim, q_dim, bias = False)
-        self.to_k = nn.Linear(dim, k_dim, bias = False)
-        # shared key / values, for further memory savings during inference
-        assert not (shared_kv and value_dim_head != dim_head), 'key and value head dimensions must be equal for shared key / values'
-        self.to_v = nn.Linear(dim, v_dim, bias = False) if not shared_kv else None
-        # relations projection from tp-attention
-        self.to_r = nn.Linear(dim, v_dim, bias = False) if tensor_product else None
-        # add GLU gating for aggregated values, from alphafold2
-        self.to_v_gate = None
-        if gate_values:
-            self.to_v_gate = nn.Linear(dim, out_dim)
-            nn.init.constant_(self.to_v_gate.weight, 0)
-            nn.init.constant_(self.to_v_gate.bias, 10)
-        # add per head gating of the output values, from 'Attend to nothing' paper
-        self.to_v_head_gate = None
-        if gate_value_heads:
-            self.to_v_head_gate = nn.Linear(dim, heads)
-            nn.init.constant_(self.to_v_head_gate.weight, 0)
-            nn.init.constant_(self.to_v_head_gate.bias, 10)
-        # cosine sim attention
-        self.qk_norm = qk_norm
-        self.qk_norm_groups = qk_norm_groups
-        self.qk_norm_scale = qk_norm_scale
-        # whether to use the rmsnorm (equivalent to cosine sim attention when scale is equal to 1) - https://arxiv.org/abs/2302.05442
-        self.qk_norm_dim_scale = qk_norm_dim_scale
-        self.qk_norm_q_scale = self.qk_norm_k_scale = 1
-        if qk_norm and qk_norm_dim_scale:
-            self.qk_norm_q_scale = nn.Parameter(torch.ones(heads, 1, dim_head))
-            self.qk_norm_k_scale = nn.Parameter(torch.ones(heads, 1, dim_head))
-        assert (not qk_norm) or divisible_by(dim_head, qk_norm_groups), 'dimension per attention head must be divisible by the qk norm groups'
-        assert not (qk_norm and (dim_head // qk_norm_groups) <= 2), 'the group dimension may be too small (2 was too small in my tests, but 4 still works, surprisingly)'
-        # attend class - includes core attention algorithm + talking heads
-        self.attend = Attend(
-            heads = heads,
-            causal = causal,
-            talking_heads = talking_heads,
-            dropout = dropout,
-            sparse_topk = sparse_topk,
-            qk_norm = qk_norm,
-            scale = qk_norm_scale if qk_norm else self.scale,
-            add_zero_kv = add_zero_kv,
-            flash = flash,
-            onnxable = onnxable
-        )
-        # head scaling
-        self.head_scale = head_scale
-        if head_scale:
-            self.head_scale_params = nn.Parameter(torch.ones(1, heads, 1, 1))
-        # explicit topk sparse attention
-        self.sparse_topk = sparse_topk
-        # add memory key / values
-        self.num_mem_kv = num_mem_kv
-        if num_mem_kv > 0:
-            self.mem_k = nn.Parameter(torch.randn(heads, num_mem_kv, dim_head))
-            self.mem_v = nn.Parameter(torch.randn(heads, num_mem_kv, dim_head))
-        # attention on attention
-        self.attn_on_attn = on_attn
-        self.to_out = nn.Sequential(nn.Linear(out_dim, dim * 2, bias = False), nn.GLU()) if on_attn else nn.Linear(out_dim, dim, bias = False)
-        # whether to rotate positions into values, for absolute positions in addition to relative
-        self.rotary_embed_values = rotary_embed_values
-        # init output projection 0
-        if zero_init_output:
-            init_zero_(self.to_out)
-    def forward(
-        self,
-        x,
-        context = None,
-        mask = None,
-        context_mask = None,
-        attn_mask = None,
-        rel_pos = None,
-        rotary_pos_emb = None,
-        prev_attn = None,
-        mem = None,
-        return_intermediates = False,
-        cache: Optional[Intermediates] = None,
-    ):
-        b, n, _, h, kv_h, head_scale, device, has_context = *x.shape, self.heads, self.kv_heads, self.head_scale, x.device, exists(context)
-        kv_input = default(context, x)
-        q_input = x
-        k_input = kv_input
-        v_input = kv_input
-        r_input = x
-        if exists(mem):
-            k_input, mem_packed_shape = pack([mem, k_input], 'b * d')
-            v_input, _ = pack([mem, v_input], 'b * d')
-        q = self.to_q(q_input)
-        k = self.to_k(k_input)
-        v = self.to_v(v_input) if exists(self.to_v) else k
-        r = self.to_r(r_input) if exists(self.to_r) else None
-        q = rearrange(q, 'b n (h d) -> b h n d', h = h)
-        k, v, r = map(lambda t: maybe(rearrange)(t, 'b n (h d) -> b h n d', h = kv_h), (k, v, r))
-        if exists(cache) and not has_context:
-            ck, cv = cache.cached_kv
-            if exists(mem):
-                mk, k = unpack(k, mem_packed_shape, 'b h * d')
-                mv, v = unpack(v, mem_packed_shape, 'b h * d')
-            k = torch.cat((ck, k), dim = -2)
-            v = torch.cat((cv, v), dim = -2)
-            if exists(mem):
-                k = torch.cat((mk, k), dim = -2)
-                v = torch.cat((mv, v), dim = -2)
-        if return_intermediates:
-            mem_len = mem.shape[-2] if exists(mem) else 0
-            cached_kv = (k[..., mem_len:, :], v[..., mem_len:, :])
-        if self.qk_norm:
-            qk_l2norm = partial(l2norm, groups = self.qk_norm_groups)
-            q, k = map(qk_l2norm, (q, k))
-            scale = self.qk_norm_scale
-            q = q * self.qk_norm_q_scale
-            k = k * self.qk_norm_k_scale
-        if exists(rotary_pos_emb) and not has_context:
-            freqs, xpos_scale = rotary_pos_emb
-            q_xpos_scale, k_xpos_scale = (xpos_scale, xpos_scale ** -1.) if exists(xpos_scale) else (1., 1.)
-            q = apply_rotary_pos_emb(q, freqs, q_xpos_scale)
-            k = apply_rotary_pos_emb(k, freqs, k_xpos_scale)
-            if self.rotary_embed_values:
-                v = apply_rotary_pos_emb(v, freqs, k_xpos_scale)
-        input_mask = context_mask
-        if not exists(input_mask) and not has_context:
-            input_mask = mask
-        if self.num_mem_kv > 0:
-            mem_k, mem_v = map(lambda t: repeat(t, 'h n d -> b h n d', b = b), (self.mem_k, self.mem_v))
-            if self.qk_norm:
-                mem_k = l2norm(mem_k)
-                mem_k = mem_k * self.qk_norm_k_scale
-            k = torch.cat((mem_k, k), dim = -2)
-            v = torch.cat((mem_v, v), dim = -2)
-            if exists(input_mask):
-                input_mask = pad_at_dim(input_mask, (self.num_mem_kv, 0), dim = -1, value = True)
-        i, j = map(lambda t: t.shape[-2], (q, k))
-        # determine masking
-        mask_value = max_neg_value(q)
-        masks = []
-        final_attn_mask = None
-        if exists(input_mask):
-            input_mask = rearrange(input_mask, 'b j -> b 1 1 j')
-            masks.append(~input_mask)
-        if exists(attn_mask):
-            assert 2 <= attn_mask.ndim <= 4, 'attention mask must have greater than 2 dimensions but less than or equal to 4'
-            if attn_mask.ndim == 2:
-                attn_mask = rearrange(attn_mask, 'i j -> 1 1 i j')
-            elif attn_mask.ndim == 3:
-                attn_mask = rearrange(attn_mask, 'h i j -> 1 h i j')
-            masks.append(~attn_mask)
-        if exists(self.max_attend_past):
-            range_q = torch.arange(j - i, j, device = device)
-            range_k = torch.arange(j, device = device)
-            dist = rearrange(range_q, 'i -> 1 1 i 1') - rearrange(range_k, 'j -> 1 1 1 j')
-            max_attend_past_mask = dist > self.max_attend_past
-            masks.append(max_attend_past_mask)
-        if len(masks) > 0:
-            final_attn_mask = ~or_reduce(masks)
-        # prepare relative positional bias, if needed
-        attn_bias = None
-        if exists(rel_pos):
-            attn_bias = rel_pos(i, j)
-        # attention is all we need
-        out, intermediates = self.attend(
-            q, k, v,
-            mask = final_attn_mask,
-            attn_bias = attn_bias,
-            prev_attn = prev_attn
-        )
-        # https://arxiv.org/abs/2208.06061 proposes to add a residual for better gradients
-        if exists(r):
-            out = out * r + out
-        # normformer scaling of heads
-        if head_scale:
-            out = out * self.head_scale_params
-        # per head gating, from https://arxiv.org/abs/2306.12929
-        if exists(self.to_v_head_gate):
-            head_gate = self.to_v_head_gate(x)
-            out = out * rearrange(head_gate, 'b n h -> b h n 1').sigmoid()
-        # merge heads
-        out = rearrange(out, 'b h n d -> b n (h d)')
-        # alphafold2 styled gating of the values
-        if exists(self.to_v_gate):
-            gates = self.to_v_gate(x)
-            out = out * gates.sigmoid()
-        # combine the heads
-        out = self.to_out(out)
-        if exists(mask):
-            mask = rearrange(mask, 'b n -> b n 1')
-            out = out.masked_fill(~mask, 0.)
-        if not return_intermediates:
-            return out
-        intermediates.cached_kv = cached_kv
-        return out, intermediates
-class AttentionLayers(nn.Module):
-    def __init__(
-        self,
-        dim,
-        depth,
-        heads = 8,
-        causal = False,
-        cross_attend = False,
-        only_cross = False,
-        use_scalenorm = False,
-        use_rmsnorm = False,
-        use_simple_rmsnorm = False,
-        alibi_pos_bias = False,
-        alibi_num_heads = None,
-        rel_pos_bias = False,
-        rel_pos_num_buckets = 32,
-        rel_pos_max_distance = 128,
-        dynamic_pos_bias = False,
-        dynamic_pos_bias_log_distance = False,
-        dynamic_pos_bias_mlp_depth = 2,
-        dynamic_pos_bias_norm = False,
-        rotary_pos_emb = False,
-        rotary_emb_dim = None,
-        rotary_xpos = False,
-        rotary_interpolation_factor = 1.,
-        rotary_xpos_scale_base = 512,
-        rotary_base_rescale_factor = 1.,
-        custom_layers = None,
-        sandwich_coef = None,
-        par_ratio = None,
-        weight_tie_layers = False,   # Albert - https://arxiv.org/abs/1909.11942
-        layers_execute_order = None, # generalizes weight tying, can do arbitrary layer execution orders
-        residual_attn = False,
-        cross_residual_attn = False,
-        macaron = False,
-        pre_norm = True,
-        pre_norm_has_final_norm = True,
-        gate_residual = False,
-        scale_residual = False,
-        scale_residual_constant = 1.,
-        shift_tokens = 0,
-        sandwich_norm = False,
-        resi_dual = False,
-        resi_dual_scale = 1.,
-        zero_init_branch_output = False,
-        layer_dropout = 0.,
-        cross_attn_tokens_dropout = 0.,
-        **kwargs
-    ):
-        super().__init__()
-        rotary_pos_emb = rotary_pos_emb or rotary_xpos
-        ff_kwargs, kwargs = groupby_prefix_and_trim('ff_', kwargs)
-        attn_kwargs, kwargs = groupby_prefix_and_trim('attn_', kwargs)
-        dim_head = attn_kwargs.get('dim_head', DEFAULT_DIM_HEAD)
-        self.dim = dim
-        self.depth = depth
-        self.causal = causal
-        self.layers = nn.ModuleList([])
-        self.has_pos_emb = rel_pos_bias or rotary_pos_emb
-        rotary_emb_dim = max(default(rotary_emb_dim, dim_head // 2), 32)
-        assert not (rotary_xpos and not causal), 'rotary xpos is not compatible with bidirectional attention'
-        self.rotary_pos_emb = RotaryEmbedding(rotary_emb_dim, use_xpos = rotary_xpos, scale_base = rotary_xpos_scale_base, interpolation_factor = rotary_interpolation_factor, base_rescale_factor = rotary_base_rescale_factor) if rotary_pos_emb else None
-        assert not (alibi_pos_bias and rel_pos_bias), 'you can only choose Alibi positional bias or T5 relative positional bias, not both'
-        assert rel_pos_num_buckets <= rel_pos_max_distance, 'number of relative position buckets must be less than the relative position max distance'
-        # relative positional bias
-        flash_attn = attn_kwargs.get('flash', False)
-        assert (int(rel_pos_bias) + int(dynamic_pos_bias) + int(alibi_pos_bias)) <= 1, 'you can only choose up to one of t5, alibi, or dynamic positional bias'
-        self.rel_pos = None
-        if rel_pos_bias:
-            assert not flash_attn, 'flash attention not compatible with t5 relative positional bias'
-            self.rel_pos = RelativePositionBias(scale = dim_head ** 0.5, causal = causal, heads = heads, num_buckets = rel_pos_num_buckets, max_distance = rel_pos_max_distance)
-        elif dynamic_pos_bias:
-            assert not flash_attn, 'flash attention not compatible with dynamic positional bias'
-            self.rel_pos = DynamicPositionBias(dim = dim // 4, heads = heads, log_distance = dynamic_pos_bias_log_distance, depth = dynamic_pos_bias_mlp_depth, norm = dynamic_pos_bias_norm)
-        elif alibi_pos_bias:
-            alibi_num_heads = default(alibi_num_heads, heads)
-            assert alibi_num_heads <= heads, 'number of ALiBi heads must be less than the total number of heads'
-            self.rel_pos = AlibiPositionalBias(heads = alibi_num_heads, total_heads = heads)
-        assert (int(sandwich_norm) + int(resi_dual)) <= 1, 'either sandwich norm or resiDual is selected, but not both'
-        assert not (not pre_norm and sandwich_norm), 'sandwich norm cannot be used when not using prenorm'
-        if resi_dual:
-            pre_norm = False
-        self.pre_norm = pre_norm
-        self.sandwich_norm = sandwich_norm
-        self.resi_dual = resi_dual
-        assert 0 < resi_dual_scale <= 1., 'resiDual prenorm residual must be scaled by a factor greater than 0 and less than or equal to 1.'
-        self.resi_dual_scale = resi_dual_scale
-        self.residual_attn = residual_attn
-        self.cross_residual_attn = cross_residual_attn
-        assert not (flash_attn and (residual_attn or cross_residual_attn)), 'flash attention is not compatible with residual attention'
-        self.cross_attend = cross_attend
-        assert (int(use_scalenorm) + int(use_rmsnorm) + int(use_simple_rmsnorm)) <= 1, 'you can only use either scalenorm, rmsnorm, or simple rmsnorm'
-        if use_scalenorm:
-            norm_class = ScaleNorm
-        elif use_rmsnorm:
-            norm_class = RMSNorm
-        elif use_simple_rmsnorm:
-            norm_class = SimpleRMSNorm
-        else:
-            norm_class = nn.LayerNorm
-        norm_fn = partial(norm_class, dim)
-        if cross_attend and not only_cross:
-            default_block = ('a', 'c', 'f')
-        elif cross_attend and only_cross:
-            default_block = ('c', 'f')
-        else:
-            default_block = ('a', 'f')
-        if macaron:
-            default_block = ('f',) + default_block
-        # zero init
-        if zero_init_branch_output:
-            attn_kwargs = {**attn_kwargs, 'zero_init_output':  True}
-            ff_kwargs = {**ff_kwargs, 'zero_init_output':  True}
-        # setup weight tying, which is a special case of `layer_execute_order`
-        assert not (weight_tie_layers and any([*map(exists, (custom_layers, par_ratio, sandwich_coef))]))
-        if weight_tie_layers:
-            assert not exists(layers_execute_order)
-            layers_execute_order = tuple(range(len(default_block))) * depth
-            depth = 1
-        # calculate layer block order
-        if exists(custom_layers):
-            layer_types = custom_layers
-        elif exists(par_ratio):
-            par_depth = depth * len(default_block)
-            assert 1 < par_ratio <= par_depth, 'par ratio out of range'
-            default_block = tuple(filter(not_equals('f'), default_block))
-            par_attn  = par_depth // par_ratio
-            depth_cut = par_depth * 2 // 3  # 2 / 3 attention layer cutoff suggested by PAR paper
-            par_width = (depth_cut + depth_cut // par_attn) // par_attn
-            assert len(default_block) <= par_width, 'default block is too large for par_ratio'
-            par_block = default_block + ('f',) * (par_width - len(default_block))
-            par_head = par_block * par_attn
-            layer_types = par_head + ('f',) * (par_depth - len(par_head))
-        elif exists(sandwich_coef):
-            assert sandwich_coef > 0 and sandwich_coef <= depth, 'sandwich coefficient should be less than the depth'
-            layer_types = ('a',) * sandwich_coef + default_block * (depth - sandwich_coef) + ('f',) * sandwich_coef
-        else:
-            layer_types = default_block * depth
-        self.layer_types = layer_types
-        self.layers_execute_order = default(layers_execute_order, tuple(range(len(layer_types))))
-        assert all([i < len(self.layer_types) for i in self.layers_execute_order])
-        self.num_attn_layers = len(list(filter(equals('a'), layer_types)))
-        # stochastic depth
-        self.layer_dropouts = cast_tuple(layer_dropout, len(layer_types))
-        # structured dropout for cross attending
-        self.cross_attn_tokens_dropout = cross_attn_tokens_dropout
-        # calculate token shifting
-        shift_tokens = cast_tuple(shift_tokens, len(layer_types))
-        # whether it has post norm
-        self.final_norm = norm_fn() if pre_norm or resi_dual else nn.Identity()
-        # iterate and construct layers
-        for ind, (layer_type, layer_shift_tokens) in enumerate(zip(self.layer_types, shift_tokens)):
-            is_last_layer = ind == (len(self.layer_types) - 1)
-            if layer_type == 'a':
-                layer = Attention(dim, heads = heads, causal = causal, **attn_kwargs)
-            elif layer_type == 'c':
-                layer = Attention(dim, heads = heads, **attn_kwargs)
-            elif layer_type == 'f':
-                layer = FeedForward(dim, **ff_kwargs)
-                layer = layer if not macaron else Scale(0.5, layer)
-            else:
-                raise Exception(f'invalid layer type {layer_type}')
-            if layer_shift_tokens > 0:
-                shift_range_upper = layer_shift_tokens + 1
-                shift_range_lower = -layer_shift_tokens if not causal else 0
-                layer = ShiftTokens(range(shift_range_lower, shift_range_upper), layer)
-            residual_fn = GRUGating if gate_residual else Residual
-            residual = residual_fn(dim, scale_residual = scale_residual, scale_residual_constant = scale_residual_constant)
-            pre_branch_norm = norm_fn() if pre_norm else None
-            post_branch_norm = norm_fn() if sandwich_norm else None
-            post_main_norm = norm_fn() if not pre_norm else None
-            norms = nn.ModuleList([
-                pre_branch_norm,
-                post_branch_norm,
-                post_main_norm
-            ])
-            self.layers.append(nn.ModuleList([
-                norms,
-                layer,
-                residual
-            ]))
-    def forward(
-        self,
-        x,
-        context = None,
-        mask = None,
-        context_mask = None,
-        attn_mask = None,
-        self_attn_kv_mask = None,
-        mems = None,
-        seq_start_pos: Optional[Tensor] = None,
-        cache: Optional[LayerIntermediates] = None,
-        cache_age = 1,
-        return_hiddens = False
-    ):
-        assert not (self.cross_attend ^ exists(context)), 'context must be passed in if cross_attend is set to True'
-        # initialize accums
-        hiddens = []
-        layer_hiddens = []
-        intermediates = []
-        prev_attn = None
-        prev_cross_attn = None
-        mems = mems.copy() if exists(mems) else [None] * self.num_attn_layers
-        # handle left padded sequences
-        if exists(seq_start_pos):
-            seq_arange = torch.arange(x.shape[-2], device = x.device, dtype = torch.long)
-            left_pad_mask = seq_arange >= seq_start_pos[..., None]
-            if exists(self_attn_kv_mask):
-                self_attn_kv_mask = self_attn_kv_mask & left_pad_mask
-            else:
-                self_attn_kv_mask = left_pad_mask
-        # rotary positions
-        rotary_pos_emb = None
-        if exists(self.rotary_pos_emb):
-            max_rotary_emb_length = max(list(map(lambda m: (m.shape[1] if exists(m) else 0) + x.shape[1], mems)))
-            rotary_pos_emb = self.rotary_pos_emb(max_rotary_emb_length)
-        # assume cached key / values
-        attn_cache = []
-        if exists(cache):
-            assert not self.training and self.causal and not any([*map(exists, (mask, attn_mask))])
-            if cache_age > 0:
-                x = x[:, -cache_age:] # for spec decoding, may be greater than 1
-            attn_cache = cache.attn_intermediates
-        iter_attn_cache = iter(attn_cache)
-        # outer residual - for resiDual paper
-        outer_residual = x * self.resi_dual_scale
-        # get layers to be executed
-        layer_variables = (
-            self.layer_types,
-            self.layers,
-            self.layer_dropouts
-        )
-        layer_variables = tuple(tuple(layer_variable[i] for i in self.layers_execute_order) for layer_variable in layer_variables)
-        # go through the attention and feedforward layers
-        for ind, (layer_type, (norm, block, residual_fn), layer_dropout) in enumerate(zip(*layer_variables)):
-            is_last = ind == (len(self.layers) - 1)
-            if self.training and layer_dropout > 0. and random() < layer_dropout:
-                continue
-            if layer_type == 'a':
-                if return_hiddens:
-                    hiddens.append(x)
-                layer_mem = mems.pop(0) if mems else None
-            if layer_type == 'c':
-                if self.training and self.cross_attn_tokens_dropout > 0.:
-                    context, context_mask = dropout_seq(context, context_mask, self.cross_attn_tokens_dropout)
-            inner_residual = x
-            if return_hiddens:
-                layer_hiddens.append(x)
-            pre_norm, post_branch_norm, post_main_norm = norm
-            if exists(pre_norm):
-                x = pre_norm(x)
-            if layer_type == 'a':
-                out, inter = block(x, mask = mask, context_mask = self_attn_kv_mask, attn_mask = attn_mask, rel_pos = self.rel_pos, rotary_pos_emb = rotary_pos_emb, prev_attn = prev_attn, cache = next(iter_attn_cache, None), mem = layer_mem, return_intermediates = True)
-            elif layer_type == 'c':
-                out, inter = block(x, context = context, mask = mask, context_mask = context_mask, prev_attn = prev_cross_attn, cache = next(iter_attn_cache, None), return_intermediates = True)
-            elif layer_type == 'f':
-                out = block(x)
-            if self.resi_dual:
-                outer_residual = outer_residual + out * self.resi_dual_scale
-            if exists(post_branch_norm):
-                out = post_branch_norm(out)
-            x = residual_fn(out, inner_residual)
-            if layer_type in ('a', 'c') and return_hiddens:
-                intermediates.append(inter)
-            if layer_type == 'a' and self.residual_attn:
-                prev_attn = inter.pre_softmax_attn
-            elif layer_type == 'c' and self.cross_residual_attn:
-                prev_cross_attn = inter.pre_softmax_attn
-            if exists(post_main_norm):
-                x = post_main_norm(x)
-        if return_hiddens:
-            layer_hiddens.append(x)
-        if self.resi_dual:
-            x = x + self.final_norm(outer_residual)
-        else:
-            x = self.final_norm(x)
-        if not return_hiddens:
-            return x
-        intermediates = LayerIntermediates(
-            hiddens = hiddens,
-            attn_intermediates = intermediates,
-            layer_hiddens = layer_hiddens
-        )
-        return x, intermediates
-class Encoder(AttentionLayers):
-    def __init__(self, **kwargs):
-        assert 'causal' not in kwargs, 'cannot set causality on encoder'
-        super().__init__(causal = False, **kwargs)
-class Decoder(AttentionLayers):
-    def __init__(self, **kwargs):
-        assert 'causal' not in kwargs, 'cannot set causality on decoder'
-        super().__init__(causal = True, **kwargs)
-class CrossAttender(AttentionLayers):
-    def __init__(self, **kwargs):
-        super().__init__(cross_attend = True, only_cross = True, **kwargs)
-class ViTransformerWrapper(nn.Module):
-    def __init__(
-        self,
-        *,
-        image_size,
-        patch_size,
-        attn_layers,
-        channels = 3,
-        num_classes = None,
-        post_emb_norm = False,
-        num_register_tokens = 0,
-        emb_dropout = 0.
-    ):
-        super().__init__()
-        assert isinstance(attn_layers, Encoder), 'attention layers must be an Encoder'
-        assert divisible_by(image_size, patch_size), 'image dimensions must be divisible by the patch size'
-        dim = attn_layers.dim
-        num_patches = (image_size // patch_size) ** 2
-        patch_dim = channels * patch_size ** 2
-        self.patch_size = patch_size
-        self.pos_embedding = nn.Parameter(torch.randn(1, num_patches, dim))
-        has_register_tokens = num_register_tokens > 0
-        self.has_register_tokens = has_register_tokens
-        if has_register_tokens:
-            self.register_tokens = nn.Parameter(torch.randn(num_register_tokens, dim))
-        self.patch_to_embedding = nn.Sequential(
-            nn.LayerNorm(patch_dim),
-            nn.Linear(patch_dim, dim),
-            nn.LayerNorm(dim)
-        )
-        self.post_emb_norm = nn.LayerNorm(dim) if post_emb_norm else nn.Identity()
-        self.dropout = nn.Dropout(emb_dropout)
-        self.attn_layers = attn_layers
-        self.mlp_head = nn.Linear(dim, num_classes) if exists(num_classes) else nn.Identity()
-    def forward(
-        self,
-        img,
-        return_embeddings = False
-    ):
-        b, p = img.shape[0], self.patch_size
-        x = rearrange(img, 'b c (h p1) (w p2) -> b (h w) (p1 p2 c)', p1 = p, p2 = p)
-        x = self.patch_to_embedding(x)
-        n = x.shape[1]
-        x = x + self.pos_embedding[:, :n]
-        x = self.post_emb_norm(x)
-        x = self.dropout(x)
-        if self.has_register_tokens:
-            r = repeat(self.register_tokens, 'n d -> b n d', b = b)
-            x, ps = pack((x, r), 'b * d')
-        x = self.attn_layers(x)
-        if self.has_register_tokens:
-            x, _ = unpack(x, ps, 'b * d')
-        if not exists(self.mlp_head) or return_embeddings:
-            return x
-        x = x.mean(dim = -2)
-        return self.mlp_head(x)
-class TransformerWrapper(nn.Module):
-    def __init__(
-        self,
-        *,
-        num_tokens,
-        max_seq_len,
-        attn_layers,
-        emb_dim = None,
-        max_mem_len = 0,
-        shift_mem_down = 0,
-        emb_dropout = 0.,
-        post_emb_norm = False,
-        num_memory_tokens = None,
-        memory_tokens_interspersed_every = None,
-        tie_embedding = False,
-        logits_dim = None,
-        use_abs_pos_emb = True,
-        scaled_sinu_pos_emb = False,
-        l2norm_embed = False,
-        emb_frac_gradient = 1., # GLM-130B and Cogview successfully used this, set at 0.1
-        attn_z_loss_weight = 1e-4,
-    ):
-        super().__init__()
-        assert isinstance(attn_layers, AttentionLayers), 'attention layers must be one of Encoder or Decoder'
-        dim = attn_layers.dim
-        emb_dim = default(emb_dim, dim)
-        self.emb_dim = emb_dim
-        self.num_tokens = num_tokens
-        self.max_seq_len = max_seq_len
-        self.max_mem_len = max_mem_len
-        self.shift_mem_down = shift_mem_down
-        self.l2norm_embed = l2norm_embed
-        self.token_emb = TokenEmbedding(emb_dim, num_tokens, l2norm_embed = l2norm_embed)
-        if not (use_abs_pos_emb and not attn_layers.has_pos_emb):
-            self.pos_emb = always(0)
-        elif scaled_sinu_pos_emb:
-            self.pos_emb = ScaledSinusoidalEmbedding(emb_dim)
-        else:
-            self.pos_emb = AbsolutePositionalEmbedding(emb_dim, max_seq_len, l2norm_embed = l2norm_embed)
-        self.emb_frac_gradient = emb_frac_gradient # fraction of the gradient that should go to the embedding, https://arxiv.org/abs/2105.13290
-        self.post_emb_norm = nn.LayerNorm(emb_dim) if post_emb_norm else nn.Identity()
-        self.emb_dropout = nn.Dropout(emb_dropout)
-        self.project_emb = nn.Linear(emb_dim, dim) if emb_dim != dim else nn.Identity()
-        self.attn_layers = attn_layers
-        self.init_()
-        logits_dim = default(logits_dim, num_tokens)
-        self.to_logits = nn.Linear(dim, logits_dim) if not tie_embedding else lambda t: t @ self.token_emb.emb.weight.t()
-        # memory tokens (like [cls]) from Memory Transformers paper
-        num_memory_tokens = default(num_memory_tokens, 0)
-        self.num_memory_tokens = num_memory_tokens
-        if num_memory_tokens > 0:
-            self.memory_tokens = nn.Parameter(torch.randn(num_memory_tokens, dim))
-        self.memory_tokens_interspersed_every = memory_tokens_interspersed_every
-        # whether can do cached kv decoding
-        self.can_cache_kv = self.num_memory_tokens == 0
-    def init_(self):
-        if self.l2norm_embed:
-            nn.init.normal_(self.token_emb.emb.weight, std = 1e-5)
-            if not isinstance(self.pos_emb, always):
-                nn.init.normal_(self.pos_emb.emb.weight, std = 1e-5)
-            return
-        nn.init.kaiming_normal_(self.token_emb.emb.weight)
-    def forward(
-        self,
-        x,
-        return_embeddings = False,
-        return_logits_and_embeddings = False,
-        return_intermediates = False,
-        mask = None,
-        return_mems = False,
-        return_attn = False,
-        mems = None,
-        pos = None,
-        prepend_embeds = None,
-        sum_embeds = None,
-        return_attn_z_loss = False,
-        attn_z_loss_weight = 1e-4,
-        seq_start_pos = None,
-        cache: Optional[LayerIntermediates] = None,
-        **kwargs
-    ):
-        b, n, device, num_mems, has_memory_tokens, emb_frac_gradient = *x.shape, x.device, self.num_memory_tokens, self.num_memory_tokens > 0, self.emb_frac_gradient
-        return_hiddens = return_mems | return_attn | return_intermediates | return_attn_z_loss
-        # absolute positional embedding
-        external_pos_emb = exists(pos) and pos.dtype != torch.long
-        pos_emb = self.pos_emb(x, pos = pos, seq_start_pos = seq_start_pos) if not external_pos_emb else pos
-        x = self.token_emb(x) + pos_emb
-        # for summing embeddings passed externally - needs this for self-conditioning in non-autoregressive training
-        if exists(sum_embeds):
-            x = x + sum_embeds
-        # post embedding norm, purportedly leads to greater stabilization
-        x = self.post_emb_norm(x)
-        # whether to append embeds, as in PaLI, for image embeddings
-        if exists(prepend_embeds):
-            prepend_seq, prepend_dim = prepend_embeds.shape[1:]
-            assert prepend_dim == x.shape[-1], 'prepended embeddings need to have same dimensions as text model dimensions'
-            x = torch.cat((prepend_embeds, x), dim = -2)
-        # whether to reduce the gradient going to the embedding, from cogview paper, corroborated by GLM-130B model
-        if emb_frac_gradient < 1:
-            assert emb_frac_gradient > 0
-            x = x * emb_frac_gradient + x.detach() * (1 - emb_frac_gradient)
-        # embedding dropout
-        x = self.emb_dropout(x)
-        x = self.project_emb(x)
-        if has_memory_tokens:
-            mem_every = self.memory_tokens_interspersed_every
-            if exists(mem_every):
-                assert mem_every > 0
-                assert isinstance(self.attn_layers, Decoder), 'only for decoder'
-                next_seq_len = math.ceil(n / mem_every) * mem_every
-                x = pad_at_dim(x, (0, next_seq_len - n), dim = -2, value = 0.)
-                x = rearrange(x, 'b (n m) d -> (b n) m d', m = mem_every)
-            mem = repeat(self.memory_tokens, 'n d -> b n d', b = x.shape[0])
-            x, mem_packed_shape = pack((mem, x), 'b * d')
-            # auto-handle masking after appending memory tokens
-            if not exists(mem_every) and exists(mask):
-                mask = pad_at_dim(mask, (num_mems, 0), dim = -1, value = True)
-            if exists(mem_every):
-                x = rearrange(x, '(b n) m d -> b (n m) d', b = b)
-        if self.shift_mem_down and exists(mems):
-            mems_l, mems_r = mems[:self.shift_mem_down], mems[self.shift_mem_down:]
-            mems = [*mems_r, *mems_l]
-        x, intermediates = self.attn_layers(x, mask = mask, mems = mems, cache = cache, return_hiddens = True, seq_start_pos = seq_start_pos, **kwargs)
-        if has_memory_tokens:
-            if exists(mem_every):
-                x = rearrange(x, 'b (n m) d -> (b n) m d', m = (mem_every + num_mems))
-            mem, x = unpack(x, mem_packed_shape, 'b * d')
-            if exists(mem_every):
-                x = rearrange(x, '(b n) m d -> b (n m) d', b = b)
-            x = x[:, :n]
-        if return_logits_and_embeddings:
-            out = (self.to_logits(x), x)
-        elif return_embeddings:
-            out = x
-        else:
-            out = self.to_logits(x)
-        if return_attn_z_loss:
-            pre_softmax_attns = list(map(lambda t: t.pre_softmax_attn, intermediates.attn_intermediates))
-            intermediates.attn_z_loss = calc_z_loss(pre_softmax_attns, weight = attn_z_loss_weight)
-            return_intermediates = True
-        if return_mems:
-            hiddens = intermediates.hiddens
-            new_mems = list(map(lambda pair: torch.cat(pair, dim = -2), zip(mems, hiddens))) if exists(mems) else hiddens
-            new_mems = list(map(lambda t: t[..., -self.max_mem_len:, :].detach(), new_mems))
-            if not return_intermediates:
-                return out, new_mems
-            intermediates.mems = new_mems
-        if return_intermediates:
-            return out, intermediates
-        if return_attn:
-            attn_maps = list(map(lambda t: t.post_softmax_attn, intermediates.attn_intermediates))
-            return out, attn_maps
-        return out
-class ContinuousTransformerWrapper(nn.Module):
-    def __init__(
-        self,
-        *,
-        max_seq_len,
-        attn_layers,
-        dim_in = None,
-        dim_out = None,
-        emb_dim = None,
-        max_mem_len = 0,
-        post_emb_norm = False,
-        emb_dropout = 0.,
-        use_abs_pos_emb = True,
-        scaled_sinu_pos_emb = False
-    ):
-        super().__init__()
-        assert isinstance(attn_layers, AttentionLayers), 'attention layers must be one of Encoder or Decoder'
-        dim = attn_layers.dim
-        self.max_seq_len = max_seq_len
-        self.max_mem_len = max_mem_len
-        if not (use_abs_pos_emb and not attn_layers.has_pos_emb):
-            self.pos_emb = always(0)
-        elif scaled_sinu_pos_emb:
-            self.pos_emb = ScaledSinusoidalEmbedding(dim)
-        else:
-            self.pos_emb = AbsolutePositionalEmbedding(dim, max_seq_len)
-        self.post_emb_norm = nn.LayerNorm(dim) if post_emb_norm else nn.Identity()
-        self.emb_dropout = nn.Dropout(emb_dropout)
-        self.project_in = nn.Linear(dim_in, dim) if exists(dim_in) else nn.Identity()
-        self.attn_layers = attn_layers
-        self.project_out = nn.Linear(dim, dim_out) if exists(dim_out) else nn.Identity()
-    def forward(
-        self,
-        x,
-        return_embeddings = False,
-        return_intermediates = False,
-        return_mems = False,
-        mask = None,
-        return_attn = False,
-        mems = None,
-        pos = None,
-        prepend_embeds = None,
-        **kwargs
-    ):
-        x = self.project_in(x)
-        x = x + self.pos_emb(x, pos = pos)
-        x = self.post_emb_norm(x)
-        # whether to append embeds, as in PaLI, for image embeddings
-        if exists(prepend_embeds):
-            _, prepend_dim = prepend_embeds.shape[1:]
-            assert prepend_dim == x.shape[-1], 'prepended embeddings need to have same dimensions as model dimensions'
-            x = torch.cat((prepend_embeds, x), dim = -2)
-        x = self.emb_dropout(x)
-        x, intermediates = self.attn_layers(x, mask = mask, mems = mems, return_hiddens = True, **kwargs)
-        out = self.project_out(x) if not return_embeddings else x
-        if return_intermediates:
-            return out, intermediates
-        if return_mems:
-            hiddens = intermediates.hiddens
-            new_mems = list(map(lambda t: t[..., -self.max_mem_len:, :].detach(), hiddens))
-            return out, new_mems
-        if return_attn:
-            attn_maps = list(map(lambda t: t.post_softmax_attn, intermediates.attn_intermediates))
-            return out, attn_maps
-        return out
-class XTransformer(nn.Module):
-    def __init__(
-        self,
-        *,
-        dim,
-        tie_token_emb = False,
-        ignore_index = -100,
-        pad_value = 0,
-        cross_attn_tokens_dropout = 0.,
-        **kwargs
-    ):
-        super().__init__()
-        enc_kwargs, kwargs = groupby_prefix_and_trim('enc_', kwargs)
-        dec_kwargs, kwargs = groupby_prefix_and_trim('dec_', kwargs)
-        assert 'dim' not in enc_kwargs and 'dim' not in dec_kwargs, 'dimension of either encoder or decoder must be set with `dim` keyword'
-        enc_transformer_kwargs = pick_and_pop(['num_tokens', 'max_seq_len'], enc_kwargs)
-        enc_transformer_kwargs['emb_dropout'] = enc_kwargs.pop('emb_dropout', 0)
-        enc_transformer_kwargs['num_memory_tokens'] = enc_kwargs.pop('num_memory_tokens', None)
-        enc_transformer_kwargs['scaled_sinu_pos_emb'] = enc_kwargs.pop('scaled_sinu_pos_emb', False)
-        enc_transformer_kwargs['use_abs_pos_emb'] = enc_kwargs.pop('use_abs_pos_emb', True)
-        dec_transformer_kwargs = pick_and_pop(['num_tokens', 'max_seq_len'], dec_kwargs)
-        dec_transformer_kwargs['emb_dropout'] = dec_kwargs.pop('emb_dropout', 0)
-        dec_transformer_kwargs['scaled_sinu_pos_emb'] = dec_kwargs.pop('scaled_sinu_pos_emb', False)
-        dec_transformer_kwargs['use_abs_pos_emb'] = dec_kwargs.pop('use_abs_pos_emb', True)
-        self.cross_attn_tokens_dropout = cross_attn_tokens_dropout  # how many tokens from the encoder to dropout when cross attending from decoder - seen in a couple papers, including Perceiver AR - this will also be very effective regularization when cross attending to very long memories
-        self.encoder = TransformerWrapper(
-            **enc_transformer_kwargs,
-            attn_layers = Encoder(dim = dim, **enc_kwargs)
-        )
-        self.decoder = TransformerWrapper(
-            **dec_transformer_kwargs,
-            attn_layers = Decoder(dim = dim, cross_attend = True, **dec_kwargs)
-        )
-        if tie_token_emb:
-            self.decoder.token_emb = self.encoder.token_emb
-        self.decoder = AutoregressiveWrapper(self.decoder, ignore_index=ignore_index, pad_value=pad_value)
-    @torch.no_grad()
-    def generate(self, seq_in, seq_out_start, seq_len, mask = None, attn_mask = None, **kwargs):
-        encodings = self.encoder(seq_in, mask = mask, attn_mask = attn_mask, return_embeddings = True)
-        return self.decoder.generate(seq_out_start, seq_len, context = encodings, context_mask = mask, **kwargs)
-    def forward(self, src, tgt, mask = None, attn_mask = None, src_prepend_embeds = None):
-        if exists(src_prepend_embeds) and exists(mask):
-            mask = pad_at_dim(mask, (src_prepend_embeds.shape[-2], 0), dim = -1, value = True)
-        enc = self.encoder(src, mask = mask, attn_mask = attn_mask, prepend_embeds = src_prepend_embeds, return_embeddings = True)
-        if self.training and self.cross_attn_tokens_dropout > 0:
-            enc, mask = dropout_seq(enc, mask, self.cross_attn_tokens_dropout)
-        out = self.decoder(tgt, context = enc, context_mask = mask)
-        return out