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configuration_retnet.py

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+# modified from https://github.com/syncdoth/RetNet/blob/main/retnet/configuration_retnet.py
+
+from dataclasses import dataclass
+import json
+
+from transformers.configuration_utils import PretrainedConfig
+
+
+def load_config_from_json(config_file):
+    with open(config_file, "r") as f:
+        config = json.load(f)
+        config = RetNetConfig.from_dict(config)
+    return config
+
+
+@dataclass
+class RetNetConfig(PretrainedConfig):
+    model_type = "retnet"
+    initializer_range: float = 0.02
+    activation_fn: str = "gelu"
+    dropout: float = 0.0  # dropout probability
+    activation_dropout: float = 0.0  # dropout probability after activation in FFN.
+    drop_path_rate: float = 0.0
+    decoder_embed_dim: int = 768  # decoder embedding dimension
+    decoder_value_embed_dim: int = 1280  # decoder value embedding dimension
+    decoder_ffn_embed_dim: int = 1280  # decoder embedding dimension for FFN
+    decoder_layers: int = 12  # num decoder layers
+    decoder_retention_heads: int = 3  # num decoder retention heads
+    decoder_normalize_before: bool = True  # apply layernorm before each decoder block
+    layernorm_embedding: bool = False  # add layernorm to embedding
+    no_scale_embedding: bool = True  # if True, dont scale embeddings
+    recurrent_chunk_size: int = 512
+    use_lm_decay: bool = False
+    use_glu: bool = True  # use GLU instead of FFN
+    z_loss_coeff: float = 0.0  # coefficient for z loss: TODO: 1e-4
+    deepnorm: bool = False
+    subln: bool = True
+    use_ffn_rms_norm: bool = False
+    layernorm_eps: float = 1e-6
+    tie_word_embeddings: bool = False
+
+    def __init__(
+        self,
+        vocab_size: int = 50257,
+        initializer_range: float = 0.02,
+        is_decoder: bool = True,
+        pad_token_id: int = 0,
+        eos_token_id: int = 0,
+        output_retentions: bool = False,
+        use_cache: bool = True,
+        forward_impl: str = "parallel",
+        activation_fn: str = "gelu",
+        dropout: float = 0.0,  # dropout probability
+        activation_dropout: float = 0.0,  # dropout probability after activation in FFN.
+        drop_path_rate: float = 0.0,
+        decoder_embed_dim: int = 768,  # decoder embedding dimension
+        decoder_value_embed_dim: int = 1280,  # decoder value embedding dimension
+        decoder_ffn_embed_dim: int = 1280,  # decoder embedding dimension for FFN
+        decoder_layers: int = 12,  # num decoder layers
+        decoder_retention_heads: int = 3,  # num decoder retention heads
+        decoder_normalize_before: bool = True,  # apply layernorm before each decoder block
+        layernorm_embedding: bool = False,  # add layernorm to embedding
+        no_scale_embedding: bool = True,  # if True, dont scale embeddings
+        recurrent_chunk_size: int = 512,
+        use_glu: bool = True,  # use GLU instead of FFN
+        z_loss_coeff: float = 0.0,  # coefficient for z loss: TODO: 1e-4
+        use_lm_decay: bool = False,
+        deepnorm: bool = False,
+        subln: bool = True,
+        use_ffn_rms_norm: bool = False,  # use RMSNorm instead of LayerNorm in FFN
+        layernorm_eps: float = 1e-6,
+        tie_word_embeddings: bool = False,
+        **kwargs
+    ):
+        self.vocab_size = vocab_size
+        self.initializer_range = initializer_range
+        self.output_retentions = output_retentions
+        self.use_lm_decay = use_lm_decay
+        self.use_glu = use_glu
+        self.z_loss_coeff = z_loss_coeff
+        # size related
+        self.decoder_embed_dim = decoder_embed_dim
+        self.decoder_value_embed_dim = decoder_value_embed_dim
+        self.decoder_retention_heads = decoder_retention_heads
+        self.decoder_ffn_embed_dim = decoder_ffn_embed_dim
+        self.decoder_layers = decoder_layers
+        # normalization related
+        self.decoder_normalize_before = decoder_normalize_before
+        self.activation_fn = activation_fn
+        self.dropout = dropout
+        self.drop_path_rate = drop_path_rate
+        self.activation_dropout = activation_dropout
+        self.no_scale_embedding = no_scale_embedding
+        self.layernorm_embedding = layernorm_embedding
+        self.deepnorm = deepnorm
+        self.subln = subln
+        self.use_ffn_rms_norm = use_ffn_rms_norm
+        self.layernorm_eps = layernorm_eps
+        # Blockwise
+        self.recurrent_chunk_size = recurrent_chunk_size
+        self.forward_impl = forward_impl
+
+        if self.deepnorm:
+            self.decoder_normalize_before = False
+            self.subln = False
+        if self.subln:
+            self.decoder_normalize_before = True
+            self.deepnorm = False
+
+        super().__init__(
+            is_decoder=is_decoder,
+            pad_token_id=pad_token_id,
+            eos_token_id=eos_token_id,
+            use_cache=use_cache,
+            tie_word_embeddings=tie_word_embeddings,
+            **kwargs
+        )
+
+    def override(self, args):
+        for hp in self.__dict__.keys():
+            if getattr(args, hp, None) is not None:
+                self.__dict__[hp] = getattr(args, hp, None)

modeling_retnet.py

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+# modified from https://github.com/syncdoth/RetNet/blob/main/retnet/modeling_retnet.py
+
+import math
+from dataclasses import dataclass
+from typing import Dict, List, Optional, Tuple, Union
+
+import numpy as np
+import torch
+import torch.nn.functional as F
+import torch.utils.checkpoint
+
+from torch import nn
+from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
+from transformers import top_k_top_p_filtering
+from transformers.activations import ACT2FN
+from transformers.modeling_outputs import ModelOutput, SequenceClassifierOutputWithPast
+from transformers.modeling_utils import PreTrainedModel
+from transformers.utils import logging
+
+from .configuration_retnet import RetNetConfig
+
+logger = logging.get_logger(__name__)
+
+
+# helper functions
+def split_heads(tensors, bsz, seqlen, num_heads):
+    assert isinstance(tensors, (tuple, list))
+    return [x.view(bsz, seqlen, num_heads, -1).transpose(1, 2) for x in tensors]
+
+
+def rotate_every_two(x):
+    x1 = x[:, :, :, ::2]
+    x2 = x[:, :, :, 1::2]
+    x = torch.stack((-x2, x1), dim=-1)
+    return x.flatten(-2)  # in einsum notation: rearrange(x, '... d j -> ... (d j)')\
+
+
+def theta_shift(x, sin, cos):
+    return (x * cos) + (rotate_every_two(x) * sin)
+
+
+def get_activation_fn(activation):
+    return ACT2FN[activation]
+
+
+# Copied from https://github.com/huggingface/pytorch-image-models/blob/bbe798317fb26f063c18279827c038058e376479/timm/layers/drop.py#L137C1-L154C29
+def drop_path(
+    x, drop_prob: float = 0.0, training: bool = False, scale_by_keep: bool = True
+):
+    """Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks).
+
+    This is the same as the DropConnect impl I created for EfficientNet, etc networks, however,
+    the original name is misleading as 'Drop Connect' is a different form of dropout in a separate paper...
+    See discussion: https://github.com/tensorflow/tpu/issues/494#issuecomment-532968956 ... I've opted for
+    changing the layer and argument names to 'drop path' rather than mix DropConnect as a layer name and use
+    'survival rate' as the argument.
+
+    """
+    if drop_prob == 0.0 or not training:
+        return x
+    keep_prob = 1 - drop_prob
+    shape = (x.shape[0],) + (1,) * (
+        x.ndim - 1
+    )  # work with diff dim tensors, not just 2D ConvNets
+    random_tensor = x.new_empty(shape).bernoulli_(keep_prob)
+    if keep_prob > 0.0 and scale_by_keep:
+        random_tensor.div_(keep_prob)
+    return x * random_tensor
+
+
+class RMSNorm(nn.Module):
+    def __init__(self, dim: int, eps: float = 1e-6, elementwise_affine=True):
+        super().__init__()
+        self.normalized_shape = dim
+        self.eps = eps
+        self.elementwise_affine = elementwise_affine
+        if self.elementwise_affine:
+            self.weight = nn.Parameter(torch.ones(dim))
+        else:
+            self.register_parameter("weight", None)
+
+    def _norm(self, x):
+        return x * torch.rsqrt(x.pow(2).mean(-1, keepdim=True) + self.eps)
+
+    def forward(self, x):
+        output = self._norm(x.float()).type_as(x)
+        if self.weight is not None:
+            output = output * self.weight
+        return output
+
+
+try:
+    from apex.normalization import FusedRMSNorm
+
+    RMSNorm = FusedRMSNorm  # noqa
+
+    logger.info(
+        "Discovered apex.normalization.FusedRMSNorm - will use it instead of RMSNorm"
+    )
+except ImportError:
+    # using the normal RMSNorm
+    pass
+except Exception:
+    logger.warning("discovered apex but it failed to load, falling back to RMSNorm")
+    pass
+
+
+class RetNetRelPos(nn.Module):
+    def __init__(self, config: RetNetConfig):
+        super().__init__()
+        self.config = config
+        num_heads = config.decoder_retention_heads
+
+        angle = 1.0 / (
+            10000 ** torch.linspace(0, 1, config.decoder_embed_dim // num_heads // 2)
+        )
+        angle = angle.unsqueeze(-1).repeat(1, 2).flatten()
+        # decay (gamma)
+        if config.use_lm_decay:
+            # NOTE: alternative way described in the paper
+            s = torch.log(torch.tensor(1 / 32))
+            e = torch.log(torch.tensor(1 / 512))
+            decay = torch.log(1 - torch.exp(torch.linspace(s, e, num_heads)))  # [h,]
+        else:
+            decay = torch.log(
+                1 - 2 ** (-5 - torch.arange(num_heads, dtype=torch.float))
+            )
+        self.register_buffer("angle", angle)
+        self.register_buffer("decay", decay)
+        self.recurrent_chunk_size = config.recurrent_chunk_size
+
+    def forward(
+        self,
+        slen,
+        forward_impl="parallel",
+        recurrent_chunk_size=None,
+        retention_mask=None,
+        get_decay_scale=True,
+    ):
+        if forward_impl == "recurrent":
+            sin = torch.sin(self.angle * (slen - 1))
+            cos = torch.cos(self.angle * (slen - 1))
+            retention_rel_pos = ((sin, cos), self.decay.view(1, -1, 1, 1).exp())
+        elif forward_impl == "chunkwise":
+            if recurrent_chunk_size is None:
+                recurrent_chunk_size = self.recurrent_chunk_size
+            index = torch.arange(slen).to(self.decay)
+            sin = torch.sin(index[:, None] * self.angle[None, :])
+            cos = torch.cos(index[:, None] * self.angle[None, :])
+
+            block_index = torch.arange(recurrent_chunk_size).to(self.decay)
+            mask = torch.tril(
+                torch.ones(recurrent_chunk_size, recurrent_chunk_size)
+            ).to(self.decay)
+            mask = torch.masked_fill(
+                block_index[:, None] - block_index[None, :], ~mask.bool(), float("inf")
+            )
+            mask = torch.exp(mask * self.decay[:, None, None])
+            mask = torch.nan_to_num(mask)
+            mask = mask.unsqueeze(0)  # [1, h, t, t]
+            # TODO: need to handle retention_mask
+            # scaling
+            value_inner_decay = mask[:, :, -1] / mask[:, :, -1].sum(
+                dim=-1, keepdim=True
+            )
+            value_inner_decay = value_inner_decay.unsqueeze(-1)
+            scale = mask.sum(dim=-1, keepdim=True).sqrt()
+            inner_mask = mask / scale
+
+            cross_decay = torch.exp(self.decay * recurrent_chunk_size)
+            query_inner_decay = torch.exp(self.decay[:, None] * (block_index + 1))
+            cross_decay = cross_decay[None, :, None, None]
+            query_inner_decay = query_inner_decay[None, :, :, None] / (
+                scale / mask[:, :, -1].sum(dim=-1)[:, :, None, None]
+            )
+            # decay_scale (used for kv cache)
+            if get_decay_scale:
+                decay_scale = self.compute_decay_scale(slen, retention_mask)
+            else:
+                decay_scale = None
+            retention_rel_pos = (
+                (sin, cos),
+                (
+                    inner_mask,
+                    cross_decay,
+                    query_inner_decay,
+                    value_inner_decay,
+                    decay_scale,
+                ),
+            )
+        else:  # parallel
+            index = torch.arange(slen).to(self.decay)
+            sin = torch.sin(index[:, None] * self.angle[None, :])
+            cos = torch.cos(index[:, None] * self.angle[None, :])
+            mask = torch.tril(torch.ones(slen, slen)).to(self.decay)
+            mask = torch.masked_fill(
+                index[:, None] - index[None, :], ~mask.bool(), float("inf")
+            )
+            mask = torch.exp(mask * self.decay[:, None, None])
+            mask = torch.nan_to_num(mask)
+            mask = mask.unsqueeze(0)  # [1, h, t, t]
+            if retention_mask is not None:
+                # this is required for left padding
+                mask = mask * retention_mask.float().view(-1, 1, 1, slen).to(mask)
+
+            # scaling
+            mask = mask / mask.sum(dim=-1, keepdim=True).sqrt()
+            mask = torch.nan_to_num(mask, nan=0.0)
+            # decay_scale (used for kv cache)
+            if get_decay_scale:
+                decay_scale = self.compute_decay_scale(slen, retention_mask)
+            else:
+                decay_scale = None
+            # mask processing for intra decay
+            if retention_mask is not None:
+                max_non_zero = (
+                    torch.cumsum(retention_mask, dim=-1).max(dim=-1).indices
+                )  # [b,]
+                intra_decay = mask[range(mask.shape[0]), :, max_non_zero]
+            else:
+                intra_decay = mask[:, :, -1]
+
+            retention_rel_pos = ((sin, cos), (mask, intra_decay, decay_scale))
+
+        return retention_rel_pos
+
+    def compute_decay_scale(self, slen, retention_mask=None):
+        exponent = torch.arange(slen, device=self.decay.device).float()
+        decay_scale = self.decay.exp().view(-1, 1) ** exponent.view(1, -1)  # [h, t]
+        if retention_mask is not None:
+            seqlen = retention_mask.sum(dim=-1)  # [b,]
+            bsz = seqlen.size(0)
+            decay_scale = decay_scale.unsqueeze(0).repeat(bsz, 1, 1)  # [b, h, t]
+            for i, pos in enumerate(seqlen):
+                # the formula for decay_scale is `sum(gamma^i) for i in [0, slen).`
+                # Since the retention_mask is 0 for padding, we can set the decay_scale
+                # to 0 for the padding positions.
+                decay_scale[i, :, pos.item() :] = 0
+        else:
+            bsz = 1
+        decay_scale = decay_scale.sum(-1).view(bsz, -1, 1, 1)  # [b, h, 1, 1]
+        return decay_scale
+
+
+class MultiScaleRetention(nn.Module):
+    def __init__(
+        self,
+        config: RetNetConfig,
+        gate_fn="swish",
+        use_bias=False,
+        tensor_parallel=False,
+    ):
+        super().__init__()
+        self.config = config
+        self.embed_dim = config.decoder_embed_dim
+        self.value_dim = config.decoder_value_embed_dim
+        self.num_heads = config.decoder_retention_heads
+        self.head_dim = self.value_dim // self.num_heads
+        self.key_dim = self.embed_dim // self.num_heads
+        self.scaling = self.key_dim**-0.5
+
+        self.gate_fn = get_activation_fn(activation=str(gate_fn))
+
+        self.q_proj = nn.Linear(self.embed_dim, self.embed_dim, bias=use_bias)
+        self.k_proj = nn.Linear(self.embed_dim, self.embed_dim, bias=use_bias)
+        self.v_proj = nn.Linear(self.embed_dim, self.value_dim, bias=use_bias)
+        self.g_proj = nn.Linear(self.embed_dim, self.value_dim, bias=use_bias)
+
+        self.out_proj = nn.Linear(self.value_dim, self.embed_dim, bias=use_bias)
+
+        self.group_norm = RMSNorm(
+            self.head_dim, eps=config.layernorm_eps, elementwise_affine=False
+        )
+        self.reset_parameters()
+
+        if tensor_parallel:
+            self.decay_proj = nn.Linear(self.num_heads, self.num_heads, bias=False)
+        else:
+            self.decay_proj = None
+
+    def reset_parameters(self):
+        nn.init.xavier_uniform_(self.q_proj.weight, gain=2**-2.5)
+        nn.init.xavier_uniform_(self.k_proj.weight, gain=2**-2.5)
+        nn.init.xavier_uniform_(self.v_proj.weight, gain=2**-2.5)
+        nn.init.xavier_uniform_(self.g_proj.weight, gain=2**-2.5)
+        nn.init.xavier_uniform_(self.out_proj.weight)
+
+    def parallel_retention(self, q, k, v, decay_mask):
+        """
+        q,  # bsz * num_head * len * qk_dim
+        k,  # bsz * num_head * len * qk_dim
+        v,  # bsz * num_head * len * v_dim
+        decay_mask,  # (1 or bsz) * num_head * len * len
+        """
+        decay_mask, intra_decay, scale = decay_mask
+        # just return retention_rel_pos projected
+        # TODO: for shardformer
+        if self.decay_proj is not None:
+            decay_mask = self.decay_proj(decay_mask.transpose(-1, -3)).transpose(-3, -1)
+
+        # [b, h, t, t]
+        retention = q @ k.transpose(-1, -2)  # (scaled dot-product)
+        retention = retention * decay_mask
+
+        # invariant after normalization
+        retention = retention / retention.detach().sum(
+            dim=-1, keepdim=True
+        ).abs().clamp(min=1)
+
+        output = retention.type_as(v) @ v  # [b, h, t, v_dim / h]
+        output = output.transpose(1, 2)  # [b, t, h, v_dim / h]
+
+        if self.training:  # skip cache
+            return output, None, retention
+
+        if self.decay_proj is not None:
+            intra_decay = self.decay_proj(intra_decay.transpose(-1, -2)).transpose(
+                -2, -1
+            )
+
+        # kv cache: [b, h, t, v_dim, qk_dim]
+        current_kv = k.unsqueeze(-2) * v.unsqueeze(-1)
+        intra_decay = intra_decay[:, :, :, None, None]  # [b, h, t, 1, 1]
+        current_kv = (current_kv * intra_decay).sum(2)  # [b, h, v_dim, qk_dim]
+
+        cache = {"prev_key_value": current_kv, "scale": scale}
+        return output, cache, retention
+
+    def recurrent_retention(
+        self, q, k, v, decay, past_key_value=None, retention_mask=None
+    ):
+        """
+        q, k, v, # bsz * num_head * 1 * qkv_dim
+        past_key_value:
+            - "prev_key_value"  # bsz * num_head * v_dim * qk_dim
+            - "scale"  # (1 or bsz) * num_head * 1 * 1
+        decay # (1 or bsz) * num_head * 1 * 1
+        retention_mask # bsz * 1
+        """
+        if retention_mask is not None:
+            retention_mask = retention_mask.float().view(-1, 1, 1, 1).to(decay)
+        else:
+            retention_mask = torch.ones(k.size(0), 1, 1, 1).to(decay)
+        # (b, h, v_dim, qk_dim)
+        current_kv = k * v.transpose(-1, -2) * retention_mask
+
+        if past_key_value is not None and "prev_key_value" in past_key_value:
+            prev_kv = past_key_value["prev_key_value"]
+            prev_scale = past_key_value["scale"]
+            scale = torch.where(retention_mask == 0, prev_scale, prev_scale * decay + 1)
+            # connect prev_kv and current_kv
+            # how much to decay prev_kv
+            decay_amount = prev_scale.sqrt() * decay / scale.sqrt()
+            decay_amount = torch.where(retention_mask == 0, 1, decay_amount)
+            prev_kv = prev_kv * decay_amount  # decay prev_kv
+            current_kv = current_kv / scale.sqrt()  # scale current_kv
+            current_kv = torch.nan_to_num(
+                current_kv, nan=0.0
+            )  # remove nan, scale might be 0
+
+            current_kv = prev_kv + current_kv
+        else:
+            scale = torch.ones_like(decay)
+            # when retention_mask is 0 at the beginning, setting scale to 1 will
+            # make the first retention to use the padding incorrectly. Hence,
+            # setting it to 0 here. This is a little ugly, so we might want to
+            # change this later. TODO: improve
+            scale = torch.where(retention_mask == 0, torch.zeros_like(decay), scale)
+
+        output = torch.sum(q * current_kv, dim=3).unsqueeze(1)  # (b, 1, h, d_v)
+
+        cache = {"prev_key_value": current_kv, "scale": scale}
+        return output, cache
+
+    def chunkwise_retention(self, q, k, v, decay_mask):
+        """
+        q, k, v,  # bsz * num_head * seqlen * qkv_dim
+        past_key_value:
+            - "prev_key_value"  # bsz * num_head * v_dim * qk_dim
+            - "scale"  # (1 or bsz) * num_head * 1 * 1
+        decay_mask,  # 1 * num_head * chunk_size * chunk_size
+        cross_decay,  # 1 * num_head * 1 * 1
+        inner_decay,  # 1 * num_head * chunk_size * 1
+        """
+        # TODO: not working properly
+        (
+            decay_mask,
+            cross_decay,
+            query_inner_decay,
+            value_inner_decay,
+            decay_scale,
+        ) = decay_mask
+        bsz, _, tgt_len, _ = v.size()
+        chunk_len = decay_mask.size(-1)
+        assert tgt_len % chunk_len == 0
+        num_chunks = tgt_len // chunk_len
+
+        # [b, n_c, h, t_c, qkv_dim]
+        q = q.view(bsz, self.num_heads, num_chunks, chunk_len, self.key_dim).transpose(
+            1, 2
+        )
+        k = k.view(bsz, self.num_heads, num_chunks, chunk_len, self.key_dim).transpose(
+            1, 2
+        )
+        v = v.view(bsz, self.num_heads, num_chunks, chunk_len, self.head_dim).transpose(
+            1, 2
+        )
+
+        k_t = k.transpose(-1, -2)
+
+        qk_mat = q @ k_t  # [b, n_c, h, t_c, t_c]
+        qk_mat = qk_mat * decay_mask.unsqueeze(1)
+        inner_scale = qk_mat.detach().abs().sum(dim=-1, keepdim=True).clamp(min=1)
+        qk_mat = qk_mat / inner_scale
+        # [b, n_c, h, t_c, v_dim]
+        inner_output = torch.matmul(qk_mat, v)
+
+        # reduce kv in one chunk
+        # [b, n_c, h, qk_dim, v_dim]
+        kv = k_t @ (v * value_inner_decay)
+        # kv = kv.view(bsz, num_chunks, self.num_heads, self.key_dim, self.head_dim)
+
+        kv_recurrent = []
+        cross_scale = []
+        kv_state = torch.zeros(bsz, self.num_heads, self.key_dim, self.head_dim).to(v)
+        kv_scale = torch.ones(bsz, self.num_heads, 1, 1).to(v)
+
+        # accumulate kv by loop
+        for i in range(num_chunks):
+            kv_recurrent.append(kv_state / kv_scale)
+            cross_scale.append(kv_scale)
+            kv_state = kv_state * cross_decay + kv[:, i]
+            kv_scale = (
+                kv_state.detach()
+                .abs()
+                .sum(dim=-2, keepdim=True)
+                .max(dim=-1, keepdim=True)
+                .values.clamp(min=1)
+            )
+
+        kv_recurrent = torch.stack(kv_recurrent, dim=1)
+        cross_scale = torch.stack(cross_scale, dim=1)
+
+        all_scale = torch.maximum(inner_scale, cross_scale)
+        align_inner_scale = all_scale / inner_scale
+        align_cross_scale = all_scale / cross_scale
+
+        cross_output = (q * query_inner_decay.unsqueeze(1)) @ kv_recurrent
+        output = inner_output / align_inner_scale + cross_output / align_cross_scale
+        output = output.transpose(2, 3)  # [b, n_c, t_c, h, v_dim]
+
+        cache = {"prev_key_value": kv_state.transpose(-2, -1), "scale": decay_scale}
+        return output, cache
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        rel_pos: Tuple[Tuple[torch.Tensor]],
+        retention_mask: Optional[torch.Tensor] = None,
+        past_key_value: Optional[Tuple[torch.Tensor]] = None,
+        forward_impl: str = "parallel",
+        output_retentions: Optional[bool] = False,
+    ) -> Tuple[torch.FloatTensor, torch.FloatTensor, Optional[torch.FloatTensor]]:
+        B, T, H = hidden_states.size()
+        (sin, cos), decay_mask = rel_pos
+        # projections
+        q = self.q_proj(hidden_states)
+        k = self.k_proj(hidden_states)
+        v = self.v_proj(hidden_states)
+        g = self.g_proj(hidden_states)
+        # multi-head
+        q, k, v = split_heads((q, k, v), B, T, self.num_heads)
+        k *= self.scaling  # for scaled dot product
+        # rotate
+        # NOTE: theta_shift has bug with mps device.
+        qr = theta_shift(q, sin, cos)
+        kr = theta_shift(k, sin, cos)
+
+        # retention
+        if forward_impl == "parallel":
+            retention_out, curr_kv, retention_weights = self.parallel_retention(
+                qr, kr, v, decay_mask
+            )
+        elif forward_impl == "recurrent":
+            retention_out, curr_kv = self.recurrent_retention(
+                qr,
+                kr,
+                v,
+                decay_mask,
+                past_key_value=past_key_value,
+                retention_mask=retention_mask,
+            )
+        elif forward_impl == "chunkwise":
+            retention_out, curr_kv = self.chunkwise_retention(qr, kr, v, decay_mask)
+        else:
+            raise ValueError(f"forward_impl {forward_impl} not supported.")
+
+        # concaat heads
+        normed = self.group_norm(retention_out).reshape(B, T, self.value_dim)
+        # out gate & proj
+        out = self.gate_fn(g) * normed
+        out = self.out_proj(out.type_as(hidden_states))
+
+        outputs = (out, curr_kv)
+        if output_retentions:
+            outputs += (retention_weights,) if forward_impl == "parallel" else (None,)
+        return outputs
+
+
+class FeedForwardNetwork(nn.Module):
+    def __init__(
+        self,
+        embed_dim,
+        ffn_dim,
+        activation_fn,
+        dropout,
+        activation_dropout,
+        layernorm_eps,
+        subln=False,
+        use_rms_norm=False,
+    ):
+        super().__init__()
+        self.embed_dim = embed_dim
+        self.activation_fn = get_activation_fn(activation=str(activation_fn))
+        self.activation_dropout_module = torch.nn.Dropout(activation_dropout)
+        self.dropout_module = torch.nn.Dropout(dropout)
+        self.fc1 = nn.Linear(self.embed_dim, ffn_dim)
+        self.fc2 = nn.Linear(ffn_dim, self.embed_dim)
+        if subln:
+            if use_rms_norm:
+                self.ffn_layernorm = RMSNorm(ffn_dim, eps=layernorm_eps)
+            else:
+                self.ffn_layernorm = LayerNorm(ffn_dim, eps=layernorm_eps)
+        else:
+            self.ffn_layernorm = None
+
+    def reset_parameters(self):
+        self.fc1.reset_parameters()
+        self.fc2.reset_parameters()
+        if self.ffn_layernorm is not None:
+            self.ffn_layernorm.reset_parameters()
+
+    def forward(self, x):
+        x_shape = x.shape
+        x = x.reshape(-1, x.size(-1))
+        x = self.fc1(x)
+        x = self.activation_fn(x.float()).type_as(x)
+        x = self.activation_dropout_module(x)
+        if self.ffn_layernorm is not None:
+            x = self.ffn_layernorm(x)
+        x = self.fc2(x)
+        x = x.view(x_shape)
+        x = self.dropout_module(x)
+        return x
+
+
+class GLU(nn.Module):
+    def __init__(
+        self,
+        embed_dim,
+        ffn_dim,
+        activation_fn,
+        dropout,
+        activation_dropout,
+    ):
+        super().__init__()
+        self.embed_dim = embed_dim
+        self.activation_fn = get_activation_fn(activation=str(activation_fn))
+        self.activation_dropout_module = torch.nn.Dropout(activation_dropout)
+        self.dropout_module = torch.nn.Dropout(dropout)
+        self.fc1 = nn.Linear(self.embed_dim, ffn_dim, bias=False)
+        self.fc2 = nn.Linear(ffn_dim, self.embed_dim, bias=False)
+        self.gate = nn.Linear(self.embed_dim, ffn_dim, bias=False)
+
+    def reset_parameters(self):
+        self.fc1.reset_parameters()
+        self.fc2.reset_parameters()
+        self.gate.reset_parameters()
+
+    def forward(self, x):
+        x_shape = x.shape
+        x = x.reshape(-1, x.size(-1))
+        g = self.gate(x)
+        x = self.fc1(x)
+        x = self.activation_fn(x.float()).type_as(x) * g
+        x = self.activation_dropout_module(x)
+        x = self.fc2(x)
+        x = x.view(x_shape)
+        x = self.dropout_module(x)
+        return x
+
+
+class DropPath(nn.Module):
+    """Drop paths (Stochastic Depth) per sample  (when applied in main path of residual blocks)."""
+
+    def __init__(self, drop_prob=None):
+        super(DropPath, self).__init__()
+        self.drop_prob = drop_prob
+
+    def forward(self, x):
+        return drop_path(x, self.drop_prob, self.training)
+
+    def extra_repr(self):
+        return "p={}".format(self.drop_prob)
+
+
+class RetNetDecoderLayer(nn.Module):
+    def __init__(self, config: RetNetConfig, depth: int, tensor_parallel: bool = False):
+        super().__init__()
+        self.config = config
+        self.embed_dim = config.decoder_embed_dim
+        self.dropout_module = torch.nn.Dropout(config.dropout)
+
+        if config.drop_path_rate > 0:
+            drop_path_prob = np.linspace(
+                0, config.drop_path_rate, config.decoder_layers
+            )[depth]
+            self.drop_path = DropPath(drop_path_prob)
+        else:
+            self.drop_path = None
+
+        self.retention = MultiScaleRetention(
+            config, use_bias=False, tensor_parallel=tensor_parallel
+        )
+
+        self.normalize_before = config.decoder_normalize_before
+
+        self.retention_layer_norm = RMSNorm(self.embed_dim, eps=config.layernorm_eps)
+
+        self.ffn_dim = config.decoder_ffn_embed_dim
+
+        self.ffn = self.build_ffn()
+
+        self.final_layer_norm = RMSNorm(self.embed_dim, eps=config.layernorm_eps)
+
+        if config.deepnorm:
+            self.alpha = math.pow(2.0 * config.decoder_layers, 0.25)
+        else:
+            self.alpha = 1.0
+
+    def build_ffn(self):
+        if self.config.use_glu:
+            return GLU(
+                self.embed_dim,
+                self.ffn_dim,
+                self.config.activation_fn,
+                self.config.dropout,
+                self.config.activation_dropout,
+            )
+        else:
+            return FeedForwardNetwork(
+                self.embed_dim,
+                self.ffn_dim,
+                self.config.activation_fn,
+                self.config.dropout,
+                self.config.activation_dropout,
+                self.config.layernorm_eps,
+                self.config.subln,
+                self.config.use_ffn_rms_norm,
+            )
+
+    def residual_connection(self, x, residual):
+        return residual * self.alpha + x
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        retention_rel_pos: Tuple[Tuple[torch.Tensor]],
+        retention_mask: Optional[torch.Tensor] = None,
+        forward_impl: str = "parallel",
+        past_key_value: Optional[Tuple[torch.Tensor]] = None,
+        output_retentions: Optional[bool] = False,
+    ) -> Tuple[torch.FloatTensor, torch.FloatTensor, Optional[torch.FloatTensor]]:
+        residual = hidden_states
+        if self.normalize_before:
+            hidden_states = self.retention_layer_norm(hidden_states)
+
+        msr_outs = self.retention(
+            hidden_states,
+            retention_rel_pos,
+            retention_mask=retention_mask,
+            past_key_value=past_key_value,
+            forward_impl=forward_impl,
+            output_retentions=output_retentions,
+        )
+        hidden_states = msr_outs[0]
+        curr_kv = msr_outs[1]
+
+        hidden_states = self.dropout_module(hidden_states)
+
+        if self.drop_path is not None:
+            hidden_states = self.drop_path(hidden_states)
+
+        hidden_states = self.residual_connection(hidden_states, residual)
+        if not self.normalize_before:
+            hidden_states = self.retention_layer_norm(hidden_states)
+
+        residual = hidden_states
+        if self.normalize_before:
+            hidden_states = self.final_layer_norm(hidden_states)
+
+        hidden_states = self.ffn(hidden_states)
+
+        if self.drop_path is not None:
+            hidden_states = self.drop_path(hidden_states)
+
+        hidden_states = self.residual_connection(hidden_states, residual)
+        if not self.normalize_before:
+            hidden_states = self.final_layer_norm(hidden_states)
+
+        outputs = (hidden_states, curr_kv)
+
+        if output_retentions:
+            outputs += (msr_outs[2],)
+        return outputs
+
+
+class RetNetPreTrainedModel(PreTrainedModel):
+    # copied from LlamaPretrainedModel
+    config_class = RetNetConfig
+    base_model_prefix = "model"
+    supports_gradient_checkpointing = True
+    _no_split_modules = ["RetNetDecoderLayer"]
+    _keys_to_ignore_on_load_unexpected = [r"decoder\.version"]
+
+    def _init_weights(self, module):
+        """
+        Following original retnet, weights are already initialized in their own
+        ways within their own init.
+        """
+        pass
+        # below is copied from LlamaPretrainedModel
+        # std = self.config.initializer_range
+        # if isinstance(module, nn.Linear):
+        #     module.weight.data.normal_(mean=0.0, std=std)
+        #     if module.bias is not None:
+        #         module.bias.data.zero_()
+        # elif isinstance(module, nn.Embedding):
+        #     module.weight.data.normal_(mean=0.0, std=std)
+        #     if module.padding_idx is not None:
+        #         module.weight.data[module.padding_idx].zero_()
+
+
+@dataclass
+class RetNetOutputWithPast(ModelOutput):
+    """
+    class for RetNet model's outputs that may also contain a past key/values (to speed up sequential decoding).
+
+    config:
+        last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, decoder_embed_dim)`):
+            Sequence of hidden-states at the output of the last layer of the model.
+
+            If `past_key_values` is used only the last hidden-state of the sequences of shape `(batch_size, 1,
+            decoder_embed_dim)` is output.
+        past_key_values (`List(Dict(str, torch.FloatTensor))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`):
+            - "prev_key_value": shape=(bsz * num_head * v_dim * qk_dim)
+            - "scale": shape=((1 or bsz) * num_head * 1 * 1)
+
+            Contains pre-computed hidden-states (key and values in the multi-scale retention blocks)
+            that can be used (see `past_key_values` input) to speed up sequential decoding.
+        hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
+            Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, +
+            one for the output of each layer) of shape `(batch_size, sequence_length, decoder_embed_dim)`.
+
+            Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.
+        retentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_retentions=True` is passed or when `config.output_retentions=True`):
+            Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
+            sequence_length)`.
+
+            Retentions weights, used for visualization.
+
+        attentions (`tuple(torch.FloatTensor)`, *optional*, for backward compatibility. Same as retentions.
+    """
+
+    last_hidden_state: torch.FloatTensor = None
+    past_key_values: Optional[List[Dict[str, torch.FloatTensor]]] = None
+    hidden_states: Optional[Tuple[torch.FloatTensor]] = None
+    retentions: Optional[Tuple[torch.FloatTensor]] = None
+    attentions: Optional[Tuple[torch.FloatTensor]] = None
+
+
+class RetNetModel(RetNetPreTrainedModel):
+    def __init__(
+        self,
+        config: RetNetConfig,
+        embed_tokens: nn.Embedding = None,
+        tensor_parallel: bool = False,
+    ):
+        super().__init__(config)
+        self.config = config
+
+        self.dropout_module = torch.nn.Dropout(config.dropout)
+
+        self.embed_dim = config.decoder_embed_dim
+        self.embed_scale = (
+            1.0 if config.no_scale_embedding else math.sqrt(self.embed_dim)
+        )
+
+        if embed_tokens is None:
+            embed_tokens = nn.Embedding(
+                config.vocab_size, config.decoder_embed_dim, config.pad_token_id
+            )
+        self.embed_tokens = embed_tokens
+
+        if config.layernorm_embedding:
+            self.layernorm_embedding = RMSNorm(self.embed_dim, eps=config.layernorm_eps)
+        else:
+            self.layernorm_embedding = None
+
+        self.layers = nn.ModuleList([])
+
+        for i in range(config.decoder_layers):
+            self.layers.append(
+                RetNetDecoderLayer(config, depth=i, tensor_parallel=tensor_parallel)
+            )
+
+        self.decoder_layers = len(self.layers)
+
+        if config.decoder_normalize_before:
+            self.layer_norm = RMSNorm(self.embed_dim, eps=config.layernorm_eps)
+        else:
+            self.layer_norm = None
+
+        self.retnet_rel_pos = RetNetRelPos(config)
+        self.recurrent_chunk_size = config.recurrent_chunk_size
+
+        if config.deepnorm:
+            init_scale = math.pow(8.0 * config.decoder_layers, 0.25)
+            for name, p in self.named_parameters():
+                if (
+                    "fc1" in name
+                    or "fc2" in name
+                    or "out_proj" in name
+                    or "v_proj" in name
+                ):
+                    p.data.div_(init_scale)
+
+        if config.subln and not config.use_glu:
+            init_scale = math.sqrt(math.log(config.decoder_layers * 2))
+            for name, p in self.named_parameters():
+                if (
+                    "fc1" in name
+                    or "fc2" in name
+                    or "out_proj" in name
+                    or "v_proj" in name
+                ):
+                    p.data.mul_(init_scale)
+
+        self.gradient_checkpointing = False
+        self.post_init()
+
+    def get_input_embeddings(self):
+        return self.embed_tokens
+
+    def set_input_embeddings(self, value):
+        self.embed_tokens = value
+
+    def forward_embedding(
+        self,
+        input_ids,
+        forward_impl,
+        inputs_embeds=None,
+        past_key_values=None,
+    ):
+        # Check if input_ids are within the range
+        if input_ids.max() >= self.config.vocab_size:
+            raise ValueError("All input_ids must be less than vocab_size")
+            
+        # if past_key_values is not None:
+        if forward_impl == "recurrent":
+            input_ids = input_ids[:, -1:]
+
+        if inputs_embeds is None:
+            inputs_embeds = self.embed_tokens(input_ids)
+
+        embed = self.embed_scale * inputs_embeds
+
+        if self.layernorm_embedding is not None:
+            embed = self.layernorm_embedding(embed)
+
+        embed = self.dropout_module(embed)
+
+        return embed
+
+    def forward(
+        self,
+        input_ids: torch.LongTensor = None,
+        retention_mask: Optional[torch.Tensor] = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        past_key_values: Optional[List[Dict[str, torch.FloatTensor]]] = None,
+        inputs_embeds: Optional[torch.FloatTensor] = None,
+        output_retentions: Optional[bool] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        use_cache: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+        forward_impl: Optional[str] = "parallel",
+        recurrent_chunk_size: Optional[int] = None,
+        retention_rel_pos: Optional[Tuple[torch.Tensor]] = None,
+    ) -> Union[Tuple, RetNetOutputWithPast]:
+        if output_retentions is None and output_attentions is not None:
+            output_retentions = output_attentions
+        output_retentions = (
+            output_retentions
+            if output_retentions is not None
+            else self.config.output_retentions
+        )
+        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:
+            batch_size, seq_length = input_ids.shape
+        elif inputs_embeds is not None:
+            batch_size, seq_length, _ = inputs_embeds.shape
+        else:
+            raise ValueError("You have to specify either input_ids or inputs_embeds")
+
+        # embed tokens
+        if inputs_embeds is None:
+            inputs_embeds = self.forward_embedding(
+                input_ids, forward_impl, inputs_embeds, past_key_values
+            )
+
+        if retention_mask is None and attention_mask is not None:
+            retention_mask = attention_mask
+        if retention_mask is not None and forward_impl == "recurrent":
+            retention_mask = retention_mask[:, -1:]
+
+        hidden_states = inputs_embeds
+
+        # handling chunking here
+        if recurrent_chunk_size is None:
+            recurrent_chunk_size = self.recurrent_chunk_size
+        need_pad_for_chunkwise = (
+            forward_impl == "chunkwise" and seq_length % recurrent_chunk_size != 0
+        )
+        if need_pad_for_chunkwise:
+            padding_len = recurrent_chunk_size - seq_length % recurrent_chunk_size
+            slen = seq_length + padding_len
+            hidden_states = F.pad(hidden_states, (0, 0, 0, padding_len))
+        else:
+            slen = seq_length
+        # relative position
+        if retention_rel_pos is None:
+            retention_rel_pos = self.retnet_rel_pos(
+                slen,
+                forward_impl=forward_impl,
+                recurrent_chunk_size=recurrent_chunk_size,
+                retention_mask=retention_mask,
+                get_decay_scale=not self.training,
+            )
+
+        # start running through the decoder layers
+        all_hidden_states = () if output_hidden_states else None
+        all_retentions = () if output_retentions else None
+        # layers * [bsz, num_head, qk_dim, decoder_embed_dim]
+        next_decoder_cache = () if use_cache else None
+
+        for idx, layer in enumerate(self.layers):
+            if output_hidden_states:
+                all_hidden_states += (hidden_states,)
+            past_key_value = (
+                past_key_values[idx] if past_key_values is not None else None
+            )
+
+            if self.gradient_checkpointing and self.training:
+
+                def create_custom_forward(module):
+                    def custom_forward(*inputs):
+                        return module(*inputs, output_retentions)
+
+                    return custom_forward
+
+                layer_outputs = torch.utils.checkpoint.checkpoint(
+                    create_custom_forward(layer),
+                    hidden_states,
+                    retention_rel_pos,
+                    retention_mask,
+                    forward_impl,
+                    past_key_value,
+                )
+            else:
+                layer_outputs = layer(
+                    hidden_states,
+                    retention_rel_pos,
+                    retention_mask=retention_mask,
+                    forward_impl=forward_impl,
+                    past_key_value=past_key_value,
+                    output_retentions=output_retentions,
+                )
+
+            hidden_states = layer_outputs[0]
+
+            if use_cache:
+                next_decoder_cache += (layer_outputs[1],)
+
+            if output_retentions:
+                all_retentions += (layer_outputs[2],)
+
+        next_cache = next_decoder_cache if use_cache else None
+
+        if need_pad_for_chunkwise:
+            hidden_states = hidden_states[:, :seq_length, :]
+
+        if self.layer_norm is not None:
+            hidden_states = self.layer_norm(hidden_states)
+
+        # add hidden states from the last decoder layer
+        if output_hidden_states:
+            all_hidden_states += (hidden_states,)
+
+        if not return_dict:
+            return tuple(
+                v
+                for v in [hidden_states, next_cache, all_hidden_states, all_retentions]
+                if v is not None
+            )
+        return RetNetOutputWithPast(
+            last_hidden_state=hidden_states,
+            past_key_values=next_cache,
+            hidden_states=all_hidden_states,
+            retentions=all_retentions,
+            attentions=all_retentions,
+        )
+
+
+@dataclass
+class RetNetCausalLMOutputWithPast(ModelOutput):
+    """
+    class for RetNet causal language model (or autoregressive) outputs.
+
+    config:
+        loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided):
+            Language modeling loss (for next-token prediction).
+        logits (`torch.FloatTensor` of shape `(batch_size, sequence_length, config.vocab_size)`):
+            Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).
+        past_key_values (`List(Dict(str, torch.FloatTensor))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`):
+            - "prev_key_value": shape=(bsz * num_head * v_dim * qk_dim)
+            - "scale": shape=((1 or bsz) * num_head * 1 * 1)
+
+            Contains pre-computed hidden-states (key and values in the multi-scale retention blocks)
+            that can be used (see `past_key_values` input) to speed up sequential decoding.
+        hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
+            Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, +
+            one for the output of each layer) of shape `(batch_size, sequence_length, decoder_embed_dim)`.
+
+            Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.
+        retentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_retentions=True` is passed or when `config.output_retentions=True`):
+            Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
+            sequence_length)`.
+
+            Retentions weights, used for visualization.
+
+        attentions (`tuple(torch.FloatTensor)`, *optional*, for backward compatibility. Same as retentions.
+    """
+
+    loss: Optional[torch.FloatTensor] = None
+    logits: torch.FloatTensor = None
+    past_key_values: Optional[List[Dict[str, torch.FloatTensor]]] = None
+    hidden_states: Optional[Tuple[torch.FloatTensor]] = None
+    retentions: Optional[Tuple[torch.FloatTensor]] = None
+    attentions: Optional[Tuple[torch.FloatTensor]] = None
+
+
+class RetNetForCausalLM(RetNetPreTrainedModel):
+    def __init__(
+        self,
+        config: RetNetConfig,
+        embed_tokens: nn.Embedding = None,
+        tensor_parallel: bool = False,
+    ) -> None:
+        super().__init__(config)
+        self.model = RetNetModel(
+            config, embed_tokens=embed_tokens, tensor_parallel=tensor_parallel
+        )
+        self.lm_head = nn.Linear(
+            config.decoder_embed_dim, config.vocab_size, bias=False
+        )
+        # init here
+        torch.nn.init.normal_(
+            self.lm_head.weight, mean=0, std=config.decoder_embed_dim**-0.5
+        )
+
+        self.post_init()
+
+    def get_input_embeddings(self):
+        return self.model.embed_tokens
+
+    def set_input_embeddings(self, value):
+        self.model.embed_tokens = value
+
+    def get_output_embeddings(self):
+        return self.lm_head
+
+    def set_output_embeddings(self, new_embeddings):
+        self.lm_head = new_embeddings
+
+    def set_decoder(self, decoder):
+        self.model = decoder
+
+    def get_decoder(self):
+        return self.model
+
+    def forward(
+        self,
+        input_ids: torch.LongTensor = None,
+        retention_mask: Optional[torch.Tensor] = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        past_key_values: Optional[List[torch.FloatTensor]] = None,
+        inputs_embeds: Optional[torch.FloatTensor] = None,
+        labels: Optional[torch.LongTensor] = None,
+        use_cache: Optional[bool] = None,
+        output_retentions: Optional[bool] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+        forward_impl: Optional[str] = None,
+        recurrent_chunk_size: Optional[int] = None,
+        retention_rel_pos: Optional[Tuple[torch.Tensor]] = None,
+    ) -> Union[Tuple, RetNetCausalLMOutputWithPast]:
+        if output_retentions is None and output_attentions is not None:
+            output_retentions = output_attentions
+        output_retentions = (
+            output_retentions
+            if output_retentions is not None
+            else self.config.output_retentions
+        )
+        output_hidden_states = (
+            output_hidden_states
+            if output_hidden_states is not None
+            else self.config.output_hidden_states
+        )
+        return_dict = (
+            return_dict if return_dict is not None else self.config.use_return_dict
+        )
+        forward_impl = (
+            forward_impl if forward_impl is not None else self.config.forward_impl
+        )
+        recurrent_chunk_size = (
+            recurrent_chunk_size
+            if recurrent_chunk_size is not None
+            else self.config.recurrent_chunk_size
+        )
+
+        if retention_mask is None and attention_mask is not None:
+            retention_mask = attention_mask
+
+        outputs = self.model(
+            input_ids,
+            retention_mask=retention_mask,
+            past_key_values=past_key_values,
+            inputs_embeds=inputs_embeds,
+            output_retentions=output_retentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+            forward_impl=forward_impl,
+            use_cache=use_cache,
+            recurrent_chunk_size=recurrent_chunk_size,
+            retention_rel_pos=retention_rel_pos,
+        )
+
+        hidden_states = outputs[0]
+        logits = self.lm_head(hidden_states)
+
+        loss = None
+        if labels is not None:
+            # Shift so that tokens < n predict n
+            shift_logits = logits[..., :-1, :].contiguous()
+            shift_labels = labels[..., 1:].contiguous()
+            # Flatten the tokens
+            loss_fct = nn.CrossEntropyLoss()
+            shift_logits = shift_logits.view(-1, self.config.vocab_size)
+            shift_labels = shift_labels.view(-1)
+            # Enable model parallelism
+            shift_labels = shift_labels.to(shift_logits.device)
+            loss = loss_fct(shift_logits, shift_labels)
+
+            if self.config.z_loss_coeff > 0:
+                # z_loss from PaLM paper
+                # z_loss = 1e-4 * log(log(z)), where z = sum(exp(logits))
+                z_loss = torch.logsumexp(shift_logits, dim=-1).log().mean()
+                loss += self.config.z_loss_coeff * z_loss
+
+        if not return_dict:
+            output = (logits,) + outputs[1:]
+            return (loss,) + output if loss is not None else output
+
+        return RetNetCausalLMOutputWithPast(
+            loss=loss,
+            logits=logits,
+            past_key_values=outputs.past_key_values,
+            hidden_states=outputs.hidden_states,
+            retentions=outputs.retentions,
+            attentions=outputs.retentions,
+        )
+
+    def _crop_past_key_values(model, past_key_values, maximum_length):
+        """Since retnet's kv do not have length, no need to crop. Just return"""
+        return past_key_values
+
+    def prepare_inputs_for_generation(
+        self,
+        input_ids,
+        past_key_values=None,
+        attention_mask=None,
+        inputs_embeds=None,
+        **kwargs,
+    ):
+        # if `inputs_embeds` are passed, we only want to use them in the 1st generation step
+        if inputs_embeds is not None and past_key_values is None:
+            model_inputs = {"inputs_embeds": inputs_embeds}
+        else:
+            model_inputs = {"input_ids": input_ids}
+
+        forward_impl = kwargs.get("forward_impl", "parallel")
+        if past_key_values is not None:
+            forward_impl = "recurrent"
+
+        model_inputs.update(
+            {
+                "past_key_values": past_key_values,
+                "use_cache": kwargs.get("use_cache"),
+                "attention_mask": attention_mask,
+                "forward_impl": forward_impl,
+            }
+        )
+        return model_inputs
+
+    @staticmethod
+    def _reorder_cache(past_key_values, beam_idx):
+        reordered_past = ()
+        for layer_past in past_key_values:  # dict
+            layer_past_kv = layer_past["prev_key_value"]  # [b, h, v_dim / h, qk_dim]
+            layer_past_scale = layer_past["scale"]  # [b, h, 1, 1]
+            if layer_past_scale.size(0) > 1:
+                # this means that retention_mask is not None, so the scale for
+                # each batch is different. We need to select the correct scale then.
+                # NOTE: during huggingface generate, it will generate attention_mask
+                # if it is None, so this linke will always be true. Still, having
+                # this line here for safety.
+                layer_past_scale = layer_past_scale.index_select(0, beam_idx)
+            reordered_past += (
+                {
+                    "prev_key_value": layer_past_kv.index_select(0, beam_idx),
+                    "scale": layer_past_scale,
+                },
+            )
+        return reordered_past
+
+    def sample_token(self, logit, do_sample=False, top_k=1, top_p=1.0, temperature=1.0):
+        if not do_sample:
+            return torch.argmax(logit, dim=-1, keepdim=True)
+        filtered = top_k_top_p_filtering(logit / temperature, top_k=top_k, top_p=top_p)
+        return torch.multinomial(torch.softmax(filtered, dim=-1), num_samples=1)
+
+    @torch.inference_mode()
+    def custom_generate(
+        self,
+        input_ids: torch.LongTensor = None,
+        retention_mask: Optional[torch.Tensor] = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        parallel_compute_prompt=True,
+        max_new_tokens=20,
+        bos_token_id=0,
+        eos_token_id=0,
+        do_sample=False,
+        top_k=0,
+        top_p=1.0,
+        temperature=1.0,
+        early_stopping=True,
+    ):
+        if retention_mask is None and attention_mask is not None:
+            retention_mask = attention_mask
+
+        if input_ids is not None:
+            if input_ids.shape[1] == 1:
+                past_key_values = None
+            elif parallel_compute_prompt:
+                ret_mask = (
+                    retention_mask[:, :-1] if retention_mask is not None else None
+                )
+                outputs = self(
+                    input_ids[:, :-1],
+                    retention_mask=ret_mask,
+                    forward_impl="parallel",
+                    return_dict=True,
+                    use_cache=True,
+                )
+                past_key_values = outputs.past_key_values
+            else:
+                past_key_values = None
+                for p_i in range(input_ids.shape[1] - 1):
+                    ret_mask = (
+                        retention_mask[:, : p_i + 1]
+                        if retention_mask is not None
+                        else None
+                    )
+                    outputs = self(
+                        input_ids[:, : p_i + 1],
+                        retention_mask=ret_mask,
+                        forward_impl="recurrent",
+                        past_key_values=past_key_values,
+                        return_dict=True,
+                        use_cache=True,
+                    )
+                    past_key_values = outputs.past_key_values
+
+            generated = input_ids
+        else:
+            generated = torch.tensor([[bos_token_id]]).to(self.lm_head.weight.device)
+            past_key_values = None
+
+        for i in range(max_new_tokens):
+            outputs = self(
+                generated,
+                retention_mask=retention_mask,
+                forward_impl="recurrent",
+                past_key_values=past_key_values,
+                use_cache=True,
+                return_dict=True,
+            )
+            logit = outputs.logits[:, -1, :]  # [batch_size, vocab_size]
+            past_key_values = outputs.past_key_values
+            token = self.sample_token(
+                logit,
+                do_sample=do_sample,
+                top_k=top_k,
+                top_p=top_p,
+                temperature=temperature,
+            )
+            generated = torch.cat([generated, token], dim=-1)
+            if retention_mask is not None:
+                retention_mask = torch.cat(
+                    [retention_mask, torch.ones_like(token)], dim=-1
+                )
+            if early_stopping and (token == eos_token_id).all():
+                break
+        return generated
+
+
+class RetNetForSequenceClassification(RetNetPreTrainedModel):
+    def __init__(self, config, tensor_parallel=False):
+        super().__init__(config)
+        self.num_labels = config.num_labels
+        self.model = RetNetModel(config, tensor_parallel=tensor_parallel)
+        self.score = nn.Linear(config.decoder_embed_dim, self.num_labels, bias=False)
+
+        # Initialize weights and apply final processing
+        self.post_init()
+
+    def get_input_embeddings(self):
+        return self.model.embed_tokens
+
+    def set_input_embeddings(self, value):
+        self.model.embed_tokens = value
+
+    def forward(
+        self,
+        input_ids: torch.LongTensor = None,
+        retention_mask: Optional[torch.Tensor] = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        past_key_values: Optional[List[torch.FloatTensor]] = None,
+        inputs_embeds: Optional[torch.FloatTensor] = None,
+        labels: Optional[torch.LongTensor] = None,
+        use_cache: Optional[bool] = None,
+        output_retentions: Optional[bool] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+        forward_impl: Optional[str] = None,
+        recurrent_chunk_size: Optional[int] = None,
+        retention_rel_pos: Optional[Tuple[torch.Tensor]] = None,
+    ) -> Union[Tuple, SequenceClassifierOutputWithPast]:
+        if output_retentions is None and output_attentions is not None:
+            output_retentions = output_attentions
+        output_retentions = (
+            output_retentions
+            if output_retentions is not None
+            else self.config.output_retentions
+        )
+        output_hidden_states = (
+            output_hidden_states
+            if output_hidden_states is not None
+            else self.config.output_hidden_states
+        )
+        return_dict = (
+            return_dict if return_dict is not None else self.config.use_return_dict
+        )
+        forward_impl = (
+            forward_impl if forward_impl is not None else self.config.forward_impl
+        )
+        recurrent_chunk_size = (
+            recurrent_chunk_size
+            if recurrent_chunk_size is not None
+            else self.config.recurrent_chunk_size
+        )
+
+        if retention_mask is None and attention_mask is not None:
+            retention_mask = attention_mask
+
+        outputs = self.model(
+            input_ids,
+            retention_mask=retention_mask,
+            past_key_values=past_key_values,
+            inputs_embeds=inputs_embeds,
+            output_retentions=output_retentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+            forward_impl=forward_impl,
+            use_cache=use_cache,
+            recurrent_chunk_size=recurrent_chunk_size,
+            retention_rel_pos=retention_rel_pos,
+        )
+
+        hidden_states = outputs[0]
+        logits = self.score(hidden_states)
+
+        if input_ids is not None:
+            batch_size = input_ids.shape[0]
+        else:
+            batch_size = inputs_embeds.shape[0]
+
+        if self.config.pad_token_id is None and batch_size != 1:
+            raise ValueError(
+                "Cannot handle batch sizes > 1 if no padding token is defined."
+            )
+        if self.config.pad_token_id is None:
+            sequence_lengths = -1
+        else:
+            if input_ids is not None:
+                sequence_lengths = (
+                    torch.eq(input_ids, self.config.pad_token_id).long().argmax(-1) - 1
+                ).to(logits.device)
+            else:
+                sequence_lengths = -1
+
+        pooled_logits = logits[
+            torch.arange(batch_size, device=logits.device), sequence_lengths
+        ]
+
+        loss = None
+        if labels is not None:
+            labels = labels.to(logits.device)
+            if self.config.problem_type is None:
+                if self.num_labels == 1:
+                    self.config.problem_type = "regression"
+                elif self.num_labels > 1 and (
+                    labels.dtype == torch.long or labels.dtype == torch.int
+                ):
+                    self.config.problem_type = "single_label_classification"
+                else:
+                    self.config.problem_type = "multi_label_classification"
+
+            if self.config.problem_type == "regression":
+                loss_fct = MSELoss()
+                if self.num_labels == 1:
+                    loss = loss_fct(pooled_logits.squeeze(), labels.squeeze())
+                else:
+                    loss = loss_fct(pooled_logits, labels)
+            elif self.config.problem_type == "single_label_classification":
+                loss_fct = CrossEntropyLoss()
+                loss = loss_fct(
+                    pooled_logits.view(-1, self.num_labels), labels.view(-1)
+                )
+            elif self.config.problem_type == "multi_label_classification":
+                loss_fct = BCEWithLogitsLoss()
+                loss = loss_fct(pooled_logits, labels)
+        if not return_dict:
+            output = (pooled_logits,) + outputs[1:]
+            return ((loss,) + output) if loss is not None else output
+
+        return SequenceClassifierOutputWithPast(
+            loss=loss,
+            logits=pooled_logits,
+            past_key_values=outputs.past_key_values,
+            hidden_states=outputs.hidden_states,
+            attentions=outputs.attentions,
+        )