Upload modeling_llada.py with huggingface_hub

modeling_llada.py

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+from __future__ import annotations
+
+import logging
+import math
+import sys
+from abc import abstractmethod
+from collections import defaultdict
+from dataclasses import fields
+from functools import partial
+from typing import (
+    Callable,
+    Dict,
+    Iterable,
+    List,
+    NamedTuple,
+    Optional,
+    Sequence,
+    Set,
+    Tuple,
+    Union,
+    cast,
+)
+
+import torch
+import torch.backends.cuda
+import torch.nn as nn
+import torch.nn.functional as F
+from torch import einsum
+from transformers import PreTrainedModel
+from transformers.cache_utils import Cache
+from transformers.modeling_outputs import CausalLMOutputWithPast
+from transformers.models.auto import AutoModel
+
+from .configuration_llada import (
+    ActivationCheckpointingStrategy,
+    ActivationType,
+    BlockType,
+    InitFnType,
+    LayerNormType,
+    LLaDAConfig,
+    ModelConfig,
+    StrEnum,
+)
+
+if sys.version_info.minor > 8:
+    from collections.abc import MutableMapping
+elif sys.version_info.minor == 8:
+    from typing import MutableMapping
+else:
+    raise SystemExit("This script supports Python 3.8 or higher")
+
+__all__ = [
+    "LayerNormBase",
+    "LayerNorm",
+    "RMSLayerNorm",
+    "GemmaRMSLayerNorm",
+    "RotaryEmbedding",
+    "Activation",
+    "GELU",
+    "ReLU",
+    "SwiGLU",
+    "LLaDABlock",
+    "LLaDASequentialBlock",
+    "LLaDAModel",
+    "LLaDAOutput",
+    "LLaDAGenerateOutput",
+]
+
+
+log = logging.getLogger(__name__)
+
+
+class ModuleType(StrEnum):
+    in_module = "in"
+    out_module = "out"
+    emb = "emb"
+    final_out = "final_out"
+
+
+def init_weights(
+    config: ModelConfig,
+    module: Union[nn.Linear, nn.Embedding],
+    d: Optional[int] = None,
+    layer_id: Optional[int] = None,
+    std_factor: float = 1.0,
+    type_of_module: Optional[ModuleType] = None,
+) -> None:
+    """
+    Initialize weights of a linear or embedding module.
+
+    :param config: The model config.
+    :param module: The linear or embedding submodule to initialize.
+    :param d: The effective input dimensionality of the weights. This could be smaller than the actual dimensions
+        for fused layers.
+    :param layer_id: When set, the standard deviation for the "mitchell" method will be adjusted by
+        ``1 / sqrt(2 * (layer_id + 1))``.
+    """
+    d = d if d is not None else config.d_model
+    if config.init_fn == InitFnType.normal:
+        std = config.init_std * std_factor
+        if config.init_cutoff_factor is not None:
+            cutoff_value = config.init_cutoff_factor * std
+            nn.init.trunc_normal_(module.weight, mean=0.0, std=std, a=-cutoff_value, b=cutoff_value)
+        else:
+            nn.init.normal_(module.weight, mean=0.0, std=std)
+    elif config.init_fn == InitFnType.mitchell:
+        std = std_factor / math.sqrt(d)
+        if layer_id is not None:
+            std = std / math.sqrt(2 * (layer_id + 1))
+        nn.init.trunc_normal_(module.weight, mean=0.0, std=std, a=-3 * std, b=3 * std)
+    elif config.init_fn == InitFnType.kaiming_normal:
+        nn.init.kaiming_normal_(module.weight, nonlinearity="relu")
+    elif config.init_fn == InitFnType.fan_in:
+        std = std_factor / math.sqrt(d)
+        nn.init.normal_(module.weight, mean=0.0, std=std)
+    elif config.init_fn == InitFnType.full_megatron:
+        if type_of_module is None:
+            raise RuntimeError(
+                f"When using the {InitFnType.full_megatron} init, every module must have a type."
+            )
+
+        cutoff_factor = config.init_cutoff_factor
+        if cutoff_factor is None:
+            cutoff_factor = 3
+
+        if type_of_module == ModuleType.in_module:
+            # for att_proj (same as QKV), ff_proj
+            std = config.init_std
+        elif type_of_module == ModuleType.out_module:
+            # for attn_out, ff_out
+            std = config.init_std / math.sqrt(2.0 * config.n_layers)
+        elif type_of_module == ModuleType.emb:
+            # positional embeddings (wpe)
+            # token embeddings (wte)
+            std = config.init_std
+        elif type_of_module == ModuleType.final_out:
+            # final output (ff_out)
+            std = config.d_model**-0.5
+        else:
+            raise RuntimeError(f"Unknown module type '{type_of_module}'")
+        nn.init.trunc_normal_(
+            module.weight,
+            mean=0.0,
+            std=std,
+            a=-cutoff_factor * std,
+            b=cutoff_factor * std,
+        )
+    else:
+        raise NotImplementedError(config.init_fn)
+
+    if isinstance(module, nn.Linear):
+        if module.bias is not None:
+            nn.init.zeros_(module.bias)
+
+        if config.init_fn == InitFnType.normal and getattr(module, "_is_residual", False):
+            with torch.no_grad():
+                module.weight.div_(math.sqrt(2 * config.n_layers))
+
+
+def ensure_finite_(x: torch.Tensor, check_neg_inf: bool = True, check_pos_inf: bool = False):
+    """
+    Modify ``x`` in place to replace ``float("-inf")`` with the minimum value of the dtype when ``check_neg_inf``
+    is ``True`` and to replace ``float("inf")`` with the maximum value of the dtype when ``check_pos_inf`` is ``True``.
+    """
+    if check_neg_inf:
+        x.masked_fill_(x == float("-inf"), torch.finfo(x.dtype).min)
+    if check_pos_inf:
+        x.masked_fill_(x == float("inf"), torch.finfo(x.dtype).max)
+
+
+def activation_checkpoint_function(cfg: ModelConfig):
+    preserve_rng_state = (
+        (cfg.attention_dropout == 0.0)
+        and (cfg.embedding_dropout == 0.0)
+        and (cfg.residual_dropout == 0.0)
+    )
+    from torch.utils.checkpoint import checkpoint
+
+    return partial(
+        checkpoint,
+        preserve_rng_state=preserve_rng_state,
+        use_reentrant=False,
+    )
+
+
+class BufferCache(dict, MutableMapping[str, torch.Tensor]):
+    """
+    Cache for attention biases and other things that would normally be stored as buffers.
+    We avoid using buffers because we've run into various issues doing so with FSDP.
+    In general it appears the way FSDP handles buffers is not well-defined.
+    It doesn't shard them but apparently it does synchronize them across processes, which we want to avoid
+    since (A) it isn't necessary, and (B) we sometimes have `-inf` in these biases which might get turned into
+    NaNs when they're synchronized due to casting or some other issue.
+    """
+
+
+def _non_meta_init_device(config: ModelConfig) -> torch.device:
+    if config.init_device is not None and config.init_device != "meta":
+        return torch.device(config.init_device)
+    else:
+        return torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
+
+class Dropout(nn.Dropout):
+    def forward(self, input: torch.Tensor) -> torch.Tensor:
+        if self.p == 0.0:
+            return input
+        else:
+            return F.dropout(input, self.p, self.training, self.inplace)
+
+
+class LayerNormBase(nn.Module):
+    def __init__(
+        self,
+        config: ModelConfig,
+        *,
+        size: Optional[int] = None,
+        elementwise_affine: Optional[bool] = True,
+        eps: float = 1e-05,
+    ):
+        super().__init__()
+        self.config = config
+        self.eps = eps
+        self.normalized_shape = (size or config.d_model,)
+        if elementwise_affine or (
+            elementwise_affine is None and self.config.layer_norm_with_affine
+        ):
+            self.weight = nn.Parameter(torch.ones(self.normalized_shape, device=config.init_device))
+            use_bias = self.config.bias_for_layer_norm
+            if use_bias is None:
+                use_bias = self.config.include_bias
+            if use_bias:
+                self.bias = nn.Parameter(
+                    torch.zeros(self.normalized_shape, device=config.init_device)
+                )
+            else:
+                self.register_parameter("bias", None)
+        else:
+            self.register_parameter("bias", None)
+            self.register_parameter("weight", None)
+
+    @abstractmethod
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        raise NotImplementedError
+
+    @classmethod
+    def build(cls, config: ModelConfig, size: Optional[int] = None, **kwargs) -> LayerNormBase:
+        if config.layer_norm_type == LayerNormType.default:
+            return LayerNorm(config, size=size, low_precision=False, **kwargs)
+        elif config.layer_norm_type == LayerNormType.low_precision:
+            return LayerNorm(config, size=size, low_precision=True, **kwargs)
+        elif config.layer_norm_type == LayerNormType.rms:
+            return RMSLayerNorm(config, size=size, **kwargs)
+        elif config.layer_norm_type == LayerNormType.gemma_rms:
+            return GemmaRMSLayerNorm(config, size=size, **kwargs)
+        else:
+            raise NotImplementedError(f"Unknown LayerNorm type: '{config.layer_norm_type}'")
+
+    def _cast_if_autocast_enabled(
+        self, tensor: torch.Tensor, dtype: Optional[torch.dtype] = None
+    ) -> torch.Tensor:
+        # NOTE: `is_autocast_enabled()` only checks for CUDA autocast, so we use the separate function
+        # `is_autocast_cpu_enabled()` for CPU autocast.
+        # See https://github.com/pytorch/pytorch/issues/110966.
+        if tensor.device.type == "cuda" and torch.is_autocast_enabled():
+            return tensor.to(dtype=dtype if dtype is not None else torch.get_autocast_gpu_dtype())
+        elif tensor.device.type == "cpu" and torch.is_autocast_cpu_enabled():
+            return tensor.to(dtype=dtype if dtype is not None else torch.get_autocast_cpu_dtype())
+        else:
+            return tensor
+
+    def reset_parameters(self):
+        if self.weight is not None:
+            torch.nn.init.ones_(self.weight)  # type: ignore
+        if self.bias is not None:
+            torch.nn.init.zeros_(self.bias)  # type: ignore
+
+
+class LayerNorm(LayerNormBase):
+    """
+    The default :class:`LayerNorm` implementation which can optionally run in low precision.
+    """
+
+    def __init__(
+        self,
+        config: ModelConfig,
+        size: Optional[int] = None,
+        low_precision: bool = False,
+        elementwise_affine: Optional[bool] = None,
+        eps: float = 1e-05,
+    ):
+        super().__init__(config, size=size, elementwise_affine=elementwise_affine, eps=eps)
+        self.low_precision = low_precision
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        if self.low_precision:
+            module_device = x.device
+            downcast_x = self._cast_if_autocast_enabled(x)
+            downcast_weight = (
+                self._cast_if_autocast_enabled(self.weight)
+                if self.weight is not None
+                else self.weight
+            )
+            downcast_bias = (
+                self._cast_if_autocast_enabled(self.bias) if self.bias is not None else self.bias
+            )
+            with torch.autocast(enabled=False, device_type=module_device.type):
+                return F.layer_norm(
+                    downcast_x,
+                    self.normalized_shape,
+                    weight=downcast_weight,
+                    bias=downcast_bias,
+                    eps=self.eps,
+                )
+        else:
+            return F.layer_norm(
+                x, self.normalized_shape, weight=self.weight, bias=self.bias, eps=self.eps
+            )
+
+
+class RMSLayerNorm(LayerNormBase):
+    """
+    RMS layer norm, a simplified :class:`LayerNorm` implementation
+    """
+
+    def __init__(
+        self,
+        config: ModelConfig,
+        size: Optional[int] = None,
+        elementwise_affine: Optional[bool] = None,
+        eps: float = 1e-5,
+    ):
+        super().__init__(
+            config, size=size, elementwise_affine=elementwise_affine, eps=config.rms_norm_eps
+        )
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        with torch.autocast(enabled=False, device_type=x.device.type):
+            og_dtype = x.dtype
+            x = x.to(torch.float32)
+            variance = x.pow(2).mean(-1, keepdim=True)
+            x = x * torch.rsqrt(variance + self.eps)
+            x = x.to(og_dtype)
+
+        if self.weight is not None:
+            if self.bias is not None:
+                return self.weight * x + self.bias
+            else:
+                return self.weight * x
+        else:
+            return x
+
+
+class GemmaRMSLayerNorm(LayerNormBase):
+    """
+    Gemma RMS layer norm, a simplified :class:`LayerNorm` implementation
+    """
+
+    def __init__(
+        self,
+        config: ModelConfig,
+        size: Optional[int] = None,
+        elementwise_affine: Optional[bool] = None,
+        eps: float = 1e-5,
+    ):
+        super().__init__(
+            config, size=size, elementwise_affine=elementwise_affine, eps=config.rms_norm_eps
+        )
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        with torch.autocast(enabled=False, device_type=x.device.type):
+            og_dtype = x.dtype
+            x = x.to(torch.float32)
+            variance = x.pow(2).mean(-1, keepdim=True)
+            x = x * torch.rsqrt(variance + self.eps)
+            x = x.to(og_dtype)
+
+        if self.weight is not None:
+            if self.bias is not None:
+                return x * (1 + self.weight) + self.bias
+            else:
+                return x * (1 + self.weight)
+        else:
+            return x
+
+
+class RotaryEmbedding(nn.Module):
+    """
+    [Rotary positional embeddings (RoPE)](https://arxiv.org/abs/2104.09864).
+    """
+
+    def __init__(self, config: ModelConfig, cache: BufferCache):
+        super().__init__()
+        self.config = config
+        self.__cache = cache
+        # Warm up cache.
+        self.rope_theta = config.rope_theta
+        self.get_rotary_embedding(config.max_sequence_length, _non_meta_init_device(config))
+
+    def get_rotary_embedding(
+        self, seq_len: int, device: torch.device
+    ) -> Tuple[torch.Tensor, torch.Tensor]:
+        if (
+            (pos_sin := self.__cache.get("rope_pos_sin")) is not None
+            and (pos_cos := self.__cache.get("rope_pos_cos")) is not None
+            and pos_sin.shape[-2] >= seq_len
+            and pos_cos.shape[-2] >= seq_len
+        ):
+            if pos_sin.device != device:
+                pos_sin = pos_sin.to(device)
+                self.__cache["rope_pos_sin"] = pos_sin
+            if pos_cos.device != device:
+                pos_cos = pos_cos.to(device)
+                self.__cache["rope_pos_cos"] = pos_cos
+            return pos_sin[:, :, :seq_len, :], pos_cos[:, :, :seq_len, :]
+
+        with torch.autocast(device.type, enabled=False):
+            dim = self.config.d_model // self.config.n_heads
+            inv_freq = 1.0 / (
+                self.rope_theta ** (torch.arange(0, dim, 2, device=device, dtype=torch.float) / dim)
+            )
+            seq = torch.arange(seq_len, device=device, dtype=torch.float)
+            freqs = einsum("i , j -> i j", seq, inv_freq)
+            positions = torch.cat((freqs, freqs), dim=-1)
+            pos_sin, pos_cos = positions.sin()[None, None, :, :], positions.cos()[None, None, :, :]
+        self.__cache["rope_pos_sin"] = pos_sin
+        self.__cache["rope_pos_cos"] = pos_cos
+        return pos_sin, pos_cos
+
+    def rotate_half(self, x: torch.Tensor) -> torch.Tensor:
+        B, nh, T, hs = x.size()
+        x = x.view(B, nh, T, 2, hs // 2)
+        x1, x2 = x.unbind(dim=-2)
+        return torch.cat((-x2, x1), dim=-1)
+
+    def apply_rotary_pos_emb(
+        self, pos_sin: torch.Tensor, pos_cos: torch.Tensor, t: torch.Tensor
+    ) -> torch.Tensor:
+        return ((t * pos_cos) + (self.rotate_half(t) * pos_sin)).to(t.dtype)
+
+    def forward(self, q: torch.Tensor, k: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]:
+        if self.config.rope_full_precision:
+            q_, k_ = q.float(), k.float()
+        else:
+            q_, k_ = q, k
+
+        with torch.autocast(q.device.type, enabled=False):
+            query_len, key_len = (
+                q_.shape[-2],
+                k_.shape[-2],
+            )  # could be different if layer_past not None
+            pos_sin, pos_cos = self.get_rotary_embedding(key_len, q_.device)
+            pos_sin = pos_sin.type_as(q_)
+            pos_cos = pos_cos.type_as(q_)
+            q_ = self.apply_rotary_pos_emb(
+                pos_sin[:, :, key_len - query_len : key_len, :],
+                pos_cos[:, :, key_len - query_len : key_len, :],
+                q_,
+            )
+            k_ = self.apply_rotary_pos_emb(pos_sin, pos_cos, k_)
+        return q_.type_as(q), k_.type_as(k)
+
+
+class Activation(nn.Module):
+    def __init__(self, config: ModelConfig):
+        super().__init__()
+        self.config = config
+
+    @abstractmethod
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        raise NotImplementedError
+
+    @property
+    @abstractmethod
+    def output_multiplier(self) -> float:
+        raise NotImplementedError
+
+    @classmethod
+    def build(cls, config: ModelConfig) -> Activation:
+        if config.activation_type == ActivationType.gelu:
+            return cast(Activation, GELU(approximate="none"))
+        elif config.activation_type == ActivationType.relu:
+            return cast(Activation, ReLU(inplace=False))
+        elif config.activation_type == ActivationType.silu:
+            return cast(Activation, SiLU(inplace=False))
+        elif config.activation_type == ActivationType.swiglu:
+            return SwiGLU(config)
+        else:
+            raise NotImplementedError(f"Unknown activation: '{config.activation_type}'")
+
+
+class GELU(nn.GELU):
+    @property
+    def output_multiplier(self) -> float:
+        return 1.0
+
+
+class ReLU(nn.ReLU):
+    @property
+    def output_multiplier(self) -> float:
+        return 1.0
+
+
+class SiLU(nn.SiLU):
+    @property
+    def output_multiplier(self) -> float:
+        return 1.0
+
+
+class SwiGLU(Activation):
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        x, gate = x.chunk(2, dim=-1)
+        return F.silu(gate) * x
+
+    @property
+    def output_multiplier(self) -> float:
+        return 0.5
+
+
+def causal_attention_bias(seq_len: int, device: torch.device) -> torch.FloatTensor:
+    att_bias = torch.triu(
+        torch.ones(seq_len, seq_len, device=device, dtype=torch.float),
+        diagonal=1,
+    )
+    att_bias.masked_fill_(att_bias == 1, torch.finfo(att_bias.dtype).min)
+    return att_bias.view(1, 1, seq_len, seq_len)  # type: ignore
+
+
+def get_causal_attention_bias(
+    cache: BufferCache, seq_len: int, device: torch.device
+) -> torch.Tensor:
+    if (causal_bias := cache.get("causal_attention_bias")) is not None and causal_bias.shape[
+        -1
+    ] >= seq_len:
+        if causal_bias.device != device:
+            causal_bias = causal_bias.to(device)
+            cache["causal_attention_bias"] = causal_bias
+        return causal_bias
+    with torch.autocast(device.type, enabled=False):
+        causal_bias = causal_attention_bias(seq_len, device)
+    cache["causal_attention_bias"] = causal_bias
+    return causal_bias
+
+
+def alibi_attention_bias(
+    seq_len: int, config: ModelConfig, device: torch.device
+) -> torch.FloatTensor:
+    alibi_bias = torch.arange(1 - seq_len, 1, dtype=torch.float, device=device).view(
+        1, 1, 1, seq_len
+    )
+
+    # shape: (1, 1, seq_len, seq_len)
+    alibi_bias = alibi_bias - torch.arange(1 - seq_len, 1, dtype=torch.float, device=device).view(
+        1, 1, seq_len, 1
+    )
+    alibi_bias.abs_().mul_(-1)
+
+    # shape: (n_heads,)
+    m = torch.arange(1, config.n_heads + 1, dtype=torch.float, device=device)
+    m.mul_(config.alibi_bias_max / config.n_heads)
+
+    # shape: (1, n_heads, seq_len, seq_len)
+    return alibi_bias * (1.0 / (2 ** m.view(1, config.n_heads, 1, 1)))  # type: ignore
+
+
+class LLaDABlock(nn.Module):
+    """
+    A base class for transformer block implementations.
+    """
+
+    def __init__(self, layer_id: int, config: ModelConfig, cache: BufferCache):
+        super().__init__()
+        self.layer_id = layer_id
+        self.config = config
+        self.hidden_size = (
+            config.mlp_hidden_size
+            if config.mlp_hidden_size is not None
+            else config.mlp_ratio * config.d_model
+        )
+        self.__cache = cache
+        assert config.d_model % config.n_heads == 0
+
+        self._activation_checkpoint_fn = None
+
+        # Dropout.
+        self.dropout = Dropout(config.residual_dropout)
+
+        # Layer norms.
+        self.k_norm: Optional[LayerNormBase] = None
+        self.q_norm: Optional[LayerNormBase] = None
+        if config.attention_layer_norm:
+            self.k_norm = LayerNormBase.build(
+                config,
+                size=(config.d_model // config.n_heads) * config.effective_n_kv_heads,
+                elementwise_affine=config.attention_layer_norm_with_affine,
+            )
+            self.q_norm = LayerNormBase.build(
+                config, elementwise_affine=config.attention_layer_norm_with_affine
+            )
+
+        # Activation function.
+        self.act = Activation.build(config)
+        assert (self.act.output_multiplier * self.hidden_size) % 1 == 0
+
+        # Attention output projection.
+        self.attn_out = nn.Linear(
+            config.d_model, config.d_model, bias=config.include_bias, device=config.init_device
+        )
+
+        # Feed-forward output projection.
+        self.ff_out = nn.Linear(
+            int(self.act.output_multiplier * self.hidden_size),
+            config.d_model,
+            bias=config.include_bias,
+            device=config.init_device,
+        )
+        self.ff_out._is_residual = True  # type: ignore
+
+        # Rotary embeddings.
+        if self.config.rope:
+            self.rotary_emb = RotaryEmbedding(config, self.__cache)
+
+        self.flash_attn_func = None
+        if config.flash_attention:
+            try:
+                from flash_attn import flash_attn_func  # type: ignore
+
+                self.flash_attn_func = flash_attn_func
+            except ModuleNotFoundError:
+                pass
+
+    def reset_parameters(self):
+        if self.k_norm is not None:
+            self.k_norm.reset_parameters()
+        if self.q_norm is not None:
+            self.q_norm.reset_parameters()
+        init_weights(
+            self.config,
+            self.attn_out,
+            d=self.config.d_model,
+            layer_id=self.layer_id,
+            type_of_module=ModuleType.out_module,
+        )
+        init_weights(
+            self.config,
+            self.ff_out,
+            d=self.ff_out.in_features,
+            layer_id=self.layer_id,
+            type_of_module=ModuleType.out_module,
+        )
+
+    def set_activation_checkpointing(self, strategy: Optional[ActivationCheckpointingStrategy]):
+        if strategy == ActivationCheckpointingStrategy.fine_grained:
+            self._activation_checkpoint_fn = activation_checkpoint_function(self.config)
+        else:
+            self._activation_checkpoint_fn = None
+
+    @classmethod
+    def _cast_attn_bias(cls, bias: torch.Tensor, input_dtype: torch.dtype) -> torch.Tensor:
+        target_dtype = input_dtype
+        # NOTE: `is_autocast_enabled()` only checks for CUDA autocast, so we use the separate function
+        # `is_autocast_cpu_enabled()` for CPU autocast.
+        # See https://github.com/pytorch/pytorch/issues/110966.
+        if bias.device.type == "cuda" and torch.is_autocast_enabled():
+            target_dtype = torch.get_autocast_gpu_dtype()
+        elif bias.device.type == "cpu" and torch.is_autocast_cpu_enabled():
+            target_dtype = torch.get_autocast_cpu_dtype()
+        if bias.dtype != target_dtype:
+            bias = bias.to(target_dtype)
+            ensure_finite_(bias, check_neg_inf=True, check_pos_inf=False)
+        return bias
+
+    def _scaled_dot_product_attention(
+        self,
+        q: torch.Tensor,
+        k: torch.Tensor,
+        v: torch.Tensor,
+        attn_mask: Optional[torch.Tensor] = None,
+        dropout_p: float = 0.0,
+        is_causal: bool = False,
+    ) -> torch.Tensor:
+        """
+        Computes scaled dot product attention on query, key and value tensors, using an optional
+        attention mask if passed, and applying dropout if a probability greater than 0.0 is specified.
+        """
+        if self.flash_attn_func is not None and attn_mask is None:
+            r = self.flash_attn_func(
+                q.transpose(1, 2),
+                k.transpose(1, 2),
+                v.transpose(1, 2),
+                dropout_p=dropout_p,
+                causal=False,
+            )
+            return r.transpose(1, 2)
+        else:
+            # torch's sdpa doesn't support GQA, so we're doing this
+            assert k.size(1) == v.size(1)
+            num_kv_heads = k.size(1)
+            num_q_heads = q.size(1)
+            if num_q_heads != num_kv_heads:
+                assert num_q_heads % num_kv_heads == 0
+                k = k.repeat_interleave(num_q_heads // num_kv_heads, dim=1, output_size=num_q_heads)
+                v = v.repeat_interleave(num_q_heads // num_kv_heads, dim=1, output_size=num_q_heads)
+
+            # Modify: MDM set causal to False.
+            return F.scaled_dot_product_attention(
+                q,
+                k,
+                v,
+                attn_mask=attn_mask,
+                dropout_p=dropout_p,
+                is_causal=False,
+            )
+
+    def attention(
+        self,
+        q: torch.Tensor,
+        k: torch.Tensor,
+        v: torch.Tensor,
+        attention_bias: Optional[torch.Tensor] = None,
+        layer_past: Optional[Tuple[torch.Tensor, torch.Tensor]] = None,
+        use_cache: bool = False,
+    ) -> Tuple[torch.Tensor, Optional[Tuple[torch.Tensor, torch.Tensor]]]:
+        B, T, C = q.size()  # batch size, sequence length, d_model
+        dtype = k.dtype
+
+        # Optionally apply layer norm to keys and queries.
+        if self.q_norm is not None and self.k_norm is not None:
+            q = self.q_norm(q).to(dtype=dtype)
+            k = self.k_norm(k).to(dtype=dtype)
+
+        # Move head forward to be next to the batch dim.
+        # shape: (B, nh, T, hs)
+        q = q.view(B, T, self.config.n_heads, C // self.config.n_heads).transpose(1, 2)
+        # shape: (B, n_kv_h, T, hs)
+        k = k.view(B, T, self.config.effective_n_kv_heads, C // self.config.n_heads).transpose(1, 2)
+        # shape: (B, n_kv_h, T, hs)
+        v = v.view(B, T, self.config.effective_n_kv_heads, C // self.config.n_heads).transpose(1, 2)
+
+        if layer_past is not None:
+            past_key, past_value = layer_past
+            k = torch.cat((past_key, k), dim=-2)
+            v = torch.cat((past_value, v), dim=-2)
+
+        present = (k, v) if use_cache else None
+        query_len, key_len = q.shape[-2], k.shape[-2]  # could be different if layer_past not None
+
+        if self.config.rope:
+            # Apply rotary embeddings.
+            q, k = self.rotary_emb(q, k)
+
+        if attention_bias is not None:
+            # Resize and cast attention bias.
+            # The current dtype of the attention bias might not match the dtype that the SDP attn function will
+            # run in if AMP is enabled, and this can be a problem if some tokens are masked out due to padding
+            # as down-casting the attention bias to the autocast precision will result in -infs, which will
+            # cause the SDP attn function to produce NaNs.
+            attention_bias = self._cast_attn_bias(
+                attention_bias[:, :, key_len - query_len : key_len, :key_len], dtype
+            )
+
+        # Get the attention scores.
+        # shape: (B, nh, T, hs)
+        att = self._scaled_dot_product_attention(
+            q,
+            k,
+            v,
+            attn_mask=attention_bias,
+            dropout_p=0.0 if not self.training else self.config.attention_dropout,
+            is_causal=False,
+        )
+
+        # Re-assemble all head outputs side-by-side.
+        att = att.transpose(1, 2).contiguous().view(B, T, C)
+
+        # Apply output projection.
+        return self.attn_out(att), present
+
+    @abstractmethod
+    def forward(
+        self,
+        x: torch.Tensor,
+        attention_bias: Optional[torch.FloatTensor] = None,
+        layer_past: Optional[Tuple[torch.Tensor, torch.Tensor]] = None,
+        use_cache: bool = False,
+    ) -> Tuple[torch.Tensor, Optional[Tuple[torch.Tensor, torch.Tensor]]]:
+        raise NotImplementedError
+
+    @classmethod
+    def build(cls, layer_id: int, config: ModelConfig, cache: BufferCache) -> LLaDABlock:
+        if config.block_type == BlockType.sequential:
+            return LLaDASequentialBlock(layer_id, config, cache)
+        elif config.block_type == BlockType.llama:
+            return LLaDALlamaBlock(layer_id, config, cache)
+        else:
+            raise NotImplementedError(f"Unknown block type: '{config.block_type}'")
+
+
+class LLaDASequentialBlock(LLaDABlock):
+    """
+    This is a typical transformer block where the output is computed as ``MLP(LN(x + Attention(LN(x))))``
+    (plus another skip connection).
+    """
+
+    def __init__(self, layer_id: int, config: ModelConfig, cache: BufferCache):
+        super().__init__(layer_id, config, cache)
+        # Layer norms.
+        self.attn_norm = LayerNorm.build(config)
+        self.ff_norm = LayerNorm.build(config)
+        # Attention input projection. Projects x -> (q, k, v)
+        head_dim = config.d_model // config.n_heads
+        self.fused_dims = (
+            config.d_model,
+            config.effective_n_kv_heads * head_dim,
+            config.effective_n_kv_heads * head_dim,
+        )
+        self.att_proj = nn.Linear(
+            config.d_model,
+            sum(self.fused_dims),
+            bias=config.include_bias | config.include_qkv_bias,
+            device=config.init_device,
+        )
+        # Feed-forward input projection.
+        self.ff_proj = nn.Linear(
+            config.d_model, self.hidden_size, bias=config.include_bias, device=config.init_device
+        )
+
+    def reset_parameters(self):
+        super().reset_parameters()
+        self.attn_norm.reset_parameters()
+        self.ff_norm.reset_parameters()
+        # NOTE: the standard deviation for these weights does not depend on the layer.
+        init_weights(
+            self.config,
+            self.att_proj,
+            d=self.config.d_model,
+            layer_id=None,
+            type_of_module=ModuleType.in_module,
+        )
+        init_weights(
+            self.config,
+            self.ff_proj,
+            d=self.config.d_model,
+            layer_id=None,
+            type_of_module=ModuleType.in_module,
+        )
+
+    def forward(
+        self,
+        x: torch.Tensor,
+        attention_bias: Optional[torch.Tensor] = None,
+        layer_past: Optional[Tuple[torch.Tensor, torch.Tensor]] = None,
+        use_cache: bool = False,
+    ) -> Tuple[torch.Tensor, Optional[Tuple[torch.Tensor, torch.Tensor]]]:
+        # Get query, key, value projections.
+        # shape:
+        #  - for regular attn q, k, v: (batch_size, seq_len, d_model)
+        #  - for multi-query attn q: (batch_size, seq_len, d_model)
+        #                      k, v: (batch_size, seq_len, d_model // n_heads)
+        #  - for group query attn q: (batch_size, seq_len, d_model)
+        #                      k, v: (batch_size, seq_len, d_model // n_kv_heads)
+        if self._activation_checkpoint_fn is not None:
+            q, k, v = self.att_proj(self._activation_checkpoint_fn(self.attn_norm, x)).split(
+                self.fused_dims, dim=-1
+            )
+        else:
+            q, k, v = self.att_proj(self.attn_norm(x)).split(self.fused_dims, dim=-1)
+
+        # Get attention scores.
+        if self._activation_checkpoint_fn is not None:
+            att, cache = self._activation_checkpoint_fn(  # type: ignore
+                self.attention, q, k, v, attention_bias, layer_past=layer_past, use_cache=use_cache
+            )
+        else:
+            att, cache = self.attention(
+                q, k, v, attention_bias, layer_past=layer_past, use_cache=use_cache
+            )
+
+        # Add attention scores.
+        # shape: (B, T, C)
+        x = x + self.dropout(att)
+
+        # Add feed-forward projection.
+        # shape: (batch_size, seq_len, d_model)
+        og_x = x
+        if self._activation_checkpoint_fn is not None:
+            x = self._activation_checkpoint_fn(self.ff_norm, x)  # type: ignore
+        else:
+            x = self.ff_norm(x)
+        x = self.ff_proj(x)
+        if self._activation_checkpoint_fn is not None:
+            x = self._activation_checkpoint_fn(self.act, x)  # type: ignore
+        else:
+            x = self.act(x)
+        x = self.ff_out(x)
+        x = self.dropout(x)
+        x = og_x + x
+
+        return x, cache
+
+
+class LLaDALlamaBlock(LLaDABlock):
+    """
+    This is a transformer block where the output is computed as ``MLP(LN(x + Attention(LN(x))))``
+    (plus another skip connection). This block is similar to `LLaDASequentialBlock`
+    but some operations have slightly different implementations to imitate the
+    behavior of Llama.
+    """
+
+    def __init__(self, layer_id: int, config: ModelConfig, cache: BufferCache):
+        super().__init__(layer_id, config, cache)
+        # Layer norms.
+        self.attn_norm = LayerNorm.build(config)
+        self.ff_norm = LayerNorm.build(config)
+        self.__cache = cache
+
+        # Attention input projection. Projects x -> (q, k, v)
+        head_dim = config.d_model // config.n_heads
+        q_proj_out_dim = config.d_model
+        k_proj_out_dim = config.effective_n_kv_heads * head_dim
+        v_proj_out_dim = config.effective_n_kv_heads * head_dim
+        self.q_proj = nn.Linear(
+            config.d_model,
+            q_proj_out_dim,
+            bias=config.include_bias | config.include_qkv_bias,
+            device=config.init_device,
+        )
+        self.k_proj = nn.Linear(
+            config.d_model,
+            k_proj_out_dim,
+            bias=config.include_bias | config.include_qkv_bias,
+            device=config.init_device,
+        )
+        self.v_proj = nn.Linear(
+            config.d_model,
+            v_proj_out_dim,
+            bias=config.include_bias | config.include_qkv_bias,
+            device=config.init_device,
+        )
+
+        # Feed-forward input projection.
+        self.ff_proj = nn.Linear(
+            config.d_model, self.hidden_size, bias=config.include_bias, device=config.init_device
+        )
+        # new add
+        self.up_proj = nn.Linear(
+            config.d_model, self.hidden_size, bias=config.include_bias, device=config.init_device
+        )
+
+    def reset_parameters(self):
+        super().reset_parameters()
+        self.attn_norm.reset_parameters()
+        self.ff_norm.reset_parameters()
+        # NOTE: the standard deviation for these weights does not depend on the layer.
+        init_weights(self.config, self.q_proj, d=self.config.d_model, layer_id=None)
+        init_weights(self.config, self.k_proj, d=self.config.d_model, layer_id=None)
+        init_weights(self.config, self.v_proj, d=self.config.d_model, layer_id=None)
+        init_weights(self.config, self.ff_proj, d=self.config.d_model, layer_id=None)
+        init_weights(self.config, self.up_proj, d=self.config.d_model, layer_id=None)  # new add
+
+    def forward(
+        self,
+        x: torch.Tensor,
+        attention_bias: Optional[torch.Tensor] = None,
+        layer_past: Optional[Tuple[torch.Tensor, torch.Tensor]] = None,
+        use_cache: bool = False,
+    ) -> Tuple[torch.Tensor, Optional[Tuple[torch.Tensor, torch.Tensor]]]:
+        # Get query, key, value projections.
+        # shape:
+        #  - for regular attn q, k, v: (batch_size, seq_len, d_model)
+        #  - for multi-query attn q: (batch_size, seq_len, d_model)
+        #                      k, v: (batch_size, seq_len, d_model // n_heads)
+        #  - for group query attn q: (batch_size, seq_len, d_model)
+        #                      k, v: (batch_size, seq_len, d_model // n_kv_heads)
+        x_normed = self.attn_norm(x)
+        q = self.q_proj(x_normed)
+        k = self.k_proj(x_normed)
+        v = self.v_proj(x_normed)
+
+        # Get attention scores.
+        if self._activation_checkpoint_fn is not None:
+            att, cache = self._activation_checkpoint_fn(  # type: ignore
+                self.attention, q, k, v, attention_bias, layer_past=layer_past, use_cache=use_cache
+            )
+        else:
+            att, cache = self.attention(
+                q, k, v, attention_bias, layer_past=layer_past, use_cache=use_cache
+            )
+
+        # Add attention scores.
+        # shape: (B, T, C)
+        x = x + self.dropout(att)
+
+        # Add feed-forward projection.
+        # shape: (batch_size, seq_len, d_model)
+        og_x = x
+        if self._activation_checkpoint_fn is not None:
+            x = self._activation_checkpoint_fn(self.ff_norm, x)  # type: ignore
+        else:
+            x = self.ff_norm(x)
+        x, x_up = self.ff_proj(x), self.up_proj(x)  # new add
+        if self._activation_checkpoint_fn is not None:
+            x = self._activation_checkpoint_fn(self.act, x)  # type: ignore
+        else:
+            x = self.act(x)
+        x = x * x_up  # new add
+        x = self.ff_out(x)
+        x = self.dropout(x)
+        x = og_x + x
+
+        return x, cache
+
+
+class LLaDAOutput(NamedTuple):
+    logits: torch.FloatTensor
+    """
+    A tensor of shape `(batch_size, seq_len, vocab_size)` representing the log probabilities
+    for the next token *before* normalization via (log) softmax.
+    """
+
+    attn_key_values: Optional[List[Tuple[torch.Tensor, torch.Tensor]]]
+    """
+    Attention keys and values from each block.
+    """
+
+    hidden_states: Optional[Tuple[torch.Tensor]]
+    """
+    Hidden states from each block.
+    """
+
+
+class LLaDAGenerateOutput(NamedTuple):
+    token_ids: torch.LongTensor
+    """
+    The generated token IDs, a tensor of shape `(batch_size, beam_size, max_steps)`.
+    These do *not* include the original input IDs.
+    """
+
+    scores: torch.FloatTensor
+    """
+    The scores of the generated sequences, a tensor of shape `(batch_size, beam_size)`.
+    """
+
+
+class LLaDABlockGroup(nn.ModuleList):
+    def __init__(
+        self, config: ModelConfig, layer_offset: int, modules: Optional[Iterable[nn.Module]] = None
+    ):
+        super().__init__(modules)
+        self.config = config
+        self.layer_offset = layer_offset
+        self.activation_checkpointing_strategy: Optional[ActivationCheckpointingStrategy] = None
+        self._activation_checkpoint_fn = activation_checkpoint_function(self.config)
+
+    def forward(
+        self,
+        x: torch.Tensor,
+        attention_bias: Optional[torch.FloatTensor] = None,
+        layers_past: Optional[List[Tuple[torch.Tensor, torch.Tensor]]] = None,
+        use_cache: bool = False,
+    ) -> Tuple[torch.Tensor, Optional[List[Tuple[torch.Tensor, torch.Tensor]]]]:
+        attn_key_values: Optional[List[Tuple[torch.Tensor, torch.Tensor]]] = (
+            [] if use_cache else None
+        )
+        for block_idx, block in enumerate(self):
+            layer_past = None if layers_past is None else layers_past[block_idx]
+            block_idx += self.layer_offset
+            if (
+                (
+                    self.activation_checkpointing_strategy
+                    == ActivationCheckpointingStrategy.whole_layer
+                )
+                or (
+                    self.activation_checkpointing_strategy
+                    == ActivationCheckpointingStrategy.one_in_two
+                    and block_idx % 2 == 0
+                )
+                or (
+                    self.activation_checkpointing_strategy
+                    == ActivationCheckpointingStrategy.one_in_three
+                    and block_idx % 3 == 0
+                )
+                or (
+                    self.activation_checkpointing_strategy
+                    == ActivationCheckpointingStrategy.one_in_four
+                    and block_idx % 4 == 0
+                )
+            ):
+                # shape: (batch_size, seq_len, d_model)
+                x, cache = self._activation_checkpoint_fn(  # type: ignore
+                    block,
+                    x,
+                    attention_bias=attention_bias,
+                    layer_past=layer_past,
+                    use_cache=use_cache,
+                )
+            else:
+                # shape: (batch_size, seq_len, d_model)
+                x, cache = block(
+                    x, attention_bias=attention_bias, layer_past=layer_past, use_cache=use_cache
+                )
+            if attn_key_values is not None:
+                assert cache is not None
+                attn_key_values.append(cache)
+        return x, attn_key_values
+
+    def reset_parameters(self):
+        for block in self:
+            block.reset_parameters()
+
+    def set_activation_checkpointing(self, strategy: Optional[ActivationCheckpointingStrategy]):
+        self.activation_checkpointing_strategy = strategy
+        for block in self:
+            block.set_activation_checkpointing(strategy)
+
+
+class LLaDAModel(nn.Module):
+    def __init__(self, config: ModelConfig, init_params: bool = True):
+        super().__init__()
+        self.config = config
+        self.__cache = BufferCache()
+
+        # Validate config.
+        if self.config.alibi and self.config.flash_attention:
+            raise Exception("ALiBi is currently not supported with FlashAttention")
+
+        if self.config.alibi and self.config.rope:
+            raise Exception("ALiBi and RoPE are mutually exclusive")
+
+        if (
+            self.config.embedding_size is not None
+            and self.config.embedding_size != self.config.vocab_size
+        ):
+            if self.config.embedding_size < self.config.vocab_size:
+                raise Exception("embedding size should be at least as big as vocab size")
+            elif self.config.embedding_size % 128 != 0:
+                import warnings
+
+                warnings.warn(
+                    "Embedding size is not a multiple of 128! This could hurt throughput performance.",
+                    UserWarning,
+                )
+
+        self.activation_checkpointing_strategy: Optional[ActivationCheckpointingStrategy] = None
+        self._activation_checkpoint_fn: Callable = activation_checkpoint_function(self.config)
+
+        if not (
+            0 < self.config.block_group_size <= self.config.n_layers
+            and self.config.n_layers % self.config.block_group_size == 0
+        ):
+            raise Exception("n layers must be divisible by block group size")
+
+        torch.backends.cuda.enable_flash_sdp(True)
+        torch.backends.cuda.enable_mem_efficient_sdp(
+            False
+        )  # this is super slow so make sure torch won't use it
+
+        self.transformer = nn.ModuleDict(
+            dict(
+                wte=nn.Embedding(
+                    config.embedding_size or config.vocab_size,
+                    config.d_model,
+                    device=config.init_device,
+                ),
+                emb_drop=Dropout(config.embedding_dropout),
+                ln_f=LayerNorm.build(config),
+            )
+        )
+
+        blocks = [LLaDABlock.build(i, config, self.__cache) for i in range(config.n_layers)]
+        if self.config.block_group_size > 1:
+            block_groups = [
+                LLaDABlockGroup(config, i, blocks[i : i + config.block_group_size])
+                for i in range(0, config.n_layers, config.block_group_size)
+            ]
+            self.transformer.update({"block_groups": nn.ModuleList(block_groups)})
+        else:
+            self.transformer.update({"blocks": nn.ModuleList(blocks)})
+
+        if not (self.config.alibi or self.config.rope):
+            self.transformer.update(
+                {
+                    "wpe": nn.Embedding(
+                        config.max_sequence_length, config.d_model, device=config.init_device
+                    )
+                }
+            )
+        if not config.weight_tying:
+            self.transformer.update(
+                {
+                    "ff_out": nn.Linear(
+                        config.d_model,
+                        config.embedding_size or config.vocab_size,
+                        bias=config.include_bias,
+                        device=config.init_device,
+                    )
+                }
+            )
+        # When `init_device="meta"` FSDP will call `reset_parameters()` to initialize weights.
+        if init_params and self.config.init_device != "meta":
+            self.reset_parameters()
+        self.__num_fwd_flops: Optional[int] = None
+
+        # Warm up cache.
+        if self.config.alibi:
+            get_causal_attention_bias(
+                self.__cache, config.max_sequence_length, _non_meta_init_device(config)
+            )
+            self.get_alibi_attention_bias(config.max_sequence_length, _non_meta_init_device(config))
+
+    def set_activation_checkpointing(self, strategy: Optional[ActivationCheckpointingStrategy]):
+        self.activation_checkpointing_strategy = strategy
+        if self.config.block_group_size != 1:
+            for block_group in self.transformer.block_groups:
+                block_group.set_activation_checkpointing(strategy)
+        else:
+            for block in self.transformer.blocks:
+                block.set_activation_checkpointing(strategy)
+
+    @property
+    def device(self) -> torch.device:
+        device: torch.device = self.transformer.wte.weight.device  # type: ignore
+        if device.type == "meta":
+            return _non_meta_init_device(self.config)
+        else:
+            return device
+
+    def reset_parameters(self):
+        log.info("Initializing model parameters...")
+        # Top-level embeddings / linear layers.
+        init_weights(
+            self.config,
+            self.transformer.wte,  # type: ignore
+            std_factor=(0.5 * math.sqrt(self.config.d_model)) if self.config.scale_logits else 1.0,
+            type_of_module=ModuleType.emb,
+        )
+        if hasattr(self.transformer, "wpe"):
+            init_weights(self.config, self.transformer.wpe, type_of_module=ModuleType.emb)  # type: ignore
+
+        # Top-level layer norm.
+        self.transformer.ln_f.reset_parameters()  # type: ignore
+
+        # Output weights.
+        if hasattr(self.transformer, "ff_out"):
+            init_weights(self.config, self.transformer.ff_out, type_of_module=ModuleType.final_out)  # type: ignore
+
+        # Let the blocks handle themselves.
+        if self.config.block_group_size == 1:
+            for block in self.transformer.blocks:
+                block.reset_parameters()
+        else:
+            for block_group in self.transformer.block_groups:
+                block_group.reset_parameters()
+
+    def get_alibi_attention_bias(self, seq_len: int, device: torch.device) -> torch.Tensor:
+        if (
+            alibi_bias := self.__cache.get("alibi_attention_bias")
+        ) is not None and alibi_bias.shape[-1] >= seq_len:
+            if alibi_bias.device != device:
+                alibi_bias = alibi_bias.to(device)
+                self.__cache["alibi_attention_bias"] = alibi_bias
+            return alibi_bias
+        with torch.autocast(device.type, enabled=False):
+            alibi_bias = alibi_attention_bias(seq_len, self.config, device)
+        self.__cache["alibi_attention_bias"] = alibi_bias
+        return alibi_bias
+
+    def get_bidirectional_attention_bias(self, seq_len: int, device: torch.device) -> torch.Tensor:
+        if (
+            bidirectional_bias := self.__cache.get("bidirectional_attention_bias")
+        ) is not None and bidirectional_bias.shape[-1] >= seq_len:
+            if bidirectional_bias.device != device:
+                bidirectional_bias = bidirectional_bias.to(device)
+                self.__cache["bidirectional_attention_bias"] = bidirectional_bias
+            return bidirectional_bias
+        with torch.autocast(device.type, enabled=False):
+            bidirectional_bias = torch.zeros(
+                (1, 1, seq_len, seq_len), device=device, dtype=torch.float
+            )
+        self.__cache["bidirectional_attention_bias"] = bidirectional_bias
+        return bidirectional_bias
+
+    def forward(
+        self,
+        input_ids: torch.LongTensor,
+        input_embeddings: Optional[torch.FloatTensor] = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        attention_bias: Optional[torch.Tensor] = None,
+        past_key_values: Optional[Sequence[Tuple[torch.Tensor, torch.Tensor]]] = None,
+        use_cache: bool = False,
+        last_logits_only: bool = False,
+        output_hidden_states: Optional[bool] = None,
+    ) -> LLaDAOutput:
+        """
+        :param input_ids: A tensor of shape `(batch_size, seq_len)`.
+        :param input_embeddings: A tensor of shape `(batch_size, seq_len, d_model)` with input
+            embeddings. When provided, it is treated as the output of the input embedding layer.
+        :param attention_mask: A tensor of shape `(batch_size, seq_len)` that indicates
+            which input IDs are masked. A `1` value in the mask means that
+            the corresponding input ID should *not* be ignored. A `0` means
+            that the corresponding input ID is masked.
+
+            This has the same meaning as the `attention_mask` in HuggingFace's `transformers`
+            library.
+        :param attention_bias: A tensor of shape `(batch_size, 1, seq_len, seq_len)`,
+            `(1, 1, seq_len, seq_len)`, or `(seq_len, seq_len)`. This is used
+            to introduce causal or other biases.
+
+            If the tensor is a bool or byte tensor, a `True` or `1` at `attention_bias[:, :, i, j]`
+            indicates that the i-th element in the sequence is allowed to attend to the j-th
+            element in the sequence.
+
+            If the tensor is a float tensor, it will just be added to the attention
+            scores before the softmax.
+
+            The default is causal, which corresponds to a lower-diagonal byte matrix of ones.
+        :param past_key_values: Pre-computed keys and values for each attention block.
+            Can be used to speed up sequential decoding. The `input_ids` which have
+            their past given to this model should not be passed as `input_ids` as they have already been computed.
+        :param use_cache: If `True`, return key and value tensors for each block.
+        :param last_logits_only: If `True`, only compute the logits for the last token of each sequence.
+            This can speed up decoding when you only care about the next token.
+        """
+        # Add Basic MDM Model config check
+        assert not self.config.alibi, "Alibi length extrapolation is not supported for MDM."
+        assert self.config.rope, "Rope must be used in Llama-Encoder for MDM."
+        assert past_key_values is None and not use_cache, "The kvcache is not suppotred for MDM."
+
+        output_hidden_states = output_hidden_states if output_hidden_states is not None else False
+
+        if past_key_values:
+            assert len(past_key_values) == self.config.n_layers
+
+        batch_size, seq_len = (
+            input_ids.size() if input_embeddings is None else input_embeddings.size()[:2]
+        )
+        if past_key_values is None:
+            past_length = 0
+        else:
+            past_length = past_key_values[0][0].size(-2)
+
+        # Get embeddings of input.
+        # shape: (batch_size, seq_len, d_model)
+        x = self.transformer.wte(input_ids) if input_embeddings is None else input_embeddings  # type: ignore
+
+        if self.config.input_emb_norm:
+            x = x * (self.config.d_model**0.5)
+
+        if not (self.config.alibi or self.config.rope):
+            # Get positional embeddings.
+            # shape: (1, seq_len)
+            pos = torch.arange(
+                past_length, past_length + seq_len, dtype=torch.long, device=x.device
+            ).unsqueeze(0)
+            # shape: (1, seq_len, d_model)
+            pos_emb = self.transformer.wpe(pos)  # type: ignore
+            x = pos_emb + x
+
+        # Add input + positional embeddings and apply dropout.
+        # shape: (batch_size, seq_len, d_model)
+        x = self.transformer.emb_drop(x)  # type: ignore
+
+        # Transform the attention mask into what the blocks expect.
+        if attention_mask is not None and 0.0 in attention_mask:
+            # shape: (batch_size, 1, 1, seq_len)
+            attention_mask = attention_mask.to(dtype=torch.float).view(batch_size, -1)[
+                :, None, None, :
+            ]
+            attention_mask = (1.0 - attention_mask) * torch.finfo(attention_mask.dtype).min
+        else:
+            attention_mask = None
+
+        # Merge attention mask with attention bias.
+        if (
+            attention_bias is not None
+            or attention_mask is not None
+            or self.config.alibi
+            # NOTE (epwalsh): we need to initialize the attn bias in order for attn to work properly
+            # with key+value cache. Otherwise `F.scaled_dot_product_attention()` doesn't seem to compute
+            # scores correctly.
+            or past_key_values is not None
+        ):
+            if attention_bias is None and self.config.alibi:
+                attention_bias = get_causal_attention_bias(
+                    self.__cache, past_length + seq_len, x.device
+                ) + self.get_alibi_attention_bias(past_length + seq_len, x.device)
+            elif attention_bias is None:
+                attention_bias = self.get_bidirectional_attention_bias(
+                    past_length + seq_len, x.device
+                )
+            elif attention_bias.dtype in (torch.int8, torch.bool):
+                attention_bias = attention_bias.to(dtype=torch.float)
+                attention_bias.masked_fill_(
+                    attention_bias == 0.0, torch.finfo(attention_bias.dtype).min
+                )
+
+            # Transform to the right shape and data type.
+            mask_len = seq_len
+            if attention_mask is not None:
+                mask_len = attention_mask.shape[-1]
+            elif past_key_values is not None:
+                mask_len = past_key_values[0][0].shape[-2] + seq_len
+            attention_bias = attention_bias[:, :, :mask_len, :mask_len].to(dtype=torch.float)
+
+            # Add in the masking bias.
+            if attention_mask is not None:
+                attention_bias = attention_bias + attention_mask
+                # Might get -infs after adding attention mask, since dtype.min + dtype.min = -inf.
+                # `F.scaled_dot_product_attention()` doesn't handle -inf like you'd expect, instead
+                # it can produce NaNs.
+                ensure_finite_(attention_bias, check_neg_inf=True, check_pos_inf=False)
+
+        attn_key_values: Optional[List[Tuple[torch.Tensor, torch.Tensor]]] = (
+            [] if use_cache else None
+        )
+
+        # decoder layers
+        all_hidden_states = []
+
+        # Apply blocks one-by-one.
+        if self.config.block_group_size == 1:
+            for block_idx, block in enumerate(self.transformer.blocks):
+                if output_hidden_states:
+                    # add hidden states
+                    all_hidden_states.append(x)
+
+                layer_past = None if past_key_values is None else past_key_values[block_idx]
+                if (
+                    (
+                        self.activation_checkpointing_strategy
+                        == ActivationCheckpointingStrategy.whole_layer
+                    )
+                    or (
+                        self.activation_checkpointing_strategy
+                        == ActivationCheckpointingStrategy.one_in_two
+                        and block_idx % 2 == 0
+                    )
+                    or (
+                        self.activation_checkpointing_strategy
+                        == ActivationCheckpointingStrategy.one_in_three
+                        and block_idx % 3 == 0
+                    )
+                    or (
+                        self.activation_checkpointing_strategy
+                        == ActivationCheckpointingStrategy.one_in_four
+                        and block_idx % 4 == 0
+                    )
+                ):
+                    # shape: (batch_size, seq_len, d_model)
+                    x, cache = self._activation_checkpoint_fn(
+                        block,
+                        x,
+                        attention_bias=attention_bias,
+                        layer_past=layer_past,
+                        use_cache=use_cache,
+                    )
+                else:
+                    # shape: (batch_size, seq_len, d_model)
+                    x, cache = block(
+                        x, attention_bias=attention_bias, layer_past=layer_past, use_cache=use_cache
+                    )
+                if attn_key_values is not None:
+                    assert cache is not None
+                    attn_key_values.append(cache)
+        else:
+            for group_idx, block_group in enumerate(self.transformer.block_groups):
+                if output_hidden_states:
+                    # add hidden states
+                    all_hidden_states.append(x)
+
+                layers_past = (
+                    None
+                    if past_key_values is None
+                    else past_key_values[
+                        group_idx
+                        * self.config.block_group_size : (group_idx + 1)
+                        * self.config.block_group_size
+                    ]
+                )
+                x, cache = block_group(
+                    x, attention_bias=attention_bias, layers_past=layers_past, use_cache=use_cache
+                )
+                if attn_key_values is not None:
+                    assert cache is not None
+                    attn_key_values.extend(cache)
+
+        if last_logits_only:
+            # shape: (batch_size, 1, d_model)
+            x = x[:, -1, :].unsqueeze(1)
+
+        # Apply final layer norm.
+        # shape: (batch_size, seq_len or 1, d_model)
+        x = self.transformer.ln_f(x)  # type: ignore
+        if output_hidden_states:
+            # add final hidden state post-final-layernorm, following HuggingFace's convention
+            all_hidden_states.append(x)
+
+        # Get logits.
+        # shape: (batch_size, seq_len or 1, vocab_size)
+        if self.config.weight_tying:
+            logits = F.linear(x, self.transformer.wte.weight, None)  # type: ignore
+        else:
+            logits = self.transformer.ff_out(x)  # type: ignore
+        if self.config.scale_logits:
+            logits.mul_(1 / math.sqrt(self.config.d_model))
+
+        return LLaDAOutput(logits=logits, attn_key_values=attn_key_values, hidden_states=tuple(all_hidden_states) if output_hidden_states else None)  # type: ignore[arg-type]
+
+
+def create_model_config_from_pretrained_config(config: LLaDAConfig):
+    """
+    Utility function
+    """
+
+    kwargs = {}
+    for field in fields(ModelConfig):
+        kwargs[field.name] = getattr(config, field.name)
+
+    model_config = ModelConfig(**kwargs)
+    return model_config
+
+
+class LLaDAModelLM(PreTrainedModel):
+    """
+    Extremely barebones HF model wrapper.
+    """
+
+    config_class = LLaDAConfig
+    base_model_prefix = "model"
+    _no_split_modules = ["LLaDABlock", "LLaDASequentialBlock", "LLaDALlamaBlock"]
+
+    def __init__(
+        self, config: LLaDAConfig, model: Optional[LLaDAModel] = None, init_params: bool = False
+    ):
+        super().__init__(config)
+
+        if not model:
+            model_config = create_model_config_from_pretrained_config(config)
+            # Initialize model (always on CPU to start with so we don't run out of GPU memory).
+            model_config.init_device = "cpu"
+            self.model = LLaDAModel(model_config, init_params=init_params)
+        else:
+            self.model = model
+
+    def forward(
+        self,
+        input_ids: torch.LongTensor = None,
+        inputs_embeds: Optional[torch.FloatTensor] = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        attention_bias: Optional[torch.Tensor] = None,
+        past_key_values: Optional[List[torch.FloatTensor]] = None,
+        labels: Optional[torch.LongTensor] = None,
+        use_cache: Optional[bool] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+        cache_position: Optional[
+            Cache
+        ] = None,  # This is a hack mitigation of an issue in transformers `4.39.x`
+    ) -> Union[Tuple, CausalLMOutputWithPast]:
+        if use_cache is None:
+            use_cache = self.config.use_cache
+
+        if output_attentions:
+            raise ValueError("output_attentions is not yet supported in LLaDA")
+
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+        # decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)
+        outputs = self.model.forward(
+            input_ids=input_ids,
+            input_embeddings=inputs_embeds,
+            attention_mask=attention_mask,
+            attention_bias=attention_bias,
+            past_key_values=past_key_values,
+            use_cache=use_cache,
+            output_hidden_states=output_hidden_states,
+        )
+
+        logits = outputs.logits
+        hidden_states = outputs.hidden_states
+
+        loss = None
+        if labels is not None:
+            import warnings
+
+            warnings.warn("Note that for LLaDA, you cannot calculate the loss here.", UserWarning)
+        if not return_dict:
+            output = (logits,) + outputs[1:]
+            return (loss,) + output if loss is not None else output
+
+        return CausalLMOutputWithPast(
+            logits=logits,
+            past_key_values=outputs.attn_key_values,
+            hidden_states=hidden_states,
+        )
+
+    def can_generate(self) -> bool:
+        return True
+
+    def prepare_inputs_for_generation(
+        self, input_ids: torch.LongTensor, past_key_values: Optional[List[Tuple]] = None, **kwargs
+    ):
+        if past_key_values:
+            # This is because we want the model to only process the last generated token.
+            input_ids = input_ids[:, -1:]
+        model_inputs = {"input_ids": input_ids, "past_key_values": past_key_values}
+
+        model_inputs.update(kwargs)
+        model_inputs["use_cache"] = kwargs.pop("use_cache", self.config.use_cache)
+        return model_inputs
+
+    # TODO: these are required to make the implementation complete.
+    # def resize_position_embeddings(self, new_num_position_embeddings: int):
+    #     pass
+    #
+    # def get_position_embeddings(self) -> Union[nn.Embedding, Tuple[nn.Embedding]]:
+    #     pass
+    #
+    # def _reorder_cache(self, past_key_values, beam_idx):
+    #     pass
+
+    def get_input_embeddings(self) -> torch.nn.Module:
+        return self.model.transformer.wte
+
+    def set_input_embeddings(self, value: torch.nn.Module):
+        self.model.transformer.wte = value
+
+    def get_output_embeddings(self):
+        if self.config.weight_tying:
+            return self.model.transformer.wte
+        else:
+            return self.model.transformer.ff_out
+
+    def set_output_embeddings(self, value: torch.nn.Module):
+        if self.config.weight_tying:
+            self.model.transformer.wte = value
+        else:
+            self.model.transformer.ff_out = value
+
+    def tie_weights(self):
+        if self.config.weight_tying:
+            self.model.transformer.ff_out = self.model.transformer.wte
+
+
+# Register the model so that it is available for transformer pipelines, auto-loading, etc.
+AutoModel.register(LLaDAConfig, LLaDAModelLM)