Delete model.py

model.py

@@ -1,1824 +0,0 @@
-"""
-Adapted from
-[MosaiclML](https://github.com/mosaicml/examples.git) and
-[minGPT](https://github.com/karpathy/minGPT.git)
-"""
-
-from __future__ import annotations
-
-import logging
-import math
-import sys
-from abc import abstractmethod
-from collections import defaultdict
-from functools import partial
-from typing import (
-    Callable,
-    Dict,
-    Iterable,
-    List,
-    NamedTuple,
-    Optional,
-    Sequence,
-    Set,
-    Tuple,
-    cast,
-)
-
-import torch
-import torch.backends.cuda
-import torch.nn as nn
-import torch.nn.functional as F
-from torch import einsum
-
-from transformers.modeling_outputs import BaseModelOutputWithPast
-
-from .aliases import PathOrStr
-from .beam_search import BeamSearch, Constraint, FinalSequenceScorer, Sampler
-from .config import (
-    ActivationCheckpointingStrategy,
-    ActivationType,
-    BlockType,
-    CheckpointType,
-    FSDPWrapStrategy,
-    LayerNormType,
-    ModelConfig,
-)
-from .exceptions import OLMoConfigurationError
-from .initialization import ModuleType, init_weights
-from .torch_util import ensure_finite_
-
-import copy 
-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",
-    "RotaryEmbedding",
-    "Activation",
-    "GELU",
-    "ReLU",
-    "SwiGLU",
-    "BitLinear158",
-    "OLMoBlock",
-    "OLMoSequentialBlock",
-    "OLMoParallelBlock",
-    "OLMo",
-    "OLMoOutput",
-    "OLMoGenerateOutput",
-]
-
-
-log = logging.getLogger(__name__)
-
-
-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)
-        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=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 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.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 / (10000 ** (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.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 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
-
-def activation_quant(x):
-    """Per−token quantization to 8 bits. No grouping is needed for quantization.
-    Args:
-    x: an activation tensor with shape [n, d]
-    Returns:
-    y: a quantized activation tensor with shape [n, d]
-    """
-    scale = 127.0 / x.abs().max(dim=-1, keepdim=True).values.clamp_(min=1e-5)
-    y = (x * scale).round().clamp_(-128, 127) / scale
-    return y
-
-def weight_quant(w):
-    """Per−tensor quantization to 1.58 bits. No grouping is needed for quantization.
-    Args:
-    w: a weight tensor with shape [d, k]
-    Returns:
-    u: a quantized weight with shape [d, k]
-    """
-    scale = 1.0 / w.abs().mean().clamp_(min=1e-5)
-    u = (w * scale).round().clamp_(-1, 1) / scale
-    return u
-
-def activation_norm_quant(x):
-    """ 
-    same as activation_quant  definition - but returning y and scale seperately 
-    Args:
-    x: an activation tensor with shape [n, d]
-    Returns:
-    y: a quantized activation tensor with shape [n, d]
-    scale: a scalar for dequantization with shape [1]
-    """
-    scale = 127.0 / x.abs().max(dim=-1, keepdim=True).values.clamp_(min=1e-5)
-    y = (x * scale).round().clamp_(-128, 127) 
-    return y, scale
-
-def gemm_lowbit_kernel(x, w): 
-    y = F.linear(x, w)
-    return y
-
-class BitLinear158(nn.Linear):
-    """
-    This is only for training, and kernel optimization is needed for efficiency.
-    """
-    def __init__(self, in_features: int, out_features: int, bias: bool = True,
-                 device=None, dtype=None, config=None):
-        super().__init__(in_features, out_features, bias, device, dtype)
-        self.norm = RMSLayerNorm(config, elementwise_affine=False)
-
-    def forward(self, x):
-        """
-        Args:
-        x: an input tensor with shape [n, d]
-        Returns:
-        y: an output tensor with shape [n, d]
-        """
-        w = self.weight  # a weight tensor with shape [d, k]
-        x_norm = self.norm(x)
-        # Atrick for implementing Straight−Through−Estimator (STE) using detach()
-        x_quant = x_norm + (activation_quant(x_norm) - x_norm).detach()
-        w_quant = w + (weight_quant(w) - w).detach()
-        y = F.linear(x_quant, w_quant)
-        return y
-
-class BitLinear158_inference(nn.Linear):
-    """
-    Use quantized weights for inference .
-    """
-    def __init__(self, in_features: int, out_features: int, bias: bool = True,
-                 device=None, dtype=None, config=None):
-        super().__init__(in_features, out_features, bias, device, dtype)
-        self.norm = RMSLayerNorm(config, elementwise_affine=False)
-        self.weight_scale = nn.Parameter(torch.ones(1))
-
-    def forward(self, x):
-        """
-        Args:
-        x: an input tensor with shape [n, d]
-        Returns:
-        y: an output tensor with shape [n, d]
-        """
-        w = self.weight # a 1.58−bit weight tensor with shape [d, k]
-        w_scale = self.weight_scale # a full−precision weight scale tensor with shape [1]
-        x_norm = self.norm(x)
-        x_quant, x_scale = activation_norm_quant(x_norm)
-        y = gemm_lowbit_kernel(x_quant, w) / w_scale / x_scale
-        return y
-
-
-class OLMoBlock(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
-
-        Linear = BitLinear158_inference if config.inference_mode else BitLinear158 if config.ternary else nn.Linear
-
-        # 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 if config.multi_query_attention else None,
-                elementwise_affine=config.attention_layer_norm_with_affine,
-            )
-            self.q_norm = LayerNormBase.build(config, elementwise_affine=config.attention_layer_norm_with_affine)
-
-        # Make sure QKV clip coefficient is positive, otherwise it's not well-defined.
-        if config.clip_qkv is not None:
-            assert config.clip_qkv > 0
-
-        # Activation function.
-        self.act = Activation.build(config)
-        assert (self.act.output_multiplier * self.hidden_size) % 1 == 0
-
-        # Attention output projection.
-        self.attn_out = Linear(
-            config.d_model, config.d_model, bias=config.include_bias, device=config.init_device,
-            config=config
-        )
-
-        # Feed-forward output projection.
-        self.ff_out = Linear(
-            int(self.act.output_multiplier * self.hidden_size),
-            config.d_model,
-            bias=config.include_bias,
-            device=config.init_device,
-            config=config,
-        )
-        self.ff_out._is_residual = True  # type: ignore
-
-        # Rotary embeddings.
-        if self.config.rope:
-            self.rotary_emb = RotaryEmbedding(config, self.__cache)
-
-    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.
-
-        This method is based on PyTorch's `scaled_dot_product_attention`.
-        """
-        return F.scaled_dot_product_attention(
-            q,
-            k,
-            v,
-            attn_mask=attn_mask,
-            dropout_p=dropout_p,
-            is_causal=is_causal,
-        )
-
-    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)
-        if self.config.multi_query_attention:
-            # shape: (B, 1, T, hs)
-            k = k.view(B, T, 1, C // self.config.n_heads).transpose(1, 2)
-            # shape: (B, 1, T, hs)
-            v = v.view(B, T, 1, C // self.config.n_heads).transpose(1, 2)
-        else:
-            # shape: (B, nh, T, hs)
-            k = k.view(B, T, self.config.n_heads, C // self.config.n_heads).transpose(1, 2)
-            # shape: (B, nh, T, hs)
-            v = v.view(B, T, self.config.n_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=attention_bias is None,
-        )
-
-        # 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) -> OLMoBlock:
-        if config.block_type == BlockType.sequential:
-            return OLMoSequentialBlock(layer_id, config, cache)
-        elif config.block_type == BlockType.parallel:
-            return OLMoParallelBlock(layer_id, config, cache)
-        elif config.block_type == BlockType.llama:
-            return OLMoLlamaBlock(layer_id, config, cache)
-        else:
-            raise NotImplementedError(f"Unknown block type: '{config.block_type}'")
-
-
-class OLMoSequentialBlock(OLMoBlock):
-    """
-    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)
-        Linear = BitLinear158_inference if config.inference_mode else BitLinear158 if config.ternary else nn.Linear
-        # Attention input projection. Projects x -> (q, k, v)
-        if config.multi_query_attention:
-            self.fused_dims = (config.d_model, config.d_model // config.n_heads, config.d_model // config.n_heads)
-        else:
-            self.fused_dims = (config.d_model, config.d_model, config.d_model)
-        self.att_proj = Linear(
-            config.d_model, sum(self.fused_dims), bias=config.include_bias, device=config.init_device,
-            config=config
-        )
-        # Feed-forward input projection.
-        self.ff_proj = Linear(
-            config.d_model, self.hidden_size, bias=config.include_bias, device=config.init_device,
-            config=config
-        )
-
-    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)
-        if self._activation_checkpoint_fn is not None:
-            qkv = self.att_proj(self._activation_checkpoint_fn(self.attn_norm, x))
-        else:
-            qkv = self.att_proj(self.attn_norm(x))
-
-        if self.config.clip_qkv is not None:
-            qkv.clamp_(min=-self.config.clip_qkv, max=self.config.clip_qkv)
-
-        q, k, v = qkv.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 OLMoParallelBlock(OLMoBlock):
-    """
-    This is a transformer block where the output is computed as ``MLP(LN(x)) + Attention(LN(x))``
-    as in the PaLM architecture, as opposed to the typical ``MLP(LN(x + Attention(LN(x))))``
-    as in :class:`OLMoSequentialBlock` (ignoring some skip connections).
-
-    The decoupling of the MLP and Attention functions allow us to fuse the separate input projections
-    into a single linear layer to increase throughput. In this configuration it's also straight-forward
-    to fuse the output projections, but we found that didn't help.
-    """
-
-    def __init__(self, layer_id: int, config: ModelConfig, cache: BufferCache):
-        super().__init__(layer_id, config, cache)
-        self.norm = LayerNorm.build(config)
-        Linear = BitLinear158_inference if config.inference_mode else BitLinear158 if config.ternary else nn.Linear
-
-        # Fused attention and feed-forward projection.
-        # NOTE: we could also fuse the attention and feed-forward output projections but we
-        # found that didn't help, possibly because of the overhead of joining the `att` and
-        # `ff` activations together. See https://github.com/allenai/LLM/pull/79 for details.
-        if config.multi_query_attention:
-            self.fused_dims = (
-                config.d_model,
-                config.d_model // config.n_heads,
-                config.d_model // config.n_heads,
-                self.hidden_size,
-            )
-        else:
-            self.fused_dims = (config.d_model, config.d_model, config.d_model, self.hidden_size)
-        self.fused_attn_ff_proj = Linear(
-            config.d_model, sum(self.fused_dims), bias=config.include_bias, device=config.init_device,
-            config=config
-        )
-
-    def reset_parameters(self):
-        super().reset_parameters()
-        self.norm.reset_parameters()
-        # NOTE: the standard deviation for these weights does not depend on the layer.
-        init_weights(
-            self.config,
-            self.fused_attn_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, and feed-forward projections.
-        # shape of q, k, v:
-        #  - 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)
-        # shape of ff:      (batch_size, seq_len, hidden_size)
-        if self._activation_checkpoint_fn is not None:
-            q, k, v, ff = self.fused_attn_ff_proj(self._activation_checkpoint_fn(self.norm, x)).split(
-                self.fused_dims, dim=-1
-            )
-        else:
-            q, k, v, ff = self.fused_attn_ff_proj(self.norm(x)).split(self.fused_dims, dim=-1)
-
-        if self.config.clip_qkv is not None:
-            q.clamp_(min=-self.config.clip_qkv, max=self.config.clip_qkv)
-            k.clamp_(min=-self.config.clip_qkv, max=self.config.clip_qkv)
-            v.clamp_(min=-self.config.clip_qkv, max=self.config.clip_qkv)
-
-        # Get attention scores.
-        # shape: (B, T, C)
-        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)
-
-        # Apply output projections (and activation function) and sum the results.
-        # We keep these projections separate because we found that we got better throughput this
-        # way compared to fusing them.
-        if self._activation_checkpoint_fn is not None:
-            return (
-                x + self.dropout(self.ff_out(self._activation_checkpoint_fn(self.act, ff))) + self.dropout(att),
-                cache,
-            )
-        else:
-            return (
-                x + self.dropout(self.ff_out(self.act(ff))) + self.dropout(att),
-                cache,
-            )
-
-
-class OLMoLlamaBlock(OLMoBlock):
-    """
-    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 `OLMoSequentialBlock`
-    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
-        Linear = BitLinear158_inference if config.inference_mode else BitLinear158 if config.ternary else nn.Linear
-
-
-        # Attention input projection. Projects x -> (q, k, v)
-        if config.multi_query_attention:
-            q_proj_out_dim = config.d_model
-            k_proj_out_dim = config.d_model // config.n_heads
-            v_proj_out_dim = config.d_model // config.n_heads
-        else:
-            q_proj_out_dim = config.d_model
-            k_proj_out_dim = config.d_model
-            v_proj_out_dim = config.d_model
-        self.q_proj = Linear(
-            config.d_model, q_proj_out_dim, bias=config.include_bias, device=config.init_device,
-            config=config
-        )
-        self.k_proj = Linear(
-            config.d_model, k_proj_out_dim, bias=config.include_bias, device=config.init_device,
-            config=config
-        )
-        self.v_proj = Linear(
-            config.d_model, v_proj_out_dim, bias=config.include_bias, device=config.init_device,
-            config=config
-        )
-
-        # Feed-forward input projection.
-        self.ff_proj = Linear(
-            config.d_model, self.hidden_size, bias=config.include_bias, device=config.init_device,
-            config=config
-        )
-
-    def reset_parameters(self):
-        super().reset_parameters()
-        if self.attn_norm:
-            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)
-
-    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:
-        attn_weights = torch.matmul(q, k.transpose(-2, -1)) / math.sqrt(q.size(-1))
-
-        if is_causal:
-            assert attn_mask is None
-
-            query_len, key_len = q.shape[-2], k.shape[-2]  # could be different if layer_past not None
-            attn_bias = get_causal_attention_bias(self.__cache, key_len, q.device)[:, :, :query_len, :key_len]
-        elif attn_mask is not None:
-            attn_bias = attn_mask.to(q.dtype)
-        else:
-            attn_bias = torch.zeros_like(attn_weights)
-
-        attn_weights += attn_bias
-        attn_weights = nn.functional.softmax(attn_weights, dim=-1).to(q.dtype)
-        attn_weights = nn.functional.dropout(attn_weights, p=dropout_p)
-        return torch.matmul(attn_weights, v)
-
-    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)
-        x_normed = self.attn_norm(x)
-        q = self.q_proj(x_normed)
-        k = self.k_proj(x_normed)
-        v = self.v_proj(x_normed)
-
-        if self.config.clip_qkv is not None:
-            q.clamp_(min=-self.config.clip_qkv, max=self.config.clip_qkv)
-            k.clamp_(min=-self.config.clip_qkv, max=self.config.clip_qkv)
-            v.clamp_(min=-self.config.clip_qkv, max=self.config.clip_qkv)
-
-        # Get attention scores.
-        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 OLMoOutput(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 OLMoGenerateOutput(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 OLMoBlockGroup(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 OLMo(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 OLMoConfigurationError("ALiBi is currently not supported with FlashAttention")
-
-        if self.config.alibi and self.config.rope:
-            raise OLMoConfigurationError("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 OLMoConfigurationError("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 OLMoConfigurationError("n layers must be divisible by block group size")
-
-        torch.backends.cuda.enable_flash_sdp(self.config.flash_attention)
-        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 = [OLMoBlock.build(i, config, self.__cache) for i in range(config.n_layers)]
-        if self.config.block_group_size > 1:
-            block_groups = [
-                OLMoBlockGroup(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 embed_tokens(self, input_ids):
-        return self.transformer.wte(input_ids)
-
-    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 forward(
-        self,
-        input_ids: torch.LongTensor,
-        inputs_embeds: 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,
-    ) -> OLMoOutput:
-        """
-        :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.
-        """
-        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 inputs_embeds is None else inputs_embeds.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 inputs_embeds is None else inputs_embeds  # type: ignore
-
-        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:
-            # 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
-
-        # 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 = get_causal_attention_bias(self.__cache, 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 BaseModelOutputWithPast(
-            last_hidden_state=x,
-            past_key_values=tuple(attn_key_values) if attn_key_values is not None else None,
-            hidden_states=tuple(all_hidden_states) if output_hidden_states else None,
-        )
-
-    def get_fsdp_wrap_policy(self, wrap_strategy: Optional[FSDPWrapStrategy] = None):
-        if wrap_strategy is None:
-            return None
-
-        # The 'recurse' mode for the wrap function does not behave like you'd expect.
-        # Even if we return False, it may still recurse because PyTorch does what it wants,
-        # not what you want. This causes issues when, for example, we want to wrap 'ff_out' (a linear layer)
-        # but not other linear layers within a block.
-        # So we have to explicitly tell PyTorch which linear layers to wrap, and we also just
-        # return True in 'recurse' mode for simplicity.
-        size_based_module_to_wrap = {self.transformer.wte}
-        if hasattr(self.transformer, "ff_out"):
-            size_based_module_to_wrap.add(self.transformer.ff_out)
-
-        if wrap_strategy == FSDPWrapStrategy.by_block:
-
-            def fsdp_wrap_fn(module, recurse: bool = True, nonwrapped_numel: int = 0):
-                del nonwrapped_numel
-                wrap = isinstance(module, OLMoBlock)
-                if recurse:
-                    return True
-                else:
-                    return wrap
-
-            return fsdp_wrap_fn
-        elif wrap_strategy == FSDPWrapStrategy.by_block_and_size:
-
-            def fsdp_wrap_fn(module, recurse: bool = True, nonwrapped_numel: int = 0):
-                del nonwrapped_numel
-                wrap = isinstance(module, (OLMoBlock,)) or module in size_based_module_to_wrap
-                if recurse:
-                    return True
-                else:
-                    return wrap
-
-            return fsdp_wrap_fn
-        elif wrap_strategy == FSDPWrapStrategy.by_block_group:
-            if self.config.block_group_size <= 1:
-                raise OLMoConfigurationError(
-                    "'by_block_group' FSDP wrapping strategy requires block group size greater than 1"
-                )
-
-            def fsdp_wrap_fn(module, recurse: bool = True, nonwrapped_numel: int = 0):
-                del nonwrapped_numel
-                wrap = isinstance(module, OLMoBlockGroup)
-                if recurse:
-                    return True
-                else:
-                    return wrap
-
-            return fsdp_wrap_fn
-        elif wrap_strategy == FSDPWrapStrategy.by_block_group_and_size:
-            if self.config.block_group_size <= 1:
-                raise OLMoConfigurationError(
-                    "'by_block_group_and_size' FSDP wrapping strategy requires block group size greater than 1"
-                )
-
-            def fsdp_wrap_fn(module, recurse: bool = True, nonwrapped_numel: int = 0):
-                del nonwrapped_numel
-                wrap = isinstance(module, (OLMoBlockGroup,)) or module in size_based_module_to_wrap
-                if recurse:
-                    return True
-                else:
-                    return wrap
-
-            return fsdp_wrap_fn
-        elif wrap_strategy == FSDPWrapStrategy.size_based:
-            from torch.distributed.fsdp.wrap import size_based_auto_wrap_policy
-
-            return size_based_auto_wrap_policy
-        elif wrap_strategy in {
-            FSDPWrapStrategy.one_in_two,
-            FSDPWrapStrategy.one_in_three,
-            FSDPWrapStrategy.one_in_four,
-            FSDPWrapStrategy.one_in_five,
-        }:
-            c = {
-                FSDPWrapStrategy.one_in_two: 2,
-                FSDPWrapStrategy.one_in_three: 3,
-                FSDPWrapStrategy.one_in_four: 4,
-                FSDPWrapStrategy.one_in_five: 5,
-            }[wrap_strategy]
-
-            def fsdp_wrap_fn(module, recurse: bool = True, nonwrapped_numel: int = 0):
-                del nonwrapped_numel
-                wrap = isinstance(module, OLMoBlock) and module.layer_id % c == 0
-                if recurse:
-                    return True
-                else:
-                    return wrap
-
-            return fsdp_wrap_fn
-        else:
-            raise NotImplementedError(wrap_strategy)
-
-    def num_params(self, include_embedding: bool = True) -> int:
-        """
-        Get the total number of parameters.
-        """
-        params = (np for np in self.named_parameters())
-        if not include_embedding:
-            params = filter(  # type: ignore
-                lambda np: ".wte." not in np[0] and ".wpe." not in np[0],
-                params,
-            )
-        return sum(p.numel() for _, p in params)
-
-    @property
-    def num_fwd_flops(self):
-        if self.__num_fwd_flops:
-            return self.__num_fwd_flops
-        n_params = self.num_params()
-        # the number of parameters is approximately the number of multiply-accumulates (MAC) in the network
-        # each MAC has 2 FLOPs - we multiply by 2 ie 2 * n_param
-        # this gets us FLOPs / token
-        params_flops_per_token = 2 * n_params
-        params_flops_per_seq = params_flops_per_token * self.config.max_sequence_length
-        # there are 2 FLOPS per mac; there is A=Q*K^T and out=A*V ops (ie mult by 2)
-        attn_flops_per_seq = (
-            self.config.n_layers * 2 * 2 * (self.config.d_model * (self.config.max_sequence_length**2))
-        )
-        self.__num_fwd_flops = params_flops_per_seq + attn_flops_per_seq
-        return self.__num_fwd_flops
-
-    def generate(
-        self,
-        input_ids: torch.LongTensor,
-        attention_mask: Optional[torch.Tensor] = None,
-        attention_bias: Optional[torch.Tensor] = None,
-        max_steps: int = 10,
-        beam_size: int = 1,
-        per_node_beam_size: Optional[int] = None,
-        sampler: Optional[Sampler] = None,
-        min_steps: Optional[int] = None,
-        final_sequence_scorer: Optional[FinalSequenceScorer] = None,
-        constraints: Optional[List[Constraint]] = None,
-    ) -> OLMoGenerateOutput:
-        """
-        Generate token IDs using beam search.
-
-        Note that by default ``beam_size`` is set to 1, which is greedy decoding.
-
-        :param input_ids: A tensor of shape `(batch_size, seq_len)`.
-        :param attention_mask: A optional tensor of shape `(batch_size, seq_len)`, the same
-            as for the forward method.
-        :param attention_bias: A tensor of shape
-            `(batch_size, 1, seq_len + tokens_to_generate, seq_len + tokens_to_generate)`,
-            the same as for the forward method except only one shape is excepted here.
-
-        For an explanation of the other arguments, see :class:`BeamSearch`.
-        """
-        beam_search = BeamSearch(
-            self.config.eos_token_id,
-            max_steps=max_steps,
-            beam_size=beam_size,
-            per_node_beam_size=per_node_beam_size,
-            sampler=sampler,
-            min_steps=min_steps,
-            final_sequence_scorer=final_sequence_scorer,
-            constraints=constraints,
-        )
-
-        # Validate inputs.
-        batch_size, seq_len = input_ids.shape
-        if attention_mask is not None:
-            assert attention_mask.shape == (batch_size, seq_len)
-        if attention_bias is not None:
-            assert len(attention_bias.shape) == 4
-            assert attention_bias.shape[:2] == (batch_size, 1)
-            assert (
-                seq_len + beam_search.max_steps
-                <= attention_bias.shape[2]
-                == attention_bias.shape[3]
-                <= self.config.max_sequence_length
-            )
-
-        tokens_generated = 0
-
-        def flatten_past_key_values(
-            past_key_values: List[Tuple[torch.Tensor, torch.Tensor]],
-        ) -> Dict[str, torch.Tensor]:
-            out = {}
-            for i, (key, value) in enumerate(past_key_values):
-                out[f"past_key_{i}"] = key
-                out[f"past_value_{i}"] = value
-            return out
-
-        def unflatten_past_key_values(
-            past_key_values: Dict[str, torch.Tensor],
-        ) -> List[Tuple[torch.Tensor, torch.Tensor]]:
-            out = []
-            for i in range(self.config.n_layers):
-                past_key = past_key_values[f"past_key_{i}"]
-                past_value = past_key_values[f"past_value_{i}"]
-                out.append((past_key, past_value))
-            return out
-
-        def step(
-            last_predictions: torch.Tensor, state: dict[str, torch.Tensor]
-        ) -> tuple[torch.Tensor, dict[str, torch.Tensor]]:
-            nonlocal tokens_generated
-
-            attention_mask = state.get("attention_mask")
-            attention_bias = state.get("attention_bias")
-
-            if tokens_generated > 0:
-                past_key_values = unflatten_past_key_values(state)
-                input_ids = last_predictions.unsqueeze(1)
-                if attention_mask is not None:
-                    group_size = input_ids.shape[0]
-                    attention_mask = torch.cat((attention_mask, attention_mask.new_ones((group_size, 1))), dim=-1)
-            else:
-                past_key_values = None
-                input_ids = state["input_ids"]
-
-            tokens_generated += 1
-
-            # Run forward pass of model to get logits, then normalize to get log probs.
-            output = self(
-                input_ids,
-                attention_mask=attention_mask,
-                attention_bias=attention_bias,
-                past_key_values=past_key_values,
-                use_cache=True,
-                last_logits_only=True,
-            )
-            log_probs = F.log_softmax(output.logits[:, -1, :], dim=-1)
-
-            # Create new state.
-            state = flatten_past_key_values(output.attn_key_values)
-            if attention_mask is not None:
-                state["attention_mask"] = attention_mask
-            if attention_bias is not None:
-                state["attention_bias"] = attention_bias
-
-            return log_probs, state
-
-        initial_preds = input_ids.new_zeros((batch_size,))  # This is arbitrary, we won't use this.
-        state: dict[str, torch.Tensor] = {"input_ids": input_ids}
-        if attention_mask is not None:
-            state["attention_mask"] = attention_mask
-        if attention_bias is not None:
-            state["attention_bias"] = attention_bias
-        with torch.no_grad():
-            token_ids, scores = beam_search.search(initial_preds, state, step)
-
-        return OLMoGenerateOutput(
-            token_ids=token_ids,  # type: ignore[arg-type]
-            scores=scores,  # type: ignore[arg-type]
-        )
-
-    @classmethod
-    def from_checkpoint(
-        cls, checkpoint_dir: PathOrStr, device: str = "cpu", checkpoint_type: Optional[CheckpointType] = None
-    ) -> OLMo:
-        """
-        Load an OLMo model from a checkpoint.
-        """
-        from .util import resource_path
-
-        # Guess checkpoint type.
-        if checkpoint_type is None:
-            try:
-                if resource_path(checkpoint_dir, "model.pt").is_file():
-                    checkpoint_type = CheckpointType.unsharded
-                else:
-                    checkpoint_type = CheckpointType.sharded
-            except FileNotFoundError:
-                checkpoint_type = CheckpointType.sharded
-
-        # Load config.
-        config_path = resource_path(checkpoint_dir, "config.yaml")
-        model_config = ModelConfig.load(config_path, key="model", validate_paths=False)
-
-        if checkpoint_type == CheckpointType.unsharded:
-            # Initialize model (always on CPU to start with so we don't run out of GPU memory).
-            model_config.init_device = "cpu"
-            model = OLMo(model_config)
-
-            # Load state dict directly to target device.
-            state_dict_path = resource_path(checkpoint_dir, "model.pt")
-            state_dict = torch.load(state_dict_path, map_location="cpu")
-            model.load_state_dict(model._make_state_dict_compatible(state_dict)[0])
-            model = model.to(torch.device(device))
-        else:
-            from .checkpoint import load_model_state
-
-            # Initialize model on target device. In this case the state dict is loaded in-place
-            # so it's not necessary to start on CPU if the target device is a GPU.
-            model_config.init_device = device
-            model = OLMo(model_config)
-
-            # Load state dict in place.
-            load_model_state(checkpoint_dir, model)
-
-        return model.eval()
-
-    def _make_state_dict_compatible(
-        self, state_dict: Dict[str, torch.Tensor]
-    ) -> Tuple[Dict[str, torch.Tensor], Dict[str, Set[str]]]:
-        """
-        Handles some cases where the state dict is valid yet may need to be transformed in order to
-        be loaded.
-
-        This modifies the state dict in-place and also returns it, along with a mapping of original key
-        names to new key names in cases where the keys were simply renamed. That mapping can be used
-        to make a corresponding optimizer state dict compatible as well.
-        """
-        import re
-        from fnmatch import fnmatch
-
-        new_keys_to_og_keys: Dict[str, str] = {}
-
-        # Remove "_fsdp_wrapped_module." prefix from all keys. We don't want this prefix when the model is
-        # not wrapped in FSDP. And when the model is wrapped in FSDP, loading this state dict will still work
-        # fine without the prefixes. This also simplifies the other steps below.
-        for key in list(state_dict.keys()):
-            state_dict[(new_key := key.replace("_fsdp_wrapped_module.", ""))] = state_dict.pop(key)
-            new_keys_to_og_keys[new_key] = key
-
-        # For backwards compatibility prior to fixing https://github.com/allenai/LLM/issues/222
-        if self.config.block_type == BlockType.sequential:
-            for key in list(state_dict.keys()):
-                if fnmatch(key, "transformer.*.norm.weight"):
-                    tensor = state_dict.pop(key)
-                    state_dict[(new_key := key.replace("norm.weight", "attn_norm.weight"))] = tensor
-                    new_keys_to_og_keys[new_key] = new_keys_to_og_keys[key]
-                    state_dict[(new_key := key.replace("norm.weight", "ff_norm.weight"))] = tensor.clone()
-                    new_keys_to_og_keys[new_key] = new_keys_to_og_keys[key]
-                    del new_keys_to_og_keys[key]
-                elif fnmatch(key, "transformer.*.norm.bias"):
-                    tensor = state_dict.pop(key)
-                    state_dict[(new_key := key.replace("norm.bias", "attn_norm.bias"))] = tensor
-                    new_keys_to_og_keys[new_key] = new_keys_to_og_keys[key]
-                    state_dict[(new_key := key.replace("norm.bias", "ff_norm.bias"))] = tensor.clone()
-                    new_keys_to_og_keys[new_key] = new_keys_to_og_keys[key]
-                    del new_keys_to_og_keys[key]
-
-        # For loading a state dict that was saved with a different `block_group_size`.
-        if "transformer.block_groups.0.0.attn_out.weight" in state_dict.keys():
-            state_dict_block_group_size = len(
-                [k for k in state_dict.keys() if fnmatch(k, "transformer.block_groups.0.*.attn_out.weight")]
-            )
-        else:
-            state_dict_block_group_size = 1
-        if self.config.block_group_size != state_dict_block_group_size:
-            log.info(
-                f"Regrouping state dict blocks from group size {state_dict_block_group_size} to "
-                f"group size {self.config.block_group_size}"
-            )
-            # For simplicity we're first going to flatten out the block groups in the state dict (if necessary)
-            # and then (re-)group them into the right block sizes.
-            if state_dict_block_group_size > 1:
-                for key in list(state_dict.keys()):
-                    if (m := re.match(r"transformer.block_groups\.(\d+)\.(\d+)\..*", key)) is not None:
-                        group_idx, group_block_idx = int(m.group(1)), int(m.group(2))
-                        block_idx = (group_idx * state_dict_block_group_size) + group_block_idx
-                        state_dict[
-                            (
-                                new_key := key.replace(
-                                    f"block_groups.{group_idx}.{group_block_idx}.", f"blocks.{block_idx}."
-                                )
-                            )
-                        ] = state_dict.pop(key)
-                        new_keys_to_og_keys[new_key] = new_keys_to_og_keys.pop(key)
-
-            if self.config.block_group_size > 1:
-                # Group the state dict blocks into the right block size.
-                for key in list(state_dict.keys()):
-                    if (m := re.match(r"transformer.blocks\.(\d+)\..*", key)) is not None:
-                        block_idx = int(m.group(1))
-                        group_idx, group_block_idx = (
-                            block_idx // self.config.block_group_size,
-                            block_idx % self.config.block_group_size,
-                        )
-                        state_dict[
-                            (
-                                new_key := key.replace(
-                                    f"blocks.{block_idx}.", f"block_groups.{group_idx}.{group_block_idx}."
-                                )
-                            )
-                        ] = state_dict.pop(key)
-                        new_keys_to_og_keys[new_key] = new_keys_to_og_keys.pop(key)
-
-        og_keys_to_new: Dict[str, Set[str]] = defaultdict(set)
-        for new_key, og_key in new_keys_to_og_keys.items():
-            og_keys_to_new[og_key].add(new_key)
-
-        return state_dict, og_keys_to_new