Upload RavenForCausalLM

README.md

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+---
+library_name: transformers
+tags: []
+---
+
+# Model Card for Model ID
+
+<!-- Provide a quick summary of what the model is/does. -->
+
+
+
+## Model Details
+
+### Model Description
+
+<!-- Provide a longer summary of what this model is. -->
+
+This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
+
+- **Developed by:** [More Information Needed]
+- **Funded by [optional]:** [More Information Needed]
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+- **Model type:** [More Information Needed]
+- **Language(s) (NLP):** [More Information Needed]
+- **License:** [More Information Needed]
+- **Finetuned from model [optional]:** [More Information Needed]
+
+### Model Sources [optional]
+
+<!-- Provide the basic links for the model. -->
+
+- **Repository:** [More Information Needed]
+- **Paper [optional]:** [More Information Needed]
+- **Demo [optional]:** [More Information Needed]
+
+## Uses
+
+<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
+
+### Direct Use
+
+<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
+
+[More Information Needed]
+
+### Downstream Use [optional]
+
+<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
+
+[More Information Needed]
+
+### Out-of-Scope Use
+
+<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
+
+[More Information Needed]
+
+## Bias, Risks, and Limitations
+
+<!-- This section is meant to convey both technical and sociotechnical limitations. -->
+
+[More Information Needed]
+
+### Recommendations
+
+<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
+
+Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
+
+## How to Get Started with the Model
+
+Use the code below to get started with the model.
+
+[More Information Needed]
+
+## Training Details
+
+### Training Data
+
+<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
+
+[More Information Needed]
+
+### Training Procedure
+
+<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
+
+#### Preprocessing [optional]
+
+[More Information Needed]
+
+
+#### Training Hyperparameters
+
+- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
+
+#### Speeds, Sizes, Times [optional]
+
+<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
+
+[More Information Needed]
+
+## Evaluation
+
+<!-- This section describes the evaluation protocols and provides the results. -->
+
+### Testing Data, Factors & Metrics
+
+#### Testing Data
+
+<!-- This should link to a Dataset Card if possible. -->
+
+[More Information Needed]
+
+#### Factors
+
+<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
+
+[More Information Needed]
+
+#### Metrics
+
+<!-- These are the evaluation metrics being used, ideally with a description of why. -->
+
+[More Information Needed]
+
+### Results
+
+[More Information Needed]
+
+#### Summary
+
+
+
+## Model Examination [optional]
+
+<!-- Relevant interpretability work for the model goes here -->
+
+[More Information Needed]
+
+## Environmental Impact
+
+<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
+
+Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
+
+- **Hardware Type:** [More Information Needed]
+- **Hours used:** [More Information Needed]
+- **Cloud Provider:** [More Information Needed]
+- **Compute Region:** [More Information Needed]
+- **Carbon Emitted:** [More Information Needed]
+
+## Technical Specifications [optional]
+
+### Model Architecture and Objective
+
+[More Information Needed]
+
+### Compute Infrastructure
+
+[More Information Needed]
+
+#### Hardware
+
+[More Information Needed]
+
+#### Software
+
+[More Information Needed]
+
+## Citation [optional]
+
+<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
+
+**BibTeX:**
+
+[More Information Needed]
+
+**APA:**
+
+[More Information Needed]
+
+## Glossary [optional]
+
+<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
+
+[More Information Needed]
+
+## More Information [optional]
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+[More Information Needed]
+
+## Model Card Authors [optional]
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+[More Information Needed]
+
+## Model Card Contact
+
+[More Information Needed]

config.json

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+{
+  "activation_checkpoint_impl": "per-iteration",
+  "architecture_class_name": "RecurrentGPT",
+  "architectures": [
+    "RavenForCausalLM"
+  ],
+  "auto_map": {
+    "AutoConfig": "raven_config_minimal.RavenConfig",
+    "AutoModelForCausalLM": "raven_modeling_minimal.RavenForCausalLM"
+  },
+  "bias": false,
+  "block_class_name": "SandwichBlock",
+  "block_size": 4096,
+  "effective_expected_depth": 132,
+  "head_dim": 96,
+  "init_orthogonal": false,
+  "init_strategy": "takase",
+  "init_values": {
+    "embed_scale": 72.6636084983398,
+    "embedding": 0.008703882797784892,
+    "out_proj": 0.0005356869554443541,
+    "std": 0.008703882797784892
+  },
+  "injection_type": "linear",
+  "intermediate_size": 17920,
+  "mean_backprop_depth": 8,
+  "mean_recurrence": 32,
+  "mlp_class_name": "GatedMLP",
+  "model_type": "huginn_raven",
+  "n_embd": 5280,
+  "n_heads": 55,
+  "n_layers": 8,
+  "n_layers_in_coda": 2,
+  "n_layers_in_prelude": 2,
+  "n_layers_in_recurrent_block": 4,
+  "nonlin_name": "SiLU",
+  "norm_class_name": "RMSNorm_llama",
+  "norm_eps": 1e-06,
+  "num_key_value_heads": 55,
+  "padded_vocab_size": 65536,
+  "padding_multiple": 4096,
+  "qk_bias": true,
+  "rope_base": 50000,
+  "sampling_scheme": "poisson-lognormal-filling",
+  "state_init": "like-init",
+  "tie_embeddings": true,
+  "torch_dtype": "float32",
+  "transformers_version": "4.46.3",
+  "vocab_size": 65536
+}

generation_config.json

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+{
+  "_from_model_config": true,
+  "transformers_version": "4.46.3"
+}

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raven_config_minimal.py

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+"""A HuggingFace-style model configuration."""
+
+from transformers import PretrainedConfig
+from math import sqrt
+
+
+class RavenConfig(PretrainedConfig):
+    model_type = "huginn_raven"
+    keys_to_ignore_at_inference = [""]
+    attribute_map = {"num_attention_heads": "n_heads", "hidden_size": "n_embd", "num_hidden_layers": "n_layers"}
+
+    def __init__(
+        self,
+        n_embd: int = 5280,
+        n_heads: int = 55,
+        n_layers: int = 8,  # total of prelude + recurrent + coda
+        block_size: int = 4096,
+        vocab_size: int = 65536,
+        padding_multiple: int = 4096,
+        tie_embeddings: bool = True,
+        intermediate_size: int = 17920,
+        bias: bool = False,
+        architecture_class_name: str = "RecurrentGPT",
+        block_class_name: str = "SandwichBlock",
+        norm_class_name: str = "RMSNorm_llama",
+        norm_eps: float = 0.000001,
+        mlp_class_name: str = "GatedMLP",
+        nonlin_name: str = "SiLU",
+        init_strategy: str = "takase",
+        init_orthogonal: bool = False,
+        state_init: str = "like-init",
+        injection_type: str = "linear",
+        n_layers_in_recurrent_block: int = 4,
+        mean_recurrence: int = 32,
+        sampling_scheme: str = "poisson-lognormal-filling",
+        mean_backprop_depth: int = 8,
+        n_layers_in_prelude: int = 2,
+        n_layers_in_coda: int = 2,
+        qk_bias: bool = True,
+        activation_checkpoint_impl: str = "per-iteration",
+        rope_base: float = 50_000,
+        torch_dtype: str = "bfloat16",
+        transformers_version: str = "4.47.1",
+        **kwargs,
+    ):
+        self.n_embd = n_embd
+        self.n_heads = n_heads
+        self.n_layers = n_layers
+        self.block_size = block_size
+        self.vocab_size = self.padded_vocab_size = vocab_size
+        self.padding_multiple = padding_multiple
+        self.tie_embeddings = tie_embeddings
+        self.intermediate_size = intermediate_size
+        self.bias = bias
+        self.architecture_class_name = architecture_class_name
+        self.block_class_name = block_class_name
+        self.norm_class_name = norm_class_name
+        self.norm_eps = norm_eps
+        self.mlp_class_name = mlp_class_name
+        self.nonlin_name = nonlin_name
+        self.init_strategy = init_strategy
+        self.init_orthogonal = init_orthogonal
+        self.state_init = state_init
+        self.injection_type = injection_type
+        self.n_layers_in_recurrent_block = n_layers_in_recurrent_block
+        self.mean_recurrence = mean_recurrence
+        self.sampling_scheme = sampling_scheme
+        self.mean_backprop_depth = mean_backprop_depth
+        self.n_layers_in_prelude = n_layers_in_prelude
+        self.n_layers_in_coda = n_layers_in_coda
+        self.qk_bias = qk_bias
+        self.activation_checkpoint_impl = activation_checkpoint_impl
+        self.rope_base = rope_base
+        self.torch_dtype = torch_dtype  # Added from JSON
+        self.transformers_version = transformers_version  # Added from JSON
+        # Derived
+        self.num_key_value_heads = n_heads
+        self.num_attention_heads = n_heads
+        self.head_dim = n_embd // n_heads
+        self.effective_expected_depth = (
+            self.n_layers_in_prelude + self.n_layers_in_coda + self.n_layers_in_recurrent_block * self.mean_recurrence
+        )
+        self.init_values = {
+            "std": sqrt(2 / (5 * self.n_embd)),
+            "out_proj": sqrt(2 / (5 * self.n_embd)) / sqrt(2 * self.effective_expected_depth),
+            "embedding": sqrt(2 / (5 * self.n_embd)),
+            "embed_scale": sqrt(self.n_embd),
+        }
+
+        super().__init__(
+            # pad_token_id=65509,
+            # bos_token_id=65504,
+            # eos_token_id=65505,
+            tie_word_embeddings=tie_embeddings,
+            **kwargs,
+        )

raven_modeling_minimal.py

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+"""Minimal modeling.py file for HF compatibility and funny zero-shot experiments. Use only for inference."""
+
+import torch
+import math
+
+from torch import Tensor
+from dataclasses import dataclass
+from typing import Optional, Union, Any
+
+from .raven_config_minimal import RavenConfig
+from transformers.cache_utils import Cache, DynamicCache
+
+###################### Huggingface Glue code I ##################################################################
+from transformers import PreTrainedModel
+from transformers.utils import ModelOutput
+from transformers.generation.utils import GenerateDecoderOnlyOutput
+
+import torch.nn.functional as F
+from transformers import GenerationConfig
+
+
+class RavenPreTrainedModel(PreTrainedModel):
+    config_class = RavenConfig
+    base_model_prefix = "model"
+    supports_gradient_checkpointing = True
+    _no_split_modules = ["SandwichBlock"]
+    _skip_keys_device_placement = ["past_key_values"]
+    _supports_flash_attn_2 = True
+    _supports_sdpa = True
+    _supports_cache_class = True
+    _supports_quantized_cache = False
+    _supports_static_cache = False
+
+    def _init_weights(self, module):
+        print("Random Initialization not implemented.")
+
+
+@dataclass
+class CausalLMOutputRecurrentLatents(ModelOutput):
+    loss: Optional[torch.Tensor] = None
+    log_ppl: Optional[torch.Tensor] = None
+    logits: Optional[torch.Tensor] = None
+    past_key_values: Optional[Cache] = None
+    latent_states: Optional[torch.Tensor] = None
+    hidden_states: Optional[torch.Tensor] = None
+    attention_maps: Optional[dict[int, torch.Tensor]] = None
+    stats: Optional[dict] = None
+
+
+###################### Minimal implementation from here ############################################################
+
+
+class RMSNorm(torch.nn.Module):
+    """Saner dtype handling and slightly better for fusion"""
+
+    def __init__(self, dim: int, eps: float = 1e-6):
+        super().__init__()
+        self.eps = eps
+        self.weight = torch.nn.Parameter(torch.ones(dim))
+
+    def _norm(self, x):
+        return x * torch.rsqrt(x.pow(2).mean(-1, keepdim=True) + self.eps)
+
+    def forward(self, x):
+        with torch.autocast(enabled=False, device_type=x.device.type):
+            return self._norm(x.float()).type_as(x) * self.weight
+
+    def reset_parameters(self) -> None:
+        torch.nn.init.ones_(self.weight)
+
+
+class HuginnDynamicCache(DynamicCache):
+    def __init__(self, lookup_strategy: str = "latest") -> None:
+        super().__init__()
+        self._seen_tokens = 0
+        self.key_cache: dict[int, dict[int, torch.Tensor]] = {}
+        self.value_cache: dict[int, dict[int, torch.Tensor]] = {}
+        # structure: cache[index_of_layer_or_recurrent_step][index_in_sequence]
+        # the cache is held uncoalesced because certain recurrent steps may be missing for some sequence ids if using
+        # per-token adaptive compute. In those cases, the "lookup_strategy" determines how to proceed
+        # Also, It is critical that the head indices do not overlap with the recurrent iteration indices
+        self.lookup_strategy = lookup_strategy
+
+    def update(
+        self,
+        key_states: torch.Tensor,
+        value_states: torch.Tensor,
+        step_idx: int,
+        lookup_strategy: Optional[str] = None,
+    ) -> tuple[torch.Tensor, torch.Tensor]:
+        lookup_strategy = self.lookup_strategy if lookup_strategy is None else lookup_strategy
+        # Init
+        if step_idx not in self.key_cache:
+            self.key_cache[step_idx] = {}
+            self.value_cache[step_idx] = {}
+        # Update the number of seen tokens, we assume that step_idx=0 (first prelude) is always hit
+        if step_idx == 0:
+            self._seen_tokens += key_states.shape[-2]
+        # Add entries to cache
+        for idx, entry in enumerate(key_states.unbind(dim=-2)):
+            assert step_idx < 0 or self._seen_tokens - key_states.shape[-2] + idx not in self.key_cache[step_idx]
+            # print(f"Overwrote cache entry for step_idx {step_idx}") # likely the head
+            self.key_cache[step_idx][self._seen_tokens - key_states.shape[-2] + idx] = entry
+        for idx, entry in enumerate(value_states.unbind(dim=-2)):
+            self.value_cache[step_idx][self._seen_tokens - value_states.shape[-2] + idx] = entry
+
+        # Materialize past state based on lookup strategy:
+        if len(self.key_cache[step_idx]) == self._seen_tokens:
+            # All entries are present, materialize cache as normal
+            return (
+                torch.stack(list(self.key_cache[step_idx].values()), dim=-2),
+                torch.stack(list(self.value_cache[step_idx].values()), dim=-2),
+            )
+        else:  # some entries where not previously computed
+            if lookup_strategy == "latest":
+                latest_keys = []
+                latest_values = []
+                for token_pos in range(self._seen_tokens):
+                    # Find the latest step that has this token position
+                    max_step = max((s for s in range(step_idx + 1) if token_pos in self.key_cache[s]), default=None)
+                    if max_step is None:
+                        raise ValueError(f"No cache entry found for token position {token_pos}")
+                    latest_keys.append(self.key_cache[max_step][token_pos])
+                    latest_values.append(self.value_cache[max_step][token_pos])
+                return torch.stack(latest_keys, dim=-2), torch.stack(latest_values, dim=-2)
+            elif lookup_strategy == "skip":
+                existing_keys = []
+                existing_values = []
+                for token_pos in range(self._seen_tokens):
+                    if token_pos in self.key_cache[step_idx]:
+                        existing_keys.append(self.key_cache[step_idx][token_pos])
+                        existing_values.append(self.value_cache[step_idx][token_pos])
+                return torch.stack(existing_keys, dim=-2), torch.stack(existing_values, dim=-2)
+            elif lookup_strategy == "randomized":  # sanity check
+                rand_keys = []
+                rand_values = []
+                for token_pos in range(self._seen_tokens):
+                    # Find steps that have this token position
+                    steps = [s for s in range(step_idx + 1) if token_pos in self.key_cache[s]]
+                    rand_step = steps[torch.randint(len(steps), (1,))]
+                    rand_keys.append(self.key_cache[rand_step][token_pos])
+                    rand_values.append(self.value_cache[rand_step][token_pos])
+                return torch.stack(rand_keys, dim=-2), torch.stack(rand_values, dim=-2)
+            else:
+                raise ValueError(f"Unknown lookup strategy: {lookup_strategy}")
+
+    def reset(self) -> None:
+        """Reset the cache state."""
+        self._seen_tokens = 0
+        self.key_cache.clear()
+        self.value_cache.clear()
+
+    def get_seq_length(self, step_idx: int = 0) -> int:
+        return self._seen_tokens
+
+
+class CausalSelfAttention(torch.nn.Module):
+    def __init__(self, config: RavenConfig) -> None:
+        super().__init__()
+        self.config = config
+        self.n_head = config.num_attention_heads
+        self.n_kv_heads = config.num_key_value_heads
+        self.head_dim = config.n_embd // self.n_head
+
+        shape = (self.n_head + 2 * self.n_kv_heads) * self.head_dim
+        self.chunks = [config.n_embd, self.n_kv_heads * self.head_dim, self.n_kv_heads * self.head_dim]
+        self.Wqkv = torch.nn.Linear(config.n_embd, shape, bias=False)
+        if config.qk_bias:
+            self.qk_bias = torch.nn.Parameter(torch.zeros(2, 1, self.n_head, self.head_dim))
+        self.proj = torch.nn.Linear(config.n_embd, config.n_embd, bias=False)
+
+    def forward(
+        self,
+        x: Tensor,
+        freqs_cis: Tensor,
+        step_idx: int,
+        mask: Optional[Tensor] = None,
+        past_key_values: Optional[Cache] = None,
+        return_attn: bool = False,
+    ) -> tuple[Tensor, Optional[Tensor]]:
+        B, S, E = x.shape  # batch size, sequence length, embedding dimensionality (n_embd)
+        q, k, v = self.Wqkv(x).split(self.chunks, dim=2)
+        q = q.view(B, S, self.n_head, self.head_dim)
+        k = k.view(B, S, self.n_kv_heads, self.head_dim)
+        v = v.view(B, S, self.n_kv_heads, self.head_dim)
+        # bias?
+        if self.config.qk_bias:
+            q_bias, k_bias = self.qk_bias.split(1, dim=0)
+            q, k = (q + q_bias).to(q.dtype), (k + k_bias).to(q.dtype)
+        # apply rotary
+        q, k = apply_rotary_emb_complex_like(q, k, freqs_cis=freqs_cis)
+
+        q = q.transpose(1, 2)  # (B, nh, S, hs)
+        k = k.transpose(1, 2)
+        v = v.transpose(1, 2)
+
+        if past_key_values is not None:
+            k, v = past_key_values.update(k, v, step_idx)
+
+        if return_attn:
+            y, attention_map = self.compute_eager_sdpa(q, k, v, attn_mask=mask)
+        else:
+            y = torch.nn.functional.scaled_dot_product_attention(
+                q, k, v, attn_mask=mask, dropout_p=0.0, is_causal=q.shape[2] > 1
+            )
+        y = y.transpose(1, 2).reshape(B, S, E).contiguous()  # reshape is a view if possible (it mostly is)
+        return self.proj(y), attention_map if return_attn else None
+
+    def compute_eager_sdpa(self, q, k, v, attn_mask):
+        scale = 1.0 / math.sqrt(self.head_dim)
+        scores = torch.matmul(q, k.transpose(-2, -1)) * scale
+
+        if attn_mask is not None:
+            scores = scores + attn_mask
+        if q.shape[2] > 1:
+            causal_mask = torch.triu(torch.ones(q.shape[2], q.shape[2]), diagonal=1).bool()
+            scores.masked_fill_(causal_mask.to(scores.device), float("-inf"))
+
+        attention_weights = torch.nn.functional.softmax(scores, dim=-1)
+        y = torch.matmul(attention_weights, v)
+        return y, attention_weights.max(dim=1)[0]
+
+
+class GatedMLP(torch.nn.Module):
+    def __init__(self, config: RavenConfig, in_features: int = 0) -> None:
+        super().__init__()
+        in_features = config.n_embd if in_features == 0 else in_features
+        self.fc = torch.nn.Linear(in_features, config.intermediate_size * 2, bias=False)
+
+        self.proj = torch.nn.Linear(config.intermediate_size, config.n_embd, bias=False)
+        self.nonlin = torch.nn.SiLU()
+
+    def forward(self, x: Tensor) -> Tensor:
+        # modified to single FC layer to improve parallelism
+        x_fc_1, x_fc_2 = self.fc(x).chunk(2, dim=-1)
+        x = self.nonlin(x_fc_1) * x_fc_2
+        return self.proj(x)
+
+
+class SandwichBlock(torch.nn.Module):
+    expanded = False
+
+    def __init__(self, config: RavenConfig, layer_id: int) -> None:
+        super().__init__()
+        self.norm_1 = RMSNorm(config.n_embd, eps=config.norm_eps)
+        self.attn = CausalSelfAttention(config)
+        self.norm_2 = RMSNorm(config.n_embd, eps=config.norm_eps)
+        self.mlp = GatedMLP(config)
+        self.norm_3 = RMSNorm(config.n_embd, eps=config.norm_eps)
+        self.norm_4 = RMSNorm(config.n_embd, eps=config.norm_eps)
+        self.layer_id = layer_id
+
+    def forward(
+        self,
+        x: Tensor,
+        freqs_cis: Tensor,
+        step_idx: int,
+        mask: Optional[Tensor] = None,
+        past_key_values: Optional[Cache] = None,
+        return_attn: bool = False,
+    ) -> tuple[Tensor, Optional[Tensor]]:
+        attn_out, attn_map = self.attn(self.norm_1(x), freqs_cis, step_idx, mask, past_key_values, return_attn)
+        x = self.norm_2(attn_out + x)
+        x = self.norm_4(self.mlp(self.norm_3(x)) + x)
+        return x, attn_map
+
+
+class RavenForCausalLM(RavenPreTrainedModel):
+    def __init__(
+        self,
+        config: RavenConfig,
+    ) -> None:
+        super().__init__(config)
+        self.config = config
+
+        # Transformer layers
+        prelude = torch.nn.ModuleList(SandwichBlock(config, layer_id=i) for i in range(config.n_layers_in_prelude))
+        adapter = torch.nn.Linear(config.n_embd * 2, config.n_embd, bias=config.bias)
+        core_block = torch.nn.ModuleList(
+            SandwichBlock(config, layer_id=i + config.n_layers_in_prelude)
+            for i in range(config.n_layers_in_recurrent_block)
+        )
+        o = config.n_layers_in_prelude + config.n_layers_in_recurrent_block * config.mean_recurrence
+        coda = torch.nn.ModuleList(SandwichBlock(config, layer_id=i + o) for i in range(config.n_layers_in_coda))
+
+        self.transformer = torch.nn.ModuleDict(
+            dict(
+                wte=torch.nn.Embedding(config.padded_vocab_size, config.n_embd),
+                prelude=prelude,
+                adapter=adapter,
+                core_block=core_block,
+                coda=coda,
+                ln_f=RMSNorm(config.n_embd, eps=config.norm_eps),  # used twice :>
+            )
+        )
+        self.emb_scale = config.init_values["embed_scale"]
+        # Head
+        self.lm_head = torch.nn.Linear(config.n_embd, config.padded_vocab_size, bias=False)
+        if self.config.tie_embeddings:
+            self.lm_head.weight = self.transformer.wte.weight
+        # rope
+        self.register_buffer("freqs_cis", self._precompute_freqs_cis(), persistent=True)
+
+    def _precompute_freqs_cis(self):
+        # can actually be a buffer now, and remains in fp32! (at least in the settings I tested)
+        freqs_cis = precompute_freqs_cis(
+            self.config.n_embd // self.config.num_attention_heads, self.config.block_size, self.config.rope_base, 1
+        )
+        return freqs_cis
+
+    def forward(
+        self,
+        input_ids: torch.Tensor,
+        input_embeds: Optional[torch.Tensor] = None,
+        input_states: Optional[torch.Tensor] = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.Tensor] = None,
+        labels: Optional[torch.Tensor] = None,
+        num_steps: Optional[torch.Tensor] = None,
+        past_key_values: Optional[Cache] = None,
+        output_details: dict = {
+            "return_logits": True,
+            "return_latents": True,
+            "return_attention": False,
+            "return_head": False,
+            "return_stats": True,
+        },
+        use_cache: bool = False,
+        cache_position: Optional[torch.Tensor] = None,
+        **kwargs,
+    ) -> CausalLMOutputRecurrentLatents:
+        # Support multiple position formats:
+        if position_ids is None and cache_position is None:
+            freqs_cis = self.freqs_cis[:, : input_ids.shape[1]]
+        elif position_ids is not None:
+            freqs_cis = self.freqs_cis.index_select(1, position_ids.squeeze())
+        elif cache_position is not None:
+            freqs_cis = self.freqs_cis[:, cache_position]
+
+        if input_embeds is None:
+            input_embeds = self.transformer.wte(input_ids)
+
+        if self.emb_scale != 1:
+            input_embeds = input_embeds * self.emb_scale  # type: ignore
+
+        if use_cache and past_key_values is None:
+            past_key_values = HuginnDynamicCache()
+        attn_maps = {}
+        return_attn = output_details["return_attention"]
+
+        # Non-recurrent prelude
+        for block_idx, block in enumerate(self.transformer.prelude):
+            input_embeds, attn_map = block(
+                input_embeds, freqs_cis, block_idx, attention_mask, past_key_values, return_attn
+            )
+            attn_maps[block_idx] = attn_map
+
+        # Main recurrence
+        x, num_steps_no_grad, num_steps_with_grad, xk, block_idx, attn_maps = self.iterate_forward(
+            input_embeds,  # type: ignore
+            input_states,
+            freqs_cis,
+            block_idx,
+            attention_mask,
+            past_key_values,
+            num_steps,
+            attn_maps,
+        )
+        latent_states = x.clone().detach()
+
+        # Coda layers
+        for block_idx, block in enumerate(self.transformer.coda, start=1):
+            x, attn_map = block(x, freqs_cis, -block_idx, attention_mask, past_key_values, return_attn)
+            attn_maps[-block_idx] = attn_map
+        x = self.transformer.ln_f(x)
+
+        # Prediction head, assuming labels really are labels and not equal to input_ids
+        if labels is not None:
+            logits = self.lm_head(x).float()
+            loss = torch.nn.functional.cross_entropy(logits.view(-1, logits.shape[-1]), labels.view(-1))
+            log_ppl = loss.clone().detach()
+        else:
+            logits = self.lm_head(x).float()
+            loss, log_ppl = torch.as_tensor(0.0), torch.as_tensor(0.0)
+
+        return CausalLMOutputRecurrentLatents(
+            loss=loss,
+            log_ppl=log_ppl,
+            logits=logits if output_details["return_logits"] else None,
+            past_key_values=past_key_values,
+            hidden_states=x if output_details["return_head"] else None,
+            latent_states=latent_states if output_details["return_latents"] else None,
+            attention_maps=attn_maps if output_details["return_attention"] else None,  # type: ignore
+            stats=self.get_stats(logits, x, latent_states, xk, input_embeds, num_steps_no_grad, num_steps_with_grad)
+            if output_details["return_stats"]
+            else None,
+        )
+
+    @torch._dynamo.disable(recursive=False)  # type: ignore
+    def iterate_forward(
+        self,
+        input_embeds,
+        input_states,
+        freqs_cis,
+        block_idx,
+        mask,
+        past_key_values: Optional[Cache] = None,
+        num_steps: Optional[torch.Tensor] = None,
+        attn_maps: dict = {},
+    ):
+        x = xk = self.initialize_state(input_embeds) if input_states is None else input_states.clone()
+        if num_steps is None:
+            num_steps_no_grad, num_steps_with_grad = self.randomized_iteration_sampler()  # type: ignore
+        elif hasattr(num_steps, "__len__") and len(num_steps) > 1:
+            num_steps_no_grad, num_steps_with_grad = num_steps
+        else:
+            num_steps_no_grad, num_steps_with_grad = num_steps, torch.tensor(0)
+
+        with torch.no_grad():
+            # ultra annoying in ddp due to
+            # https://discuss.pytorch.org/t/does-distributeddataparallel-work-with-torch-no-grad-and-find-unused-parameters-false/122594
+            # for now running with find_unused_params=True enabled even though the graph structure is (technically) clear
+            # and all parameters are always used
+            for step in range(num_steps_no_grad):
+                xk = x
+                x, block_idx, attn_maps = self.core_block_forward(
+                    xk, input_embeds, freqs_cis, mask, past_key_values, block_idx, attn_maps
+                )
+
+        for step in range(num_steps_with_grad):
+            xk = x
+            x, block_idx, attn_maps = self.core_block_forward(
+                xk, input_embeds, freqs_cis, mask, past_key_values, block_idx, attn_maps
+            )
+        return self.transformer.ln_f(x), num_steps_no_grad, num_steps_with_grad, xk.detach(), block_idx, attn_maps
+
+    def core_block_forward(
+        self,
+        x,
+        input_embeds,
+        freqs_cis,
+        mask,
+        past_key_values,
+        block_idx: Union[torch.Tensor, int],
+        attn_maps: dict = {},
+    ):
+        x = self.transformer.adapter(torch.cat([x, input_embeds], dim=-1))
+        for idx, block in enumerate(self.transformer.core_block, start=1):
+            x, attn_map = block(x, freqs_cis, block_idx + idx, mask, past_key_values, return_attn=len(attn_maps) > 0)
+            attn_maps[block_idx + idx] = attn_map
+        return x, block_idx + idx, attn_maps
+
+    @torch.no_grad()
+    def iterate_one_step(
+        self,
+        input_embeds,
+        input_states,
+        position_ids: Optional[torch.Tensor] = None,
+        cache_position: Optional[torch.Tensor] = None,
+        block_idx: Union[torch.Tensor, int] = 0,
+        attention_mask: Optional[Tensor] = None,
+        past_key_values: Optional[Cache] = None,
+        attn_maps: dict = {},
+    ):
+        if position_ids is None and cache_position is None:
+            freqs_cis = self.freqs_cis[:, : input_embeds.shape[1]]
+        elif position_ids is not None:
+            freqs_cis = self.freqs_cis.index_select(1, position_ids.squeeze())
+        elif cache_position is not None:
+            freqs_cis = self.freqs_cis[:, cache_position]
+        x, block_idx, attn_maps = self.core_block_forward(
+            input_states, input_embeds, freqs_cis, attention_mask, past_key_values, block_idx, attn_maps
+        )
+        return x, block_idx, attn_maps
+
+    def predict_from_latents(
+        self,
+        latents,
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.Tensor] = None,
+        cache_position: Optional[torch.Tensor] = None,
+        past_key_values: Optional[Cache] = None,
+        return_attn: bool = False,
+        attn_maps: dict = {},
+    ):
+        if position_ids is None and cache_position is None:
+            freqs_cis = self.freqs_cis[:, : latents.shape[1]]
+        elif position_ids is not None:
+            freqs_cis = self.freqs_cis.index_select(1, position_ids.squeeze())
+        elif cache_position is not None:
+            freqs_cis = self.freqs_cis[:, cache_position]
+        x = self.transformer.ln_f(latents)
+        # Coda layers
+        for block_idx, block in enumerate(self.transformer.coda, start=1):
+            x, attn_map = block(x, freqs_cis, -block_idx, attention_mask, past_key_values)
+        attn_maps[block_idx] = attn_map
+        x = self.transformer.ln_f(x)
+
+        logits = self.lm_head(x).float()
+
+        return CausalLMOutputRecurrentLatents(
+            loss=torch.as_tensor(0.0),
+            log_ppl=torch.as_tensor(0.0),
+            logits=logits,
+            past_key_values=past_key_values,
+            attention_maps=attn_maps if len(attn_maps) > 0 else None,
+        )
+
+    def embed_inputs(
+        self,
+        input_ids: torch.Tensor,
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.Tensor] = None,
+        past_key_values: Optional[Cache] = None,
+        use_cache: bool = False,
+        cache_position: Optional[torch.Tensor] = None,
+        return_attn: bool = False,
+        **kwargs,
+    ) -> tuple[torch.Tensor, int, dict[int, Tensor]]:
+        # Support multiple position formats:
+        if position_ids is None and cache_position is None:
+            freqs_cis = self.freqs_cis[:, : input_ids.shape[1]]
+        elif position_ids is not None:
+            freqs_cis = self.freqs_cis.index_select(1, position_ids.squeeze())
+        elif cache_position is not None:
+            freqs_cis = self.freqs_cis[:, cache_position]
+
+        input_embeds = self.transformer.wte(input_ids)
+
+        if self.emb_scale != 1:
+            input_embeds = input_embeds * self.emb_scale  # type: ignore
+
+        if use_cache and past_key_values is None:
+            past_key_values = HuginnDynamicCache()
+
+        # Non-recurrent prelude
+        attn_maps = {}
+        for block_idx, block in enumerate(self.transformer.prelude):
+            input_embeds, attn_maps = block(
+                input_embeds, freqs_cis, block_idx, attention_mask, past_key_values, return_attn
+            )
+        return input_embeds, block_idx, attn_maps
+
+    @torch._dynamo.disable(recursive=False)  # type: ignore
+    def randomized_iteration_sampler(self) -> tuple[torch.Tensor, torch.Tensor]:
+        """Outputs are long tensors so that they can be passed through compiled functions"""
+        t = max(self.config.mean_recurrence - self.config.mean_backprop_depth, 0)
+        s = self.config.mean_backprop_depth
+        if self.training:
+            sigma = 0.5
+            mu = math.log(t + s) - (sigma**2 / 2)
+            rate = torch.zeros((1,)).log_normal_(mean=mu, std=sigma)
+            p = torch.poisson(torch.tensor([rate], dtype=torch.float)) + 1
+            n = torch.clamp(p - s, min=0)
+            k = torch.as_tensor(torch.minimum(torch.as_tensor(s), p))
+        else:
+            n, k = torch.as_tensor(self.config.mean_recurrence), torch.as_tensor(0)
+
+        return n.to(dtype=torch.long), k.to(dtype=torch.long)
+
+    def initialize_state(self, input_embeds, deterministic: bool = False):
+        x = torch.randn_like(input_embeds)
+        std = self.config.init_values["std"]
+        torch.nn.init.trunc_normal_(x, mean=0.0, std=std, a=-3 * std, b=3 * std)
+        if self.emb_scale != 1:
+            x = x * self.emb_scale
+        return x if not deterministic else x.zero_()
+
+    def prepare_inputs_for_generation(
+        self,
+        input_ids: torch.LongTensor,
+        past_key_values: Optional[Cache] = None,
+        attention_mask: Optional[torch.LongTensor] = None,
+        inputs_embeds: Optional[torch.FloatTensor] = None,
+        cache_position: Optional[torch.LongTensor] = None,
+        **kwargs,
+    ):
+        model_inputs = {}
+        model_inputs["cache_position"] = cache_position
+        current_input_length = input_ids.shape[1]
+        if past_key_values is not None:
+            if type(past_key_values) == DynamicCache:
+                # Need to use custom cache, detect and replace HF dynamic cache if generate injects it
+                assert past_key_values.get_seq_length() == 0
+                past_key_values = HuginnDynamicCache()
+            model_inputs["past_key_values"] = past_key_values if kwargs["use_cache"] else None
+            input_ids = input_ids[:, cache_position]  # type: ignore
+        model_inputs["input_ids"] = input_ids.clone(memory_format=torch.contiguous_format)
+
+        if cache_position is None:
+            position_ids = torch.arange(current_input_length)[None, :].to(input_ids.device)
+            model_inputs["position_ids"] = position_ids[:, -current_input_length:].clone(
+                memory_format=torch.contiguous_format
+            )  # some form of position_ids is a critical argument for the model to correctly apply rope!
+
+        # forward all other entries
+        for key, value in kwargs.items():
+            if key not in model_inputs:
+                model_inputs[key] = value
+        return model_inputs
+
+    @torch.no_grad()
+    def generate_minimal(
+        self,
+        input_ids: torch.LongTensor,
+        generation_config: Optional[GenerationConfig] = None,  # type: ignore
+        tokenizer=None,
+        streamer=None,
+        continuous_compute=False,  # warm-start state / continuous CoT
+        cache_kwargs: dict = {},
+        **model_kwargs,
+    ) -> Union[torch.Tensor, dict[str, Any]]:
+        """Minimal single-sequence generation. Template for more complicated generate tasks"""
+        # Setup
+        if generation_config is None:
+            generation_config: GenerationConfig = self.generation_config  # type: ignore
+        model_kwargs["past_key_values"] = HuginnDynamicCache(**cache_kwargs)
+        model_kwargs["use_cache"] = True
+        model_kwargs = self._get_initial_cache_position(input_ids, model_kwargs)
+        stop_tokens = self._get_stops(generation_config, tokenizer).to(input_ids.device)
+        if continuous_compute:
+            embedded_inputs, _, _ = self.embed_inputs(input_ids)
+            model_kwargs["input_states"] = self.initialize_state(embedded_inputs)
+        # Generate tokens
+        for _ in range(generation_config.max_length - input_ids.shape[1]):
+            # Forward pass
+            model_inputs = self.prepare_inputs_for_generation(input_ids, **model_kwargs)
+            outputs = self(**model_inputs)
+            next_token_logits = outputs.logits[0, -1, :]
+            if continuous_compute:
+                current_last_latent = outputs.latent_states[:, -1:, :]
+
+            # Sample or select next token
+            if generation_config.do_sample:
+                if generation_config.temperature:
+                    next_token_logits = next_token_logits / generation_config.temperature
+
+                probs = F.softmax(next_token_logits, dim=-1)
+
+                # Apply top_k
+                if generation_config.top_k:
+                    top_k_probs, _ = torch.topk(probs, generation_config.top_k)
+                    probs[probs < top_k_probs[-1]] = 0
+                # Apply top_p
+                if generation_config.top_p:
+                    sorted_probs = torch.sort(probs, descending=True)[0]
+                    cumsum = torch.cumsum(sorted_probs, dim=-1)
+                    probs[cumsum > generation_config.top_p] = 0
+                # Apply min_p
+                if generation_config.min_p:
+                    probs[probs < generation_config.min_p * probs.max()] = 0
+
+                probs = probs / probs.sum()
+                next_token = torch.multinomial(probs, num_samples=1)
+            else:
+                next_token = torch.argmax(next_token_logits, dim=-1, keepdim=True)
+
+            input_ids = torch.cat([input_ids, next_token[None, :]], dim=-1)  # type: ignore
+
+            if streamer:
+                streamer.put(next_token.cpu())
+
+            # Update model kwargs
+            model_kwargs = self._update_model_kwargs_for_generation(outputs, model_kwargs)
+            if continuous_compute:
+                model_kwargs["input_states"] = current_last_latent
+
+            # Check if we hit a stop token
+            if stop_tokens is not None and next_token in stop_tokens:
+                break
+
+        if streamer:
+            streamer.end()
+
+        if generation_config.return_dict_in_generate:
+            return GenerateDecoderOnlyOutput(
+                sequences=input_ids,
+                scores=None,
+                logits=None,
+                attentions=None,
+                hidden_states=None,
+                past_key_values=model_kwargs.get("past_key_values"),
+            )
+        return input_ids
+
+    @torch.no_grad()
+    def generate_with_adaptive_compute(
+        self,
+        input_ids: torch.LongTensor,
+        generation_config: Optional[GenerationConfig] = None,  # type: ignore
+        tokenizer=None,
+        streamer=None,
+        continuous_compute=False,  # warm-start state / continuous CoT
+        latent_dampening=False,
+        criterion="entropy-diff",
+        cache_kwargs: dict = {},
+        **model_kwargs,
+    ) -> Union[torch.Tensor, GenerateDecoderOnlyOutput]:
+        """Minimal single-sequence generation. Template for more complicated generate tasks"""
+        # Setup
+        if generation_config is None:
+            generation_config: GenerationConfig = self.generation_config  # type: ignore
+        model_kwargs["past_key_values"] = HuginnDynamicCache(**cache_kwargs)
+        model_kwargs["use_cache"] = True
+        model_kwargs = self._get_initial_cache_position(input_ids, model_kwargs)
+        stop_tokens = self._get_stops(generation_config, tokenizer).to(input_ids.device)
+        if continuous_compute:
+            embedded_inputs, _, _ = self.embed_inputs(input_ids)
+            current_last_latent = self.initialize_state(embedded_inputs)
+        compute_steps = []
+
+        # Generate tokens
+        for step in range(generation_config.max_length - input_ids.shape[1]):
+            # Adaptive compute forward
+            model_inputs = self.prepare_inputs_for_generation(input_ids, **model_kwargs)
+            aux_inputs = {
+                k: model_inputs[k] for k in ["cache_position", "past_key_values", "attention_mask"] if k in model_inputs
+            }
+            embedded_inputs, block_idx, _ = self.embed_inputs(model_inputs["input_ids"], **aux_inputs)
+            if not continuous_compute:
+                current_latents = self.initialize_state(embedded_inputs, deterministic=False)
+            else:
+                current_latents = current_last_latent
+
+            # Prep criterions:
+            if criterion == "entropy-diff":
+                entropy = torch.tensor(100.0, device=input_ids.device)
+            elif criterion in ["latent-diff", "none"]:
+                pass
+            elif criterion == "kl":
+                V = self.config.padded_vocab_size
+                log_probs = (1 / V * torch.ones(V, device=input_ids.device)).log()
+            elif criterion == "argmax-stability":
+                stable_for_n_steps = 0
+                current_argmax = torch.tensor(-1, dtype=torch.long, device=input_ids.device)
+            else:
+                raise ValueError("Invalid adaptive compute strategy.")
+
+            all_latents = []
+            for compute_step in range(1, model_inputs["num_steps"]):
+                prev_latents = current_latents.clone()
+                current_latents, block_idx, _ = self.iterate_one_step(
+                    embedded_inputs, current_latents, block_idx=block_idx, **aux_inputs
+                )
+                all_latents.append(current_latents if latent_dampening else None)
+                if compute_step > 1 and step > 0:  # do not exit in prefill:
+                    if criterion == "entropy-diff":
+                        prev_entropy = entropy.clone()
+                        outputs = self.predict_from_latents(current_latents, **aux_inputs)
+                        probs = F.softmax(outputs.logits[:, -1, :], dim=-1)  # type: ignore
+                        entropy = -torch.sum(probs * torch.log(probs + 1e-10), dim=-1).mean()
+                        entropy_diff = (entropy - prev_entropy).abs()
+                        if entropy_diff < 1e-3:
+                            compute_steps.append([compute_step, entropy_diff.item()])
+                            break
+                    elif criterion == "latent-diff":
+                        norm_diff = (prev_latents - current_latents).norm()
+                        if norm_diff < 1:
+                            compute_steps.append([compute_step, norm_diff.item()])
+                            break
+                    elif criterion == "kl":
+                        prev_log_probs = log_probs.clone()
+                        outputs = self.predict_from_latents(current_latents, **aux_inputs)
+                        log_probs = F.log_softmax(outputs.logits[:, -1, :], dim=-1)  # type: ignore
+                        kl = F.kl_div(log_probs, prev_log_probs, reduction="none", log_target=True).sum(dim=-1)
+                        if kl < 2e-4:
+                            compute_steps.append([compute_step, kl.item()])
+                            break
+                    elif criterion == "argmax-stability":
+                        prev_argmax = current_argmax.clone()
+                        outputs = self.predict_from_latents(current_latents, **aux_inputs)
+                        current_argmax = outputs.logits[0, -1, :].argmax(dim=-1)  # type: ignore
+                        if current_argmax == prev_argmax:
+                            stable_for_n_steps += 1
+                        else:
+                            stable_for_n_steps = 0
+                        if stable_for_n_steps >= 10:
+                            compute_steps.append([compute_step, stable_for_n_steps])
+                            break
+                    elif criterion == "none":
+                        pass
+
+            else:
+                compute_steps.append([compute_step, float("NaN")])
+                if not latent_dampening:
+                    outputs = self.predict_from_latents(current_latents, **aux_inputs)
+                else:
+                    dampened_latents = torch.sum(torch.cat(all_latents, dim=0), dim=0, keepdim=True)
+                    outputs = self.predict_from_latents(dampened_latents, **aux_inputs)
+
+            next_token_logits = outputs.logits[0, -1, :]  # type: ignore
+            if continuous_compute:  # Save last latent
+                current_last_latent = current_latents[:, -1:, :]
+
+            # Sample or select next token
+            if generation_config.do_sample:
+                if generation_config.temperature:
+                    next_token_logits = next_token_logits / generation_config.temperature
+
+                probs = F.softmax(next_token_logits, dim=-1)
+                # Apply top_k
+                if generation_config.top_k:
+                    top_k_probs, _ = torch.topk(probs, generation_config.top_k)
+                    probs[probs < top_k_probs[-1]] = 0
+                # Apply top_p
+                if generation_config.top_p:
+                    sorted_probs = torch.sort(probs, descending=True)[0]
+                    cumsum = torch.cumsum(sorted_probs, dim=-1)
+                    probs[cumsum > generation_config.top_p] = 0
+                # Apply min_p
+                if generation_config.min_p:
+                    probs[probs < generation_config.min_p * probs.max()] = 0
+
+                probs = probs / probs.sum()
+                next_token = torch.multinomial(probs, num_samples=1)
+            else:
+                next_token = torch.argmax(next_token_logits, dim=-1, keepdim=True)
+
+            input_ids = torch.cat([input_ids, next_token[None, :]], dim=-1)  # type: ignore
+
+            if streamer:
+                streamer.put(next_token.cpu())
+
+            # Update model kwargs
+            model_kwargs = self._update_model_kwargs_for_generation(outputs, model_kwargs)
+
+            # Check if we hit a stop token
+            if stop_tokens is not None and next_token in stop_tokens:
+                break
+
+        if streamer:
+            streamer.end()
+
+        if generation_config.return_dict_in_generate:
+            return GenerateDecoderOnlyOutput(
+                sequences=input_ids,
+                scores=compute_steps,  # type: ignore
+                logits=None,
+                attentions=None,
+                hidden_states=None,
+                past_key_values=model_kwargs.get("past_key_values"),
+            )
+        return input_ids
+
+    def _get_stops(self, generation_config, tokenizer):
+        stop_tokens = set()
+        if generation_config.eos_token_id is not None:
+            stop_tokens.add(generation_config.eos_token_id)
+        if hasattr(generation_config, "stop_strings") and tokenizer and generation_config.stop_strings:
+            for s in generation_config.stop_strings:
+                token_id = tokenizer(s, add_special_tokens=False)["input_ids"][0]
+                stop_tokens.add(token_id)
+        return torch.tensor(list(stop_tokens))
+
+    def get_stats(self, logits, x, latent_states, xk, input_embeds, num_steps_no_grad, num_steps_with_grad):
+        probs = torch.softmax(logits.float(), dim=-1)
+        prob_entropy = torch.where(probs > 0, -probs * probs.log(), 0).sum(dim=-1)
+        residual_diff = (x - latent_states).norm(dim=-1)
+        rel_residual = residual_diff / latent_states.norm(dim=-1)
+        stats = {
+            "entropy": prob_entropy,
+            "residual_diff": residual_diff,
+            "rel_residual": rel_residual,
+            "num_steps_no_grad": num_steps_no_grad,
+            "num_steps_with_grad": num_steps_with_grad,
+        }
+        return stats
+
+
+#################################### Utils #######################################################################
+def precompute_freqs_cis(dim: int, end: int, theta: float = 10000.0, condense_ratio: int = 1):
+    with torch.autocast("cuda", enabled=False):
+        inv_freqs = 1.0 / (theta ** (torch.arange(0, dim, 2, dtype=torch.float32) / dim))
+        t = torch.arange(end, dtype=torch.float32, device=inv_freqs.device) / condense_ratio
+        freqs = torch.outer(t, inv_freqs).float()
+        return torch.stack([torch.cos(freqs)[None, :, None, :], torch.sin(freqs)[None, :, None, :]], dim=4)
+        # equivalent to
+        # freqs_cis = torch.polar(torch.ones_like(freqs), freqs)
+        # cache = torch.stack([freqs_cis.real, freqs_cis.imag], dim=-1)
+
+
+def apply_rotary_emb_complex_like(q: Tensor, k: Tensor, freqs_cis: Tensor) -> tuple[Tensor, Tensor]:
+    with torch.autocast("cuda", enabled=False):
+        qk_r2 = torch.cat([q, k], dim=2).unflatten(dim=-1, sizes=(-1, 2)).float()  # cast to float32 for smooth skin
+        rotated_qk_r2 = torch.stack(
+            [
+                qk_r2[..., 0] * freqs_cis[..., 0] - qk_r2[..., 1] * freqs_cis[..., 1],
+                qk_r2[..., 1] * freqs_cis[..., 0] + qk_r2[..., 0] * freqs_cis[..., 1],
+            ],
+            -1,
+        ).flatten(3)
+        rotated_qk = rotated_qk_r2
+        return torch.split(rotated_qk.type_as(q), q.shape[2], dim=2)  # type: ignore
+
+
+#################################### HF registration ############################################################
+
+from transformers import AutoConfig, AutoModel, AutoModelForCausalLM
+
+# New
+RavenConfig.register_for_auto_class()
+
+RavenForCausalLM.register_for_auto_class("AutoModel")
+RavenForCausalLM.register_for_auto_class("AutoModelForCausalLM")
+
+# Old?
+AutoConfig.register("huginn_raven", RavenConfig)
+AutoModel.register(RavenConfig, RavenForCausalLM)
+AutoModelForCausalLM.register(RavenConfig, RavenForCausalLM)