Delete raven

raven/__init__.py

@@ -1,7 +0,0 @@
-# -*- coding: utf-8 -*-
-
-from raven.models.raven import RavenConfig, RavenForCausalLM, RavenModel
-
-__all__ = [
-    'RavenConfig', 'RavenForCausalLM', 'RavenModel',
-]

raven/layers/__init__.py

@@ -1,5 +0,0 @@
-# -*- coding: utf-8 -*-
-
-from raven.layers.raven import RavenAttention
-
-__all__ = ['RavenAttention']

raven/layers/raven.py

@@ -1,348 +0,0 @@
-
-
-from __future__ import annotations
-
-import warnings
-from typing import TYPE_CHECKING, Dict, Optional, Tuple
-
-import torch
-import torch.nn as nn
-import math
-import torch.nn.functional as F
-from einops import rearrange, repeat
-
-from fla.layers.utils import get_unpad_data, index_first_axis, pad_input
-from fla.modules.feature_map import ReLUFeatureMap, SwishFeatureMap, T2RFeatureMap
-from fla.modules.layernorm import rms_norm_linear
-from fla.ops.gsa import chunk_gsa as chunk_raven, fused_recurrent_gsa as fused_recurrent_raven
-
-
-from fla.ops.utils.index import prepare_lens_from_mask
-
-
-from fla.modules import FusedRMSNormGated , RMSNorm, RotaryEmbedding  #Added for RoPE
-
-if TYPE_CHECKING:
-    from transformers.processing_utils import Unpack
-
-    from fla.models.utils import Cache
-
-def _max_offset(seqlen_offset):
-    if seqlen_offset is None:
-        return 0
-    if isinstance(seqlen_offset, int):
-        # scalar offset, nothing fancy
-        return seqlen_offset
-    if isinstance(seqlen_offset, torch.Tensor):
-        # tensor of offsets -> take max
-        return int(seqlen_offset.max().item())
-    # list/tuple/other iterables
-    return max(seqlen_offset)
-
-
-class RavenAttention(nn.Module):
-
-    def __init__(
-        self,
-        mode: str = 'chunk',
-        hidden_size: int = 1024,
-        expand_k: float = 1.,
-        expand_v: float = 1.,
-        num_heads: int = 4,
-        num_kv_heads: Optional[int] = None,
-        num_slots: Optional[int] = None,
-        elementwise_affine: Optional[bool] = True,
-        norm_eps: float = 1e-5,
-        gate_logit_normalizer: int = 8,
-        feature_map: str = 'swish',
-        use_output_gate: bool = False,
-        use_norm: bool = True,
-        layer_idx: Optional[int] = None,
-        scale: Optional[float] = 1.,
-        decay_type: str = 'Mamba2',
-        topk: int = 32,
-        bias_rmm: bool = False,
-        add_gumbel_noise: bool = True,
-        router_score: str = 'sigmoid',
-        router_type: str = 'lin',
-        use_rope: bool = False,
-        **kwargs
-    ) -> RavenAttention:
-        super().__init__()
-
-        self.mode = mode
-        self.decay_type = decay_type
-        self.hidden_size = hidden_size
-        self.expand_k = expand_k
-        self.expand_v = expand_v
-        self.num_heads = num_heads
-        self.num_kv_heads = num_heads if num_kv_heads is None else num_kv_heads
-        self.num_kv_groups = self.num_heads // self.num_kv_heads
-        self.key_dim = int(hidden_size * expand_k)
-        self.value_dim = int(hidden_size * expand_v)
-        self.key_dim_per_group = self.key_dim // self.num_kv_groups
-        self.value_dim_per_group = self.value_dim // self.num_kv_groups
-        self.head_k_dim = self.key_dim // self.num_heads
-        self.head_v_dim = self.value_dim // self.num_heads
-        self.topk = topk
-        self.use_output_gate = use_output_gate
-        self.use_rope = use_rope
-        self.gate_logit_normalizer = gate_logit_normalizer
-        self.use_norm = use_norm
-        self.scale = scale
-        self.rope_theta = 10000.
-
-        ##  For Router Design
-        self.bias_rmm = bias_rmm  # For no gumbel router with bias
-        self.add_gumbel_noise = add_gumbel_noise  # For no gumbel router with bias
-        self.router_score = router_score
-        self.router_type = router_type
-
-        if num_slots is None:
-            num_slots = self.head_k_dim
-        self.num_slots = num_slots
-
-        self.layer_idx = layer_idx
-
-        if layer_idx is None:
-            warnings.warn(
-                f"Instantiating {self.__class__.__name__} without passing `layer_idx` is not recommended and will "
-                "to errors during the forward call, if caching is used. Please make sure to provide a `layer_idx` "
-                "when creating this class."
-            )
-
-        self.register_module('feature_map', None)
-        if feature_map == 'swish':
-            self.feature_map = SwishFeatureMap()
-        elif feature_map == 'relu':
-            self.feature_map = ReLUFeatureMap()
-        elif feature_map == 't2r':
-            self.feature_map = T2RFeatureMap(self.head_k_dim, self.head_k_dim)
-        else:
-            raise NotImplementedError(f"Feature map `{feature_map}` is not supported now.")
-
-        ## ===== QKV Proj =====
-        self.q_proj = nn.Linear(self.hidden_size, self.key_dim, bias=False)
-        self.k_proj = nn.Linear(self.hidden_size, self.key_dim_per_group, bias=False)
-        self.v_proj = nn.Linear(self.hidden_size, self.value_dim_per_group, bias=False)
-
-        ## ===== Forget Gate/ Decay =====
-        if self.decay_type == 'Mamba2':
-            # Decay of Mamba2
-            self.a_proj = nn.Linear(self.hidden_size, self.num_heads, bias=False)
-            A = torch.empty(self.num_heads, dtype=torch.float32).uniform_(0, 16)
-            self.A_log = nn.Parameter(torch.log(A))
-            self.A_log._no_weight_decay = True
-            # hard coded for now
-            dt_min = 0.001
-            dt_max = 0.1
-            dt_init_floor = 1e-4
-            dt = torch.exp(
-                torch.rand(self.num_heads) * (math.log(dt_max) - math.log(dt_min))
-                + math.log(dt_min)
-            )
-            dt = torch.clamp(dt, min=dt_init_floor)
-            # Inverse of softplus: https://github.com/pytorch/pytorch/issues/72759
-            inv_dt = dt + torch.log(-torch.expm1(-dt))
-            self.dt_bias = nn.Parameter(inv_dt)
-            # Just to be explicit. Without this we already don't put wd on dt_bias because of the check
-            # name.endswith("bias") in param_grouping.py
-            self.dt_bias._no_weight_decay = True
-
-        elif self.decay_type == 'GLA':
-            self.f_proj = nn.Linear(self.hidden_size, self.num_kv_heads * self.num_slots, bias=False)
-
-        ## ===== Router  =====
-        if self.bias_rmm:
-            self.r_bias =  nn.Parameter(torch.empty( ( self.num_heads , self.num_slots) , dtype=torch.float32))
-
-        if  self.router_type == 'lin':
-            self.r_proj = nn.Linear(self.hidden_size, self.num_heads * self.num_slots , bias=False)
-
-        elif  self.router_type == 'mlp':
-            self.r_proj = nn.Sequential(
-            nn.Linear(self.hidden_size, self.hidden_size, bias=True),
-            nn.GELU(),
-            nn.Linear(self.hidden_size, self.num_heads * self.num_slots, bias=False) )
-
-        self.score_fn = (
-            (lambda x: torch.sigmoid(x))
-            if self.router_score == "sigmoid"
-            else (lambda x: torch.softmax(x, dim=-1))
-        )
-
-
-        ## ===== RoPE  =====
-        if self.use_rope:
-            self.rotary = RotaryEmbedding(dim=self.head_k_dim, base=self.rope_theta)   # Added for RoPE
-
-        ## ===== QK Norm  =====
-        self.q_norm = RMSNorm(self.head_k_dim, elementwise_affine, eps=norm_eps)
-        self.k_norm = RMSNorm(self.head_k_dim, elementwise_affine, eps=norm_eps)
-
-        ## ===== Output Layer  =====
-        if self.use_output_gate:
-            self.o_gate_proj = nn.Linear(hidden_size, self.value_dim, bias=False)
-            self.o_norm = FusedRMSNormGated(self.head_v_dim, eps=norm_eps)
-        else:
-            self.g_norm = RMSNorm(self.hidden_size, elementwise_affine, eps=norm_eps)
-
-        self.o_proj = nn.Linear(self.value_dim, self.hidden_size, bias=False)
-
-    def reset_parameters(self) -> None:
-        for module in self.children():
-            reset = getattr(module, "reset_parameters", None)
-            if callable(reset):
-                reset()
-
-    def forward(
-        self,
-        hidden_states: torch.Tensor,
-        attention_mask: Optional[torch.Tensor] = None,
-        past_key_values: Optional[Cache] = None,
-        use_cache: Optional[bool] = False,
-        output_attentions: Optional[bool] = False,
-        **kwargs: Unpack[Dict]
-    ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Cache]]:
-        if attention_mask is not None:
-            assert len(attention_mask.shape) == 2, (
-                "Expected attention_mask as a 0-1 matrix with shape [batch_size, seq_len] "
-                "for padding purposes (0 indicating padding). "
-                "Arbitrary attention masks of shape [batch_size, seq_len, seq_len] are not allowed."
-            )
-
-        batch_size, q_len, _ = hidden_states.shape
-        mode = self.mode
-        if (not self.training) and q_len == 1 and use_cache and past_key_values is not None and mode == 'chunk':
-            mode = 'fused_recurrent'
-        seqlen_offset, max_seqlen = 0, q_len # Added for RoPE
-
-        last_state = None
-        if past_key_values is not None and len(past_key_values) > self.layer_idx:
-            seqlen_offset = past_key_values.get_seq_length(self.layer_idx)   # Added for RoPE
-            last_state = past_key_values[self.layer_idx]
-            max_seqlen = q_len + seqlen_offset  # Added for RoPE
-
-        cu_seqlens = kwargs.get('cu_seqlens', None)
-        if attention_mask is not None:
-            indices, cu_seqlens, _ = get_unpad_data(attention_mask[:, -q_len:])
-            hidden_states = index_first_axis(rearrange(hidden_states, "b s ... -> (b s) ..."), indices).unsqueeze(0) # to deliminate the offsets of padding tokens
-            seqlen_offset = seqlen_offset + prepare_lens_from_mask(attention_mask) - attention_mask.shape[-1]   # Added for RoPE
-            max_seqlen = max(q_len, q_len + _max_offset(seqlen_offset))    # Added for RoPE
-
-        q = rearrange(self.q_proj(hidden_states), '... (h d) -> ... h d', d=self.head_k_dim)
-        k = rearrange(self.k_proj(hidden_states), '... (h d) -> ... h d', d=self.head_k_dim)
-        v = rearrange(self.v_proj(hidden_states), '... (h d) -> ... h d', d=self.head_v_dim)
-        router = rearrange(self.r_proj(hidden_states) , '... (h m) -> ... h m', m=self.num_slots)
-
-        if self.decay_type == 'Mamba2':
-            # Build Mamba2 Decay
-            f = (- self.A_log.float().exp() * F.softplus(self.a_proj(hidden_states).float() + self.dt_bias)).unsqueeze(-1)
-        elif self.decay_type == 'GLA':
-            f = rearrange(self.f_proj(hidden_states) , '... (h m) -> ... h m', m=self.num_slots)
-            f =  F.logsigmoid(f) / self.gate_logit_normalizer
-
-        if self.feature_map is not None:
-            q, k = map(lambda x: self.feature_map(x), (q, k))
-
-        # QK Norm
-        q, k = self.q_norm(q), self.k_norm(k)
-
-        if self.use_rope:
-            assert batch_size == 1, "RoPE is not supported for batch size > 1"
-            max_seqlen = max(max_seqlen, 8192)  # Added for RoPE
-            q, k = self.rotary(q, k, seqlen_offset=seqlen_offset, max_seqlen=max_seqlen, cu_seqlens=cu_seqlens)  #Added for RoPE
-
-        # V Feature map
-        v = F.silu(v)
-
-        # Build RMM Router
-        if self.add_gumbel_noise:
-            if self.training:
-                # bf16 exponential/log can underflow to zero and produce infinities,
-                # which then poison the sparse router and the GSA Triton kernels.
-                noise = torch.empty(
-                    router.shape,
-                    device=router.device,
-                    dtype=torch.float32,
-                ).exponential_().clamp_min_(1e-6).log_()
-                router = router.float() - noise
-
-        router = torch.nan_to_num(router.float(), nan=0.0, posinf=20.0, neginf=-20.0).clamp_(-20.0, 20.0)
-
-        orig_scores = self.score_fn(router)
-        if self.bias_rmm:
-            scores = orig_scores + self.r_bias.float()
-        else:
-            scores = orig_scores
-
-        route_idx = scores.topk(self.topk, dim=-1).indices
-        topk_weights =  torch.gather(orig_scores, dim=-1, index=route_idx)
-
-        if self.router_score == 'sigmoid':
-            topk_weights /= (topk_weights.sum(dim=-1, keepdim=True) + 1e-9)
-
-        s_multihot = torch.zeros_like(router).scatter_(-1, route_idx, topk_weights.to(router.dtype))
-
-        f = torch.nan_to_num(f.float(), nan=0.0, posinf=0.0, neginf=-20.0).clamp_(-20.0, 0.0)
-        s_multihot = torch.nan_to_num(s_multihot.float(), nan=0.0, posinf=1.0, neginf=0.0).clamp_(0.0, 1.0)
-        f = (f * s_multihot).to(q.dtype)
-        s = torch.nan_to_num((1 - f.float().exp()), nan=0.0, posinf=1.0, neginf=0.0).clamp_(0.0, 1.0).to(q.dtype)
-
-        q = torch.nan_to_num(q, nan=0.0, posinf=0.0, neginf=0.0)
-        k = torch.nan_to_num(k, nan=0.0, posinf=0.0, neginf=0.0)
-        v = torch.nan_to_num(v, nan=0.0, posinf=0.0, neginf=0.0)
-
-        recurrent_state = last_state['recurrent_state'] if last_state is not None else None
-        if self.num_kv_groups > 1:
-            k, v = map(lambda x: repeat(x, '... h d -> ... (h g) d', g=self.num_kv_groups), (k, v))
-
-        if mode == 'fused_recurrent':
-            o, recurrent_state = fused_recurrent_raven(
-                q=q,
-                k=k,
-                v=v,
-                s=s,
-                g=f,
-                initial_state=recurrent_state,
-                output_final_state=use_cache,
-                scale=self.scale,
-                cu_seqlens=cu_seqlens,
-            )
-        elif mode == 'chunk':
-            o, recurrent_state = chunk_raven(
-                q=q,
-                k=k,
-                v=v,
-                s=s,
-                g=f,
-                initial_state=recurrent_state,
-                output_final_state=use_cache,
-                scale=self.scale,
-                cu_seqlens=cu_seqlens,
-            )
-        else:
-            raise NotImplementedError(f"Not supported mode `{mode}`.")
-
-        if past_key_values is not None:
-            past_key_values.update(
-                recurrent_state=recurrent_state,
-                conv_state=None,
-                layer_idx=self.layer_idx,
-                offset=q_len
-            )
-
-
-        if self.use_output_gate:
-            gate_out = rearrange(self.o_gate_proj(hidden_states), '... (h d) -> ... h d', d=self.head_v_dim)
-            o = self.o_norm(F.silu(o), gate_out)
-            o = rearrange(o, '... h d -> ... (h d)')
-            o = self.o_proj(o)
-        else:
-            o = rearrange(o, '... h d -> ... (h d)')
-            o = rms_norm_linear(F.silu(o), self.g_norm.weight, self.g_norm.bias, self.o_proj.weight, self.o_proj.bias)
-
-        if attention_mask is not None:
-            o = pad_input(o.squeeze(0), indices, batch_size, q_len)
-
-        return o, None, past_key_values

raven/models/__init__.py

@@ -1 +0,0 @@
-# -*- coding: utf-8 -*-

raven/models/raven/__init__.py

@@ -1,12 +0,0 @@
-# -*- coding: utf-8 -*-
-
-from transformers import AutoConfig, AutoModel, AutoModelForCausalLM
-
-from raven.models.raven.configuration_raven import RavenConfig
-from raven.models.raven.modeling_raven import RavenForCausalLM, RavenModel
-
-AutoConfig.register(RavenConfig.model_type, RavenConfig, exist_ok=True)
-AutoModel.register(RavenConfig, RavenModel, exist_ok=True)
-AutoModelForCausalLM.register(RavenConfig, RavenForCausalLM, exist_ok=True)
-
-__all__ = ['RavenConfig', 'RavenForCausalLM', 'RavenModel']

raven/models/raven/configuration_raven.py

@@ -1,111 +0,0 @@
-# -*- coding: utf-8 -*-
-
-from typing import Dict, Optional
-
-from transformers.configuration_utils import PretrainedConfig
-
-
-class RavenConfig(PretrainedConfig):
-
-    model_type = 'raven'
-    keys_to_ignore_at_inference = ['past_key_values']
-
-    def __init__(
-        self,
-        hidden_size: int = 2048,
-        gate_logit_normalizer: Optional[int] = 8,
-        clamp_min: Optional[float] = None,
-        clamp_max: Optional[float] = None,
-        hidden_ratio: Optional[int] = 4,
-        intermediate_size: Optional[int] = None,
-        num_hidden_layers: int = 24,
-        num_heads: int = 4,
-        num_kv_heads: Optional[int] = None,
-        num_slots: Optional[int] = 64,
-        use_short_conv: bool = False,
-        conv_size: int = 4,
-        exapnd_k: float = 1,
-        exapnd_v: float = 1,
-        feature_map: str = 'swish',
-        use_output_gate: bool = False,
-        use_norm: bool = True,
-        max_position_embeddings: int = 2048,
-        hidden_act: str = "swish",
-        decay_type: str = 'Mamba2',
-        bias_rmm: bool = False,
-        add_gumbel_noise: bool = True,
-        router_score: str = 'sigmoid',
-        router_type: str = 'lin',
-        topk = 32,
-        elementwise_affine: Optional[bool] = True,
-        norm_eps: float = 1e-6,
-        attn: Optional[Dict] = None,
-        use_cache: bool = True,
-        pad_token_id: Optional[int] = None,
-        bos_token_id: int = 1,
-        eos_token_id: int = 2,
-        initializer_range: float = 0.02,
-        tie_word_embeddings: bool = False,
-        fuse_norm: bool = True,
-        fuse_swiglu: bool = True,
-        fuse_cross_entropy: bool = True,
-        use_l2warp: bool = False,
-        vocab_size: int = 32000,
-        **kwargs
-    ):
-        self.hidden_size = hidden_size
-        self.gate_logit_normalizer = gate_logit_normalizer
-        self.clamp_min = clamp_min
-        self.clamp_max = clamp_max
-        self.hidden_ratio = hidden_ratio
-        self.intermediate_size = intermediate_size
-        self.num_hidden_layers = num_hidden_layers
-        self.num_heads = num_heads
-        self.num_kv_heads = num_kv_heads
-        self.num_slots = num_slots
-        self.use_short_conv = use_short_conv
-        self.conv_size = conv_size
-        self.expand_k = exapnd_k
-        self.expand_v = exapnd_v
-        self.feature_map = feature_map
-        self.use_output_gate = use_output_gate
-        self.use_norm = use_norm
-        self.max_position_embeddings = max_position_embeddings
-        self.hidden_act = hidden_act
-        self.elementwise_affine = elementwise_affine
-        self.norm_eps = norm_eps
-        self.attn = attn
-        self.use_cache = use_cache
-        self.initializer_range = initializer_range
-        self.decay_type = decay_type
-        self.topk = topk
-        self.bias_rmm = bias_rmm   
-        self.add_gumbel_noise = add_gumbel_noise   
-        self.router_score = router_score
-        self.router_type = router_type
-        self.fuse_norm = fuse_norm
-        self.fuse_swiglu = fuse_swiglu
-        self.fuse_cross_entropy = fuse_cross_entropy
-        self.use_l2warp = use_l2warp
-        self.vocab_size = vocab_size
-
-        if attn is not None:
-            if not isinstance(attn, Dict):
-                raise ValueError("attn must be a dictionary")
-            if 'layers' not in attn:
-                raise ValueError("Layer indices must be provided to initialize hybrid attention layers")
-            if 'num_heads' not in attn:
-                raise ValueError("Number of heads must be provided to initialize hybrid attention layers")
-            attn['num_kv_heads'] = attn.get('num_kv_heads', attn['num_heads'])
-            attn['qkv_bias'] = attn.get('qkv_bias', False)
-            attn['window_size'] = attn.get('window_size', None)
-            attn['rope_theta'] = attn.get('rope_theta', 10000.)
-            attn['use_rope'] = attn.get('use_rope', True)
-
-        super().__init__(
-            pad_token_id=pad_token_id,
-            bos_token_id=bos_token_id,
-            eos_token_id=eos_token_id,
-            tie_word_embeddings=tie_word_embeddings,
-            **kwargs,
-        )

raven/models/raven/modeling_raven.py

@@ -1,429 +0,0 @@
-# -*- coding: utf-8 -*-
-
-from __future__ import annotations
-
-import math
-import warnings
-from typing import TYPE_CHECKING, Dict, List, Optional, Tuple, Union
-
-import torch
-import torch.nn as nn
-import torch.utils.checkpoint
-from transformers.generation import GenerationMixin
-from transformers.modeling_outputs import BaseModelOutputWithPast, CausalLMOutputWithPast
-from transformers.modeling_utils import PreTrainedModel
-from transformers.utils import logging
-from transformers.utils.deprecation import deprecate_kwarg
-
-from fla.layers.attn import Attention
-from raven.layers.raven import RavenAttention
-from raven.models.raven.configuration_raven import RavenConfig
-from fla.models.utils import Cache
-from fla.modules import FusedCrossEntropyLoss, FusedLinearCrossEntropyLoss
-from fla.modules import GatedMLP as GSAMLP
-from fla.modules import RMSNorm
-from fla.modules.l2warp import l2_warp
-
-if TYPE_CHECKING:
-    from transformers.processing_utils import Unpack
-
-logger = logging.get_logger(__name__)
-
-
-class RavenBlock(nn.Module):
-    def __init__(self, config: RavenConfig, layer_idx: int):
-        super().__init__()
-
-        self.config = config
-        self.layer_idx = layer_idx
-
-        self.attn_norm = (RMSNorm if config.fuse_norm else nn.RMSNorm)(config.hidden_size, eps=config.norm_eps)
-        if config.attn is not None and layer_idx in config.attn['layers']:
-            self.attn = Attention(
-                hidden_size=config.hidden_size,
-                num_heads=config.attn['num_heads'],
-                num_kv_heads=config.attn['num_kv_heads'],
-                qkv_bias=config.attn['qkv_bias'],
-                window_size=config.attn['window_size'],
-                use_rope=config.attn['use_rope'],
-                rope_theta=config.attn['rope_theta'],
-                max_position_embeddings=config.max_position_embeddings,
-                layer_idx=layer_idx
-            )
-        else:
-            self.attn = RavenAttention(
-                hidden_size=config.hidden_size,
-                expand_k=config.expand_k,
-                expand_v=config.expand_v,
-                num_heads=config.num_heads,
-                num_kv_heads=config.num_kv_heads,
-                num_slots=config.num_slots,
-                use_short_conv=config.use_short_conv,
-                conv_size=config.conv_size,
-                feature_map=config.feature_map,
-                use_output_gate=config.use_output_gate,
-                use_norm=config.use_norm,
-                gate_fn=config.hidden_act,
-                topk=config.topk,
-                bias_rmm=config.bias_rmm,
-                add_gumbel_noise=config.add_gumbel_noise,
-                router_score=config.router_score,
-                router_type=config.router_type,
-                decay_type = config.decay_type,
-                gate_logit_normalizer=config.gate_logit_normalizer,
-                elementwise_affine=config.elementwise_affine,
-                norm_eps=config.norm_eps,
-                fuse_norm=config.fuse_norm,
-                layer_idx=layer_idx
-            )
-        self.mlp_norm = (RMSNorm if config.fuse_norm else nn.RMSNorm)(config.hidden_size, eps=config.norm_eps)
-        self.mlp = GSAMLP(
-            hidden_size=config.hidden_size,
-            hidden_ratio=config.hidden_ratio,
-            intermediate_size=config.intermediate_size,
-            hidden_act=config.hidden_act,
-            fuse_swiglu=config.fuse_swiglu
-        )
-
-    def forward(
-        self,
-        hidden_states: torch.Tensor,
-        attention_mask: Optional[torch.Tensor] = None,
-        past_key_values: Optional[Union[Cache, List[torch.FloatTensor]]] = None,
-        use_cache: Optional[bool] = False,
-        output_attentions: Optional[bool] = False,
-        **kwargs: Unpack[Dict]
-    ) -> Tuple[torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]]]:
-        residual = hidden_states
-        hidden_states = self.attn_norm(hidden_states)
-        hidden_states, attentions, past_key_values = self.attn(
-            hidden_states=hidden_states,
-            attention_mask=attention_mask,
-            past_key_values=past_key_values,
-            use_cache=use_cache,
-            output_attentions=output_attentions,
-            **kwargs
-        )
-        if self.config.fuse_norm:
-            hidden_states, residual = self.mlp_norm(hidden_states, residual, True)
-        else:
-            hidden_states = residual + hidden_states
-            residual = hidden_states
-            hidden_states = self.mlp_norm(hidden_states)
-        hidden_states = self.mlp(hidden_states, **kwargs)
-        hidden_states = residual + hidden_states
-
-        outputs = (hidden_states, attentions, past_key_values)
-
-        return outputs
-
-
-class RavenPreTrainedModel(PreTrainedModel):
-
-    config_class = RavenConfig
-    base_model_prefix = 'model'
-    supports_gradient_checkpointing = True
-    _no_split_modules = ['RavenBlock']
-    _supports_cache_class = True
-
-    def __init__(self, *inputs, **kwargs):
-        super().__init__(*inputs, **kwargs)
-
-    def _init_weights(
-        self,
-        module: nn.Module,
-        prenorm_residual_strategy: Optional[str] = None,
-        num_residuals_per_layer: int = 2,
-    ):
-        if isinstance(module, (nn.Linear, nn.Conv1d)):
-            # Slightly different from the TF version which uses truncated_normal for initialization
-            # cf https://github.com/pytorch/pytorch/pull/5617
-            nn.init.normal_(module.weight, mean=0.0, std=self.config.initializer_range)
-            if module.bias is not None:
-                nn.init.zeros_(module.bias)
-        elif isinstance(module, nn.Embedding):
-            nn.init.normal_(module.weight, mean=0.0, std=self.config.initializer_range)
-        elif hasattr(module, 'reset_parameters'):
-            module.reset_parameters()
-
-        if prenorm_residual_strategy is not None:
-            # Reinitialize selected weights subject to the OpenAI GPT-2 Paper Scheme:
-            #   > A modified initialization which accounts for the accumulation on the residual path with model depth. Scale
-            #   > the weights of residual layers at initialization by a factor of 1/√N where N is the # of residual layers.
-            #   >   -- GPT-2 :: https://openai.com/blog/better-language-models/
-            #
-            # Reference (Megatron-LM): https://github.com/NVIDIA/Megatron-LM/blob/main/megatron/model/gpt_model.py
-            p = None
-            if hasattr(module, 'o_proj'):
-                p = module.o_proj.weight
-            elif hasattr(module, 'down_proj'):
-                p = module.down_proj.weight
-            if p is not None:
-                # Special Scaled Initialization --> There are 2 Layer Norms per Transformer Block
-                # Following Pytorch init, except scale by 1/sqrt(2 * n_layer)
-                # We need to reinit p since this code could be called multiple times
-                # Having just p *= scale would repeatedly scale it down
-                if prenorm_residual_strategy == 'rescale':
-                    nn.init.kaiming_uniform_(p, a=math.sqrt(5))
-                    with torch.no_grad():
-                        p /= math.sqrt(num_residuals_per_layer * self.config.num_hidden_layers)
-                elif prenorm_residual_strategy == 'zero':
-                    nn.init.zeros_(p)
-                else:
-                    raise ValueError(f"Invalid prenorm_residual_strategy: {prenorm_residual_strategy}")
-
-
-class RavenModel(RavenPreTrainedModel):
-
-    def __init__(self, config: RavenConfig):
-        super().__init__(config)
-        self.padding_idx = config.pad_token_id
-        self.vocab_size = config.vocab_size
-
-        self.embeddings = nn.Embedding(config.vocab_size, config.hidden_size, self.padding_idx)
-        self.layers = nn.ModuleList([RavenBlock(config, layer_idx) for layer_idx in range(config.num_hidden_layers)])
-        self.norm = (RMSNorm if config.fuse_norm else nn.RMSNorm)(config.hidden_size, eps=config.norm_eps)
-
-        self.gradient_checkpointing = False
-
-        self.post_init()
-
-    def get_input_embeddings(self):
-        return self.embeddings
-
-    def set_input_embeddings(self, value):
-        self.embeddings = value
-
-    def forward(
-        self,
-        input_ids: Optional[torch.LongTensor] = None,
-        attention_mask: Optional[torch.Tensor] = None,  # noqa
-        inputs_embeds: Optional[torch.FloatTensor] = None,
-        past_key_values: Optional[Union[Cache, List[torch.FloatTensor]]] = None,
-        use_cache: Optional[bool] = None,
-        output_attentions: Optional[bool] = None,
-        output_hidden_states: Optional[bool] = None,
-        return_dict: Optional[bool] = None,
-        **kwargs: Unpack[Dict]
-    ) -> Union[Tuple, BaseModelOutputWithPast]:
-        if output_attentions:
-            warnings.warn("`RavenModel` does not `output_attentions` now, setting it to `False`.")
-            output_attentions = False
-        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
-        output_hidden_states = output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
-        use_cache = use_cache if use_cache is not None else (self.config.use_cache if not self.training else False)
-        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
-
-        # retrieve input_ids and inputs_embeds
-        if input_ids is not None and inputs_embeds is not None:
-            raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time")
-        if input_ids is None and inputs_embeds is None:
-            raise ValueError("You have to specify either input_ids or inputs_embeds")
-
-        if inputs_embeds is None:
-            inputs_embeds = self.embeddings(input_ids)
-        hidden_states = inputs_embeds
-
-        if use_cache and not isinstance(past_key_values, Cache):
-            past_key_values = Cache.from_legacy_cache(past_key_values)
-
-        if self.gradient_checkpointing and self.training and use_cache:
-            logger.warning_once("`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`...")
-            use_cache = False
-
-        all_hidden_states = () if output_hidden_states else None
-        all_attns = () if output_attentions else None
-        for layer in self.layers:
-            if output_hidden_states:
-                all_hidden_states += (hidden_states,)
-
-            if self.gradient_checkpointing and self.training:
-                hidden_states, attentions, past_key_values = self._gradient_checkpointing_func(
-                    layer.__call__,
-                    hidden_states,
-                    attention_mask,
-                    past_key_values,
-                    use_cache,
-                    output_attentions,
-                    **kwargs
-                )
-            else:
-                hidden_states, attentions, past_key_values = layer(
-                    hidden_states,
-                    attention_mask=attention_mask,
-                    past_key_values=past_key_values,
-                    use_cache=use_cache,
-                    output_attentions=output_attentions,
-                    **kwargs
-                )
-
-            if output_attentions:
-                all_attns += (attentions,)
-
-        hidden_states = self.norm(hidden_states)
-
-        # add hidden states from the last decoder layer
-        if output_hidden_states:
-            all_hidden_states += (hidden_states,)
-
-        if not return_dict:
-            return tuple(i for i in [hidden_states, past_key_values, all_hidden_states, all_attns] if i is not None)
-        return BaseModelOutputWithPast(
-            last_hidden_state=hidden_states,
-            past_key_values=past_key_values,
-            hidden_states=all_hidden_states,
-            attentions=all_attns
-        )
-
-
-class RavenForCausalLM(RavenPreTrainedModel, GenerationMixin):
-
-    _tied_weights_keys = ["lm_head.weight"]
-
-    def __init__(self, config):
-
-        super().__init__(config)
-        self.model = RavenModel(config)
-        self.vocab_size = config.vocab_size
-        self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
-        self.criterion = None
-
-        # Initialize weights and apply final processing
-        self.post_init()
-
-    def get_input_embeddings(self):
-        return self.model.embeddings
-
-    def set_input_embeddings(self, value):
-        self.model.embeddings = value
-
-    def get_output_embeddings(self):
-        return self.lm_head
-
-    def set_output_embeddings(self, new_embeddings):
-        self.lm_head = new_embeddings
-
-    def set_decoder(self, decoder):
-        self.model = decoder
-
-    def get_decoder(self):
-        return self.model
-
-    def generate(self, *args, **kwargs):
-        try:
-            return super().generate(*args, **kwargs)
-        except AttributeError as exception:
-            if 'past_key_values' in str(exception):
-                raise AttributeError(
-                    f"You tried to call `generate` with a decoding strategy that manipulates `past_key_values`, "
-                    f"which is not supported for {self.__class__.__name__}. "
-                    f"Try another generation strategy instead. "
-                    f"For the available generation strategies, check this doc: "
-                    f"https://huggingface.co/docs/transformers/en/generation_strategies#decoding-strategies"
-                )
-            else:
-                raise exception
-
-    @deprecate_kwarg("num_logits_to_keep", version="4.50", new_name="logits_to_keep")
-    def prepare_inputs_for_generation(
-        self,
-        input_ids: torch.LongTensor = None,
-        past_key_values: Optional[Union[Cache, List[torch.FloatTensor]]] = None,
-        attention_mask: Optional[torch.Tensor] = None,
-        inputs_embeds: Optional[torch.Tensor] = None,
-        use_cache: bool = True,
-        logits_to_keep: Optional[int] = None,
-        **kwargs
-    ):
-        # only last token for `inputs_ids` if the `past_key_values` is not empty.
-        if past_key_values is not None and len(past_key_values) > 0:
-            input_ids = input_ids[:, -1:]
-        # if `inputs_embeds` are passed, we only want to use them in the 1st generation step
-        if inputs_embeds is not None and len(past_key_values) == 0:
-            model_inputs = {'inputs_embeds': inputs_embeds}
-        else:
-            # The `contiguous()` here is necessary to have a static stride during decoding. torchdynamo otherwise
-            # recompiles graphs as the stride of the inputs is a guard.
-            # Ref: https://github.com/huggingface/transformers/pull/29114
-            # TODO: use `next_tokens` directly instead.
-            model_inputs = {'input_ids': input_ids.contiguous()}
-
-        if logits_to_keep is not None:
-            model_inputs['logits_to_keep'] = logits_to_keep
-
-        model_inputs.update({
-            'past_key_values': past_key_values,
-            'use_cache': use_cache,
-            'attention_mask': attention_mask,
-        })
-        return model_inputs
-
-    @deprecate_kwarg("num_logits_to_keep", version="4.50", new_name="logits_to_keep")
-    def forward(
-        self,
-        input_ids: torch.LongTensor = None,
-        attention_mask: Optional[torch.Tensor] = None,
-        inputs_embeds: Optional[torch.Tensor] = None,
-        past_key_values: Optional[Union[Cache, List[torch.FloatTensor]]] = None,
-        labels: Optional[torch.LongTensor] = None,
-        use_cache: Optional[bool] = None,
-        output_attentions: Optional[bool] = None,
-        output_hidden_states: Optional[bool] = None,
-        return_dict: Optional[bool] = None,
-        logits_to_keep: Optional[int] = 0,
-        **kwargs: Unpack[Dict]
-    ) -> Union[Tuple, CausalLMOutputWithPast]:
-        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
-        output_hidden_states = (
-            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
-        )
-        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
-
-        outputs = self.model(
-            input_ids=input_ids,
-            attention_mask=attention_mask,
-            inputs_embeds=inputs_embeds,
-            past_key_values=past_key_values,
-            use_cache=use_cache,
-            output_attentions=output_attentions,
-            output_hidden_states=output_hidden_states,
-            return_dict=return_dict,
-            **kwargs
-        )
-
-        hidden_states = outputs[0]
-        fuse_linear_and_cross_entropy = self.config.fuse_cross_entropy and self.training and labels is not None
-
-        loss, logits = None, None
-        if not fuse_linear_and_cross_entropy or labels is None:
-            logits = self.lm_head(hidden_states if logits_to_keep is None else hidden_states[:, -logits_to_keep:])
-        if labels is not None:
-            if getattr(self, 'criterion', None) is None:
-                if fuse_linear_and_cross_entropy:
-                    criterion = FusedLinearCrossEntropyLoss(use_l2warp=self.config.use_l2warp)
-                elif self.config.fuse_cross_entropy:
-                    criterion = FusedCrossEntropyLoss(inplace_backward=True)
-                else:
-                    criterion = nn.CrossEntropyLoss()
-            else:
-                criterion = self.criterion
-            # Enable model parallelism
-            labels = labels.to(hidden_states.device)
-            labels = torch.cat((labels[..., 1:], torch.full_like(labels[:, :1], criterion.ignore_index)), 1)
-            if fuse_linear_and_cross_entropy:
-                loss = criterion(hidden_states, labels, self.lm_head.weight, self.lm_head.bias)
-            else:
-                loss = criterion(logits.view(labels.numel(), -1), labels.view(-1))
-                loss = l2_warp(loss, logits) if self.config.use_l2warp else loss
-
-        if not return_dict:
-            output = (logits,) + outputs[1:]
-            return (loss,) + output if loss is not None else output
-
-        return CausalLMOutputWithPast(
-            loss=loss,
-            logits=logits,
-            past_key_values=outputs.past_key_values,
-            hidden_states=outputs.hidden_states,
-            attentions=outputs.attentions,
-        )