Upload 2 files

configuration_rwkv5.py

@@ -53,11 +53,9 @@ class Rwkv5Config(PretrainedConfig):
         layer_norm_epsilon (`float`, *optional*, defaults to 1e-05):
             The epsilon to use in the layer normalization layers.
         bos_token_id (`int`, *optional*, defaults to 0):
-            The id of the beginning of sentence token in the vocabulary. Defaults to 0 as RWKV5 uses the same tokenizer
-            as GPTNeoX.
+            The id of the beginning of sentence token in the vocabulary. Defaults to 0.
         eos_token_id (`int`, *optional*, defaults to 0):
-            The id of the end of sentence token in the vocabulary. Defaults to 0 as RWKV5 uses the same tokenizer as
-            GPTNeoX.
+            The id of the end of sentence token in the vocabulary. Defaults to 0.
         rescale_every (`int`, *optional*, defaults to 6):
             At inference, the hidden states (and weights of the correponding output layers) are divided by 2 every
             `rescale_every` layer. If set to 0 or a negative number, no rescale is done.
@@ -90,8 +88,8 @@ class Rwkv5Config(PretrainedConfig):
         hidden_size=768,
         num_hidden_layers=24,
         attention_hidden_size=None,
-        num_attention_heads=64,
         head_size=64,
+        head_size_divisor=8,
         intermediate_size=None,
         layer_norm_epsilon=1e-5,
         bos_token_id=0,
@@ -105,8 +103,8 @@ class Rwkv5Config(PretrainedConfig):
         self.hidden_size = hidden_size
         self.num_hidden_layers = num_hidden_layers
         self.attention_hidden_size = attention_hidden_size if attention_hidden_size is not None else hidden_size
-        self.num_attention_heads = num_attention_heads
         self.head_size = head_size
+        self.head_size_divisor = head_size_divisor
         self.intermediate_size = None
         self.layer_norm_epsilon = layer_norm_epsilon
         self.rescale_every = rescale_every

modeling_rwkv5.py

@@ -1,6 +1,5 @@
 # coding=utf-8
-# Copyright 2023 Bo Peng and HuggingFace Inc. team.
-# Copyright (c) 2018, NVIDIA CORPORATION.  All rights reserved.
+# Copyright 2024 The RWKV team and HuggingFace Inc. team.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
@@ -45,12 +44,6 @@ logger = logging.get_logger(__name__)
 _CHECKPOINT_FOR_DOC = "RWKV/rwkv-5-world-1b5"
 _CONFIG_FOR_DOC = "Rwkv5Config"
 
-RWKV5_PRETRAINED_MODEL_ARCHIVE_LIST = [
-    "RWKV/rwkv-5-world-1b5",
-    "RWKV/rwkv-5-world-3b",
-    # See all RWKV models at https://huggingface.co/models?filter=rwkv
-]
-
 rwkv5_cuda_kernel = None
 
 
@@ -60,14 +53,14 @@ def load_wkv5_cuda_kernel(head_size):
 
     global rwkv5_cuda_kernel
 
-    kernel_folder = Path(__file__).resolve().parent.parent.parent / "kernels" / "rwkv5"
+    kernel_folder = Path(__file__).parent.resolve()
     cuda_kernel_files = [kernel_folder / f for f in ["wkv5_op.cpp", "wkv5_cuda.cu"]]
 
     # Only load the kernel if it's not been loaded yet or if we changed the context length
     if rwkv5_cuda_kernel is not None and rwkv5_cuda_kernel.head_size == head_size:
         return
 
-    logger.info(f"Loading CUDA kernel for RWKV at head size of {head_size}.")
+    logger.info(f"Loading CUDA kernel for RWKV5 at head size of {head_size}.")
 
     flags = [
         "-res-usage",
@@ -87,39 +80,45 @@ def load_wkv5_cuda_kernel(head_size):
     rwkv5_cuda_kernel.head_size = head_size
 
 
-class WKV_5(torch.autograd.Function):
+class Rwkv5LinearAttention(torch.autograd.Function):
     @staticmethod
     def forward(ctx, receptance, key, value, time_decay, time_first, state):
         with torch.no_grad():
-            Batch = key.shape[0]
-            SequenceLength = key.shape[1]
-            HiddenSize = key.shape[2]
-            HeadSize = HiddenSize // time_decay.shape[0]
-            ctx.Batch = Batch
-            ctx.SequenceLength = SequenceLength
-            ctx.HiddenSize = HiddenSize
-            ctx.HeadSize = HeadSize
+            assert receptance.dtype == torch.bfloat16
+            assert key.dtype == torch.bfloat16
+            assert value.dtype == torch.bfloat16
+            assert time_decay.dtype == torch.bfloat16
+            assert time_first.dtype == torch.bfloat16
+            assert state.dtype == torch.float32
+            batch, seq_length, hidden_size = key.shape
+            num_heads = time_decay.shape[0]
+            ctx.batch = batch
+            ctx.seq_length = seq_length
+            ctx.hidden_size = hidden_size
+            ctx.num_heads = num_heads
             e_time_decay = (-torch.exp(time_decay.float())).contiguous()
             ee_time_decay = (torch.exp(e_time_decay)).contiguous()
+            assert ee_time_decay.dtype == torch.float32
             ctx.save_for_backward(receptance, key, value, ee_time_decay, e_time_decay, time_first)
             out = torch.empty(
-                (Batch, SequenceLength, HiddenSize),
+                (batch, seq_length, hidden_size),
                 device=receptance.device,
                 dtype=torch.bfloat16,
                 memory_format=torch.contiguous_format,
             )
-            rwkv5_cuda_kernel.forward(
-                Batch,
-                SequenceLength,
-                HiddenSize,
-                HeadSize,
+            state = state.clone()
+            rwkv5_cuda_kernel.forward_bf16(
+                batch,
+                seq_length,
+                hidden_size,
+                num_heads,
+                state,
                 receptance,
                 key,
                 value,
                 ee_time_decay,
                 time_first,
                 out,
-                state,
             )
             return out, state
 
@@ -127,51 +126,55 @@ class WKV_5(torch.autograd.Function):
     def backward(ctx, gout):
         with torch.no_grad():
             assert gout.dtype == torch.bfloat16
-            Batch = ctx.Batch
-            SequenceLength = ctx.SequenceLength
-            HiddenSize = ctx.HiddenSize
-            HeadSize = ctx.HeadSize
+            batch = ctx.batch
+            seq_length = ctx.seq_length
+            hidden_size = ctx.hidden_size
+            num_heads = ctx.num_heads
             receptance, key, value, ee_time_decay, e_time_decay, time_first = ctx.saved_tensors
+
+            global_shape = (batch, seq_length, hidden_size)
+
+            # TODO dtype should not be forced here IMO
             greceptance = torch.empty(
-                (Batch, SequenceLength, HiddenSize),
+                global_shape,
                 device=gout.device,
                 requires_grad=False,
                 dtype=torch.bfloat16,
                 memory_format=torch.contiguous_format,
             )
             g_key = torch.empty(
-                (Batch, SequenceLength, HiddenSize),
+                global_shape,
                 device=gout.device,
                 requires_grad=False,
                 dtype=torch.bfloat16,
                 memory_format=torch.contiguous_format,
             )
             g_value = torch.empty(
-                (Batch, SequenceLength, HiddenSize),
+                global_shape,
                 device=gout.device,
                 requires_grad=False,
                 dtype=torch.bfloat16,
                 memory_format=torch.contiguous_format,
             )
             g_time_decay = torch.empty(
-                (Batch, HiddenSize),
+                (batch, hidden_size),
                 device=gout.device,
                 requires_grad=False,
                 dtype=torch.bfloat16,
                 memory_format=torch.contiguous_format,
             )
             g_time_first = torch.empty(
-                (Batch, HiddenSize),
+                (batch, hidden_size),
                 device=gout.device,
                 requires_grad=False,
                 dtype=torch.bfloat16,
                 memory_format=torch.contiguous_format,
             )
-            rwkv5_cuda_kernel.backward(
-                Batch,
-                SequenceLength,
-                HiddenSize,
-                HeadSize,
+            rwkv5_cuda_kernel.backward_bf16(
+                batch,
+                seq_length,
+                hidden_size,
+                num_heads,
                 receptance,
                 key,
                 value,
@@ -185,133 +188,69 @@ class WKV_5(torch.autograd.Function):
                 g_time_decay,
                 g_time_first,
             )
-            g_time_decay = torch.sum(g_time_decay, 0).view(HeadSize, HiddenSize // HeadSize)
-            g_time_first = torch.sum(g_time_first, 0).view(HeadSize, HiddenSize // HeadSize)
+            head_size = hidden_size // num_heads
+            g_time_decay = torch.sum(g_time_decay, 0).view(num_heads, head_size)
+            g_time_first = torch.sum(g_time_first, 0).view(num_heads, head_size)
             return (None, None, None, None, greceptance, g_key, g_value, g_time_decay, g_time_first)
 
 
-def rwkv_linear_attention_v5_cpu(
-    hidden,
-    time_decay,
-    time_first,
-    receptance,
-    key,
-    value,
-    gate,
-    layer_norm_weight,
-    layer_norm_bias,
-    output_weight,
-    state,
-):
-    Batch = hidden.shape[0]
-    AttentionHeads = time_decay.shape[0]
-    HeadSize = hidden.shape[-1] // AttentionHeads
-    SequenceLength = hidden.shape[1]
-    key = key.to(torch.float32).view(Batch, SequenceLength, AttentionHeads, HeadSize).transpose(1, 2).transpose(-2, -1)
-    value = value.to(torch.float32).view(Batch, SequenceLength, AttentionHeads, HeadSize).transpose(1, 2)
-    receptance = receptance.to(torch.float32).view(Batch, SequenceLength, AttentionHeads, HeadSize).transpose(1, 2)
-    time_decay = torch.exp(-torch.exp(time_decay.float())).reshape(-1, 1, 1).reshape(AttentionHeads, -1, 1)
-    time_first = time_first.float().reshape(-1, 1, 1).reshape(AttentionHeads, -1, 1)
-    layer_norm_weight = layer_norm_weight.float()
-    layer_norm_bias = layer_norm_bias.float()
-    out = torch.zeros_like(key).reshape(Batch, SequenceLength, AttentionHeads, HeadSize)
-    for t in range(SequenceLength):
-        rt = receptance[:, :, t : t + 1, :]
-        kt = key[:, :, :, t : t + 1]
-        vt = value[:, :, t : t + 1, :]
-        at = kt @ vt
-        out[:, t] = (rt @ (time_first * at + state)).squeeze(2)
+def rwkv5_linear_attention_cpu(receptance, key, value, time_decay, time_first, state):
+    input_dtype = receptance.dtype
+    # For CPU fallback. Will be slower and probably take more memory than the custom CUDA kernel if not executed
+    # within a torch.no_grad.
+    batch, seq_length, hidden_size = receptance.shape
+    num_heads, head_size = time_first.shape
+    key = key.float().view(batch, seq_length, num_heads, head_size).transpose(1, 2).transpose(-2, -1)
+    value = value.float().view(batch, seq_length, num_heads, head_size).transpose(1, 2)
+    receptance = receptance.float().view(batch, seq_length, num_heads, head_size).transpose(1, 2)
+    time_decay = torch.exp(-torch.exp(time_decay.float())).reshape(-1, 1, 1).reshape(num_heads, -1, 1)
+    time_first = time_first.float().reshape(-1, 1, 1).reshape(num_heads, -1, 1)
+    out = torch.zeros_like(key).reshape(batch, seq_length, num_heads, head_size)
+
+    for current_index in range(seq_length):
+        current_receptance = receptance[:, :, current_index:current_index+1, :]
+        current_key = key[:, :, :, current_index:current_index+1]
+        current_value = value[:, :, current_index:current_index+1, :]
+        attention_output = current_key @ current_value
+        out[:, current_index] = (current_receptance @ (time_first * attention_output + state)).squeeze(2)
         with torch.no_grad():
-            state = at + time_decay * state
-
-    out = out.reshape(Batch * SequenceLength, AttentionHeads * HeadSize)
-    out = F.group_norm(out, num_groups=AttentionHeads, weight=layer_norm_weight, bias=layer_norm_bias).reshape(
-        Batch, SequenceLength, AttentionHeads * HeadSize
-    )
-    out = out.to(dtype=hidden.dtype) * gate
-    out = out @ output_weight
+            state = attention_output + time_decay * state
 
     return out, state
 
-
-def rwkv_linear_attention(
-    hidden,
-    time_decay,
-    time_first,
-    receptance,
-    key,
-    value,
-    gate,
-    layer_norm_weight,
-    layer_norm_bias,
-    output_weight,
-    state,
-):
-    Batch = hidden.shape[0]
-    AttentionHeads = time_decay.shape[0]
-    HeadSize = hidden.shape[-1] // AttentionHeads
-    SequenceLength = hidden.shape[1]
-    no_cuda = any(t.device.type != "cuda" for t in [time_decay, time_first, receptance, key, value])
+# copied from RWKV but with receptance
+def RWKV5_linear_attention(training, receptance, key, value, time_decay, time_first, state):
+    no_cuda = any(t.device.type != "cuda" for t in [time_decay, time_first, key, value])
     # Launching the CUDA kernel for just one token will actually be slower (there is no for loop in the CPU version
     # in this case).
     one_token = key.size(1) == 1
-    if rwkv5_cuda_kernel is None or no_cuda or one_token:
-        return rwkv_linear_attention_v5_cpu(
-            hidden,
-            time_decay,
-            time_first,
-            receptance,
-            key,
-            value,
-            gate,
-            layer_norm_weight,
-            layer_norm_bias,
-            output_weight,
-            state,
+    if not training or rwkv5_cuda_kernel is None or no_cuda or one_token:
+        return rwkv5_linear_attention_cpu(
+            receptance, key, value, time_decay, time_first, state
         )
     else:
-        out, state = WKV_5.apply(
-            Batch,
-            SequenceLength,
-            AttentionHeads * HeadSize,
-            AttentionHeads,
-            receptance,
-            key,
-            value,
-            time_decay,
-            time_first,
-            state,
-        )
-        out = out.reshape(Batch * SequenceLength, AttentionHeads * HeadSize)
-        out = F.group_norm(out, num_groups=AttentionHeads, weight=layer_norm_weight, bias=layer_norm_bias).reshape(
-            Batch, SequenceLength, AttentionHeads * HeadSize
-        )
-        out = out.to(dtype=hidden.dtype) * gate
-        out = out @ output_weight
-        return out, state
+        return Rwkv5LinearAttention.apply(receptance, key, value, time_decay, time_first, state)
 
 
-class RwkvSelfAttention(nn.Module):
+class Rwkv5SelfAttention(nn.Module):
     def __init__(self, config, layer_id=0):
         super().__init__()
         self.config = config
         kernel_loaded = rwkv5_cuda_kernel is not None and rwkv5_cuda_kernel.head_size == config.head_size
         if is_ninja_available() and is_torch_cuda_available() and not kernel_loaded:
             try:
-                load_wkv5_cuda_kernel(config.context_length)
+                load_wkv5_cuda_kernel(config.head_size)
             except Exception:
                 logger.info("Could not load the custom CUDA kernel for RWKV5 attention.")
         self.layer_id = layer_id
         hidden_size = config.hidden_size
-        num_attention_heads = hidden_size // config.head_size
-        self.num_attention_heads = num_attention_heads
-        attention_hidden_size = (
-            config.attention_hidden_size if config.attention_hidden_size is not None else hidden_size
-        )
+        attention_hidden_size = config.attention_hidden_size
         self.attention_hidden_size = attention_hidden_size
+        head_size = config.head_size
+        num_heads = attention_hidden_size // head_size
 
-        self.time_decay = nn.Parameter(torch.empty(num_attention_heads, config.head_size))
-        self.time_faaaa = nn.Parameter(torch.empty(num_attention_heads, config.head_size))
+        self.time_decay = nn.Parameter(torch.empty(num_heads, head_size))
+        self.time_faaaa = nn.Parameter(torch.empty(num_heads, head_size))
         self.time_mix_gate = nn.Parameter(torch.empty(1, 1, hidden_size))
 
         self.time_mix_key = nn.Parameter(torch.empty(1, 1, hidden_size))
@@ -324,7 +263,7 @@ class RwkvSelfAttention(nn.Module):
         self.receptance = nn.Linear(hidden_size, attention_hidden_size, bias=False)
         self.gate = nn.Linear(hidden_size, attention_hidden_size, bias=False)
         self.output = nn.Linear(attention_hidden_size, hidden_size, bias=False)
-        self.ln_x = nn.GroupNorm(hidden_size // config.head_size, hidden_size)
+        self.ln_x = nn.GroupNorm(num_heads, hidden_size)
 
     def extract_key_value(self, hidden, state=None):
         # Mix hidden with the previous timestep to produce key, value, receptance
@@ -336,6 +275,7 @@ class RwkvSelfAttention(nn.Module):
                 shifted[:, 0] = state[0][:, :, self.layer_id]
         if len(shifted.size()) == 2:
             shifted = shifted.unsqueeze(1)
+        
         key = hidden * self.time_mix_key + shifted * (1 - self.time_mix_key)
         value = hidden * self.time_mix_value + shifted * (1 - self.time_mix_value)
         receptance = hidden * self.time_mix_receptance + shifted * (1 - self.time_mix_receptance)
@@ -353,28 +293,26 @@ class RwkvSelfAttention(nn.Module):
 
     def forward(self, hidden, state=None, use_cache=False, seq_mode=True):
         receptance, key, value, gate, state = self.extract_key_value(hidden, state=state)
+
+        B,T,C = receptance.shape
+        H, S = self.time_faaaa.shape
+
         layer_state = state[1][:, :, :, :, self.layer_id] if state is not None else None
-        rwkv, layer_state = rwkv_linear_attention(
-            hidden,
-            self.time_decay,
-            self.time_faaaa,
-            receptance,
-            key,
-            value,
-            gate,
-            self.ln_x.weight,
-            self.ln_x.bias,
-            self.output.weight.t(),
-            state=layer_state,
+        out, layer_state = RWKV5_linear_attention(
+            self.training, receptance, key, value, self.time_decay, self.time_faaaa, layer_state
         )
 
         if layer_state is not None:
             state[1][:, :, :, :, self.layer_id] = layer_state
 
-        return rwkv, state
-
+        out = out.reshape(B * T, H * S)
+        out = F.group_norm(out / self.config.head_size_divisor, num_groups=H, weight=self.ln_x.weight.to(out.dtype), bias=self.ln_x.bias.to(out.dtype), eps=self.ln_x.eps).reshape(B, T, H * S)
+        out = out.to(dtype=hidden.dtype) * gate
+        out = self.output(out)
+        return out, state
 
-class RwkvFeedForward(nn.Module):
+# Copied from rwkv exceot for the intermediate size
+class Rwkv5FeedForward(nn.Module):
     def __init__(self, config, layer_id=0):
         super().__init__()
         self.config = config
@@ -416,7 +354,7 @@ class RwkvFeedForward(nn.Module):
         return receptance * value, state
 
 
-# copied from HuggingFace https://github.com/huggingface/transformers/blob/main/src/transformers/models/rwkv/modeling_rwkv.py
+# Copied from transformers.models.rwkv.modeling_rwkv.RwkvBlock with Rwkv->Rwkv5
 class Rwkv5Block(nn.Module):
     def __init__(self, config, layer_id):
         super().__init__()
@@ -429,8 +367,8 @@ class Rwkv5Block(nn.Module):
         self.ln1 = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_epsilon)
         self.ln2 = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_epsilon)
 
-        self.attention = RwkvSelfAttention(config, layer_id)
-        self.feed_forward = RwkvFeedForward(config, layer_id)
+        self.attention = Rwkv5SelfAttention(config, layer_id)
+        self.feed_forward = Rwkv5FeedForward(config, layer_id)
 
     def forward(self, hidden, state=None, use_cache=False, output_attentions=False, seq_mode=True):
         if self.layer_id == 0:
@@ -450,6 +388,7 @@ class Rwkv5Block(nn.Module):
         return outputs
 
 
+# Copied from transformers.models.rwkv.modeling_rwkv.RwkvPreTrainedModel with Rwkv->Rwkv5
 class Rwkv5PreTrainedModel(PreTrainedModel):
     """
     An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
@@ -457,19 +396,20 @@ class Rwkv5PreTrainedModel(PreTrainedModel):
     """
 
     config_class = Rwkv5Config
-    base_model_prefix = "rwkv"
+    base_model_prefix = "rwkv5"
     _no_split_modules = ["Rwkv5Block"]
     _keep_in_fp32_modules = ["time_decay", "time_first"]
     supports_gradient_checkpointing = True
 
     def _init_weights(self, module):
         """Initialize the weights."""
-        if isinstance(module, RwkvSelfAttention):
+        if isinstance(module, Rwkv5SelfAttention):
             layer_id = module.layer_id
             num_hidden_layers = module.config.num_hidden_layers
             hidden_size = module.config.hidden_size
             attention_hidden_size = module.attention_hidden_size
-            num_attention_heads = hidden_size // module.config.num_attention_heads
+            head_size = module.config.head_size
+            num_heads = attention_hidden_size // head_size
 
             ratio_0_to_1 = layer_id / (num_hidden_layers - 1)  # 0 to 1
             ratio_1_to_almost0 = 1.0 - (layer_id / num_hidden_layers)  # 1 to ~0
@@ -496,15 +436,15 @@ class Rwkv5PreTrainedModel(PreTrainedModel):
             )
 
             with torch.no_grad():
-                module.time_decay.data = decay_speed.reshape(num_attention_heads, module.config.num_attention_heads)
-                module.time_faaaa.data = tmp.reshape(num_attention_heads, module.config.num_attention_heads)
+                module.time_decay.data = decay_speed.reshape(num_heads, head_size)
+                module.time_faaaa.data = tmp.reshape(num_heads, head_size)
                 module.time_mix_key.data = torch.pow(time_weight, ratio_1_to_almost0)
 
                 module.time_mix_value.data = torch.pow(time_weight, ratio_1_to_almost0) + 0.3 * ratio_0_to_1
                 module.time_mix_receptance.data = torch.pow(time_weight, 0.5 * ratio_1_to_almost0)
                 module.time_mix_gate.data = torch.pow(time_weight, 0.5 * ratio_1_to_almost0)
 
-        elif isinstance(module, RwkvFeedForward):
+        elif isinstance(module, Rwkv5FeedForward):
             layer_id = module.layer_id
             num_hidden_layers = module.config.num_hidden_layers
             hidden_size = module.config.hidden_size
@@ -523,11 +463,11 @@ class Rwkv5PreTrainedModel(PreTrainedModel):
                 module.time_mix_receptance.data = torch.pow(time_weight, ratio_1_to_almost0)
 
 
-# copied from HuggingFace https://github.com/huggingface/transformers/blob/main/src/transformers/models/rwkv/modeling_rwkv.py
+# Copied from transformers.models.rwkv.modeling_rwkv.RwkvOutput with Rwkv->Rwkv5
 @dataclass
 class Rwkv5Output(ModelOutput):
     """
-    Class for the RWKV model outputs.
+    Class for the RWKV5 model outputs.
 
     Args:
         last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`):
@@ -551,7 +491,7 @@ class Rwkv5Output(ModelOutput):
     attentions: Optional[Tuple[torch.FloatTensor]] = None
 
 
-# copied from HuggingFace https://github.com/huggingface/transformers/blob/main/src/transformers/models/rwkv/modeling_rwkv.py
+# Copied from transformers.models.rwkv.modeling_rwkv.RwkvCausalLMOutput with Rwkv->Rwkv5
 @dataclass
 class Rwkv5CausalLMOutput(ModelOutput):
     """
@@ -582,7 +522,7 @@ class Rwkv5CausalLMOutput(ModelOutput):
     attentions: Optional[Tuple[torch.FloatTensor]] = None
 
 
-RWKV_START_DOCSTRING = r"""
+RWKV5_START_DOCSTRING = r"""
     This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
     library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
     etc.) This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module)
@@ -595,7 +535,7 @@ RWKV_START_DOCSTRING = r"""
             configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.
 """
 
-RWKV_INPUTS_DOCSTRING = r"""
+RWKV5_INPUTS_DOCSTRING = r"""
     Args:
         input_ids (`torch.LongTensor` of shape `(batch_size, input_ids_length)`):
             `input_ids_length` = `sequence_length` if `past_key_values` is `None` else
@@ -625,8 +565,8 @@ RWKV_INPUTS_DOCSTRING = r"""
 
 
 @add_start_docstrings(
-    "The bare RWKV Model transformer outputting raw hidden-states without any specific head on top.",
-    RWKV_START_DOCSTRING,
+    "The bare RWKV5 Model transformer outputting raw hidden-states without any specific head on top.",
+    RWKV5_START_DOCSTRING,
 )
 class Rwkv5Model(Rwkv5PreTrainedModel):
     def __init__(self, config):
@@ -648,7 +588,7 @@ class Rwkv5Model(Rwkv5PreTrainedModel):
     def set_input_embeddings(self, new_embeddings):
         self.embeddings = new_embeddings
 
-    @add_start_docstrings_to_model_forward(RWKV_INPUTS_DOCSTRING)
+    @add_start_docstrings_to_model_forward(RWKV5_INPUTS_DOCSTRING)
     @add_code_sample_docstrings(
         checkpoint=_CHECKPOINT_FOR_DOC,
         output_type=Rwkv5Output,
@@ -669,6 +609,7 @@ class Rwkv5Model(Rwkv5PreTrainedModel):
         output_hidden_states = (
             output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
         )
+        # FIXME - training is supportable with the CUDA code
         # rwkv5 only support inference in huggingface.
         use_cache = use_cache if use_cache is not None else self.config.use_cache
         return_dict = return_dict if return_dict is not None else self.config.use_return_dict
@@ -686,9 +627,10 @@ class Rwkv5Model(Rwkv5PreTrainedModel):
         if inputs_embeds is None:
             inputs_embeds = self.embeddings(input_ids)
 
-        if use_cache and state is None:
+        if state is None:
             state = []
-            num_attention_heads = self.config.hidden_size // self.config.num_attention_heads
+            head_size = self.config.head_size
+            num_heads = self.config.attention_hidden_size // head_size
             state_attn_x = torch.zeros(
                 (inputs_embeds.size(0), self.config.hidden_size, self.config.num_hidden_layers),
                 dtype=inputs_embeds.dtype,
@@ -698,9 +640,9 @@ class Rwkv5Model(Rwkv5PreTrainedModel):
             state_attn_kv = torch.zeros(
                 (
                     inputs_embeds.size(0),
-                    num_attention_heads,
-                    self.config.hidden_size // num_attention_heads,
-                    self.config.hidden_size // num_attention_heads,
+                    num_heads,
+                    head_size,
+                    head_size,
                     self.config.num_hidden_layers,
                 ),
                 dtype=torch.float32,
@@ -765,8 +707,16 @@ class Rwkv5Model(Rwkv5PreTrainedModel):
                         block.attention.output.weight.mul_(2 ** int(block_id // self.config.rescale_every))
                         block.feed_forward.value.weight.mul_(2 ** int(block_id // self.config.rescale_every))
                     else:
-                        block.attention.output.weight.div_(2 ** int(block_id // self.config.rescale_every))
-                        block.feed_forward.value.weight.div_(2 ** int(block_id // self.config.rescale_every))
+                        # Deal with quantization statistics
+                        if hasattr(block.attention.output.weight, "SCB"):
+                            block.attention.output.weight.SCB.div_(2 ** int(block_id // self.config.rescale_every))
+                            block.feed_forward.value.weight.SCB.div_(2 ** int(block_id // self.config.rescale_every))
+                        elif hasattr(block.attention.output.weight, "quant_state"):
+                            self._bnb_4bit_dequantize_and_rescale(block.attention.output, block_id)
+                            self._bnb_4bit_dequantize_and_rescale(block.feed_forward.value, block_id)
+                        else:
+                            block.attention.output.weight.div_(2 ** int(block_id // self.config.rescale_every))
+                            block.feed_forward.value.weight.div_(2 ** int(block_id // self.config.rescale_every))
 
         self.layers_are_rescaled = not self.training
 
@@ -798,8 +748,9 @@ class Rwkv5Model(Rwkv5PreTrainedModel):
     The RWKV5 Model transformer with a language modeling head on top (linear layer with weights tied to the input
     embeddings).
     """,
-    RWKV_START_DOCSTRING,
+    RWKV5_START_DOCSTRING,
 )
+# Copied from transformers.models.rwkv.modeling_rwkv.RwkvForCausalLM with Rwkv->Rwkv5
 class Rwkv5ForCausalLM(Rwkv5PreTrainedModel):
     _tied_weights_keys = ["head.weight"]
 
@@ -831,7 +782,7 @@ class Rwkv5ForCausalLM(Rwkv5PreTrainedModel):
         model_inputs["state"] = state
         return model_inputs
 
-    @add_start_docstrings_to_model_forward(RWKV_INPUTS_DOCSTRING)
+    @add_start_docstrings_to_model_forward(RWKV5_INPUTS_DOCSTRING)
     @add_code_sample_docstrings(
         checkpoint=_CHECKPOINT_FOR_DOC,
         output_type=Rwkv5CausalLMOutput,
@@ -857,7 +808,7 @@ class Rwkv5ForCausalLM(Rwkv5PreTrainedModel):
         """
         return_dict = return_dict if return_dict is not None else self.config.use_return_dict
 
-        rwkv_outputs = self.rwkv(
+        outputs = self.rwkv(
             input_ids,
             inputs_embeds=inputs_embeds,
             state=state,
@@ -866,7 +817,7 @@ class Rwkv5ForCausalLM(Rwkv5PreTrainedModel):
             output_hidden_states=output_hidden_states,
             return_dict=return_dict,
         )
-        hidden_states = rwkv_outputs[0]
+        hidden_states = outputs[0]
 
         logits = self.head(hidden_states)
 
@@ -882,13 +833,13 @@ class Rwkv5ForCausalLM(Rwkv5PreTrainedModel):
             loss = loss_fct(shift_logits.view(-1, shift_logits.size(-1)), shift_labels.view(-1))
 
         if not return_dict:
-            output = (logits,) + rwkv_outputs[1:]
+            output = (logits,) + outputs[1:]
             return ((loss,) + output) if loss is not None else output
 
         return Rwkv5CausalLMOutput(
             loss=loss,
             logits=logits,
-            state=rwkv_outputs.state,
-            hidden_states=rwkv_outputs.hidden_states,
-            attentions=rwkv_outputs.attentions,
+            state=outputs.state,
+            hidden_states=outputs.hidden_states,
+            attentions=outputs.attentions,
         )