using rwkv5 batch

configuration_rwkv5.py

@@ -21,46 +21,42 @@ from transformers.utils import logging
 
 logger = logging.get_logger(__name__)
 
-RWKV_PRETRAINED_CONFIG_ARCHIVE_MAP = {
-    
-}
+RWKV5_PRETRAINED_CONFIG_ARCHIVE_MAP = {}
 
 
 class Rwkv5Config(PretrainedConfig):
     """
-    This is the configuration class to store the configuration of a [`RwkvModel`]. It is used to instantiate a RWKV
+    This is the configuration class to store the configuration of a [`Rwkv5Model`]. It is used to instantiate a RWKV5
     model according to the specified arguments, defining the model architecture. Instantiating a configuration with the
     defaults will yield a similar configuration to that of the RWVK-4
-    [RWKV/rwkv-4-169m-pile](https://huggingface.co/RWKV/rwkv-4-169m-pile) architecture.
+    [RWKV/rwkv-5-world-1b5](https://huggingface.co/RWKV/rwkv-5-world-1b5) architecture.
 
     Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
     documentation from [`PretrainedConfig`] for more information.
 
 
     Args:
-        vocab_size (`int`, *optional*, defaults to 50277):
-            Vocabulary size of the RWKV model. Defines the number of different tokens that can be represented by the
-            `inputs_ids` passed when calling [`RwkvModel`].
-        context_length (`int`, *optional*, defaults to 1024):
-            The maximum sequence length that this model can be be used with in a single forward (using it in RNN mode
-            lets use any sequence length).
-        hidden_size (`int`, *optional*, defaults to 4096):
+        vocab_size (`int`, *optional*, defaults to 65536):
+            Vocabulary size of the RWKV5 model. Defines the number of different tokens that can be represented by the
+            `inputs_ids` passed when calling [`Rwkv5Model`].
+        hidden_size (`int`, *optional*, defaults to 768):
             Dimensionality of the embeddings and hidden states.
-        num_hidden_layers (`int`, *optional*, defaults to 32):
+        num_hidden_layers (`int`, *optional*, defaults to 24):
             Number of hidden layers in the model.
         attention_hidden_size (`int`, *optional*):
             Dimensionality of the attention hidden states. Will default to `hidden_size` if unset.
+        num_attention_heads (`int`, *optional*, defaults to 64):
+            The attention heads to use in rwkv5 self_attention module.
+        head_size (`int`, *optional*, defaults to 64): head_size of rwkv5 self_attention module.
         intermediate_size (`int`, *optional*):
             Dimensionality of the inner feed-forward layers. Will default to 4 times `hidden_size` if unset.
-        layer_norm_eps (`float`, *optional*, defaults to 1e-5):
+        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 RWKV 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 RWKV uses the same tokenizer as
-            GPTNeoX.
-        rescale_every (`int`, *optional*, default to 6):
+            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.
         tie_word_embeddings (`bool`, *optional*, defaults to `False`):
@@ -72,28 +68,27 @@ class Rwkv5Config(PretrainedConfig):
     Example:
 
     ```python
-    >>> from transformers import RwkvConfig, RwkvModel
+    >>> from transformers import Rwkv5Config, Rwkv5Model
 
-    >>> # Initializing a Rwkv configuration
-    >>> configuration = RwkvConfig()
+    >>> # Initializing a Rwkv5 configuration
+    >>> configuration = Rwkv5Config()
 
     >>> # Initializing a model (with random weights) from the configuration
-    >>> model = RwkvModel(configuration)
+    >>> model = Rwkv5Model(configuration)
 
     >>> # Accessing the model configuration
     >>> configuration = model.config
     ```"""
 
     model_type = "rwkv5"
-    attribute_map = {"max_position_embeddings": "context_length"}
 
-    def __init__( #1.5B World
+    def __init__(
         self,
         vocab_size=65536,
-        context_length=4096,
         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,
@@ -103,14 +98,13 @@ class Rwkv5Config(PretrainedConfig):
         rescale_every=6,
         tie_word_embeddings=False,
         use_cache=True,
-        model_version="5_2",
         **kwargs,
     ):
         self.vocab_size = vocab_size
-        self.context_length = context_length
         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
@@ -120,7 +114,6 @@ class Rwkv5Config(PretrainedConfig):
 
         self.bos_token_id = bos_token_id
         self.eos_token_id = eos_token_id
-        self.model_version = model_version
 
         super().__init__(
             tie_word_embeddings=tie_word_embeddings, bos_token_id=bos_token_id, eos_token_id=eos_token_id, **kwargs

modeling_rwkv5.py

@@ -752,6 +752,14 @@ 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:
+                        # 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))