rwkv-5-world-3b / modeling_rwkv5.py
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# coding=utf-8
# 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.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""PyTorch RWKV5 World model."""
from dataclasses import dataclass
from pathlib import Path
from typing import List, Optional, Tuple, Union
import pkg_resources
import torch
import torch.nn.functional as F
import torch.utils.checkpoint
from torch import nn
from torch.nn import CrossEntropyLoss
from transformers.modeling_utils import PreTrainedModel
from transformers.utils import (
ModelOutput,
add_code_sample_docstrings,
add_start_docstrings,
add_start_docstrings_to_model_forward,
is_bitsandbytes_available,
is_ninja_available,
is_torch_cuda_available,
logging,
)
try:
from flash_rwkv import rwkv5_cuda_linear_attention
# Check version
required_version = pkg_resources.parse_version("0.2.1")
current_version = pkg_resources.get_distribution("flash-rwkv").parsed_version
if current_version < required_version:
raise Exception("Your version of flash-rwkv is below 0.2.1. Please use pip install --upgrade flash-rwkv to update or install the required version.")
except ImportError:
raise ImportError("The flash-rwkv package is not detected. Please install it using pip install flash-rwkv.")
except pkg_resources.DistributionNotFound:
raise ImportError("The flash-rwkv package is not detected. Please install it using pip install flash-rwkv.")
from .configuration_rwkv5 import Rwkv5Config
logger = logging.get_logger(__name__)
_CHECKPOINT_FOR_DOC = "RWKV/rwkv-5-world-1b5"
_CONFIG_FOR_DOC = "Rwkv5Config"
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 = attention_output + time_decay * state
return out, state
# 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 not training or no_cuda or one_token:
return rwkv5_linear_attention_cpu(
receptance, key, value, time_decay, time_first, state
)
else:
return rwkv5_cuda_linear_attention(receptance.float(), key.float(), value.float(), time_decay.float().flatten(), time_first.float().flatten(), state)
class Rwkv5SelfAttention(nn.Module):
def __init__(self, config, layer_id=0):
super().__init__()
self.config = config
self.layer_id = layer_id
hidden_size = config.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_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))
self.time_mix_value = nn.Parameter(torch.empty(1, 1, hidden_size))
self.time_mix_receptance = nn.Parameter(torch.empty(1, 1, hidden_size))
self.time_shift = nn.ZeroPad2d((0, 0, 1, -1))
self.key = nn.Linear(hidden_size, attention_hidden_size, bias=False)
self.value = nn.Linear(hidden_size, attention_hidden_size, bias=False)
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(num_heads, hidden_size)
def extract_key_value(self, hidden, state=None):
# Mix hidden with the previous timestep to produce key, value, receptance
if hidden.size(1) == 1 and state is not None:
shifted = state[0][:, :, self.layer_id]
else:
shifted = self.time_shift(hidden)
if state is not None:
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)
gate = hidden * self.time_mix_gate + shifted * (1 - self.time_mix_gate)
key = self.key(key)
value = self.value(value)
receptance = self.receptance(receptance)
gate = F.silu(self.gate(gate))
if state is not None:
state[0][:, :, self.layer_id] = hidden[:, -1]
return receptance, key, value, gate, state
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
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
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
# Copied from rwkv except for the intermediate size
class Rwkv5FeedForward(nn.Module):
def __init__(self, config, layer_id=0):
super().__init__()
self.config = config
self.layer_id = layer_id
hidden_size = config.hidden_size
intermediate_size = (
config.intermediate_size
if config.intermediate_size is not None
else int((config.hidden_size * 3.5) // 32 * 32)
)
self.time_shift = nn.ZeroPad2d((0, 0, 1, -1))
self.time_mix_key = nn.Parameter(torch.empty(1, 1, hidden_size))
self.time_mix_receptance = nn.Parameter(torch.empty(1, 1, hidden_size))
self.key = nn.Linear(hidden_size, intermediate_size, bias=False)
self.receptance = nn.Linear(hidden_size, hidden_size, bias=False)
self.value = nn.Linear(intermediate_size, hidden_size, bias=False)
def forward(self, hidden, state=None):
if hidden.size(1) == 1 and state is not None:
shifted = state[2][:, :, self.layer_id]
else:
shifted = self.time_shift(hidden)
if state is not None:
shifted[:, 0] = state[2][:, :, self.layer_id]
if len(shifted.size()) == 2:
shifted = shifted.unsqueeze(1)
key = hidden * self.time_mix_key + shifted * (1 - self.time_mix_key)
receptance = hidden * self.time_mix_receptance + shifted * (1 - self.time_mix_receptance)
key = torch.square(torch.relu(self.key(key)))
value = self.value(key)
receptance = torch.sigmoid(self.receptance(receptance))
if state is not None:
state[2][:, :, self.layer_id] = hidden[:, -1]
return receptance * value, state
# Copied from transformers.models.rwkv.modeling_rwkv.RwkvBlock with Rwkv->Rwkv5
class Rwkv5Block(nn.Module):
def __init__(self, config, layer_id):
super().__init__()
self.config = config
self.layer_id = layer_id
if layer_id == 0:
self.pre_ln = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_epsilon)
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 = 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:
hidden = self.pre_ln(hidden)
attention, state = self.attention(self.ln1(hidden), state=state, use_cache=use_cache, seq_mode=seq_mode)
hidden = hidden + attention
feed_forward, state = self.feed_forward(self.ln2(hidden), state=state)
hidden = hidden + feed_forward
outputs = (hidden, state)
if output_attentions:
outputs += (attention,)
else:
outputs += (None,)
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
models.
"""
config_class = Rwkv5Config
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, 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
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
time_weight = torch.tensor(
[i / hidden_size for i in range(hidden_size)],
dtype=module.time_mix_key.dtype,
device=module.time_mix_key.device,
)
time_weight = time_weight[None, None, :]
decay_speed = [
-6.0 + 5.0 * (h / (attention_hidden_size - 1)) ** (0.7 + 1.3 * ratio_0_to_1)
for h in range(attention_hidden_size)
]
decay_speed = torch.tensor(decay_speed, dtype=module.time_decay.dtype, device=module.time_decay.device)
tmp = torch.tensor(
[
(1.0 - (i / (attention_hidden_size - 1.0))) * ratio_0_to_1 + 0.1 * ((i + 1) % 3 - 1)
for i in range(attention_hidden_size)
],
dtype=module.time_faaaa.dtype,
device=module.time_faaaa.device,
)
with torch.no_grad():
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, Rwkv5FeedForward):
layer_id = module.layer_id
num_hidden_layers = module.config.num_hidden_layers
hidden_size = module.config.hidden_size
ratio_1_to_almost0 = 1.0 - (layer_id / num_hidden_layers) # 1 to ~0
time_weight = torch.tensor(
[i / hidden_size for i in range(hidden_size)],
dtype=module.time_mix_key.dtype,
device=module.time_mix_key.device,
)
time_weight = time_weight[None, None, :]
with torch.no_grad():
module.time_mix_key.data = torch.pow(time_weight, ratio_1_to_almost0)
module.time_mix_receptance.data = torch.pow(time_weight, ratio_1_to_almost0)
# Copied from transformers.models.rwkv.modeling_rwkv.RwkvOutput with Rwkv->Rwkv5
@dataclass
class Rwkv5Output(ModelOutput):
"""
Class for the RWKV5 model outputs.
Args:
last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`):
Sequence of hidden-states at the output of the last layer of the model.
state (list of five `torch.FloatTensor` of shape `(batch_size, hidden_size, num_hidden_layers)`):
The state of the model at the last time step. Can be used in a forward method with the next `input_ids` to
avoid providing the old `input_ids`.
hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, +
one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`. Hidden-states of
the model at the output of each layer plus the optional initial embedding outputs.
attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
sequence_length)`. Attentions weights after the attention softmax, used to compute the weighted average in
the self-attention heads.
"""
last_hidden_state: torch.FloatTensor = None
state: Optional[List[torch.FloatTensor]] = None
hidden_states: Optional[Tuple[torch.FloatTensor]] = None
attentions: Optional[Tuple[torch.FloatTensor]] = None
# Copied from transformers.models.rwkv.modeling_rwkv.RwkvCausalLMOutput with Rwkv->Rwkv5
@dataclass
class Rwkv5CausalLMOutput(ModelOutput):
"""
Base class for causal language model (or autoregressive) outputs.
Args:
loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided):
Language modeling loss (for next-token prediction).
logits (`torch.FloatTensor` of shape `(batch_size, sequence_length, config.vocab_size)`):
Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).
state (list of five `torch.FloatTensor` of shape `(batch_size, hidden_size, num_hidden_layers)`):
The state of the model at the last time step. Can be used in a forward method with the next `input_ids` to
avoid providing the old `input_ids`.
hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, +
one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`. Hidden-states of
the model at the output of each layer plus the optional initial embedding outputs.
attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
sequence_length)`. Attentions weights after the attention softmax, used to compute the weighted average in
the self-attention heads.
"""
loss: Optional[torch.FloatTensor] = None
logits: torch.FloatTensor = None
state: Optional[List[torch.FloatTensor]] = None
hidden_states: Optional[Tuple[torch.FloatTensor]] = None
attentions: Optional[Tuple[torch.FloatTensor]] = None
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)
subclass. Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to
general usage and behavior.
Parameters:
config ([`Rwkv5Config`]): Model configuration class with all the parameters of the model.
Initializing with a config file does not load the weights associated with the model, only the
configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.
"""
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
`past_key_values[0][0].shape[-2]` (`sequence_length` of input past key value states). Indices of input
sequence tokens in the vocabulary. If `past_key_values` is used, only `input_ids` that do not have their
past calculated should be passed as `input_ids`. Indices can be obtained using [`AutoTokenizer`]. See
[`PreTrainedTokenizer.encode`] and [`PreTrainedTokenizer.__call__`] for details. [What are input
IDs?](../glossary#input-ids)
inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This
is useful if you want more control over how to convert `input_ids` indices into associated vectors than the
model's internal embedding lookup matrix.
state (tuple of five `torch.FloatTensor` of shape `(batch_size, hidden_size, num_hidden_layers)`, *optional*):
If passed along, the model uses the previous state in all the blocks (which will give the output for the
`input_ids` provided as if the model add `state_input_ids + input_ids` as context).
use_cache (`bool`, *optional*):
If set to `True`, the last state is returned and can be used to quickly generate the next logits.
output_attentions (`bool`, *optional*):
Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
tensors for more detail.
output_hidden_states (`bool`, *optional*):
Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
more detail.
return_dict (`bool`, *optional*):
Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
"""
@add_start_docstrings(
"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):
super().__init__(config)
self.embeddings = nn.Embedding(config.vocab_size, config.hidden_size)
self.blocks = nn.ModuleList([Rwkv5Block(config, layer_id=idx) for idx in range(config.num_hidden_layers)])
self.ln_out = nn.LayerNorm(config.hidden_size)
self.layers_are_rescaled = False
self.gradient_checkpointing = False
# Initialize weights and apply final processing
self.post_init()
def get_input_embeddings(self):
return self.embeddings
def set_input_embeddings(self, new_embeddings):
self.embeddings = new_embeddings
@add_start_docstrings_to_model_forward(RWKV5_INPUTS_DOCSTRING)
@add_code_sample_docstrings(
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=Rwkv5Output,
config_class=_CONFIG_FOR_DOC,
)
def forward(
self,
input_ids: Optional[torch.LongTensor] = None,
attention_mask: Optional[torch.LongTensor] = None, # noqa
inputs_embeds: Optional[torch.FloatTensor] = None,
state: Optional[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,
) -> Union[Tuple, Rwkv5Output]:
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
)
# 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
if self.training == self.layers_are_rescaled and (
self.embeddings.weight.dtype == torch.float16 or self.embeddings.weight.dtype == torch.bfloat16
):
self._rescale_layers()
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")
elif 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)
if state is None:
state = []
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,
requires_grad=False,
device=inputs_embeds.device,
).contiguous()
state_attn_kv = torch.zeros(
(
inputs_embeds.size(0),
num_heads,
head_size,
head_size,
self.config.num_hidden_layers,
),
dtype=torch.float32,
requires_grad=False,
device=inputs_embeds.device,
).contiguous()
state_ffn_x = torch.zeros(
(inputs_embeds.size(0), self.config.hidden_size, self.config.num_hidden_layers),
dtype=inputs_embeds.dtype,
requires_grad=False,
device=inputs_embeds.device,
).contiguous()
state.append(state_attn_x)
state.append(state_attn_kv)
state.append(state_ffn_x)
seq_mode = inputs_embeds.shape[1] > 1
hidden_states = inputs_embeds
all_self_attentions = () if output_attentions else None
all_hidden_states = () if output_hidden_states else None
for idx, block in enumerate(self.blocks):
hidden_states, state, attentions = block(
hidden_states, state=state, use_cache=use_cache, output_attentions=output_attentions, seq_mode=seq_mode
)
if (
self.layers_are_rescaled
and self.config.rescale_every > 0
and (idx + 1) % self.config.rescale_every == 0
):
hidden_states = hidden_states / 2
if output_hidden_states:
all_hidden_states = all_hidden_states + (hidden_states,)
if output_attentions:
all_self_attentions = all_self_attentions + (attentions,)
hidden_states = self.ln_out(hidden_states)
if output_hidden_states:
all_hidden_states = all_hidden_states + (hidden_states,)
if not return_dict:
return (hidden_states, state, all_hidden_states, all_self_attentions)
return Rwkv5Output(
last_hidden_state=hidden_states,
state=state,
hidden_states=all_hidden_states, # None
attentions=all_self_attentions, # None
)
def _rescale_layers(self):
# Layers should be rescaled for inference only.
if self.layers_are_rescaled == (not self.training):
return
if self.config.rescale_every > 0:
with torch.no_grad():
for block_id, block in enumerate(self.blocks):
if self.training:
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))
self.layers_are_rescaled = not self.training
def _bnb_4bit_dequantize_and_rescale(self, target_layer, block_id):
r"""
Perform the dequantization and rescaling of the weights of a given layer. After that operation the layer will
be quantized again.
"""
if not is_bitsandbytes_available():
raise ImportError("Please install bitsandbytes to use this method.")
import bitsandbytes as bnb
dequant_weights = bnb.functional.dequantize_4bit(target_layer.weight.data, target_layer.weight.quant_state)
dequant_weights.div_(2 ** int(block_id // self.config.rescale_every))
# re-quantize the model:
# we need to put it first on CPU then back to the device
# this will create an overhead :/
# We set requires_grad=False as we cannot compute gradients on top of 4bit parameters anyway and to avoid
# bugs with bnb
quant_weight = bnb.nn.Params4bit(dequant_weights.to("cpu"), requires_grad=False).to(dequant_weights.device)
setattr(target_layer, "weight", quant_weight)
# copied from HuggingFace https://github.com/huggingface/transformers/blob/main/src/transformers/models/rwkv/modeling_rwkv.py
@add_start_docstrings(
"""
The RWKV5 Model transformer with a language modeling head on top (linear layer with weights tied to the input
embeddings).
""",
RWKV5_START_DOCSTRING,
)
# Copied from transformers.models.rwkv.modeling_rwkv.RwkvForCausalLM with Rwkv->Rwkv5
class Rwkv5ForCausalLM(Rwkv5PreTrainedModel):
_tied_weights_keys = ["head.weight"]
def __init__(self, config):
super().__init__(config)
self.rwkv = Rwkv5Model(config)
self.head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
# Initialize weights and apply final processing
self.post_init()
def get_output_embeddings(self):
return self.head
def set_output_embeddings(self, new_embeddings):
self.head = new_embeddings
def prepare_inputs_for_generation(self, input_ids, state=None, inputs_embeds=None, **kwargs):
# only last token for inputs_ids if the state is passed along.
if state is not None:
input_ids = input_ids[:, -1].unsqueeze(-1)
# if `inputs_embeds` are passed, we only want to use them in the 1st generation step
if inputs_embeds is not None and state is None:
model_inputs = {"inputs_embeds": inputs_embeds}
else:
model_inputs = {"input_ids": input_ids}
model_inputs["state"] = state
return model_inputs
@add_start_docstrings_to_model_forward(RWKV5_INPUTS_DOCSTRING)
@add_code_sample_docstrings(
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=Rwkv5CausalLMOutput,
config_class=_CONFIG_FOR_DOC,
)
def forward(
self,
input_ids: Optional[torch.LongTensor] = None,
attention_mask: Optional[torch.LongTensor] = None,
inputs_embeds: Optional[torch.FloatTensor] = None,
state: Optional[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,
) -> Union[Tuple, Rwkv5CausalLMOutput]:
r"""
labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
Labels for language modeling. Note that the labels **are shifted** inside the model, i.e. you can set
`labels = input_ids` Indices are selected in `[-100, 0, ..., config.vocab_size]` All labels set to `-100`
are ignored (masked), the loss is only computed for labels in `[0, ..., config.vocab_size]`
"""
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
outputs = self.rwkv(
input_ids,
inputs_embeds=inputs_embeds,
state=state,
use_cache=use_cache,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
hidden_states = outputs[0]
logits = self.head(hidden_states)
loss = None
if labels is not None:
# move labels to correct device to enable model parallelism
labels = labels.to(logits.device)
# Shift so that tokens < n predict n
shift_logits = logits[..., :-1, :].contiguous()
shift_labels = labels[..., 1:].contiguous()
# Flatten the tokens
loss_fct = CrossEntropyLoss()
loss = loss_fct(shift_logits.view(-1, shift_logits.size(-1)), shift_labels.view(-1))
if not return_dict:
output = (logits,) + outputs[1:]
return ((loss,) + output) if loss is not None else output
return Rwkv5CausalLMOutput(
loss=loss,
logits=logits,
state=outputs.state,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
)