705 lines
32 KiB
Python
705 lines
32 KiB
Python
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# coding=utf-8
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# Copyright 2024 The RWKV team and HuggingFace Inc. team.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""PyTorch RWKV5 World model."""
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from dataclasses import dataclass
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from pathlib import Path
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from typing import List, Optional, Tuple, Union
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import pkg_resources
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import torch
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import torch.nn.functional as F
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import torch.utils.checkpoint
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from torch import nn
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from torch.nn import CrossEntropyLoss
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from transformers.modeling_utils import PreTrainedModel
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from transformers.utils import (
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ModelOutput,
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add_code_sample_docstrings,
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add_start_docstrings,
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add_start_docstrings_to_model_forward,
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is_bitsandbytes_available,
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is_ninja_available,
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is_torch_cuda_available,
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logging,
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)
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try:
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from flash_rwkv import rwkv5_cuda_linear_attention
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# Check version
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required_version = pkg_resources.parse_version("0.2.1")
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current_version = pkg_resources.get_distribution("flash-rwkv").parsed_version
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if current_version < required_version:
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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.")
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except ImportError:
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raise ImportError("The flash-rwkv package is not detected. Please install it using pip install flash-rwkv.")
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except pkg_resources.DistributionNotFound:
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raise ImportError("The flash-rwkv package is not detected. Please install it using pip install flash-rwkv.")
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from .configuration_rwkv5 import Rwkv5Config
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logger = logging.get_logger(__name__)
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_CHECKPOINT_FOR_DOC = "RWKV/rwkv-5-world-1b5"
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_CONFIG_FOR_DOC = "Rwkv5Config"
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def rwkv5_linear_attention_cpu(receptance, key, value, time_decay, time_first, state):
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input_dtype = receptance.dtype
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# For CPU fallback. Will be slower and probably take more memory than the custom CUDA kernel if not executed
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# within a torch.no_grad.
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batch, seq_length, hidden_size = receptance.shape
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num_heads, head_size = time_first.shape
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key = key.float().view(batch, seq_length, num_heads, head_size).transpose(1, 2).transpose(-2, -1)
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value = value.float().view(batch, seq_length, num_heads, head_size).transpose(1, 2)
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receptance = receptance.float().view(batch, seq_length, num_heads, head_size).transpose(1, 2)
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time_decay = torch.exp(-torch.exp(time_decay.float())).reshape(-1, 1, 1).reshape(num_heads, -1, 1)
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time_first = time_first.float().reshape(-1, 1, 1).reshape(num_heads, -1, 1)
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out = torch.zeros_like(key).reshape(batch, seq_length, num_heads, head_size)
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for current_index in range(seq_length):
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current_receptance = receptance[:, :, current_index:current_index+1, :]
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current_key = key[:, :, :, current_index:current_index+1]
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current_value = value[:, :, current_index:current_index+1, :]
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attention_output = current_key @ current_value
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out[:, current_index] = (current_receptance @ (time_first * attention_output + state)).squeeze(2)
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with torch.no_grad():
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state = attention_output + time_decay * state
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return out, state
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# copied from RWKV but with receptance
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def RWKV5_linear_attention(training, receptance, key, value, time_decay, time_first, state):
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no_cuda = any(t.device.type != "cuda" for t in [time_decay, time_first, key, value])
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# Launching the CUDA kernel for just one token will actually be slower (there is no for loop in the CPU version
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# in this case).
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one_token = key.size(1) == 1
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if not training or no_cuda or one_token:
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return rwkv5_linear_attention_cpu(
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receptance, key, value, time_decay, time_first, state
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)
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else:
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return rwkv5_cuda_linear_attention(receptance.float(), key.float(), value.float(), time_decay.float().flatten(), time_first.float().flatten(), state)
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class Rwkv5SelfAttention(nn.Module):
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def __init__(self, config, layer_id=0):
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super().__init__()
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self.config = config
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self.layer_id = layer_id
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hidden_size = config.hidden_size
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attention_hidden_size = config.attention_hidden_size
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self.attention_hidden_size = attention_hidden_size
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head_size = config.head_size
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num_heads = attention_hidden_size // head_size
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self.time_decay = nn.Parameter(torch.empty(num_heads, head_size))
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self.time_faaaa = nn.Parameter(torch.empty(num_heads, head_size))
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self.time_mix_gate = nn.Parameter(torch.empty(1, 1, hidden_size))
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self.time_mix_key = nn.Parameter(torch.empty(1, 1, hidden_size))
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self.time_mix_value = nn.Parameter(torch.empty(1, 1, hidden_size))
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self.time_mix_receptance = nn.Parameter(torch.empty(1, 1, hidden_size))
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self.time_shift = nn.ZeroPad2d((0, 0, 1, -1))
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self.key = nn.Linear(hidden_size, attention_hidden_size, bias=False)
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self.value = nn.Linear(hidden_size, attention_hidden_size, bias=False)
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self.receptance = nn.Linear(hidden_size, attention_hidden_size, bias=False)
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self.gate = nn.Linear(hidden_size, attention_hidden_size, bias=False)
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self.output = nn.Linear(attention_hidden_size, hidden_size, bias=False)
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self.ln_x = nn.GroupNorm(num_heads, hidden_size)
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def extract_key_value(self, hidden, state=None):
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# Mix hidden with the previous timestep to produce key, value, receptance
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if hidden.size(1) == 1 and state is not None:
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shifted = state[0][:, :, self.layer_id]
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else:
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shifted = self.time_shift(hidden)
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if state is not None:
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shifted[:, 0] = state[0][:, :, self.layer_id]
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if len(shifted.size()) == 2:
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shifted = shifted.unsqueeze(1)
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key = hidden * self.time_mix_key + shifted * (1 - self.time_mix_key)
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value = hidden * self.time_mix_value + shifted * (1 - self.time_mix_value)
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receptance = hidden * self.time_mix_receptance + shifted * (1 - self.time_mix_receptance)
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gate = hidden * self.time_mix_gate + shifted * (1 - self.time_mix_gate)
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key = self.key(key)
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value = self.value(value)
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receptance = self.receptance(receptance)
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gate = F.silu(self.gate(gate))
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if state is not None:
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state[0][:, :, self.layer_id] = hidden[:, -1]
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return receptance, key, value, gate, state
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def forward(self, hidden, state=None, use_cache=False, seq_mode=True):
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receptance, key, value, gate, state = self.extract_key_value(hidden, state=state)
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B,T,C = receptance.shape
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H, S = self.time_faaaa.shape
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layer_state = state[1][:, :, :, :, self.layer_id] if state is not None else None
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out, layer_state = RWKV5_linear_attention(
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self.training, receptance, key, value, self.time_decay, self.time_faaaa, layer_state
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)
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if layer_state is not None:
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state[1][:, :, :, :, self.layer_id] = layer_state
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out = out.reshape(B * T, H * S)
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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)
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out = out.to(dtype=hidden.dtype) * gate
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out = self.output(out)
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return out, state
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# Copied from rwkv except for the intermediate size
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class Rwkv5FeedForward(nn.Module):
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def __init__(self, config, layer_id=0):
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super().__init__()
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self.config = config
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self.layer_id = layer_id
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hidden_size = config.hidden_size
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intermediate_size = (
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config.intermediate_size
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if config.intermediate_size is not None
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else int((config.hidden_size * 3.5) // 32 * 32)
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)
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self.time_shift = nn.ZeroPad2d((0, 0, 1, -1))
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self.time_mix_key = nn.Parameter(torch.empty(1, 1, hidden_size))
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self.time_mix_receptance = nn.Parameter(torch.empty(1, 1, hidden_size))
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self.key = nn.Linear(hidden_size, intermediate_size, bias=False)
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self.receptance = nn.Linear(hidden_size, hidden_size, bias=False)
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self.value = nn.Linear(intermediate_size, hidden_size, bias=False)
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def forward(self, hidden, state=None):
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if hidden.size(1) == 1 and state is not None:
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shifted = state[2][:, :, self.layer_id]
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else:
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shifted = self.time_shift(hidden)
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if state is not None:
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shifted[:, 0] = state[2][:, :, self.layer_id]
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if len(shifted.size()) == 2:
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shifted = shifted.unsqueeze(1)
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key = hidden * self.time_mix_key + shifted * (1 - self.time_mix_key)
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receptance = hidden * self.time_mix_receptance + shifted * (1 - self.time_mix_receptance)
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key = torch.square(torch.relu(self.key(key)))
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value = self.value(key)
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receptance = torch.sigmoid(self.receptance(receptance))
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if state is not None:
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state[2][:, :, self.layer_id] = hidden[:, -1]
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return receptance * value, state
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# Copied from transformers.models.rwkv.modeling_rwkv.RwkvBlock with Rwkv->Rwkv5
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class Rwkv5Block(nn.Module):
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def __init__(self, config, layer_id):
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super().__init__()
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self.config = config
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self.layer_id = layer_id
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if layer_id == 0:
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self.pre_ln = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_epsilon)
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self.ln1 = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_epsilon)
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self.ln2 = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_epsilon)
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self.attention = Rwkv5SelfAttention(config, layer_id)
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self.feed_forward = Rwkv5FeedForward(config, layer_id)
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def forward(self, hidden, state=None, use_cache=False, output_attentions=False, seq_mode=True):
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if self.layer_id == 0:
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hidden = self.pre_ln(hidden)
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attention, state = self.attention(self.ln1(hidden), state=state, use_cache=use_cache, seq_mode=seq_mode)
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hidden = hidden + attention
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feed_forward, state = self.feed_forward(self.ln2(hidden), state=state)
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hidden = hidden + feed_forward
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outputs = (hidden, state)
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if output_attentions:
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outputs += (attention,)
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else:
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outputs += (None,)
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return outputs
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# Copied from transformers.models.rwkv.modeling_rwkv.RwkvPreTrainedModel with Rwkv->Rwkv5
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class Rwkv5PreTrainedModel(PreTrainedModel):
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"""
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An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
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models.
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"""
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config_class = Rwkv5Config
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base_model_prefix = "rwkv5"
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_no_split_modules = ["Rwkv5Block"]
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_keep_in_fp32_modules = ["time_decay", "time_first"]
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supports_gradient_checkpointing = True
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def _init_weights(self, module):
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"""Initialize the weights."""
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if isinstance(module, Rwkv5SelfAttention):
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layer_id = module.layer_id
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num_hidden_layers = module.config.num_hidden_layers
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hidden_size = module.config.hidden_size
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attention_hidden_size = module.attention_hidden_size
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head_size = module.config.head_size
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num_heads = attention_hidden_size // head_size
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ratio_0_to_1 = layer_id / (num_hidden_layers - 1) # 0 to 1
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ratio_1_to_almost0 = 1.0 - (layer_id / num_hidden_layers) # 1 to ~0
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time_weight = torch.tensor(
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[i / hidden_size for i in range(hidden_size)],
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dtype=module.time_mix_key.dtype,
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device=module.time_mix_key.device,
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)
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time_weight = time_weight[None, None, :]
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decay_speed = [
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-6.0 + 5.0 * (h / (attention_hidden_size - 1)) ** (0.7 + 1.3 * ratio_0_to_1)
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for h in range(attention_hidden_size)
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]
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decay_speed = torch.tensor(decay_speed, dtype=module.time_decay.dtype, device=module.time_decay.device)
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tmp = torch.tensor(
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[
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(1.0 - (i / (attention_hidden_size - 1.0))) * ratio_0_to_1 + 0.1 * ((i + 1) % 3 - 1)
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for i in range(attention_hidden_size)
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],
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dtype=module.time_faaaa.dtype,
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device=module.time_faaaa.device,
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)
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with torch.no_grad():
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module.time_decay.data = decay_speed.reshape(num_heads, head_size)
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module.time_faaaa.data = tmp.reshape(num_heads, head_size)
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module.time_mix_key.data = torch.pow(time_weight, ratio_1_to_almost0)
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module.time_mix_value.data = torch.pow(time_weight, ratio_1_to_almost0) + 0.3 * ratio_0_to_1
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module.time_mix_receptance.data = torch.pow(time_weight, 0.5 * ratio_1_to_almost0)
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module.time_mix_gate.data = torch.pow(time_weight, 0.5 * ratio_1_to_almost0)
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elif isinstance(module, Rwkv5FeedForward):
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layer_id = module.layer_id
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num_hidden_layers = module.config.num_hidden_layers
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hidden_size = module.config.hidden_size
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ratio_1_to_almost0 = 1.0 - (layer_id / num_hidden_layers) # 1 to ~0
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time_weight = torch.tensor(
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[i / hidden_size for i in range(hidden_size)],
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dtype=module.time_mix_key.dtype,
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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):
|
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|
self.head = new_embeddings
|
||
|
|
|
||
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|
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,
|
||
|
|
)
|