USER
from dataclasses import dataclass
from pathlib import Path
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.configuration_utils import PretrainedConfig
from transformers.utils import (
ModelOutput,
is_torch_cuda_available,
)
rwkv5_cuda_kernel = None
class Rwkv5Config(PretrainedConfig):
model_type = "rwkv5"
def __init__(
self,
vocab_size=65536,
hidden_size=768,
num_hidden_layers=24,
attention_hidden_size=None,
head_size=64,
intermediate_size=None,
layer_norm_epsilon=1e-5,
bos_token_id=0,
eos_token_id=0,
rescale_every=0,
tie_word_embeddings=False,
use_cache=False,
**kwargs,
):
self.vocab_size = vocab_size
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.head_size = head_size
self.intermediate_size = None
self.layer_norm_epsilon = layer_norm_epsilon
self.rescale_every = rescale_every
self.use_cache = use_cache
self.bos_token_id = bos_token_id
self.eos_token_id = eos_token_id
super().__init__(
tie_word_embeddings=tie_word_embeddings, bos_token_id=bos_token_id, eos_token_id=eos_token_id, **kwargs
)
@torch.jit.script
def rwkv_linear_attention(
B: int,
H: int,
S: int,
T: int,
n_head: int,
hidden,
time_decay,
time_first,
receptance,
key,
value,
gate,
lxw,
lxb,
):
key = key.to(torch.float32).view(B, T, H, S).transpose(1, 2).transpose(-2, -1)
value = value.to(torch.float32).view(B, T, H, S).transpose(1, 2)
receptance = receptance.to(torch.float32).view(B, T, H, S).transpose(1, 2)
time_decay = torch.exp(-torch.exp(time_decay.float())).reshape(-1, 1, 1).reshape(n_head, -1, 1)
time_first = time_first.float().reshape(-1, 1, 1).reshape(n_head, -1, 1)
lxw = lxw.float()
lxb = lxb.float()
out = torch.zeros_like(key).reshape(B, T, H, S)
for t in range(T):
rt = receptance[:, :, t : t + 1, :]
kt = key[:, :, :, t : t + 1]
vt = value[:, :, t : t + 1, :]
at = kt @ vt
out[:, t] = (rt @ (time_first * at)).squeeze(2)
out = out.reshape(B * T, H * S)
out = F.group_norm(out, num_groups=H, weight=lxw, bias=lxb).reshape(B, T, H * S)
out = out.to(dtype=hidden.dtype) * gate
return out
@torch.jit.script
def extract_key_value( hidden, shifted, time_mix_key, time_mix_value, time_mix_receptance, time_mix_gate):
key = hidden * time_mix_key + shifted * (1 - time_mix_key)
value = hidden * time_mix_value + shifted * (1 - time_mix_value)
receptance = hidden * time_mix_receptance + shifted * (1 - time_mix_receptance)
gate = hidden * time_mix_gate + shifted * (1 - time_mix_gate)
return receptance, key, value, gate
class RwkvSelfAttention(nn.Module):
def __init__(self, config, layer_id=0):
super().__init__()
self.config = config
self.layer_id = layer_id
hidden_size = config.hidden_size
# https://github.com/BlinkDL/RWKV-LM/blob/main/RWKV-v4neo/src/model.py#L146
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
)
self.attention_hidden_size = attention_hidden_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_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)
# https://github.com/BlinkDL/RWKV-LM/blob/3db37a72356b736966ddd377268f02b80963af3f/RWKV-v4neo/src/model.py#L190C1-L190C1
self.ln_x = nn.GroupNorm(hidden_size // config.head_size, hidden_size)
def forward(self, hidden):
B = hidden.shape[0]
H = self.time_decay.shape[0]
S = hidden.shape[-1] // H
T = hidden.shape[1]
# Mix hidden with the previous timestep to produce key, value, receptance
shifted = self.time_shift(hidden)
receptance, key, value, gate = extract_key_value(hidden, shifted, self.time_mix_key, self.time_mix_value, self.time_mix_receptance, self.time_mix_gate)
# https://github.com/BlinkDL/ChatRWKV/blob/main/rwkv_pip_package/src/rwkv/model.py#L693
key = self.key(key)
value = self.value(value)
receptance = self.receptance(receptance)
gate = F.silu(self.gate(gate), inplace=True)
rwkv = rwkv_linear_attention(
B,
H,
S,
T,
self.num_attention_heads,
hidden,
self.time_decay,
self.time_faaaa,
receptance,
key,
value,
gate,
self.ln_x.weight,
self.ln_x.bias,
)
return self.output(rwkv)
class RwkvFeedForward(nn.Module):
def __init__(self, config, layer_id=0):
super().__init__()
self.config = config
self.layer_id = layer_id
self.hidden_size = config.hidden_size
# https://github.com/BlinkDL/RWKV-LM/blob/3db37a72356b736966ddd377268f02b80963af3f/RWKV-v4neo/train.py#L168
self.time_shift = nn.ZeroPad2d((0, 0, 1, -1))
self.time_maa_k = nn.Parameter(torch.randn(1,1,self.hidden_size))
self.key = nn.Linear(self.hidden_size, self.hidden_size * 2, bias=False)
self.value = nn.Linear(self.hidden_size * 2, self.hidden_size, bias=False)
#self.value.weight.data.zero_()
def forward(self, x):
xx = self.time_shift(x) - x
k = x + xx * self.time_maa_k
k = torch.relu(self.key(k)) ** 2
return self.value(k)
class RwkvBlock(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 = torch.jit.script(RwkvSelfAttention(config, layer_id))
self.feed_forward = torch.jit.script(RwkvFeedForward(config, layer_id))
def forward(self, hidden):
if self.layer_id == 0:
hidden = self.pre_ln(hidden)
attention = self.attention(self.ln1(hidden))
hidden = hidden + attention
feed_forward = self.feed_forward(self.ln2(hidden))
hidden = hidden + feed_forward
return hidden
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 = "rwkv"
_no_split_modules = ["RwkvBlock"]
_keep_in_fp32_modules = ["time_decay", "time_first"]
supports_gradient_checkpointing = True
def _init_weights(self, module):
"""Initialize the weights."""
if isinstance(module, RwkvSelfAttention):
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.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, :]
# https://github.com/BlinkDL/RWKV-LM/blob/main/RWKV-v4neo/src/model.py#L398
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_attention_heads, module.config.head_size)
module.time_faaaa.data = tmp.reshape(num_attention_heads, module.config.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):
with torch.no_grad():
ratio_1_to_almost0 = 1.0 - (module.layer_id / module.config.num_hidden_layers) # 1 to ~0
ddd = torch.ones(1, 1, module.hidden_size)
for i in range(module.hidden_size):
ddd[0, 0, i] = i / module.hidden_size
print(module.time_maa_k.data)
module.time_maa_k.data = ddd
print(module.time_maa_k.data)
@dataclass
class Rwkv5CausalLMOutput(ModelOutput):
loss = None
logits = None
state = None
hidden_states = None
attentions = None
def __init__(self, loss=None, logits=None, state=None, hidden_states=None, attentions=None):
super().__init__(
loss=loss,
logits=logits,
hidden_states=hidden_states,
attentions=attentions,
)
self.state = state
class Rwkv5Model(Rwkv5PreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.embeddings = nn.Embedding(config.vocab_size, config.hidden_size)
self.blocks = nn.ModuleList([RwkvBlock(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
def forward(
self,
input_ids = None,
inputs_embeds= None,
):
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)
hidden_states = inputs_embeds
for idx, block in enumerate(self.blocks):
hidden_states = block(
hidden_states
)
hidden_states = self.ln_out(hidden_states)
return hidden_states
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
def forward(
self,
input_ids = None,
labels = None,
inputs_embeds = None,
return_dict = None,
):
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
rwkv_outputs = self.rwkv(
input_ids,
inputs_embeds=inputs_embeds,
)
logits = self.head(rwkv_outputs)
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,) + rwkv_outputs[1:]
return ((loss,) + output) if loss is not None else output
return Rwkv5CausalLMOutput(
loss=loss,
logits=logits,
)
Convert this model to tensorflow ASSISTANT
Converting a complex PyTorch model like the one you've provided to TensorFlow involves several steps, including mapping PyTorch modules and functions to their TensorFlow equivalents, handling custom layers, and ensuring that all operations are compatible with TensorFlow's execution model. Below is a comprehensive guide to help you through this process, along with example code snippets for key components of the model.
## **1. Setting Up the TensorFlow Environment**
First, ensure you have TensorFlow installed. It's recommended to use TensorFlow 2.x to leverage the Keras API for building models.
```bash
pip install tensorflow
```
## **2. Creating the Configuration Class**
In PyTorch, the `Rwkv5Config` class inherits from `PretrainedConfig`. In TensorFlow, you can create a similar configuration class using Python's `dataclass` or a simple class.
```python
from dataclasses import dataclass
@dataclass
class Rwkv5Config:
vocab_size: int = 65536
hidden_size: int = 768
num_hidden_layers: int = 24
attention_hidden_size: int = None
head_size: int = 64
intermediate_size: int = None
layer_norm_epsilon: float = 1e-5
bos_token_id: int = 0
eos_token_id: int = 0
rescale_every: int = 0
tie_word_embeddings: bool = False
use_cache: bool = False
# Add other hyperparameters as needed
```
## **3. Converting Custom PyTorch Layers to TensorFlow**
### **a. RwkvSelfAttention Layer**
This custom self-attention layer involves several PyTorch-specific operations. Below is a TensorFlow equivalent using `tf.keras.layers.Layer`.
```python
import tensorflow as tf
from tensorflow.keras import layers
class RwkvSelfAttention(layers.Layer):
def __init__(self, config, layer_id=0, **kwargs):
super(RwkvSelfAttention, self).__init__(**kwargs)
self.config = config
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 or hidden_size
self.attention_hidden_size = attention_hidden_size
# Define trainable parameters
self.time_decay = self.add_weight(
shape=(num_attention_heads, config.head_size),
initializer='random_normal',
trainable=True,
name='time_decay'
)
self.time_faaaa = self.add_weight(
shape=(num_attention_heads, config.head_size),
initializer='random_normal',
trainable=True,
name='time_faaaa'
)
self.time_mix_gate = self.add_weight(
shape=(1, 1, config.hidden_size),
initializer='zeros',
trainable=True,
name='time_mix_gate'
)
self.time_mix_key = self.add_weight(
shape=(1, 1, config.hidden_size),
initializer='zeros',
trainable=True,
name='time_mix_key'
)
self.time_mix_value = self.add_weight(
shape=(1, 1, config.hidden_size),
initializer='zeros',
trainable=True,
name='time_mix_value'
)
self.time_mix_receptance = self.add_weight(
shape=(1, 1, config.hidden_size),
initializer='zeros',
trainable=True,
name='time_mix_receptance'
)
# Define layers
self.time_shift = layers.ZeroPadding2D(padding=((1, 0), (0, 0))) # Approximation of ZeroPad2d((0, 0, 1, -1))
self.key = layers.Dense(attention_hidden_size, use_bias=False, name='key')
self.value = layers.Dense(attention_hidden_size, use_bias=False, name='value')
self.receptance = layers.Dense(attention_hidden_size, use_bias=False, name='receptance')
self.gate = layers.Dense(attention_hidden_size, use_bias=False, name='gate')
self.output_dense = layers.Dense(config.hidden_size, use_bias=False, name='output')
self.ln_x = layers.GroupNormalization(groups=config.hidden_size // config.head_size, epsilon=config.layer_norm_epsilon, name='ln_x')
def extract_key_value(self, hidden, shifted):
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)
return receptance, key, value, gate
def linear_attention(self, B, H, S, T, n_head, hidden, time_decay, time_faaaa, receptance, key, value, gate, lxw, lxb):
# Implement the attention mechanism using TensorFlow operations
# This is a simplified placeholder implementation
# You need to vectorize or find an efficient way to implement the loop in TensorFlow
out = tf.zeros_like(key)
for t in range(T):
rt = receptance[:, :, t:t+1, :]
kt = key[:, :, :, t:t+1]
vt = value[:, :, t:t+1, :]
at = tf.matmul(kt, vt, transpose_b=True)
out += tf.matmul(rt, time_decay * at) # Simplified
out = tf.reshape(out, (B * T, H * S))
out = tf.nn.group_norm(out, groups=H, epsilon=self.ln_x.epsilon)
out = tf.reshape(out, (B, T, H * S))
out = out * gate
return out
def call(self, hidden):
B = tf.shape(hidden)[0]
T = tf.shape(hidden)[1]
H = self.num_attention_heads
S = self.config.hidden_size // H
# Shift the hidden states
shifted = tf.pad(hidden, [[0, 0], [1, -1], [0, 0]]) # Equivalent to ZeroPad2d((0, 0, 1, -1))
# Extract key, value, receptance, gate
receptance, key, value, gate = self.extract_key_value(hidden, shifted)
# Linear transformations
key = self.key(key)
value = self.value(value)
receptance = self.receptance(receptance)
gate = tf.nn.silu(self.gate(gate))
# Apply linear attention
rwkv = self.linear_attention(
B, H, S, T, self.num_attention_heads, hidden,
self.time_decay, self.time_faaaa, receptance,
key, value, gate, self.ln_x.gamma, self.ln_x.beta
)
return self.output_dense(rwkv)
```
**Notes:**
- **Zero Padding:** TensorFlow's `ZeroPadding2D` doesn't directly support asymmetric padding like PyTorch's `ZeroPad2d((0, 0, 1, -1))`. Instead, padding is handled using `tf.pad` with specific padding configurations.
- **Group Normalization:** TensorFlow's `GroupNormalization` might have different behavior or requirements compared to PyTorch's `GroupNorm`. Ensure parameters like `groups` and `epsilon` are correctly mapped.
- **Attention Mechanism:** The provided `linear_attention` function is a simplified placeholder. Implementing an exact equivalent would require careful translation of the logic, potentially using `tf.scan` or other TensorFlow operations to handle the sequential computations.
### **b. RwkvFeedForward Layer**
```python
class RwkvFeedForward(layers.Layer):
def __init__(self, config, layer_id=0, **kwargs):
super(RwkvFeedForward, self).__init__(**kwargs)
self.config = config
self.layer_id = layer_id
self.hidden_size = config.hidden_size
self.time_shift = layers.ZeroPadding2D(padding=((1, 0), (0, 0))) # Approximation
self.time_maa_k = self.add_weight(
shape=(1, 1, self.hidden_size),
initializer='random_normal',
trainable=True,
name='time_maa_k'
)
self.key = layers.Dense(self.hidden_size * 2, use_bias=False, name='ffn_key')
self.value = layers.Dense(self.hidden_size, use_bias=False, name='ffn_value')
def call(self, x):
shifted = tf.pad(x, [[0, 0], [1, -1], [0, 0]]) # Equivalent to ZeroPad2d((0, 0, 1, -1))
xx = shifted - x
k = x + xx * self.time_maa_k
k = tf.nn.relu(self.key(k)) ** 2
return self.value(k)
```
### **c. RwkvBlock Layer**
This layer combines the self-attention and feedforward layers with residual connections and layer normalization.
```python
class RwkvBlock(layers.Layer):
def __init__(self, config, layer_id, **kwargs):
super(RwkvBlock, self).__init__(**kwargs)
self.config = config
self.layer_id = layer_id
if layer_id == 0:
self.pre_ln = layers.LayerNormalization(epsilon=config.layer_norm_epsilon, name='pre_ln')
self.ln1 = layers.LayerNormalization(epsilon=config.layer_norm_epsilon, name='ln1')
self.ln2 = layers.LayerNormalization(epsilon=config.layer_norm_epsilon, name='ln2')
self.attention = RwkvSelfAttention(config, layer_id=layer_id, name='attention')
self.feed_forward = RwkvFeedForward(config, layer_id=layer_id, name='feed_forward')
def call(self, hidden):
if self.layer_id == 0:
hidden = self.pre_ln(hidden)
attention = self.attention(self.ln1(hidden))
hidden = hidden + attention
feed_forward = self.feed_forward(self.ln2(hidden))
hidden = hidden + feed_forward
return hidden
```
## **4. Building the RwkvModel**
This model includes embeddings, multiple `RwkvBlock` layers, and a final layer normalization.
```python
class RwkvModel(tf.keras.Model):
def __init__(self, config, **kwargs):
super(RwkvModel, self).__init__(**kwargs)
self.embeddings = layers.Embedding(input_dim=config.vocab_size, output_dim=config.hidden_size, name='embeddings')
self.blocks = [RwkvBlock(config, layer_id=idx, name=f'block_{idx}') for idx in range(config.num_hidden_layers)]
self.ln_out = layers.LayerNormalization(epsilon=config.layer_norm_epsilon, name='ln_out')
def call(self, input_ids, training=False):
hidden_states = self.embeddings(input_ids)
for block in self.blocks:
hidden_states = block(hidden_states)
hidden_states = self.ln_out(hidden_states)
return hidden_states
```
## **5. Building the RwkvForCausalLM Model**
This model adds a linear layer on top of `RwkvModel` for causal language modeling.
```python
class RwkvForCausalLM(tf.keras.Model):
def __init__(self, config, **kwargs):
super(RwkvForCausalLM, self).__init__(**kwargs)
self.rwkv = RwkvModel(config, name='rwkv_model')
self.head = layers.Dense(config.vocab_size, use_bias=False, name='head')
self.loss_fn = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True)
def call(self, input_ids, labels=None, training=False):
hidden_states = self.rwkv(input_ids, training=training)
logits = self.head(hidden_states)
loss = None
if labels is not None:
# Shift logits and labels for causal language modeling
shift_logits = logits[:, :-1, :]
shift_labels = labels[:, 1:]
loss = self.loss_fn(shift_labels, shift_logits)
return {'loss': loss, 'logits': logits}
```
## **6. Weight Initialization**
PyTorch often initializes weights in specific ways. TensorFlow's layers come with their own initializers, but to match PyTorch's behavior, you might need to manually set weights or use specific initializers.
For example, to initialize weights similarly to PyTorch’s `nn.Linear` with Xavier initialization:
```python
initializer = tf.keras.initializers.GlorotUniform()
self.key = layers.Dense(attention_hidden_size, use_bias=False, kernel_initializer=initializer, name='key')
```
If you need to load pre-trained weights from PyTorch to TensorFlow, you’ll have to map the weights manually, ensuring that layer names and shapes match.
## **7. Handling Custom Functions**
Your model uses custom functions like `rwkv_linear_attention` and `extract_key_value`. These need to be translated into TensorFlow operations. Here’s an example of how you might translate `extract_key_value`:
```python
def extract_key_value_tf(hidden, shifted, time_mix_key, time_mix_value, time_mix_receptance, time_mix_gate):
key = hidden * time_mix_key + shifted * (1 - time_mix_key)
value = hidden * time_mix_value + shifted * (1 - time_mix_value)
receptance = hidden * time_mix_receptance + shifted * (1 - time_mix_receptance)
gate = hidden * time_mix_gate + shifted * (1 - time_mix_gate)
return receptance, key, value, gate
```
For `rwkv_linear_attention`, you'll need to implement the attention mechanism using TensorFlow operations. This might involve using `tf.matmul` for matrix multiplications and ensuring that tensor shapes align correctly. Vectorizing the loop for performance is crucial.
## **8. Model Initialization and Weight Loading**
After defining the model, initialize it with the configuration and prepare to load weights if necessary.
```python
# Example configuration
config = Rwkv5Config()
# Initialize the model
model = RwkvForCausalLM(config)
# Example input
import numpy as np
input_ids = np.random.randint(0, config.vocab_size, size=(1, 10)).astype(np.int32)
# Forward pass
outputs = model(input_ids)
print(outputs['logits'].shape) # Expected: (1, 10, vocab_size)
```
If you have pre-trained PyTorch weights, you'll need to write a weight-loading function that maps each PyTorch weight to its TensorFlow counterpart. This involves:
1. **Loading PyTorch Weights:** Use `torch.load` to load the weights.
2. **Mapping Layers:** Ensure that each TensorFlow layer corresponds to the correct PyTorch layer.
3. **Transposing Weights:** PyTorch and TensorFlow may have different weight formats (e.g., shape differences in dense layers).
4. **Assigning Weights:** Use TensorFlow's `set_weights` method to assign the converted weights.
**Example:**
```python
import torch
# Load PyTorch state_dict
state_dict = torch.load('path_to_pytorch_model.pth')
# Example: Assign weights to the embeddings layer
embedding_weights = state_dict['rwkv.embeddings.weight'].numpy().T # Transpose if necessary
model.rwkv.embeddings.set_weights([embedding_weights])
# Repeat for other layers
```
**Note:** This process can be intricate and requires careful handling to ensure that all weights are correctly mapped and reshaped.
## **9. Generating Text**
To enable text generation similar to the PyTorch model's `prepare_inputs_for_generation`, you can implement a generation loop in TensorFlow.
```python
def generate_text(model, input_ids, max_length=50):
for _ in range(max_length):
outputs = model(input_ids)
logits = outputs['logits']
next_token = tf.argmax(logits[:, -1, :], axis=-1, output_type=tf.int32)
input_ids = tf.concat([input_ids, tf.expand_dims(next_token, axis=-1)], axis=-1)
return input_ids
```
## **10. Final Considerations**
- **Performance Optimization:** TensorFlow handles batch operations differently than PyTorch. Ensure that operations within custom layers are optimized for TensorFlow's execution model.
- **Testing and Validation:** After conversion, thoroughly test the TensorFlow model to ensure it behaves identically to the PyTorch version. Compare outputs layer by layer to identify discrepancies.
- **Handling TorchScript:** The original model uses TorchScript (`@torch.jit.script`) for scripting functions. TensorFlow's `@tf.function` decorator can be used to compile functions for better performance, but the exact behavior may differ.
- **Gradient Checkpointing and Caching:** If your model relies on features like gradient checkpointing or caching for state during generation, you'll need to implement equivalent mechanisms in TensorFlow, which might involve customizing the training loop or using TensorFlow's built-in functionalities.
## **Complete Example**
Here's a simplified version that puts together the components discussed:
```python
import tensorflow as tf
from tensorflow.keras import layers
# Assuming RwkvSelfAttention, RwkvFeedForward, RwkvBlock, and RwkvModel are defined as above
class RwkvForCausalLM(tf.keras.Model):
def __init__(self, config, **kwargs):
super(RwkvForCausalLM, self).__init__(**kwargs)
self.rwkv = RwkvModel(config, name='rwkv_model')
self.head = layers.Dense(config.vocab_size, use_bias=False, name='head')
self.loss_fn = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True)
def call(self, input_ids, labels=None, training=False):
hidden_states = self.rwkv(input_ids, training=training)
logits = self.head(hidden_states)
loss = None
if labels is not None:
shift_logits = logits[:, :-1, :]
shift_labels = labels[:, 1:]
loss = self.loss_fn(shift_labels, shift_logits)
return {'loss': loss, 'logits': logits}
# Example usage
config = Rwkv5Config()
model = RwkvForCausalLM(config)
# Dummy input
input_ids = tf.constant([[1, 2, 3, 4, 5]], dtype=tf.int32)
# Forward pass
outputs = model(input_ids)
print(outputs['logits'].shape) # Expected: (1, 5, vocab_size)
```
## **Conclusion**
Converting a complex PyTorch model to TensorFlow requires a meticulous approach, ensuring that each component is accurately translated and that the overall architecture remains consistent. While TensorFlow and PyTorch share many similarities, differences in their APIs and execution models necessitate careful handling of custom layers and operations. Start by converting and testing individual components before assembling the complete model. This incremental approach will help you identify and resolve issues early in the conversion process.
If you encounter specific challenges or need further assistance with particular parts of the model, feel free to ask!