Beginning of a conversation containing 4 posts (1768 tokens of 32768 were used)Uzito писал(а): ↑What's the purpose of fused_add_tanh_sigmoid_multiply and can it be done any other way faster?
The function `fused_add_tanh_sigmoid_multiply` serves the purpose of combining several operations—addition, element-wise tanh and sigmoid activations, followed by point-wise multiplication—in a single computational step. This can be useful in neural network layers where such fused operations might be common, especially in complex architectures like LSTMs or GRUs.
Here's a breakdown of what the function does:
1. **Addition**: It adds two input tensors `input_a` and `input_b`.
2. **Tanh Activation**: Applies the tanh activation function to a specified number of channels.
3. **Sigmoid Activation**: Applies the sigmoid activation function to the remaining channels.
4. **Multiplication**: Multiplies the results of the tanh and sigmoid activations element-wise.
The purpose of this fusion is to potentially save computational time and memory by performing these operations in a more optimized manner together rather than separately.
### Code Breakdown
```python
@torch.jit.script
def fused_add_tanh_sigmoid_multiply(input_a, input_b, n_channels):
n_channels_int = n_channels[0]
in_act = input_a + input_b
t_act = torch.tanh(in_act[:, :n_channels_int, :])
s_act = torch.sigmoid(in_act[:, n_channels_int:, :])
acts = t_act * s_act
return acts
```
### Can it be Done Faster?
Yes, there are some strategies that could potentially make it faster:
1. **Manual Optimization**: Ensuring that the operations are as efficient as possible. This might involve low-level optimizations depending on the specific hardware.
2. **CUDA Kernels**: If you have CUDA skills, you can write custom CUDA kernels for these operations which might offer further performance improvements.
3. **PyTorch Native Operations**: Check if there's a native PyTorch operation that combines these steps efficiently. PyTorch's library is constantly evolving, and sometimes new optimized functions are added.
### Example using PyTorch Native Functions
If we consider the code, it already uses PyTorch's built-in functions which are optimized, but you can rearrange or modify it slightly for performance gains:
```python
import torch
def fused_add_tanh_sigmoid_multiply(input_a, input_b, n_channels):
n_channels_int = n_channels[0]
in_act = input_a + input_b
# Computing tanh and sigmoid in a way that allows parallel execution
t_act_s_act = torch.cat((torch.tanh(in_act[:, :n_channels_int, :]),
torch.sigmoid(in_act[:, n_channels_int:, :])), dim=1)
# Splitting them back for multiplication
t_act = t_act_s_act[:, :n_channels_int, :]
s_act = t_act_s_act[:, n_channels_int:, :]
acts = t_act * s_act
return acts
```
So, while the native PyTorch operations are already optimized, ensuring that activations can run in parallel by manipulating the tensor dimensions (as shown) can sometimes help PyTorch to handle operations more efficiently on modern GPUs that support concurrent kernels.
### Using Conditional Compilation to Include Optimized Implementations
For certain GPUs, you might also want to use hardware-specific optimizations, such as using NVidia's TensorRT for inference optimizations. Here's how you might conditionally include those optimizations:
```python
if torch.cuda.is_available() and torch.cuda.get_device_properties(0).name == 'Your_Specific_GPU_Model':
import tensorrt as trt # Assume TensorRT is installed and properly configured
# An example of how you might use TensorRT to optimize this specific operation
def fused_add_tanh_sigmoid_multiply(input_a, input_b, n_channels):
# Fallback to manual or another optimized implementation if TensorRT is not available
pass # Use tensorrt API to implement the optimized version
else:
def fused_add_tanh_sigmoid_multiply(input_a, input_b, n_channels):
n_channels_int = n_channels[0]
in_act = input_a + input_b
t_act = torch.tanh(in_act[:, :n_channels_int, :])
s_act = torch.sigmoid(in_act[:, n_channels_int:, :])
acts = t_act * s_act
return acts
```
In conclusion, while the provided function is quite optimized using PyTorch's built-in operations, further speed-ups might be achievable through hardware-specific optimizations, leveraging parallelism, or custom CUDA kernels if needed.
Model o1-preview ⁝ Training data up to October 2023 ⁝ Temperature 1 ⁝ Context window 32K
