1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
//! Base neural network module - Foundation for all PyTorch-compatible layers
use crate::{device::PyDevice, error::PyResult, tensor::PyTensor};
use pyo3::prelude::*;
use pyo3::types::PyAny;
use std::collections::HashMap;
/// Base class for all neural network modules
#[pyclass(name = "Module", subclass)]
pub struct PyModule {
// This will be overridden by subclasses
}
#[pymethods]
impl PyModule {
#[new]
pub fn new() -> Self {
Self {}
}
/// Get all parameters of the module
fn parameters(&self) -> PyResult<Vec<PyTensor>> {
// Default implementation - subclasses should override
Ok(Vec::new())
}
/// Get all named parameters of the module
fn named_parameters(&self) -> PyResult<HashMap<String, PyTensor>> {
// Default implementation - subclasses should override
Ok(HashMap::new())
}
/// Set the module in training mode
fn train(&mut self, mode: Option<bool>) {
// Default implementation - subclasses should override
let _mode = mode.unwrap_or(true);
// Subclasses should implement actual training mode logic
}
/// Set the module in evaluation mode
fn eval(&mut self) {
// Default implementation - subclasses should override
// Subclasses should implement actual evaluation mode logic
}
/// Move module to specified device
fn to(&mut self, device: PyDevice) -> PyResult<()> {
// Default implementation - subclasses should override
let _device = device;
Ok(())
}
/// Zero out gradients of all parameters
fn zero_grad(&mut self) {
// Default implementation - subclasses should override
// Subclasses should implement actual gradient zeroing
}
/// Make module callable (forward pass)
fn __call__(&self, input: &PyTensor) -> PyResult<PyTensor> {
self.forward(input)
}
/// Forward pass - must be implemented by subclasses
fn forward(&self, _input: &PyTensor) -> PyResult<PyTensor> {
Err(PyErr::new::<pyo3::exceptions::PyNotImplementedError, _>(
"Subclasses must implement forward method",
))
}
/// String representation
fn __repr__(&self) -> String {
"Module()".to_string()
}
/// Apply a function to all submodules
fn apply(&mut self, _func: Py<PyAny>) -> PyResult<()> {
// Default implementation - subclasses should override
Ok(())
}
/// Get the state dict (parameters and buffers)
fn state_dict(&self) -> PyResult<HashMap<String, PyTensor>> {
// Default implementation returns named parameters
self.named_parameters()
}
/// Load state dict (parameters and buffers)
fn load_state_dict(&mut self, _state_dict: HashMap<String, PyTensor>) -> PyResult<()> {
// Default implementation - subclasses should override
Err(PyErr::new::<pyo3::exceptions::PyNotImplementedError, _>(
"Subclasses must implement load_state_dict method",
))
}
/// Get number of parameters
fn num_parameters(&self) -> PyResult<usize> {
let params = self.parameters()?;
Ok(params.iter().map(|p| p.numel()).sum())
}
/// Check if module is in training mode
fn training(&self) -> bool {
// Default implementation - subclasses should track this
true
}
}