torsh-python 0.1.2

//! Distributed training bindings

use pyo3::prelude::*;
use pyo3::types::PyAny;
use crate::{tensor::PyTensor, error::PyResult};

/// Process group for distributed training
#[pyclass(name = "ProcessGroup")]
pub struct PyProcessGroup {
    rank: u32,
    world_size: u32,
}

#[pymethods]
impl PyProcessGroup {
    #[new]
    fn new(rank: u32, world_size: u32) -> Self {
        Self { rank, world_size }
    }
    
    #[getter]
    fn rank(&self) -> u32 {
        self.rank
    }
    
    #[getter]
    fn world_size(&self) -> u32 {
        self.world_size
    }
    
    fn all_reduce(&self, tensor: &PyTensor, op: Option<String>) -> PyResult<()> {
        // Placeholder implementation
        Ok(())
    }
    
    fn all_gather(&self, tensors: Vec<PyTensor>, tensor: &PyTensor) -> PyResult<()> {
        // Placeholder implementation
        Ok(())
    }
    
    fn broadcast(&self, tensor: &PyTensor, src: u32) -> PyResult<()> {
        // Placeholder implementation
        Ok(())
    }
    
    fn barrier(&self) -> PyResult<()> {
        // Placeholder implementation
        Ok(())
    }
}

/// Distributed Data Parallel wrapper
#[pyclass(name = "DistributedDataParallel")]
pub struct PyDDP {
    module: Py<PyAny>,
    process_group: Option<PyProcessGroup>,
}

#[pymethods]
impl PyDDP {
    #[new]
    fn new(
        module: Py<PyAny>,
        device_ids: Option<Vec<u32>>,
        output_device: Option<u32>,
        broadcast_buffers: Option<bool>,
        process_group: Option<PyProcessGroup>,
        bucket_cap_mb: Option<f32>,
        find_unused_parameters: Option<bool>,
        check_reduction: Option<bool>,
        gradient_as_bucket_view: Option<bool>,
    ) -> Self {
        Self {
            module,
            process_group,
        }
    }
    
    fn forward(&self, inputs: Vec<PyTensor>) -> PyResult<PyTensor> {
        // Forward pass through wrapped module
        Python::attach(|py| {
            let forward_method = self.module.getattr(py, "forward")?;
            let result = forward_method.call1(py, PyTuple::new(py, &inputs))?;
            result.extract::<PyTensor>(py)
        })
    }
    
    fn __call__(&self, inputs: Vec<PyTensor>) -> PyResult<PyTensor> {
        self.forward(inputs)
    }
    
    fn parameters(&self) -> PyResult<Vec<PyTensor>> {
        Python::attach(|py| {
            let params_method = self.module.getattr(py, "parameters")?;
            let result = params_method.call0(py)?;
            result.extract::<Vec<PyTensor>>(py)
        })
    }
    
    fn named_parameters(&self) -> PyResult<std::collections::HashMap<String, PyTensor>> {
        Python::attach(|py| {
            let named_params_method = self.module.getattr(py, "named_parameters")?;
            let result = named_params_method.call0(py)?;
            result.extract::<std::collections::HashMap<String, PyTensor>>(py)
        })
    }
    
    fn train(&mut self, mode: Option<bool>) -> PyResult<()> {
        Python::attach(|py| {
            let train_method = self.module.getattr(py, "train")?;
            train_method.call1(py, (mode.unwrap_or(true),))?;
            Ok(())
        })
    }
    
    fn eval(&mut self) -> PyResult<()> {
        self.train(Some(false))
    }
}

/// Register distributed module
pub fn register_distributed_module(py: Python<'_>, m: &PyModule) -> PyResult<()> {
    // Add distributed classes
    m.add_class::<PyProcessGroup>()?;
    m.add_class::<PyDDP>()?;
    
    // Add distributed functions
    #[pyfn(m)]
    fn init_process_group(
        backend: String,
        init_method: Option<String>,
        world_size: Option<u32>,
        rank: Option<u32>,
        store: Option<Py<PyAny>>,
        timeout: Option<f64>,
        group_name: Option<String>,
        pg_options: Option<Py<PyAny>>,
    ) -> PyResult<PyProcessGroup> {
        let rank = rank.unwrap_or(0);
        let world_size = world_size.unwrap_or(1);
        Ok(PyProcessGroup::new(rank, world_size))
    }
    
    #[pyfn(m)]
    fn destroy_process_group(group: Option<PyProcessGroup>) -> PyResult<()> {
        // Cleanup process group
        Ok(())
    }
    
    #[pyfn(m)]
    fn get_rank(group: Option<PyProcessGroup>) -> u32 {
        group.map(|g| g.rank()).unwrap_or(0)
    }
    
    #[pyfn(m)]
    fn get_world_size(group: Option<PyProcessGroup>) -> u32 {
        group.map(|g| g.world_size()).unwrap_or(1)
    }
    
    #[pyfn(m)]
    fn is_initialized() -> bool {
        // Check if distributed is initialized
        false
    }
    
    #[pyfn(m)]
    fn is_available() -> bool {
        // Check if distributed is available
        true
    }
    
    #[pyfn(m)]
    fn barrier(group: Option<PyProcessGroup>) -> PyResult<()> {
        if let Some(g) = group {
            g.barrier()
        } else {
            Ok(())
        }
    }
    
    #[pyfn(m)]
    fn all_reduce(tensor: &PyTensor, op: Option<String>, group: Option<PyProcessGroup>) -> PyResult<()> {
        if let Some(g) = group {
            g.all_reduce(tensor, op)
        } else {
            Ok(())
        }
    }
    
    #[pyfn(m)]
    fn all_gather(tensor_list: Vec<PyTensor>, tensor: &PyTensor, group: Option<PyProcessGroup>) -> PyResult<()> {
        if let Some(g) = group {
            g.all_gather(tensor_list, tensor)
        } else {
            Ok(())
        }
    }
    
    #[pyfn(m)]
    fn broadcast(tensor: &PyTensor, src: u32, group: Option<PyProcessGroup>) -> PyResult<()> {
        if let Some(g) = group {
            g.broadcast(tensor, src)
        } else {
            Ok(())
        }
    }
    
    #[pyfn(m)]
    fn reduce(tensor: &PyTensor, dst: u32, op: Option<String>, group: Option<PyProcessGroup>) -> PyResult<()> {
        // Placeholder implementation
        Ok(())
    }
    
    #[pyfn(m)]
    fn scatter(tensor: &PyTensor, scatter_list: Option<Vec<PyTensor>>, src: u32, group: Option<PyProcessGroup>) -> PyResult<()> {
        // Placeholder implementation
        Ok(())
    }
    
    #[pyfn(m)]
    fn gather(tensor: &PyTensor, gather_list: Option<Vec<PyTensor>>, dst: u32, group: Option<PyProcessGroup>) -> PyResult<()> {
        // Placeholder implementation
        Ok(())
    }
    
    Ok(())
}