sample-GPT-PIMC.rs

//! A single-file skeleton demonstrating a GPU-based PIMC search approach in Rust.
//! Using `cust` for CUDA bindings. 
//! 
//! To compile/run, you'll need `cust` in your Cargo.toml, e.g.:
//! 
//! [dependencies]
//! cust = "0.7"
//! rand = "0.8"
//! 
//! Then `cargo run` or similar.  This is only a template!  Fill in real logic for:
//! - Determinization (distributing unknown cards)
//! - GPU kernel playout logic
//! - Marjapussi rule enforcement
//! - Actual card scoring (instead of trivial placeholders)

use std::error::Error;
use rand::prelude::*;
use cust::context::{Context, ContextFlags};
use cust::device::Device;
use cust::memory::{CopyDestination, DeviceBuffer};
use cust::module::Module;
use cust::stream::{Stream, StreamFlags};

///////////////////////////////////////////////////////////////////////////////
//  MARJAPUSSI GAME STATE TRAIT (PARTIAL INFO)
///////////////////////////////////////////////////////////////////////////////

/// A placeholder trait representing a Marjapussi game state with partial information.
/// You must implement this for your actual game logic.
pub trait MarjaPussiGameState {
    /// Return true if the state is terminal (no further moves possible).
    fn is_terminal(&self) -> bool;

    /// Evaluate the final score if terminal. 
    /// (Or return some heuristic if you prefer.)
    fn evaluate_terminal_score(&self) -> f32;

    /// Which player's turn it is (0..=3 for a 4-player game, etc.).
    fn current_player(&self) -> usize;

    /// Return all legal moves from this state.
    fn legal_moves(&self) -> Vec<MarjaPussiMove>;

    /// Apply a move (in place), mutating the state.
    fn apply_move(&mut self, mov: &MarjaPussiMove);

    /// Clone into a boxed trait object.
    fn clone_box(&self) -> Box<dyn MarjaPussiGameState>;

    // ... Add further methods if needed (e.g. known/unknown cards, trump announcements, etc.)
}

/// A move in Marjapussi. Could be "play this card," "announce trump," etc.
#[derive(Clone, Debug)]
pub struct MarjaPussiMove {
    pub move_id: u8,
    // Add any fields you need to represent a move in Marjapussi.
}

// We can do the clone manually or add a constructor. It's minimal here.

///////////////////////////////////////////////////////////////////////////////
//  GPU KERNEL / DATA STRUCT
///////////////////////////////////////////////////////////////////////////////

/// A compact representation of a **perfect-information** state on GPU.
///
/// Must be `#[repr(C)]` and contain only data that can be transferred easily.
/// For a real game, you'll store suit/rank arrays, who holds which cards, etc.
#[repr(C)]
#[derive(Clone, Copy, Debug, Default)]
pub struct GpuMarjaState {
    pub current_player: u32,
    pub is_terminal: u8,

    // Just a placeholder array. In real code, you might store exactly 36
    // slots for each card (0=In hand of playerX, 1=In discards, etc.),
    // or some suitable encoding for the deck:
    pub card_data: [u8; 64],
}

/// This PTX code is a minimal kernel that just sets scores[idx] = idx as float.
/// Replace with your actual rollout logic.
static PTX_SRC: &str = r#"
.version 7.0
.target sm_70
.address_size 64

.visible .entry do_rollouts(
    .param .u64 states_ptr,
    .param .u64 scores_ptr,
    .param .u32 count)
{
    .reg .pred  %p<2>;
    .reg .f32   %f<2>;
    .reg .s32   %r<6>;
    .reg .u64   %rd<6>;

    ld.param.u64    %rd1, [states_ptr];
    ld.param.u64    %rd2, [scores_ptr];
    ld.param.u32    %r2,  [count];

    mov.u32 %r3, %ctaid.x;   // blockIdx.x
    mov.u32 %r4, %ntid.x;    // blockDim.x
    mov.u32 %r5, %tid.x;     // threadIdx.x
    mad.lo.s32 %r0, %r3, %r4, %r5; // idx = blockIdx.x * blockDim.x + threadIdx.x

    setp.ge.s32 %p0, %r0, %r2;
    @%p0 bra DONE;

    // In real code, read states_ptr[idx], do a random playout, etc.
    // For demonstration, just do:
    cvt.f32.s32 %f1, %r0;

    // store into scores[idx]
    mul.wide.s32 %rd3, %r0, 4; // 4 bytes per float
    add.s64 %rd4, %rd2, %rd3;
    st.global.f32 [%rd4], %f1;

DONE:
    ret;
}
"#;

/// Launch the kernel on the GPU, returning per-state rollout scores.
fn gpu_rollouts(
    states: &mut [GpuMarjaState],
) -> Result<Vec<f32>, Box<dyn Error>> {
    let device = Device::get_device(0)?;
    let _ctx = Context::create_and_push(ContextFlags::MAP_HOST | ContextFlags::SCHED_AUTO, device)?;
    let stream = Stream::create(StreamFlags::DEFAULT, None)?;

    let module = Module::from_ptx(PTX_SRC, &[])?;

    let n = states.len();
    let mut d_states = DeviceBuffer::new(n)?;
    let mut d_scores = DeviceBuffer::<f32>::new(n)?;

    // Copy states to GPU
    d_states.copy_from(states)?;

    // kernel
    let f = module.get_function("do_rollouts")?;
    let block_size = 128u32;
    let grid_size = ((n as u32) + block_size - 1) / block_size;

    unsafe {
        cust::launch!(
            f<<<grid_size, block_size, 0, stream>>>(
                d_states.as_device_ptr(),
                d_scores.as_device_ptr(),
                n as u32
            )
        )?;
    }
    stream.synchronize()?;

    // Copy back
    let mut scores = vec![0.0f32; n];
    d_scores.copy_to(&mut scores)?;
    Ok(scores)
}

///////////////////////////////////////////////////////////////////////////////
//  PIMC SOLVER
///////////////////////////////////////////////////////////////////////////////

/// A naive PIMC solver with GPU rollouts.  
/// It does:
/// 1) For each candidate move from the partial-info state, 
///    generate `rollouts_per_move` determinized states,
/// 2) Offload them to GPU for rollouts,
/// 3) Average the resulting scores,
/// 4) Pick the best move by average outcome.
pub struct PIMCSolver {
    pub rollouts_per_move: usize,
}

impl PIMCSolver {
    /// Main entry point: choose the next move from a partial-info Marjapussi state.
    pub fn select_move(
        &self, 
        state: &dyn MarjaPussiGameState,
    ) -> MarjaPussiMove {
        let moves = state.legal_moves();
        if moves.is_empty() {
            // If no legal moves, do something sensible:
            return MarjaPussiMove { move_id: 0 };
        }
        if moves.len() == 1 {
            return moves[0].clone(); 
        }

        // We'll gather (move_index -> determinized states) in big arrays.
        let mut all_gpu_states = Vec::new();   // each is GpuMarjaState
        let mut all_move_index = Vec::new();   // which move generated this state?

        // For each move, create N determinized states
        for (m_idx, mv) in moves.iter().enumerate() {
            for _ in 0..self.rollouts_per_move {
                // 1) Make a perfect-info guess from the partial-info state:
                let det = self.determinize(state);

                // 2) Apply the candidate move immediately:
                let mut after = det.clone_box();
                after.apply_move(mv);

                // 3) Convert to GPU struct
                let gpu_st = self.to_gpu_state(&*after);
                all_gpu_states.push(gpu_st);
                all_move_index.push(m_idx);
            }
        }

        // Now run GPU rollouts on all states in one shot
        let scores = gpu_rollouts(&mut all_gpu_states)
            .expect("GPU rollouts failed");

        // Accumulate sums and counts for each move
        let mut sums = vec![0.0f32; moves.len()];
        let mut counts = vec![0; moves.len()];

        for (i, &score) in scores.iter().enumerate() {
            let m_idx = all_move_index[i];
            sums[m_idx] += score;
            counts[m_idx] += 1;
        }

        // Find best average
        let mut best_move = 0;
        let mut best_val = f32::MIN;
        for m_idx in 0..moves.len() {
            if counts[m_idx] > 0 {
                let avg = sums[m_idx] / (counts[m_idx] as f32);
                if avg > best_val {
                    best_val = avg;
                    best_move = m_idx;
                }
            }
        }

        moves[best_move].clone()
    }

    /// Convert the partial-information state into one possible perfect-information state 
    /// that is consistent with known/unknown cards in Marjapussi.
    /// (Here we do a trivial example that just clones the state; you must implement real logic.)
    fn determinize(&self, state: &dyn MarjaPussiGameState) -> Box<dyn MarjaPussiGameState> {
        // Pseudo-code example:
        //  1. clone the partial-info state
        //  2. randomly assign unknown cards to players (ensuring it doesn't conflict with known info)
        //  3. return that as a "perfect info" distribution
        let mut st = state.clone_box();
        // ...
        // e.g. shuffle, deal unknown cards to the hidden hands, etc.
        st
    }

    /// Build the GPU data structure from a fully known (perfect-info) state.
    fn to_gpu_state(&self, st: &dyn MarjaPussiGameState) -> GpuMarjaState {
        let mut out = GpuMarjaState::default();
        out.current_player = st.current_player() as u32;
        out.is_terminal = if st.is_terminal() { 1 } else { 0 };

        // Fill out.card_data with your perfect-info distribution of cards.
        // This is game-specific encoding. We'll just do dummy data for now:
        // Example: out.card_data[i] = 0 means "card i is with player 0," etc.
        // 
        // In real code, you'd examine each player's cards or the location of each card
        // and fill out.card_data accordingly.

        out
    }
}

///////////////////////////////////////////////////////////////////////////////
//  DEMO / MAIN
///////////////////////////////////////////////////////////////////////////////

// A trivial "fake" state, implementing our trait for demonstration only.
// You would replace this with your real MarjaPussi state type.
#[derive(Clone)]
pub struct FakeMarjaState {
    pub turn: usize,
    pub done: bool,
    pub moves: Vec<MarjaPussiMove>,
}

impl MarjaPussiGameState for FakeMarjaState {
    fn is_terminal(&self) -> bool {
        self.done
    }

    fn evaluate_terminal_score(&self) -> f32 {
        // For a real game, compute the final score based on stich-points, trumps, etc.
        42.0
    }

    fn current_player(&self) -> usize {
        self.turn
    }

    fn legal_moves(&self) -> Vec<MarjaPussiMove> {
        // Provide some placeholder moves
        if self.done {
            vec![]
        } else {
            self.moves.clone()
        }
    }

    fn apply_move(&mut self, mov: &MarjaPussiMove) {
        // For demonstration, just mark done if move_id is 255
        if mov.move_id == 255 {
            self.done = true;
        } else {
            // otherwise do something trivial
            self.turn = (self.turn + 1) % 4;
        }
    }

    fn clone_box(&self) -> Box<dyn MarjaPussiGameState> {
        Box::new(self.clone())
    }
}

fn main() -> Result<(), Box<dyn Error>> {
    // Create a trivial partial-info state:
    let initial_state = FakeMarjaState {
        turn: 0,
        done: false,
        moves: vec![
            MarjaPussiMove { move_id: 1 },
            MarjaPussiMove { move_id: 2 },
            MarjaPussiMove { move_id: 255 },
        ],
    };

    // Build a solver
    let solver = PIMCSolver {
        rollouts_per_move: 100,
    };

    // Pick a move
    let chosen_move = solver.select_move(&initial_state);
    println!("Chosen move = {:?}", chosen_move);

    Ok(())
}