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ai/research/cuda_proof_of_concept.py

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+"""
+GPU-Resident Environment Proof-of-Concept
+=========================================
+
+This file demonstrates how the current CPU-based Numba VectorEnv
+would be translated to a GPU-based Numba CUDA implementation.
+
+Usage:
+    This is a design reference. It requires a CUDA-capable GPU and the
+    `cudatoolkit` library to run.
+
+    To run (if hardware available):
+    $ python ai/cuda_proof_of_concept.py
+"""
+
+import time
+
+import numpy as np
+
+try:
+    from numba import cuda, float32, int32
+    from numba.cuda.random import create_xoroshiro128p_states, xoroshiro128p_uniform_float32
+
+    HAS_CUDA = True
+except ImportError:
+    print("Warning: Numba CUDA not installed or hardware not found.")
+    HAS_CUDA = False
+
+    # Mock objects for linting/viewing
+    class MockCuda:
+        def jit(self, *args, **kwargs):
+            return lambda x: x
+
+        def grid(self, x):
+            return 0
+
+        def device_array(self, *args, **kwargs):
+            return np.zeros(*args)
+
+        def to_device(self, x):
+            return x
+
+        def synchronize(self):
+            pass
+
+    cuda = MockCuda()
+
+# Constants
+CTX_VALUE = 20
+SC = 0
+HD = 3
+DK = 6
+
+# =============================================================================
+# 1. Device Functions (The "Inner Logic")
+# =============================================================================
+# Instead of @njit, we use @cuda.jit(device=True)
+# These functions can ONLY be called from other CUDA kernels/functions.
+
+
+@cuda.jit(device=True)
+def resolve_bytecode_device(bytecode, flat_ctx, global_ctx, p_hand, p_deck):
+    """
+    Equivalent to engine/game/fast_logic.py:resolve_bytecode
+    Adapted for CUDA:
+    - No recursion (CUDA doesn't support it well, though Numba has limited support).
+    - Minimal stack usage.
+    """
+    # Simple example opcode implementation
+    ip = 0
+    blen = bytecode.shape[0]
+
+    while ip < blen:
+        op = bytecode[ip, 0]
+        v = bytecode[ip, 1]
+
+        # O_DRAW (Opcode 10)
+        if op == 10:
+            # Check Deck Count
+            if global_ctx[DK] >= v:
+                global_ctx[DK] -= v
+                global_ctx[HD] += v
+                # Real implementation would move card IDs in p_hand/p_deck arrays
+
+        # O_RETURN (Opcode 1)
+        elif op == 1:
+            return 0
+
+        ip += 1
+    return 0
+
+
+# =============================================================================
+# 2. Kernels (The "Parallel Loops")
+# =============================================================================
+# Instead of `for i in prange(num_envs)`, the GPU launches thousands of threads.
+# Each thread calculates its ID and processes one environment.
+
+
+@cuda.jit
+def step_kernel(
+    rng_states, batch_stage, batch_global_ctx, batch_hand, batch_deck, bytecode_map, bytecode_index, actions
+):
+    """
+    CUDA Kernel to step N environments in parallel.
+    One thread = One Environment.
+    """
+    # 1. Calculate Thread ID
+    # This replaces the `for i in range(num_envs)` loop
+    i = cuda.grid(1)
+
+    # Bounds check (in case we launched more threads than envs)
+    if i >= batch_global_ctx.shape[0]:
+        return
+
+    # 2. Apply Action
+    act_id = actions[i]
+
+    # Lookup bytecode
+    # (Simplified for POC)
+    if act_id > 0:
+        # Get map index
+        map_idx = bytecode_index[act_id, 0]
+
+        # Get bytecode sequence
+        # Note: Accessing large global arrays is fine, but caching in shared memory
+        # is better for performance if many threads access the same data.
+        code_seq = bytecode_map[map_idx]
+
+        # Call Device Function
+        resolve_bytecode_device(
+            code_seq,
+            batch_global_ctx[i],  # Passing slice creates a local view?
+            # Numba CUDA handles array slicing carefully.
+            batch_global_ctx[i],  # using global_ctx as flat_ctx for demo
+            batch_hand[i],
+            batch_deck[i],
+        )
+
+    # 3. Randomness (Opponent Logic)
+    # CUDA requires explicit RNG states
+    rand_val = xoroshiro128p_uniform_float32(rng_states, i)
+    if rand_val > 0.5:
+        # Simulate opponent doing something
+        pass
+
+
+# =============================================================================
+# 3. Host Controller (The "Driver")
+# =============================================================================
+
+
+class CudaVectorEnv:
+    def __init__(self, num_envs=4096):
+        if not HAS_CUDA:
+            pass  # Continue with mocks
+
+        self.num_envs = num_envs
+
+        # 1. Allocate Data on GPU (Device Arrays)
+        # This is "Zero-Copy" residence. Data lives on VRAM.
+        self.d_batch_stage = cuda.device_array((num_envs, 3), dtype=np.int32)
+        self.d_batch_global_ctx = cuda.device_array((num_envs, 128), dtype=np.int32)
+        self.d_batch_hand = cuda.device_array((num_envs, 60), dtype=np.int32)
+        self.d_batch_deck = cuda.device_array((num_envs, 60), dtype=np.int32)
+
+        # Bytecode maps also go to GPU (Read-Only)
+        # Assuming we loaded them like in vector_env.py
+        self.d_bytecode_map = cuda.to_device(np.zeros((100, 64, 4), dtype=np.int32))
+        self.d_bytecode_index = cuda.to_device(np.zeros((2000, 4), dtype=np.int32))
+
+        # RNG States
+        if HAS_CUDA:
+            self.rng_states = create_xoroshiro128p_states(num_envs, seed=1234)
+        else:
+            self.rng_states = None
+
+        # Threads per Block (Hyperparameter)
+        self.threads_per_block = 128
+        self.blocks_per_grid = (num_envs + (self.threads_per_block - 1)) // self.threads_per_block
+
+    def step(self, actions):
+        """
+        1. Copy Actions to GPU (Small transfer: 4KB for 1024 envs)
+        2. Launch Kernel
+        3. (Optional) Return Observation Pointer
+        """
+        # Transfer actions to GPU
+        d_actions = cuda.to_device(actions)
+
+        # Launch Kernel
+        step_kernel[self.blocks_per_grid, self.threads_per_block](
+            self.rng_states,
+            self.d_batch_stage,
+            self.d_batch_global_ctx,
+            self.d_batch_hand,
+            self.d_batch_deck,
+            self.d_bytecode_map,
+            self.d_bytecode_index,
+            d_actions,
+        )
+
+        # Synchronize (Wait for finish)
+        cuda.synchronize()
+
+        # In a real "Isaac Gym" setup, we wouldn't copy back.
+        # We would return the device array handle to PyTorch.
+        # return self.d_batch_global_ctx
+
+        # For POC, we copy back to show it works
+        # If mock, this fails because mock device_array is numpy
+        if HAS_CUDA:
+            return self.d_batch_global_ctx.copy_to_host()
+        else:
+            return self.d_batch_global_ctx
+
+
+if __name__ == "__main__":
+    print("Initializing CUDA Env Proof of Concept...")
+    if HAS_CUDA:
+        try:
+            env = CudaVectorEnv(num_envs=1024)
+            actions = np.zeros(1024, dtype=np.int32)
+
+            start = time.time()
+            res = env.step(actions)
+            end = time.time()
+
+            print(f"Step completed in {end - start:.6f}s")
+        except Exception as e:
+            print(f"Runtime Error: {e}")
+    else:
+        print("Skipping run (No CUDA), verifying syntax only.")
+        env = CudaVectorEnv(num_envs=10)
+        print("Mock env initialized.")