import importlib.util
import unittest

try:
    import torch
except ImportError:
    torch = None

GPU = torch is not None and torch.cuda.is_available() and importlib.util.find_spec("vllm") is not None


@unittest.skipUnless(GPU, "needs vLLM and a GPU")
class KernelMatch(unittest.TestCase):
    def test_cpu_experts_compute_what_the_vllm_kernel_computes(self):
        from vllm.config import VllmConfig, set_current_vllm_config
        from vllm.model_executor.layers.fused_moe.activation import MoEActivation
        from vllm.model_executor.layers.fused_moe.fused_moe import fused_experts

        from prain.tiered import cpu_experts

        torch.manual_seed(0)
        tokens, hidden, inter, experts = 8, 256, 128, 4
        x = torch.randn(tokens, hidden, dtype=torch.bfloat16)
        w13 = torch.randn(experts, 2 * inter, hidden, dtype=torch.bfloat16) * 0.1
        w2 = torch.randn(experts, hidden, inter, dtype=torch.bfloat16) * 0.1
        topk_ids = torch.stack([torch.randperm(experts)[:2] for _ in range(tokens)]).int()
        topk_weights = torch.rand(tokens, 2)
        with set_current_vllm_config(VllmConfig()):
            gpu = fused_experts(x.cuda(), w13.cuda(), w2.cuda(), topk_weights.cuda(),
                                topk_ids.cuda(), activation=MoEActivation.GELU_TANH,
                                global_num_experts=experts).float().cpu()
        cpu = cpu_experts(x, topk_weights, topk_ids, set(range(experts)), lambda e: (w13[e], w2[e]))
        torch.testing.assert_close(cpu, gpu, rtol=3e-2, atol=3e-2)