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)

    def test_a_round_computes_only_the_experts_its_slot_table_maps(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, slot_table

        torch.manual_seed(0)
        tokens, hidden, inter, experts = 64, 256, 128, 6
        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_ids[0, 1] = experts  # a padding route
        topk_weights = torch.rand(tokens, 2)
        table = slot_table({1: 0, 4: 1}, experts, "cuda")
        with set_current_vllm_config(VllmConfig()):
            gpu = fused_experts(x.cuda(), w13[[1, 4]].cuda(), w2[[1, 4]].cuda(), topk_weights.cuda(),
                                topk_ids.cuda(), activation=MoEActivation.GELU_TANH,
                                global_num_experts=table.numel(), expert_map=table).float().cpu()
        cpu = cpu_experts(x, topk_weights, topk_ids, {1, 4}, lambda e: (w13[e], w2[e]))
        torch.testing.assert_close(cpu, gpu, rtol=3e-2, atol=3e-2)

    def test_quantized_slots_compute_what_the_cpu_path_computes(self):
        """Pack layout -> slot layout on the GPU -> vLLM's int8/int4 kernel for a round that
        holds experts 1 and 3, against the CPU kernels on the pack layout and against the
        dequantized weights."""
        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.quant import (cpu_matmul, dequantize, moe_quant_config, quantize, to_cpu_int4,
                                 to_gpu_int4)
        from prain.tiered import cpu_experts, slot_table

        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) * 0.1
        w2 = torch.randn(experts, hidden, inter) * 0.1
        topk_ids = torch.stack([torch.randperm(experts)[:2] for _ in range(tokens)]).int()
        topk_weights = torch.rand(tokens, 2)
        table = slot_table({1: 0, 3: 1}, experts, "cuda")
        for quant in ("int8", "int4"):
            parts = [quantize(w, quant) for w in (w13, w2)]  # [q, scale], gpu layout
            pack = [[to_cpu_int4(q) if quant == "int4" else q, s] for q, s in parts]
            slots = [[to_gpu_int4(q[[1, 3]].cuda()) if quant == "int4" else q[[1, 3]].cuda(),
                      s[[1, 3]].cuda()] for q, s in pack]
            with set_current_vllm_config(VllmConfig()):
                gpu = fused_experts(x.cuda(), slots[0][0], slots[1][0], topk_weights.cuda(),
                                    topk_ids.cuda(), activation=MoEActivation.GELU_TANH,
                                    global_num_experts=table.numel(), expert_map=table,
                                    quant_config=moe_quant_config(quant, slots[0][1], slots[1][1]))
            cpu = cpu_experts(x, topk_weights, topk_ids, {1, 3}, lambda e: tuple(
                cpu_matmul(quant, q[e], s[e]) for q, s in pack))
            exact = cpu_experts(x, topk_weights, topk_ids, {1, 3}, lambda e: tuple(
                dequantize(q, s, quant)[e] for q, s in parts))
            self.assertGreater(exact.norm().item(), 1.0)
            torch.testing.assert_close(cpu, exact, rtol=3e-2, atol=3e-2, msg=quant)
            torch.testing.assert_close(gpu.float().cpu(), exact, rtol=3e-2, atol=3e-2, msg=quant)