import tilelang
import tilelang.language as T
from tilelang.autotuner import *
from tvm import tir
import argparse
import itertools
import torch

tilelang.disable_cache()

torch.manual_seed(0)


def _tir_u8_to_f4_to_bf16(nbit: int, val: tir.PrimExpr, pos: tir.PrimExpr, scale: tir.PrimExpr,
                          dtype: str):
    assert nbit == 4
    assert dtype == "bfloat16"
    assert val.dtype == "uint8"
    mask = tir.const((1 << nbit) - 1, "uint16")
    f4 = (val >> (pos.astype("uint16") * tir.const(nbit, "uint16"))) & mask
    s = f4 >> tir.const(3, "uint16")
    e_f4 = (f4 & tir.const(6, "uint16")) >> tir.const(1, "uint16")
    # Exponential bias between f4 and bf16 is 2^(8-1) - 2^(2-1) = 126
    e_bf16 = e_f4 + tir.const(126, "uint16")
    # Scale is the exponential part, within the representation of uint8
    # To handle the overflow, we use the max function to limit the exponential part to 8 bits
    e_bf16 = T.min(e_bf16 + scale, tir.const((1 << 8) - 1, "uint16"))
    m_f4 = f4 & tir.const(1, "uint16")
    val_bf16 = tir.reinterpret("bfloat16",
                               ((((s << tir.const(8, "uint16")) | e_bf16) << tir.const(7, "uint16"))
                                | (m_f4 << tir.const(6, "uint16"))).astype("uint16"))
    return val_bf16


def torch_convert(tensor, scale_size=None, Scale=None):

    def print_bit(name, val):
        val_cpu = val.cpu().item()
        binary_repr = f'{val_cpu:032b}'
        print(name, binary_repr)

    def _convert(val, pos, scale=None):
        assert val.dtype == torch.uint8
        # val = val.view(torch.int8)
        mask = (1 << 4) - 1
        f4 = ((val >> (pos * 4)) & mask).to(torch.int16)
        s = f4 >> 3
        e_f4 = (f4 & 6) >> 1
        e_f16 = e_f4 + 126
        if scale is not None:
            e_f16 = min(e_f16 + scale, (1 << 8) - 1)
        m_f4 = f4 & 1
        m_f16 = m_f4
        val_f16 = (((e_f16 | (s << 8)) << 7) | (m_f16 << 6)) & 0xFFFF
        lower_16_bits = (val_f16 & 0xFFFF).to(torch.uint16)
        return lower_16_bits.view(torch.bfloat16)

    N = tensor.shape[0]
    K = tensor.shape[1]
    new_tensor = torch.empty(N, K * 2, dtype=torch.bfloat16, device=tensor.device)
    for i in range(new_tensor.shape[0]):
        for j in range(new_tensor.shape[1]):
            if scale_size is not None:
                new_tensor[i][j] = _convert(tensor[i][j // 2], j % 2, Scale[i][j // scale_size])
            else:
                new_tensor[i][j] = _convert(tensor[i][j // 2], j % 2)
    return new_tensor


@tilelang.jit(out_idx=[-1])
def convert(N, K, block_N, block_K, in_dtype, num_bits=4, threads=128):
    num_elems_per_byte = 8 // num_bits
    storage_dtype = "uint8"
    B_shape = (N, K // num_elems_per_byte)
    B_shared_shape = (block_N, block_K // num_elems_per_byte)
    B_dequantize_shared_shape = (block_N, block_K)

    @T.prim_func
    def main(
            B: T.Tensor(B_shape, storage_dtype),
            C: T.Tensor((N, K), in_dtype),
    ):
        with T.Kernel(T.ceildiv(N, block_N), threads=threads) as (bx):
            B_shared = T.alloc_shared(B_shared_shape, storage_dtype)
            B_local = T.alloc_fragment(B_shared_shape, storage_dtype)
            B_dequantize_local = T.alloc_fragment(B_dequantize_shared_shape, in_dtype)

            for k in T.Pipelined(T.ceildiv(K, block_K), num_stages=1):
                T.copy(B[bx * block_N, k * block_K // num_elems_per_byte], B_shared)
                T.copy(B_shared, B_local)
                for i, j in T.Parallel(block_N, block_K):
                    B_dequantize_local[i, j] = _tir_u8_to_f4_to_bf16(
                        num_bits,
                        B_local[i, j // num_elems_per_byte],
                        j % num_elems_per_byte,
                        0,  # No scale for test
                        dtype=in_dtype,
                    )
                T.copy(B_dequantize_local, C[bx * block_N, k * block_K])

    return main


@tilelang.jit(out_idx=[-1])
def convert_scale(N, K, block_N, block_K, in_dtype, num_bits=4, scale_size=32, threads=128):
    num_elems_per_byte = 8 // num_bits
    storage_dtype = "uint8"
    B_shape = (N, K // num_elems_per_byte)
    B_shared_shape = (block_N, block_K // num_elems_per_byte)
    B_dequantize_shared_shape = (block_N, block_K)
    Scale_shape = (N, K // scale_size)
    Scale_shared_shape = (block_N, block_K // scale_size)

    @T.prim_func
    def main(
            B: T.Tensor(B_shape, storage_dtype),
            Scale: T.Tensor(Scale_shape, storage_dtype),
            C: T.Tensor((N, K), in_dtype),
    ):
        with T.Kernel(T.ceildiv(N, block_N), threads=threads) as (bx):
            B_shared = T.alloc_shared(B_shared_shape, storage_dtype)
            B_local = T.alloc_fragment(B_shared_shape, storage_dtype)
            B_dequantize_local = T.alloc_fragment(B_dequantize_shared_shape, in_dtype)
            Scale_shared = T.alloc_shared(Scale_shared_shape, storage_dtype)
            Scale_local = T.alloc_fragment(Scale_shared_shape, storage_dtype)

            for k in T.Pipelined(T.ceildiv(K, block_K), num_stages=1):
                T.copy(B[bx * block_N, k * block_K // num_elems_per_byte], B_shared)
                T.copy(B_shared, B_local)
                T.copy(Scale[bx * block_N, k * block_K // scale_size], Scale_shared)
                T.copy(Scale_shared, Scale_local)
                for i, j in T.Parallel(block_N, block_K):
                    B_dequantize_local[i, j] = _tir_u8_to_f4_to_bf16(
                        num_bits,
                        B_local[i, j // num_elems_per_byte],
                        j % num_elems_per_byte,
                        Scale_local[
                            i, j //
                            scale_size],  # Scale is the exponential part, within the representation of uint8
                        dtype=in_dtype,
                    )
                T.copy(B_dequantize_local, C[bx * block_N, k * block_K])

    return main


def test_fp4_bf16_convert_close():
    N, K = 256, 256
    block_N, block_K = 64, 64
    kernel = convert(
        N,
        K,
        block_N,
        block_K,
        "bfloat16",
    )

    B = torch.randint(0, 16, (N, K // 2), dtype=torch.uint8, device="cuda").to(torch.uint8)
    tl_out = kernel(B)
    ref_out = torch_convert(B)
    assert torch.allclose(tl_out, ref_out, rtol=0.01, atol=0.01), (tl_out, ref_out)
    print("Convert Pass")


def test_fp4_bf16_convert_scale_close():
    N, K = 256, 256
    block_N, block_K = 64, 64
    kernel = convert_scale(N, K, block_N, block_K, "bfloat16", scale_size=32)

    B = torch.randint(0, 16, (N, K // 2), dtype=torch.uint8, device="cuda").to(torch.uint8)
    Scale = torch.randint(0, 1, (N, K // 32), dtype=torch.uint8, device="cuda").to(torch.uint8)
    tl_out = kernel(B, Scale)
    ref_out = torch_convert(B, scale_size=32, Scale=Scale)
    assert torch.allclose(tl_out, ref_out, rtol=0.01, atol=0.01), (tl_out, ref_out)
    print("Convert Scale Pass")


def get_configs():
    block_M = [128]
    block_N = [128, 256]
    block_K = [128]
    num_stages = [2]
    threads = [256]
    splits = [1]
    _configs = list(itertools.product(block_M, block_N, block_K, num_stages, threads, splits))

    configs = [{
        'block_M': c[0],
        'block_N': c[1],
        'block_K': c[2],
        'num_stages': c[3],
        'threads': c[4],
        'split': c[5]
    } for c in _configs]
    return configs


def matmul(M, N, K, in_dtype, out_dtype, accum_dtype, num_bits=4, scale_size=32, tune=False):

    @tilelang.jit(out_idx=[-1])
    def kernel_func(block_M, block_N, block_K, num_stages, threads, split=1):
        num_elems_per_byte = 8 // num_bits
        storage_dtype = "uint8"
        A_shape = (M, K)
        B_shape = (N, K // num_elems_per_byte)
        Scale_shape = (N, K // scale_size)
        A_shared_shape = (block_M, block_K)
        B_shared_shape = (block_N, block_K // num_elems_per_byte)
        B_dequantize_shared_shape = (block_N, block_K)
        Scale_shared_shape = (block_N, block_K // scale_size)
        assert K % (block_K * split) == 0
        KK = K // split

        @T.prim_func
        def main_split(
                A: T.Tensor(A_shape, in_dtype),
                B: T.Tensor(B_shape, storage_dtype),
                Scale: T.Tensor(Scale_shape, storage_dtype),
                Ct: T.Tensor((N, M), out_dtype),
        ):
            SplitC = T.alloc_buffer([
                split, (N + block_N - 1) // block_N * block_N,
                (M + block_M - 1) // block_M * block_M
            ], out_dtype)
            with T.Kernel(
                    T.ceildiv(N, block_N), T.ceildiv(M, block_M), split,
                    threads=threads) as (bx, by, bz):
                A_shared = T.alloc_shared(A_shared_shape, in_dtype)
                B_shared = T.alloc_shared(B_shared_shape, storage_dtype)
                B_local = T.alloc_fragment(B_shared_shape, storage_dtype)
                B_dequantize_local = T.alloc_fragment(B_dequantize_shared_shape, in_dtype)
                B_dequantize_prev_local = T.alloc_fragment(B_dequantize_shared_shape, in_dtype)
                Ct_local = T.alloc_fragment((block_N, block_M), accum_dtype)
                Ct_shared = T.alloc_shared((block_N, block_M), out_dtype)
                Scale_shared = T.alloc_shared(Scale_shared_shape, storage_dtype)
                Scale_local = T.alloc_fragment(Scale_shared_shape, storage_dtype)

                T.annotate_layout({
                    B_shared: tilelang.layout.make_swizzled_layout(B_shared),
                    Ct_shared: tilelang.layout.make_swizzled_layout(Ct_shared),
                    Scale_shared: tilelang.layout.make_swizzled_layout(Scale_shared),
                })

                T.clear(Ct_local)
                for k in T.Pipelined(K // (block_K * split), num_stages=num_stages):
                    T.copy(A[by * block_M, KK * bz + k * block_K], A_shared)
                    T.copy(B[bx * block_N, (KK * bz + k * block_K) // num_elems_per_byte], B_shared)
                    T.copy(B_shared, B_local)
                    T.copy(Scale[bx * block_N, (KK * bz + k * block_K) // scale_size], Scale_shared)
                    T.copy(Scale_shared, Scale_local)
                    for i, j in T.Parallel(block_N, block_K):
                        B_dequantize_local[i, j] = _tir_u8_to_f4_to_bf16(
                            num_bits,
                            B_local[i, j // num_elems_per_byte],
                            j % num_elems_per_byte,
                            Scale_local[i, j // scale_size],
                            dtype=in_dtype,
                        )
                    T.copy(B_dequantize_local, B_dequantize_prev_local)
                    T.gemm(B_dequantize_prev_local, A_shared, Ct_local, transpose_B=True)
                T.copy(Ct_local, SplitC[bz, bx * block_N:(bx + 1) * block_N,
                                        by * block_M:(by + 1) * block_M])
            with T.Kernel(T.ceildiv(N, block_N), T.ceildiv(M, block_M)) as (bx, by):
                acc = T.alloc_fragment((block_N, block_M), out_dtype)
                T.clear(acc)
                for k in range(split):
                    for i, j in T.Parallel(block_N, block_M):
                        acc[i, j] += SplitC[k, bx * block_N + i, by * block_M + j]
                T.copy(acc, Ct[bx * block_N, by * block_M])

        @T.prim_func
        def main(
                A: T.Tensor(A_shape, in_dtype),
                B: T.Tensor(B_shape, storage_dtype),
                Scale: T.Tensor(Scale_shape, storage_dtype),
                Ct: T.Tensor((N, M), out_dtype),
        ):
            with T.Kernel(
                    T.ceildiv(N, block_N), T.ceildiv(M, block_M), threads=threads) as (bx, by):
                A_shared = T.alloc_shared(A_shared_shape, in_dtype)
                B_shared = T.alloc_shared(B_shared_shape, storage_dtype)
                B_local = T.alloc_fragment(B_shared_shape, storage_dtype)
                B_dequantize_local = T.alloc_fragment(B_dequantize_shared_shape, in_dtype)
                B_dequantize_prev_local = T.alloc_fragment(B_dequantize_shared_shape, in_dtype)
                Ct_local = T.alloc_fragment((block_N, block_M), accum_dtype)
                Ct_shared = T.alloc_shared((block_N, block_M), out_dtype)
                Scale_shared = T.alloc_shared((block_N, block_K // scale_size), storage_dtype)
                Scale_local = T.alloc_fragment((block_N, block_K // scale_size), storage_dtype)

                T.annotate_layout({
                    B_shared: tilelang.layout.make_swizzled_layout(B_shared),
                    Ct_shared: tilelang.layout.make_swizzled_layout(Ct_shared),
                    Scale_shared: tilelang.layout.make_swizzled_layout(Scale_shared),
                })

                T.clear(Ct_local)
                for k in T.Pipelined(K // block_K, num_stages=num_stages):
                    T.copy(A[by * block_M, k * block_K], A_shared)
                    T.copy(B[bx * block_N, k * block_K // num_elems_per_byte], B_shared)
                    T.copy(B_shared, B_local)
                    T.copy(Scale[bx * block_N, k * block_K // scale_size], Scale_shared)
                    T.copy(Scale_shared, Scale_local)
                    for i, j in T.Parallel(block_N, block_K):
                        B_dequantize_local[i, j] = _tir_u8_to_f4_to_bf16(
                            num_bits,
                            B_local[i, j // num_elems_per_byte],
                            j % num_elems_per_byte,
                            Scale_local[i, j // scale_size],
                            dtype=in_dtype,
                        )
                    T.copy(B_dequantize_local, B_dequantize_prev_local)
                    T.gemm(B_dequantize_prev_local, A_shared, Ct_local, transpose_B=True)
                T.copy(Ct_local, Ct_shared)
                T.copy(Ct_shared, Ct[bx * block_N:(bx + 1) * block_N,
                                     by * block_M:(by + 1) * block_M])

        if split == 1:
            return main
        else:
            return main_split

    if tune:

        @autotune(
            configs=get_configs(),
            keys=["block_M", "block_N", "block_K", "num_stages", "threads", "split"],
            warmup=10,
            rep=10)
        @tilelang.jit(out_idx=[-1])
        def kernel(block_M=None,
                   block_N=None,
                   block_K=None,
                   num_stages=None,
                   threads=None,
                   split=None):
            return kernel_func(block_M, block_N, block_K, num_stages, threads, split)

        return kernel()
    else:

        def kernel(block_M, block_N, block_K, num_stages, threads, split=1):
            return kernel_func(block_M, block_N, block_K, num_stages, threads, split)

        return kernel


def ref_program(A, qB):
    dtypeC = "bfloat16"
    B = torch_convert(qB)
    C = torch.matmul(A.to(torch.float), B.T.to(torch.float))
    C = C.to(torch.__getattribute__(dtypeC))
    return C.transpose(0, 1)


def ref_program_scale(A, qB, Scale):
    dtypeC = "bfloat16"
    B = torch_convert(qB, scale_size=32, Scale=Scale)
    C = torch.matmul(A.to(torch.float), B.T.to(torch.float))
    C = C.to(torch.__getattribute__(dtypeC))
    return C.transpose(0, 1)


def main(m=256, n=256, k=256, scale_size=32, tune=False):
    total_flops = 2 * m * n * k

    if (not tune):
        kernel = matmul(
            m,
            n,
            k,
            "bfloat16",
            "bfloat16",
            "float32",
            num_bits=4,
            scale_size=scale_size,
            tune=tune)(
                block_M=128, block_N=128, block_K=128, num_stages=2, threads=256, split=1)
        profiler = kernel.get_profiler(tilelang.TensorSupplyType.Integer)
        profiler.assert_allclose(ref_program_scale, rtol=0.01, atol=0.01)
        print("All checks pass.")
        latency = profiler.do_bench(ref_program_scale, warmup=500)
        print("Ref: {:.2f} ms".format(latency))
        print("Ref: {:.2f} TFlops".format(total_flops / latency * 1e-9))
        latency = profiler.do_bench(warmup=500)
        print("Tile-lang: {:.2f} ms".format(latency))
        print("Tile-lang: {:.2f} TFlops".format(total_flops / latency * 1e-9))
    else:
        best_result = matmul(
            m,
            n,
            k,
            "bfloat16",
            "bfloat16",
            "float32",
            num_bits=4,
            scale_size=scale_size,
            tune=tune)
        best_latency = best_result.latency
        best_config = best_result.config
        print(f"Best latency: {best_latency}")
        print(f"Best TFlops: {total_flops / best_latency * 1e-9}")
        print(f"Best config: {best_config}")


def test_convert():
    test_fp4_bf16_convert_close()
    test_fp4_bf16_convert_scale_close()


if __name__ == "__main__":
    parser = argparse.ArgumentParser()
    parser.add_argument('--m', type=int, default=256, help='M')
    parser.add_argument('--n', type=int, default=256, help='N')
    parser.add_argument('--k', type=int, default=256, help='K')
    parser.add_argument(
        '--scale_size',
        type=int,
        default=32,
        help='scale size, the exponential part, within the representation of uint8')
    parser.add_argument('--tune', action='store_true', help='tune configs')
    args = parser.parse_args()
    M, N, K = args.m, args.n, args.k
    # test_convert()
    main(M, N, K, args.scale_size, args.tune)
