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import sys
import os
from typing import Tuple

import cutlass
import cutlass.cute as cute
from cutlass.cute.runtime import make_ptr


"""
An Example demonstrating how to call off-the-shelf kernel by-passing dlpack protocol

The example shows how to directly pass pointers from PyTorch tensors to off-the-shelf kernels
written by CuTe DSL with a thin customized wrapper jit function. The jit function will be
compiled with inline without introducing overhead.

To run this example:

.. code-block:: bash

    python examples/ampere/call_bypass_dlpack.py


It's worth to mention that by-passing dlpack protocol can resolve the issue that dlpack doesn't handle shape-1
mode correctly. For example, the following code will fail, because dlpack will convert the shape-1 mode
with stride-1 which propagate alignment incorrectly.

.. code-block:: python

    @cute.kernel
    def fails_kernel(gX: cute.Tensor):
        bidx, _, _ = cute.arch.block_idx()
        mX = gX[None, bidx, None]  # We wish to retain alignment
        # assert mX.iterator.alignment == 16


    @cute.jit
    def fails(gX_: cute.Tensor):
        gX = gX_
        fails_kernel(gX).launch(grid=(1, 1, 1), block=(128, 1, 1))


    gX_torch = torch.rand((128, 1, 128), device="cuda", dtype=torch.bfloat16)
    fails(from_dlpack(gX_torch, assumed_align=16))

"""

if __name__ == "__main__":
    current_dir = os.path.dirname(os.path.abspath(__file__))
    sys.path.insert(0, os.path.join(current_dir, ".."))

from cute.ampere.kernel.dense_gemm.tensorop_gemm import TensorOpGemm


@cute.jit
def tensor_op_gemm_wrapper(
    a_ptr: cute.Pointer,
    b_ptr: cute.Pointer,
    c_ptr: cute.Pointer,
    m: cutlass.Int32,
    n: cutlass.Int32,
    k: cutlass.Int32,
    l: cutlass.Int32,
):
    print("\n[DSL INFO] Input Parameters:")
    print(f"[DSL INFO]   mnkl: {(m, n, k, l)}")

    # Assume alignment of shape to call tensorop_gemm example
    m = cute.assume(m, divby=8)
    n = cute.assume(n, divby=8)

    # Torch is row major
    a_layout = cute.make_ordered_layout((m, k, l), order=(0, 1, 2))
    b_layout = cute.make_ordered_layout((n, k, l), order=(0, 1, 2))
    c_layout = cute.make_ordered_layout((m, n, l), order=(1, 0, 2))
    mA = cute.make_tensor(a_ptr, layout=a_layout)
    mB = cute.make_tensor(b_ptr, layout=b_layout)
    mC = cute.make_tensor(c_ptr, layout=c_layout)

    print(f"[DSL INFO]   mA: {mA}")
    print(f"[DSL INFO]   mB: {mB}")
    print(f"[DSL INFO]   mC: {mC}")

    tensor_op_gemm = TensorOpGemm(
        a_ptr.value_type, c_ptr.value_type, cutlass.Float32, (2, 2, 1)
    )
    print("\n[DSL INFO] Created TensorOpGemm instance")
    print(f"[DSL INFO]   Input dtype: {a_ptr.value_type}")
    print(f"[DSL INFO]   Output dtype: {c_ptr.value_type}")
    print(f"[DSL INFO]   Accumulation dtype: {cutlass.Float32}")
    print(f"[DSL INFO]   Atom layout: {(2, 2, 1)}")

    # No need to compile inside jit function
    tensor_op_gemm(mA, mB, mC)
    print("\n[DSL INFO] Executed TensorOpGemm")


def run_tensor_op_gemm_wrapper(mnkl: Tuple[int, int, int, int]):
    import torch

    print("\nRunning TensorOpGemm test with:")
    print(f"Tensor dimensions: {mnkl}")

    # (M,K,L)
    a = torch.randn(
        mnkl[3], mnkl[2], mnkl[0], dtype=torch.float16, device="cuda"
    ).permute(2, 1, 0)
    # (N,K,L)
    b = torch.randn(
        mnkl[3], mnkl[2], mnkl[1], dtype=torch.float16, device="cuda"
    ).permute(2, 1, 0)
    # (N,M,L)
    c = torch.randn(
        mnkl[3], mnkl[0], mnkl[1], dtype=torch.float16, device="cuda"
    ).permute(1, 2, 0)

    print("Input tensor shapes:")
    print(f"a: {a.shape}, dtype: {a.dtype}")
    print(f"b: {b.shape}, dtype: {b.dtype}")
    print(f"c: {c.shape}, dtype: {c.dtype}\n")

    a_ptr = make_ptr(
        cutlass.Float16, a.data_ptr(), cute.AddressSpace.gmem, assumed_align=32
    )
    b_ptr = make_ptr(
        cutlass.Float16, b.data_ptr(), cute.AddressSpace.gmem, assumed_align=32
    )
    c_ptr = make_ptr(
        cutlass.Float16, c.data_ptr(), cute.AddressSpace.gmem, assumed_align=32
    )
    tensor_op_gemm_wrapper(a_ptr, b_ptr, c_ptr, *mnkl)
    torch.cuda.synchronize()

    ref = torch.einsum("mkl,nkl->mnl", a, b)
    torch.testing.assert_close(c, ref, atol=1e-05, rtol=1e-05)
    print("\n[DSL INFO] Results verified successfully!")
    print(f"First few elements of result: \n{c[:3, :3, :3]}")


if __name__ == "__main__":
    run_tensor_op_gemm_wrapper((512, 256, 128, 16))