# Copyright (c) Microsoft Corporation.
# Licensed under the MIT License.

import tilelang
from tilelang import Profiler
import tilelang.language as T


def matmul(M, N, K, block_M, block_N, block_K, dtype="float16", accum_dtype="float"):

    @T.prim_func
    def main(
            A: T.Buffer((M, K), dtype),
            B: T.Buffer((K, N), dtype),
            C: T.Buffer((M, N), dtype),
    ):
        with T.Kernel(T.ceildiv(N, block_N), T.ceildiv(M, block_M), threads=128) as (bx, by):
            A_shared = T.alloc_shared((block_M, block_K), dtype)
            B_shared = T.alloc_shared((block_K, block_N), dtype)
            C_local = T.alloc_fragment((block_M, block_N), accum_dtype)

            # Enable rasterization for better L2 Cache Locality
            T.use_swizzle(panel_size=10)

            # Clear the local buffer
            T.clear(C_local)

            # Auto pipeline the computation
            for ko in T.Pipelined(T.ceildiv(K, block_K), num_stages=3):
                T.copy(A[by * block_M, ko * block_K], A_shared)

                # Instead of using
                # T.copy(B[k * block_K, bx * block_N], B_shared)
                # we can also use Parallel to auto map the thread
                # bindings and vectorize the copy operation.
                for k, j in T.Parallel(block_K, block_N):
                    B_shared[k, j] = B[ko * block_K + k, bx * block_N + j]

                T.gemm(A_shared, B_shared, C_local)

            T.copy(C_local, C[by * block_M, bx * block_N])

    return main


func = matmul(1024, 1024, 1024, 128, 128, 32)

print(func)

rt_mod, params = tilelang.lower(func)

profiler = Profiler(rt_mod, params, result_idx=[2])

import torch

a = torch.randn(1024, 1024).cuda().half()
b = torch.randn(1024, 1024).cuda().half()

c = profiler(a, b)

ref_c = a @ b

print(c)
print(ref_c)

torch.testing.assert_close(c, ref_c, rtol=1e-2, atol=1e-2)

# Get CUDA Source
print(rt_mod.imported_modules[0].get_source())
