[Dev] Implement test case for tilelang transformations

testing/python/autotune/test_tilelang_autotune.py

@@ -0,0 +1,297 @@
+# Copyright (c) Microsoft Corporation.
+# Licensed under the MIT License.
+
+import itertools
+import logging
+
+import tilelang as tl
+import tilelang.testing
+import tilelang.language as T
+from tilelang.autotuner import autotune, jit
+
+# Configure logger
+logger = logging.getLogger(__name__)
+logger.setLevel(logging.DEBUG)
+
+
+def ref_program(A, B):
+    """
+    A reference matrix multiplication program, used to compare performance.
+
+    Parameters
+    ----------
+    A : numpy.ndarray
+        The matrix with shape (M, K).
+    B : numpy.ndarray
+        The matrix with shape (N, K).
+
+    Returns
+    -------
+    np.ndarray
+        The result of A @ B.T, shape (M, N).
+    """
+    return A @ B.T
+
+
+def get_configs(M, N, K, with_roller=False):
+    """
+    Generate a list of configuration dictionaries that will be used for tuning.
+
+    Parameters
+    ----------
+    with_roller : bool
+        Whether to enable bitblas roller to deduce search spaces
+
+    Returns
+    -------
+    list of dict
+        Each configuration dict includes various block sizes, pipeline stages,
+        thread numbers, and other parameters to explore during autotuning.
+    """
+    if with_roller:
+        from bitblas.base.utils import get_roller_hints_from_func
+        from bitblas.ops.general_matmul.tirscript import matmul_select_implementation
+        from bitblas.base.arch import CUDA
+        from bitblas.base.roller.rasterization import NoRasterization
+        arch = CUDA("cuda")
+        topk = 20
+
+        # Simple TIR Compute Expression
+        ir_module = matmul_select_implementation(
+            M=M,
+            N=N,
+            K=K,
+            in_dtype="float16",
+            out_dtype="float16",
+            accum_dtype="float16",
+        )
+
+        roller_hints = get_roller_hints_from_func(
+            ir_module,
+            arch,
+            topk,
+            tensorcore_only=True,
+            allow_gemv=True,
+        )
+
+        if roller_hints is None:
+            raise ValueError("No Roller Hints Found for TensorCore Scheduling")
+        configs = []
+        for hint in roller_hints:
+            config = {}
+            block_m, block_n = hint.block
+            warp_m, warp_n = hint.warp
+            config["block_M"] = block_m
+            config["block_N"] = block_n
+            config["block_K"] = hint.rstep[0]
+            config["num_stages"] = 0
+            config["thread_num"] = (block_m * block_n) // (warp_m * warp_n) * 32
+            config["enable_rasteration"] = hint.rasterization_plan is not NoRasterization
+            configs.append(config)
+        for config in configs:
+            print(config)
+    else:
+
+        block_M = [64]
+        block_N = [64]
+        block_K = [32]
+        num_stages = [0, 1]
+        thread_num = [128]
+        enable_rasterization = [False]
+
+        _configs = list(
+            itertools.product(
+                block_M,
+                block_N,
+                block_K,
+                num_stages,
+                thread_num,
+                enable_rasterization,
+            ))
+
+        configs = [
+            {
+                "block_M": c[0],
+                "block_N": c[1],
+                "block_K": c[2],
+                "num_stages": c[3],
+                "thread_num": c[4],
+                "enable_rasteration": c[5],  # keep param name for backward-compat
+            } for c in _configs
+        ]
+    return configs
+
+
+def matmul(M, N, K, with_roller):
+    """
+    Create an autotuned matrix multiplication kernel for matrices of shape:
+      - A: (M, K)
+      - B: (N, K)
+      - C: (M, N)
+
+    Parameters
+    ----------
+    M : int
+        The dimension M of the matrix multiplication.
+    N : int
+        The dimension N of the matrix multiplication.
+    K : int
+        The dimension K of the matrix multiplication.
+
+    Returns
+    -------
+    (best_latency, best_config, ref_latency)
+        best_latency : float
+            The best latency found among the tuned configurations.
+        best_config : dict
+            The parameter configuration that yielded best_latency.
+        ref_latency : float
+            The baseline latency of the reference program (for computing speedup).
+    """
+
+    # Decorate the kernel with autotune & jit, specifying:
+    #  - Tuning config list
+    #  - Profiling keys
+    #  - Warmup and repetition counts for better measurement
+    #  - A reference program for correctness verification
+    #  - The "tvm" profiler backend
+    #  - HIP as the compilation target (modify as needed for your hardware)
+    if with_roller:
+        # check out bitblas is installed
+        try:
+            import bitblas  # noqa: F401
+        except ImportError as e:
+            raise ImportError(
+                "BitBlas is not installed. Please install it via 'pip install bitblas'.") from e
+
+    @autotune(
+        configs=get_configs(M, N, K, with_roller),
+        keys=[
+            "block_M",
+            "block_N",
+            "block_K",
+            "num_stages",
+            "thread_num",
+            "enable_rasteration",
+        ],
+        warmup=3,
+        rep=5,
+    )
+    @jit(
+        out_idx=[2],
+        supply_type=tl.TensorSupplyType.Integer,
+        ref_prog=ref_program,
+        skip_check=True,
+        profiler="auto",
+        target="auto",
+    )
+    def kernel(
+        block_M=None,
+        block_N=None,
+        block_K=None,
+        num_stages=None,
+        thread_num=None,
+        enable_rasteration=None,
+    ):
+        """
+        The actual kernel to compute C = A @ B^T.
+
+        Parameters
+        ----------
+        block_M : int
+            Block size in M dimension.
+        block_N : int
+            Block size in N dimension.
+        block_K : int
+            Block size in K dimension.
+        num_stages : int
+            Number of pipelined stages (for asynchronous load).
+        thread_num : int
+            Number of threads to use per block.
+        enable_rasteration : bool
+            Whether to enable rasterization (swizzling) optimization.
+        k_pack : int
+            K dimension packing factor to improve memory coalescing.
+
+        Returns
+        -------
+        Function
+            A TVM Tensor Language function (T.prim_func) that computes matmul.
+        """
+        # Use half-precision for input data to reduce memory bandwidth,
+        # accumulate in float for better numerical accuracy
+        dtype = "float16"
+        accum_dtype = "float"
+
+        @T.prim_func
+        def main(
+                A: T.Buffer((M, K), dtype),
+                B: T.Buffer((N, K), dtype),
+                C: T.Buffer((M, N), dtype),
+        ):
+            """
+            The compiled TVM function for block-level matrix multiplication.
+
+            - We divide the entire (M, N) domain into blocks of shape
+              (block_M, block_N).
+            - Each block has its own allocated shared memory for sub-blocks
+              of A and B.
+            - The partial results go into C_local, and then we copy them back
+              to global memory C.
+            """
+            # Bind x-dimension to block index in N,
+            #     y-dimension to block index in M.
+            with T.Kernel(
+                    T.ceildiv(N, block_N), T.ceildiv(M, block_M), threads=thread_num) as (bx, by):
+
+                # Allocate shared memory for A sub-block of shape (block_M, block_K)
+                A_shared = T.alloc_shared((block_M, block_K), dtype)
+                # Allocate shared memory for B sub-block of shape (block_N, block_K)
+                B_shared = T.alloc_shared((block_N, block_K), dtype)
+                # Allocate a local fragment for intermediate accumulation
+                C_local = T.alloc_fragment((block_M, block_N), accum_dtype)
+
+                # Enable (or disable) swizzling optimization
+                T.use_swizzle(panel_size=10, enable=enable_rasteration)
+
+                # Clear out the accumulation buffer
+                T.clear(C_local)
+
+                # Loop over sub-blocks in K dimension, pipelined by num_stages
+                for k in T.Pipelined(T.ceildiv(K, block_K), num_stages=num_stages):
+                    # Load a sub-block of A from global memory into A_shared
+                    T.copy(
+                        A[by * block_M, k * block_K],
+                        A_shared,
+                    )
+                    # Load a sub-block of B from global memory into B_shared
+                    T.copy(
+                        B[bx * block_N, k * block_K],
+                        B_shared,
+                    )
+                    # Perform a partial matrix multiplication:
+                    #   C_local += A_shared @ B_shared^T
+                    T.gemm(
+                        A_shared,
+                        B_shared,
+                        C_local,
+                        transpose_B=True,
+                    )
+                # Write back the results from C_local to the global memory C
+                T.copy(C_local, C[by * block_M, bx * block_N])
+
+        return main
+
+    return kernel()
+
+
+def test_autotune_get_configs():
+    get_configs(8192, 8192, 8192, with_roller=False)
+
+
+def test_autotune_matmul():
+    matmul(8192, 8192, 8192, with_roller=False)
+
+
+if __name__ == "__main__":
+    tilelang.testing.main()