[Autotune][Conv] optimize convolution examples to use autotune (#866)

examples/convolution/example_convolution_autotune.py

@@ -3,11 +3,6 @@ import argparse
 import itertools
 import tilelang
 import tilelang.language as T
-from tilelang.autotuner import AutoTuner
-from tilelang.carver.template import ConvTemplate
-from tilelang.carver.arch import CUDA
-from tilelang.carver.arch import CDNA
-from tilelang.carver.roller.rasterization import NoRasterization
 
 
 def check_hopper():
@@ -30,44 +25,7 @@ def ref_program(stride, padding, dilation):
     return main
 
 
-def get_configs(N, C, H, W, F, K, S, D, P, with_roller=False, topk=15):
-    if with_roller:
-        arch = CDNA("hip") if torch.version.hip is not None else CUDA("cuda")
-        carve_template = ConvTemplate(
-            N=N,
-            C=C,
-            H=H,
-            W=W,
-            F=F,
-            K=K,
-            S=S,
-            D=D,
-            P=P,
-            in_dtype="float16",
-            out_dtype="float16",
-            accum_dtype="float",
-        ).with_arch(arch)
-
-        func = carve_template.equivalent_function()
-        assert func is not None, "Function is None"
-        roller_hints = carve_template.recommend_hints(topk=topk)
-        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
-            # block_rows, block_cols represents warp partitioning
-            block_rows, block_cols = block_m // warp_m, block_n // warp_n
-            config["block_M"] = block_m
-            config["block_N"] = block_n
-            config["block_K"] = hint.rstep[0]
-            config["num_stages"] = hint.pipeline_stage if hint.pipeline_stage > 1 else 0
-            config["thread_num"] = block_rows * block_cols * 32
-            config["enable_rasteration"] = hint.rasterization_plan is not NoRasterization
-            configs.append(config)
-    else:
+def get_configs():
     block_M = [64, 128, 256]
     block_N = [64, 128, 256]
     block_K = [32, 64]
@@ -97,82 +55,6 @@ def get_configs(N, C, H, W, F, K, S, D, P, with_roller=False, topk=15):
     return configs
 
 
-def get_best_config(N, C, H, W, F, K, S, D, P, ref_prog, with_roller=False):
-
-    @tilelang.jit(out_idx=[2])
-    def kernel(
-        block_M=None,
-        block_N=None,
-        block_K=None,
-        num_stages=None,
-        thread_num=None,
-        enable_rasteration=None,
-    ):
-        dtype = "float16"
-        accum_dtype = "float"
-        KH, KW = K, K
-        OH = (H + 2 * P - D * (K - 1) - 1) // S + 1
-        OW = (W + 2 * P - D * (K - 1) - 1) // S + 1
-        is_hopper = check_hopper()
-
-        @T.prim_func
-        def main(
-                data: T.Tensor((N, H, W, C), dtype),
-                kernel: T.Tensor((KH, KW, C, F), dtype),
-                out: T.Tensor((N, OH, OW, F), dtype),
-        ):
-            with T.Kernel(
-                    T.ceildiv(F, block_N), T.ceildiv(N * OH * OW, block_M),
-                    threads=thread_num) as (bx, by):
-                data_shared = T.alloc_shared((block_M, block_K), dtype)
-                kernel_shared = T.alloc_shared((block_K, block_N), dtype)
-                out_local = T.alloc_fragment((block_M, block_N), accum_dtype)
-                out_shared = T.alloc_shared((block_M, block_N), dtype)
-
-                kernel_flat = T.Tensor((KH * KW * C, F), dtype, kernel.data)
-                out_flat = T.Tensor((N * OH * OW, F), dtype, out.data)
-
-                T.annotate_layout({
-                    out_shared: tilelang.layout.make_swizzled_layout(out_shared),
-                    data_shared: tilelang.layout.make_swizzled_layout(data_shared),
-                    kernel_shared: tilelang.layout.make_swizzled_layout(kernel_shared),
-                })
-
-                T.clear(out_local)
-                for k_iter in T.Pipelined(T.ceildiv(KH * KW * C, block_K), num_stages=num_stages):
-                    if is_hopper:
-                        T.c2d_im2col(data, data_shared, by, k_iter, KH, S, D, P)
-                    else:
-                        for i, j in T.Parallel(block_M, block_K):
-                            k = k_iter * block_K + j
-                            m = by * block_M + i
-                            access_h = m % (OH * OW) // OW * S + k // (KW * C) * D - P
-                            access_w = m % OW * S + k // C % KW * D - P
-                            in_bound = ((access_h >= 0) and (access_w >= 0) and (access_h < H) and
-                                        (access_w < W))
-                            data_shared[i, j] = T.if_then_else(
-                                in_bound, data[m // (OH * OW), access_h, access_w, k % C], 0)
-                    T.copy(kernel_flat[k_iter * block_K, bx * block_N], kernel_shared)
-                    T.gemm(data_shared, kernel_shared, out_local)
-
-                T.copy(out_local, out_shared)
-                T.copy(out_shared, out_flat[by * block_M, bx * block_N])
-
-        return main
-
-    autotuner = AutoTuner.from_kernel(
-        kernel=kernel, configs=get_configs(N, C, H, W, F, K, S, D, P,
-                                           with_roller)).set_compile_args(
-                                               out_idx=[2],
-                                               target="auto",
-                                           ).set_profile_args(
-                                               supply_type=tilelang.TensorSupplyType.Integer,
-                                               ref_prog=ref_prog,
-                                               skip_check=False,
-                                           )
-    return autotuner.run(warmup=3, rep=20)
-
-
 def get_heuristic_config() -> dict:
     # Get CUDA device properties
     if not torch.cuda.is_available():
@@ -210,6 +92,7 @@ def get_heuristic_config() -> dict:
         }
 
 
+@tilelang.autotune(configs=get_configs())
 @tilelang.jit(out_idx=[2])
 def convolution(N,
                 C,
@@ -252,10 +135,9 @@ def convolution(N,
             kernel_flat = T.Tensor((KH * KW * C, F), dtype, kernel.data)
             out_flat = T.Tensor((N * OH * OW, F), dtype, out.data)
 
+            if is_hopper:
                 T.annotate_layout({
                     out_shared: tilelang.layout.make_swizzled_layout(out_shared),
-                data_shared: tilelang.layout.make_swizzled_layout(data_shared),
-                kernel_shared: tilelang.layout.make_swizzled_layout(kernel_shared),
                 })
 
             T.clear(out_local)
@@ -275,8 +157,11 @@ def convolution(N,
                 T.copy(kernel_flat[k_iter * block_K, bx * block_N], kernel_shared)
                 T.gemm(data_shared, kernel_shared, out_local)
 
+            if is_hopper:
                 T.copy(out_local, out_shared)
                 T.copy(out_shared, out_flat[by * block_M, bx * block_N])
+            else:
+                T.copy(out_local, out_flat[by * block_M, bx * block_N])
 
     return main
 
@@ -296,9 +181,7 @@ def main(n: int = 128,
     ref_prog = ref_program(S, P, D)
 
     if use_autotune:
-        result = get_best_config(N, C, H, W, F, K, S, D, P, ref_prog, with_roller)
-        print(result.config)
-        kernel = result.kernel
+        kernel = convolution(N, C, H, W, F, K, S, D, P)
     else:
         config = get_heuristic_config()
         kernel = convolution(N, C, H, W, F, K, S, D, P, **config)