removed im2col profiling for conv2d

Author: masahi

python/tvm/contrib/cutlass/gen_conv2d.py

@@ -169,59 +169,39 @@ def profile(
         If profile_all is False, return immediately after the first applicable kernel is found.
         If use_multiprocessing is True, compile all profiler executables in parallel.
         """
-        if True:
-            ops = GENERATOR_FUNC_TABLE[self.sm](out_dtype, op_creator=create_conv2d_operator)
-            N, H, W, IC = d_shape
-            OC, R, S, _ = w_shape
-            ops = list(filter(lambda op: self.check_align(op["name"], IC, OC), ops))
-
-            for op in ops:
-                op["runtime"] = -1
-
-            if profile_all:
-                self.engine.compile_all(ops, use_multiprocessing)
-
-            args = [
-                "--n=%d" % N,
-                "--h=%d" % H,
-                "--w=%d" % W,
-                "--k=%d" % OC,
-                "--c=%d" % IC,
-                "--r=%d" % R,
-                "--s=%d" % S,
-                "--pad_h=%d" % padding[0],
-                "--pad_w=%d" % padding[1],
-                "--stride_h=%d" % stride[0],
-                "--stride_w=%d" % stride[1],
-                "--dilation_h=%d" % dilation[0],
-                "--dilation_w=%d" % dilation[1],
-            ]
-            for op in ops:
-                out = self.engine.evaluate(op, args)
-                op["runtime"] = out
-                if out > 0 and profile_all is False:
-                    break
-
-            valid_ops = filter(lambda op: op["runtime"] > 0, ops)
-            output = sorted(valid_ops, key=lambda i: i["runtime"])
-            # self.cache[(M, N, K)] = output[0]
-            return output[0]
-
-        else:
-            B, _, _, IC = d_shape
-            OC, R, S, _ = w_shape
-            _, P, Q, _ = out_shape
-
-            M = B * P * Q
-            N = OC
-            K = R * S * IC
-
-            gemm_profile_result = self.gemm_profiler.profile(
-                M, N, K, out_dtype, profile_all=profile_all, use_multiprocessing=use_multiprocessing
-            )
-
-            tile_description = gemm_profile_result["tile_description"]
-            alignment = gemm_profile_result["alignment"]
-            data_type = gemm_profile_result["data_type"]
-
-            return create_conv2d_operator([tile_description], data_type, [alignment])[0]
+        ops = GENERATOR_FUNC_TABLE[self.sm](out_dtype, op_creator=create_conv2d_operator)
+        N, H, W, IC = d_shape
+        OC, R, S, _ = w_shape
+        ops = list(filter(lambda op: self.check_align(op["name"], IC, OC), ops))
+
+        for op in ops:
+            op["runtime"] = -1
+
+        if profile_all:
+            self.engine.compile_all(ops, use_multiprocessing)
+
+        args = [
+            "--n=%d" % N,
+            "--h=%d" % H,
+            "--w=%d" % W,
+            "--k=%d" % OC,
+            "--c=%d" % IC,
+            "--r=%d" % R,
+            "--s=%d" % S,
+            "--pad_h=%d" % padding[0],
+            "--pad_w=%d" % padding[1],
+            "--stride_h=%d" % stride[0],
+            "--stride_w=%d" % stride[1],
+            "--dilation_h=%d" % dilation[0],
+            "--dilation_w=%d" % dilation[1],
+        ]
+        for op in ops:
+            out = self.engine.evaluate(op, args)
+            op["runtime"] = out
+            if out > 0 and profile_all is False:
+                break
+
+        valid_ops = filter(lambda op: op["runtime"] > 0, ops)
+        output = sorted(valid_ops, key=lambda i: i["runtime"])
+        # self.cache[(M, N, K)] = output[0]
+        return output[0]