Disable oneDNN adaptive pooling exhaustive check (#43236)

paddle/fluid/operators/pool_op.cc

@@ -35,27 +35,8 @@ bool CanMKLDNNSupportPool(const framework::ExecutionContext& ctx) {
   auto src_tz = phi::vectorize(ctx.Input<Tensor>("X")->dims());
   std::vector<int> ksize = ctx.Attr<std::vector<int>>("ksize");
   // Fast but not exhustive check
-  if ((src_tz[src_tz.size() - 1] % ksize[1] == 0) &&
-      (src_tz[src_tz.size() - 2] % ksize[0] == 0))
-    return true;
-
-  // Exhustive check
-  auto IH = static_cast<double>(src_tz[src_tz.size() - 2]);
-  auto IW = static_cast<double>(src_tz[src_tz.size() - 1]);
-  auto OH = static_cast<double>(ksize[0]);
-  auto OW = static_cast<double>(ksize[1]);
-
-  auto SH = static_cast<int>(floor((IH * 2.0) / OH) - floor(IH / OH));
-  auto SW = static_cast<int>(floor((IW * 2.0) / OW) - floor(IW / OW));
-  auto KH = static_cast<int>(ceil((IH * 2.0) / OH) - floor(IH / OH));
-  auto KW = static_cast<int>(ceil((IW * 2.0) / OW) - floor(IW / OW));
-
-  auto PH = (SH * (static_cast<int>(OH) - 1) + KH - static_cast<int>(IH));
-  auto PW = (SW * (static_cast<int>(OW) - 1) + KW - static_cast<int>(IW));
-  // If there is additional padding needed then
-  // this is situation that oneDNN cannot comply with
-  // paddlepaddle reference implementation
-  return (PH == 0) && (PW == 0);
+  return ((src_tz[src_tz.size() - 1] % ksize[1] == 0) &&
+          (src_tz[src_tz.size() - 2] % ksize[0] == 0));
 }
 
 framework::OpKernelType PoolOp::GetExpectedKernelType(