- disabled non-linear adaptive pooling for oneDNN kernels

test=develop
jczaja · Oct 6, 2020 · b7396f6 · b7396f6
1 parent abb6daf
commit b7396f6
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Showing 2 changed files with 18 additions and 5 deletions.
diff --git a/paddle/fluid/platform/mkldnn_reuse.h b/paddle/fluid/platform/mkldnn_reuse.h
@@ -927,10 +927,17 @@ class PoolingMKLDNNHandler : public MKLDNNHandlerT<T, mkldnn::pooling_forward,
       const std::vector<int64_t>& src_tz, std::vector<int64_t>& ksize,
       std::vector<int64_t>& strides) {
     if (ctx.Attr<bool>("adaptive")) {
-      ksize[0] = static_cast<int>(
-          ceil(static_cast<double>(src_tz[src_tz.size() - 2] / ksize[0])));
-      ksize[1] = static_cast<int>(
-          ceil(static_cast<double>(src_tz[src_tz.size() - 1] / ksize[1])));
+      // (jczaja): oneDNN is supporting only unchangable in size pool window
+      PADDLE_ENFORCE_EQ(
+          src_tz[src_tz.size() - 1] % ksize[1], 0,
+          platform::errors::Unimplemented(
+              "Input dim must be divisible by corressponding ksize dim."));
+      PADDLE_ENFORCE_EQ(
+          src_tz[src_tz.size() - 2] % ksize[0], 0,
+          platform::errors::Unimplemented(
+              "Input dim must be divisible by corressponding ksize dim."));
+      ksize[0] = src_tz[src_tz.size() - 2] / ksize[0];
+      ksize[1] = src_tz[src_tz.size() - 1] / ksize[1];
       strides[0] = ksize[0];
       strides[1] = ksize[1];
     }

diff --git a/python/paddle/fluid/tests/unittests/mkldnn/test_pool2d_mkldnn_op.py b/python/paddle/fluid/tests/unittests/mkldnn/test_pool2d_mkldnn_op.py
@@ -79,12 +79,18 @@ def init_test_case(self):
     def init_data_type(self):
         self.dtype = np.float32
 
+    def init_global_pool(self):
+        self.global_pool = False
+
 
 class TestAvgPoolAdaptive2(TestAvgPoolAdaptive):
     def init_test_case(self):
-        self.ksize = [2, 2]
+        self.ksize = [2, 3]
         self.strides = [1, 1]
 
+    def init_shape(self):
+        self.shape = [2, 3, 6, 6]
+
 
 class TestAsymPad(TestPool2D_Op):
     def init_test_case(self):