Added support for BF16 datatype for all oneDNN activation kernels

paddle/fluid/operators/abs_op.cc

@@ -30,6 +30,21 @@ namespace operators {
 class AbsOp : public framework::OperatorWithKernel {
  public:
   using framework::OperatorWithKernel::OperatorWithKernel;
+
+  framework::OpKernelType GetExpectedKernelType(
+      const framework::ExecutionContext& ctx) const override {
+    auto input_data_type =
+        framework::OperatorWithKernel::IndicateVarDataType(ctx, "X");
+
+#ifdef PADDLE_WITH_MKLDNN
+    if (this->CanMKLDNNBeUsed(ctx, input_data_type)) {
+      return framework::OpKernelType(input_data_type, ctx.GetPlace(),
+                                     framework::DataLayout::kMKLDNN,
+                                     framework::LibraryType::kMKLDNN);
+    }
+#endif
+    return framework::OpKernelType(input_data_type, ctx.GetPlace());
+  }
 };
 
 class AbsOpMaker : public framework::OpProtoAndCheckerMaker {
@@ -72,8 +87,17 @@ class AbsGradOp : public framework::OperatorWithKernel {
  protected:
   framework::OpKernelType GetExpectedKernelType(
       const framework::ExecutionContext& ctx) const override {
-    auto dtype = OperatorWithKernel::IndicateVarDataType(ctx, "X");
-    return framework::OpKernelType(dtype, ctx.GetPlace());
+    auto input_data_type =
+        framework::OperatorWithKernel::IndicateVarDataType(ctx, "X");
+
+#ifdef PADDLE_WITH_MKLDNN
+    if (this->CanMKLDNNBeUsed(ctx, input_data_type)) {
+      return framework::OpKernelType(input_data_type, ctx.GetPlace(),
+                                     framework::DataLayout::kMKLDNN,
+                                     framework::LibraryType::kMKLDNN);
+    }
+#endif
+    return framework::OpKernelType(input_data_type, ctx.GetPlace());
   }
 };
 

paddle/fluid/operators/mkldnn/activation_mkldnn_op.cc

@@ -315,15 +315,7 @@ using ExpMKLDNNGradUseOutFunctor = MKLDNNActivationGradUseOutFunc<
 
 namespace ops = paddle::operators;
 
-#define REGISTER_ACTIVATION_MKLDNN_KERNEL(act_type, functor, grad_functor) \
-  REGISTER_OP_KERNEL(act_type, MKLDNN, ::paddle::platform::CPUPlace,       \
-                     ops::MKLDNNActivationKernel<ops::functor<float>>);    \
-  REGISTER_OP_KERNEL(                                                      \
-      act_type##_grad, MKLDNN, ::paddle::platform::CPUPlace,               \
-      ops::MKLDNNActivationGradKernel<ops::grad_functor<float>>);
-
-#define REGISTER_ACTIVATION_MKLDNN_BF16_KERNEL(act_type, functor,             \
-                                               grad_functor)                  \
+#define REGISTER_ACTIVATION_MKLDNN_KERNEL(act_type, functor, grad_functor)    \
   REGISTER_OP_KERNEL(                                                         \
       act_type, MKLDNN, ::paddle::platform::CPUPlace,                         \
       ops::MKLDNNActivationKernel<ops::functor<float>>,                       \
@@ -339,30 +331,27 @@ namespace ops = paddle::operators;
                      ops::MKLDNNActivationKernel<ops::functor<float>>);
 
 #define FOR_EACH_MKLDNN_KERNEL_FUNCTOR(__macro)                            \
-  __macro(relu6, Relu6MKLDNNFunctor, Relu6MKLDNNGradFunctor);              \
-  __macro(leaky_relu, ReluMKLDNNFunctor, ReluMKLDNNGradFunctor);           \
-  __macro(swish, SwishMKLDNNFunctor, SwishMKLDNNGradFunctor);              \
-  __macro(hard_swish, HardSwishMKLDNNFunctor, HardSwishMKLDNNGradFunctor); \
-  __macro(tanh, TanhMKLDNNFunctor, TanhMKLDNNGradUseOutFunctor);           \
   __macro(abs, AbsMKLDNNFunctor, AbsMKLDNNGradFunctor);                    \
   __macro(elu, EluMKLDNNFunctor, EluMKLDNNGradUseOutFunctor);              \
-  __macro(exp, ExpMKLDNNFunctor, ExpMKLDNNGradUseOutFunctor);
+  __macro(exp, ExpMKLDNNFunctor, ExpMKLDNNGradUseOutFunctor);              \
+  __macro(gelu, GeluMKLDNNFunctor, GeluMKLDNNGradFunctor);                 \
+  __macro(hard_swish, HardSwishMKLDNNFunctor, HardSwishMKLDNNGradFunctor); \
+  __macro(leaky_relu, ReluMKLDNNFunctor, ReluMKLDNNGradFunctor);           \
+  __macro(mish, MishMKLDNNFunctor, MishMKLDNNGradFunctor);                 \
+  __macro(relu, ReluMKLDNNFunctor, ReluMKLDNNGradFunctor);                 \
+  __macro(relu6, Relu6MKLDNNFunctor, Relu6MKLDNNGradFunctor);              \
+  __macro(sigmoid, SigmoidMKLDNNFunctor, SigmoidMKLDNNGradUseOutFunctor);  \
+  __macro(sqrt, SqrtMKLDNNFunctor, SqrtMKLDNNGradUseOutFunctor);           \
+  __macro(swish, SwishMKLDNNFunctor, SwishMKLDNNGradFunctor);              \
+  __macro(tanh, TanhMKLDNNFunctor, TanhMKLDNNGradUseOutFunctor);
 
 FOR_EACH_MKLDNN_KERNEL_FUNCTOR(REGISTER_ACTIVATION_MKLDNN_KERNEL);
-REGISTER_ACTIVATION_MKLDNN_KERNEL_FWD_ONLY(round, RoundMKLDNNFunctor);
 
-REGISTER_ACTIVATION_MKLDNN_BF16_KERNEL(relu, ReluMKLDNNFunctor,
-                                       ReluMKLDNNGradFunctor);
-REGISTER_ACTIVATION_MKLDNN_BF16_KERNEL(gelu, GeluMKLDNNFunctor,
-                                       GeluMKLDNNGradFunctor);
-REGISTER_ACTIVATION_MKLDNN_BF16_KERNEL(sigmoid, SigmoidMKLDNNFunctor,
-                                       SigmoidMKLDNNGradUseOutFunctor);
-REGISTER_ACTIVATION_MKLDNN_BF16_KERNEL(sqrt, SqrtMKLDNNFunctor,
-                                       SqrtMKLDNNGradUseOutFunctor);
-REGISTER_ACTIVATION_MKLDNN_BF16_KERNEL(mish, MishMKLDNNFunctor,
-                                       MishMKLDNNGradFunctor);
+REGISTER_ACTIVATION_MKLDNN_KERNEL_FWD_ONLY(round, RoundMKLDNNFunctor);
 
 namespace ops = paddle::operators;
 REGISTER_OP_KERNEL(
     softplus, MKLDNN, paddle::platform::CPUPlace,
-    ops::MKLDNNActivationKernel<ops::SoftplusMKLDNNFunctor<float>>);
+    ops::MKLDNNActivationKernel<ops::SoftplusMKLDNNFunctor<float>>,
+    ops::MKLDNNActivationKernel<
+        ops::SoftplusMKLDNNFunctor<paddle::platform::bfloat16>>);

python/paddle/fluid/tests/unittests/mkldnn/test_activation_bf16_mkldnn_op.py

@@ -50,11 +50,11 @@ def setUp(self):
         self.dtype = np.uint16
         self.init_data()
         self.config()
+        self.set_attrs()
         self.out = self.op_forward(self.x)
 
         self.inputs = {'X': convert_float_to_uint16(self.x)}
         self.outputs = {'Out': self.out}
-        self.set_attrs()
 
     def calculate_grads(self):
         self.dx = self.op_grad(self.out, self.x)
@@ -162,5 +162,110 @@ def op_grad(self, dout, x):
         return dout * ((np.exp(x) * omega) / delta**2)
 
 
+class TestMKLDNNRelu6BF16Op(MKLDNNBF16ActivationOp, TestActivation):
+    def config(self):
+        self.op_type = "relu6"
+
+    def op_forward(self, x):
+        return np.clip(x, 0, 6)
+
+    def op_grad(self, dout, x):
+        return np.where((x > 0) & (x <= 6), dout, 0)
+
+
+class TestMKLDNNLeakyReluBF16Op(MKLDNNBF16ActivationOp, TestActivation):
+    def config(self):
+        self.op_type = "leaky_relu"
+
+    def op_forward(self, x):
+        return np.where(x > 0, x, self.alpha * x)
+
+    def op_grad(self, dout, x):
+        return np.where(x > 0, dout, self.alpha * dout)
+
+    def set_attrs(self):
+        self.alpha = 0.2
+        self.attrs = {"use_mkldnn": True, "alpha": self.alpha}
+
+
+class TestMKLDNNSwishBF16Op(MKLDNNBF16ActivationOp, TestActivation):
+    def config(self):
+        self.op_type = "swish"
+
+    def expit(self, val):
+        return 1 / (1 + np.exp(-self.beta * val))
+
+    def op_forward(self, x):
+        return x * self.expit(x)
+
+    def op_grad(self, dout, x):
+        return dout * self.expit(x) * (1 + self.beta * x * (1 - self.expit(x)))
+
+    def set_attrs(self):
+        self.beta = 0.2
+        self.attrs = {"use_mkldnn": True, "beta": self.beta}
+
+
+class TestMKLDNNHardSwishBF16Op(MKLDNNBF16ActivationOp, TestActivation):
+    def config(self):
+        self.op_type = "hard_swish"
+
+    def op_forward(self, x):
+        result = np.where(x < -3, 0, x)
+        return np.where(result > 3, result, result * (result + 3) / 6)
+
+    def op_grad(self, dout, x):
+        result = np.where(x < -3, 0, x)
+        return np.where(result > 3, dout, dout * (2 * x + 3) / 6)
+
+
+class TestMKLDNNTanhBF16Op(MKLDNNBF16ActivationOp, TestActivation):
+    def config(self):
+        self.op_type = "tanh"
+
+    def op_forward(self, x):
+        return np.tanh(x)
+
+    def op_grad(self, dout, x):
+        return dout * (1 - np.tanh(x)**2)
+
+
+class TestMKLDNNAbsBF16Op(MKLDNNBF16ActivationOp, TestActivation):
+    def config(self):
+        self.op_type = "abs"
+
+    def op_forward(self, x):
+        return np.absolute(x)
+
+    def op_grad(self, dout, x):
+        return dout * np.sign(x)
+
+
+class TestMKLDNNEluBF16Op(MKLDNNBF16ActivationOp, TestActivation):
+    def config(self):
+        self.op_type = "elu"
+
+    def op_forward(self, x):
+        return np.where(x > 0, x, self.alpha * (np.exp(x) - 1))
+
+    def op_grad(self, dout, x):
+        return np.where(x > 0, dout, dout * self.alpha * np.exp(x))
+
+    def set_attrs(self):
+        self.alpha = 0.2
+        self.attrs = {"use_mkldnn": True, "alpha": self.alpha}
+
+
+class TestMKLDNNExpBF16Op(MKLDNNBF16ActivationOp, TestActivation):
+    def config(self):
+        self.op_type = "exp"
+
+    def op_forward(self, x):
+        return np.exp(x)
+
+    def op_grad(self, dout, x):
+        return dout * np.exp(x)
+
+
 if __name__ == '__main__':
     unittest.main()

python/paddle/fluid/tests/unittests/mkldnn/test_softplus_mkldnn_op.py

@@ -16,7 +16,7 @@
 
 import unittest
 import numpy as np
-from paddle.fluid.tests.unittests.op_test import OpTest, OpTestTool
+from paddle.fluid.tests.unittests.op_test import OpTest, OpTestTool, convert_float_to_uint16
 import paddle
 import paddle.fluid as fluid
 import paddle.fluid.core as core
@@ -30,23 +30,32 @@ def ref_softplus(x, beta, threshold):
     return out
 
 
-@OpTestTool.skip_if(not (isinstance(_current_expected_place(), core.CPUPlace)),
-                    "GPU is not supported")
+@OpTestTool.skip_if_not_cpu_bf16()
 class TestSoftplusOneDNNOp(OpTest):
     def setUp(self):
         self.op_type = "softplus"
         self.beta = 1
         self.threshold = 20
         self.config()
+        self.set_dtype()
         self.attrs = {'use_mkldnn': True, 'beta': self.beta}
-        self.inputs = {'X': np.random.random(self.x_shape).astype(np.float32)}
+        self.x = np.random.random(self.x_shape)
+        self.out = ref_softplus(self.x, self.beta, self.threshold)
+
+        if self.dtype != np.float32:
+            self.x = convert_float_to_uint16(self.x)
+
+        self.inputs = {'X': self.out}
         self.outputs = {
-            'Out': ref_softplus(self.inputs['X'], self.beta, self.threshold)
+            'Out': ref_softplus(self.out, self.beta, self.threshold)
         }
 
     def config(self):
         self.x_shape = (10, 10)
 
+    def set_dtype(self):
+        self.dtype = np.float32
+
     def test_check_output(self):
         self.check_output()
 
@@ -73,6 +82,27 @@ def config(self):
         self.beta = 0.4
 
 
+class TestSoftplusBF16OneDNNOp(TestSoftplusOneDNNOp):
+    def set_dtype(self):
+        self.dtype = np.uint16
+
+
+class TestSoftplus4DBF16OneDNNOp(TestSoftplus4DOneDNNOp):
+    def set_dtype(self):
+        self.dtype = np.uint16
+
+
+class TestSoftplus6DBF16OneDNNOp(TestSoftplus6DOneDNNOp):
+    def set_dtype(self):
+        self.dtype = np.uint16
+
+
+class TestSoftplus3DExtendedFunctorBF16OneDNNOp(
+        TestSoftplus3DExtendedFunctorOneDNNOp):
+    def set_dtype(self):
+        self.dtype = np.uint16
+
+
 if __name__ == "__main__":
     paddle.enable_static()
     unittest.main()