Added Conv2D BF16 BWD oneDNN kernel

paddle/fluid/operators/mkldnn/conv_mkldnn_op.cc

@@ -1012,6 +1012,11 @@ REGISTER_OP_KERNEL_WITH_CUSTOM_TYPE(conv2d_grad, MKLDNN,
                                     ops::kConvMKLDNNFP32,
                                     ops::ConvMKLDNNGradOpKernel<float, float>);
 
+REGISTER_OP_KERNEL_WITH_CUSTOM_TYPE(
+    conv2d_grad, MKLDNN, ::paddle::platform::CPUPlace, BF16,
+    ops::kConvMKLDNNFP32,
+    ops::ConvMKLDNNGradOpKernel<paddle::platform::bfloat16, float>);
+
 REGISTER_OP_KERNEL_WITH_CUSTOM_TYPE(conv3d, MKLDNN,
                                     ::paddle::platform::CPUPlace, FP32,
                                     ops::kConvMKLDNNFP32,

python/paddle/fluid/tests/unittests/mkldnn/test_conv2d_bf16_mkldnn_op.py

@@ -19,7 +19,7 @@
 import struct
 
 import paddle.fluid.core as core
-from paddle.fluid.tests.unittests.op_test import OpTest, convert_float_to_uint16
+from paddle.fluid.tests.unittests.op_test import OpTest, convert_float_to_uint16, OpTestTool
 from paddle.fluid.tests.unittests.test_conv2d_op import conv2d_forward_naive, TestConv2DOp
 
 
@@ -31,7 +31,7 @@ def conv2d_residual_naive(out, residual):
 
 @unittest.skipIf(not core.supports_bfloat16(),
                  "place does not support BF16 evaluation")
-class TestConv2DBf16Op(TestConv2DOp):
+class TestConv2DBF16Op(TestConv2DOp):
     def setUp(self):
         self.op_type = "conv2d"
         self.use_cudnn = False
@@ -51,15 +51,19 @@ def setUp(self):
         self.init_data_type()
         self.init_force_fp32_output()
 
-        conv2d_param = {
+        self.conv2d_param = {
             'stride': self.stride,
             'pad': self.pad,
             'dilation': self.dilations
         }
+
         self.input = np.random.random(self.input_size).astype(np.float32)
         self.filter = np.random.random(self.filter_size).astype(np.float32)
-        conv_out, _, _, _, _ = conv2d_forward_naive(self.input, self.filter,
-                                                    self.groups, conv2d_param)
+
+        self.inputs_fp32 = {'Input': self.input, 'Filter': self.filter}
+
+        conv_out, _, _, _, _ = conv2d_forward_naive(
+            self.input, self.filter, self.groups, self.conv2d_param)
         self.conv_output_float = conv_out
 
         if self.fuse_residual:
@@ -71,12 +75,16 @@ def setUp(self):
             self.outputs = {'Output': self.conv_output}
         elif self.force_fp32_output:
             self.outputs = {'Output': self.conv_output_float.astype(np.float32)}
+        else:
+            self.outputs = {
+                'Output': convert_float_to_uint16(self.conv_output_float)
+            }
 
         if self.input_type is not np.float32:
             self.input = convert_float_to_uint16(self.input)
 
         self.inputs = {
-            'Input': self.input.view(self.input_type),
+            'Input': self.input,
             'Filter': OpTest.np_dtype_to_fluid_dtype(
                 self.filter.astype(self.weight_type))
         }
@@ -111,14 +119,18 @@ def test_check_grad_no_input(self):
 
     def init_test_case(self):
         TestConv2DOp.init_test_case(self)
-        self.input_size = [1, 1, 5, 5]  # NCHW
+        self.input_size = [1, 6, 12, 12]  # NCHW
         f_c = self.input_size[1] // self.groups
-        self.input_residual_size = [1, 2, 3, 3]
-        self.filter_size = [2, f_c, 3, 3]
+        o_c = 15
+        self.input_residual_size = [1, o_c, 10, 10]
+        self.filter_size = [o_c, f_c, 3, 3]
+
+    def init_padding(self):
+        pass
 
     def init_data_type(self):
         self.weight_type = np.float32
-        self.input_type = np.float32
+        self.input_type = np.uint16
 
     def init_force_fp32_output(self):
         self.force_fp32_output = False
@@ -130,7 +142,121 @@ def init_fuse_residual(self):
         self.fuse_residual = True
 
 
-class TestConv2D(TestConv2DBf16Op):
+@OpTestTool.skip_if_not_cpu_bf16()
+class TestConv2DWithGradBF16Op(TestConv2DBF16Op):
+    def init_fuse_relu(self):
+        self.fuse_activation = None
+
+    def init_fuse_residual(self):
+        self.fuse_residual = None
+
+    def test_check_grad(self):
+        dout = self.conv_output_float
+        x = self.inputs_fp32['Input']
+        w = self.inputs_fp32['Filter']
+
+        dx, dweights = conv_backward(dout, x, w, self.conv2d_param)
+
+        self.check_grad_with_place(
+            core.CPUPlace(), ["Input", "Filter"],
+            "Output",
+            user_defined_grads=[dx, dweights],
+            user_defined_grad_outputs=[convert_float_to_uint16(dout)])
+
+    def test_check_grad_no_filter(self):
+        dout = self.conv_output_float
+        x = self.inputs_fp32['Input']
+        w = self.inputs_fp32['Filter']
+
+        dx, _ = conv_backward(dout, x, w, self.conv2d_param)
+
+        self.check_grad_with_place(
+            core.CPUPlace(), ["Input"],
+            "Output",
+            set(['Filter']),
+            user_defined_grads=[dx],
+            user_defined_grad_outputs=[convert_float_to_uint16(dout)])
+
+    def test_check_grad_no_input(self):
+        dout = self.conv_output_float
+        x = self.inputs_fp32['Input']
+        w = self.inputs_fp32['Filter']
+
+        _, dweights = conv_backward(dout, x, w, self.conv2d_param)
+
+        self.check_grad_with_place(
+            core.CPUPlace(), ["Filter"],
+            "Output",
+            set(['Input']),
+            user_defined_grads=[dweights],
+            user_defined_grad_outputs=[convert_float_to_uint16(dout)])
+
+
+def conv_backward(dout, x, w, params):
+    padding = params['pad'][0]
+    stride = params['stride']
+
+    dx = np.zeros_like(x)
+    dweights = np.zeros_like(w)
+
+    N, IC, H, W = x.shape
+    OC, _, KH, KW = w.shape
+
+    H_out = int(1 + (H + 2 * padding - KH) / stride[0])
+    W_out = int(1 + (W + 2 * padding - KW) / stride[1])
+
+    x_padded = np.pad(x, ((0, ), (0, ), (padding, ), (padding, )), 'constant')
+
+    for n in range(N):
+        for oc in range(OC):
+            for i in range(KH):
+                for j in range(KW):
+                    for k in range(H_out):
+                        for l in range(W_out):
+                            for ic in range(IC):
+                                dweights[oc, ic, i, j] += x_padded[
+                                    n, ic, i + k * stride[0], j + l * stride[
+                                        1]] * dout[n, oc, k, l]
+
+    dx_padded = np.pad(dx, ((0, ), (0, ), (padding, ), (padding, )), 'constant')
+
+    w_ = np.zeros_like(w)
+    for i in range(KH):
+        for j in range(KW):
+            w_[:, :, i, j] = w[:, :, KH - i - 1, KW - j - 1]
+
+    for n in range(N):
+        for oc in range(OC):
+            for i in range(H_out):
+                for j in range(W_out):
+                    for kh in range(KH):
+                        for kw in range(KW):
+                            for ic in range(IC):
+                                dx_padded[n, ic, stride[0] * i + kh, stride[1] *
+                                          j + kw] += dout[n, oc, i, j] * w[
+                                              oc, ic, kh, kw]
+
+    if padding == 0:
+        dx = dx_padded
+    else:
+        dx = dx_padded[:, :, padding:-padding, padding:-padding]
+
+    return dx.astype(np.float32), dweights.astype(np.float32)
+
+
+class TestConv2DBF16WithPadding1(TestConv2DWithGradBF16Op):
+    def init_test_case(self):
+        TestConv2DWithGradBF16Op.init_test_case(self)
+        self.pad = [1, 1]
+
+
+class TestConv2DBF16WithStride2(TestConv2DWithGradBF16Op):
+    def init_test_case(self):
+        TestConv2DWithGradBF16Op.init_test_case(self)
+        self.stride = [2, 3]
+
+
+class TestConv2D(TestConv2DBF16Op):
     def init_test_case(self):
         self.pad = [0, 0]
         self.stride = [1, 1]
@@ -156,7 +282,7 @@ def init_group(self):
         self.groups = 3
 
 
-class TestWithStride(TestConv2DBf16Op):
+class TestWithStride(TestConv2DBF16Op):
     def init_test_case(self):
         self.pad = [1, 1]
         self.stride = [2, 2]
@@ -170,7 +296,7 @@ def init_data_type(self):
         self.input_type = np.uint16
 
 
-class TestWithDilations(TestConv2DBf16Op):
+class TestWithDilations(TestConv2DBF16Op):
     def init_test_case(self):
         self.pad = [1, 1]
         self.stride = [1, 1]
@@ -185,7 +311,7 @@ def init_data_type(self):
         self.input_type = np.uint16
 
 
-class TestWith1x1ForceFP32Output(TestConv2DBf16Op):
+class TestWith1x1ForceFP32Output(TestConv2DBF16Op):
     def init_test_case(self):
         self.pad = [0, 0]
         self.stride = [1, 1]
@@ -201,7 +327,7 @@ def init_fuse_residual(self):
         self.fuse_residual = False
 
 
-class TestWithInput1x1Filter1x1(TestConv2DBf16Op):
+class TestWithInput1x1Filter1x1(TestConv2DBF16Op):
     def init_test_case(self):
         self.pad = [0, 0]
         self.stride = [1, 1]