[TF:NN:CONVOLUTION] Don't call space to batch in depthwise convolution on TPU.

PiperOrigin-RevId: 261242540

tensorflow/compiler/tests/depthwise_conv_op_test.py

@@ -25,6 +25,7 @@ from tensorflow.compiler.tests import xla_test
 from tensorflow.python.framework import constant_op
 from tensorflow.python.framework import ops
 from tensorflow.python.ops import array_ops
+from tensorflow.python.ops import nn_impl
 from tensorflow.python.ops import nn_ops
 import tensorflow.python.ops.nn_grad  # pylint: disable=unused-import
 from tensorflow.python.platform import test
@@ -87,6 +88,32 @@ def ConfigsToTest():
     yield i, f, o, s, p
 
 
+def ConfigsWithDilationsToTest():
+  """Iterator for different convolution shapes, strides and paddings.
+
+  Yields:
+    Tuple (input_size, filter_size, out_size, stride, dilation, padding), the
+    depthwise
+    convolution parameters.
+  """
+  input_sizes = [[4, 6, 6, 48], [4, 8, 8, 84], [4, 36, 36, 2], [4, 148, 148, 2],
+                 [3, 300, 300, 3]]
+  filter_sizes = [[1, 1, 48, 2], [1, 3, 84, 1], [5, 5, 2, 1], [4, 4, 2, 8],
+                  [2, 2, 3, 8]]
+  out_sizes = [[4, 6, 6, 96], [4, 8, 8, 84], [4, 36, 36, 2], [4, 74, 74, 16],
+               [3, 296, 296, 24]]
+  strides = [1, 1, 2, 2, 1]
+  dilations = [2, 2, 4, 2, 4]
+  # pylint: disable=invalid-name
+  VALID = "VALID"
+  SAME = "SAME"
+  # pylint: enable=invalid-name
+  paddings = [SAME, SAME, SAME, SAME, VALID]
+  for i, f, o, s, d, p in zip(input_sizes, filter_sizes, out_sizes, strides,
+                              dilations, paddings):
+    yield i, f, o, s, d, p
+
+
 def CheckGradConfigsToTest():
   """Iterator for different convolution shapes, strides and paddings.
 
@@ -315,6 +342,118 @@ class DepthwiseConv2DTest(xla_test.XLATestCase):
         padding="VALID",
         expected=expected_output)
 
+  # This is testing that depthwise_conv2d with dilation produces
+  # the same results between CPU and TPU. It also tests that NCHW
+  # and NWHC formats agree.
+  def _VerifyValuesWithDilation(self,
+                                tensor_in_sizes,
+                                filter_in_sizes,
+                                stride,
+                                dilation,
+                                padding,
+                                data_type,
+                                data_format="NHWC"):
+    """Verifies the output values of the convolution function.
+
+    Args:
+      tensor_in_sizes: Input tensor dimensions in [batch, input_rows,
+        input_cols, input_depth].
+      filter_in_sizes: Filter tensor dimensions in [filter_rows, filter_cols,
+        input_depth, depth_multiplier].
+      stride: Stride.
+      dilation: Dilation.
+      padding: Padding type.
+      data_type: The data type to use.
+      data_format: The data_format of the input. "NHWC" or "NCHW".
+    """
+    total_size_1 = 1
+    total_size_2 = 1
+    for s in tensor_in_sizes:
+      total_size_1 *= s
+    for s in filter_in_sizes:
+      total_size_2 *= s
+    # Initializes the input and filter tensor with numbers incrementing from 1.
+    x1 = np.array([f * 1.0 for f in range(1, total_size_1 + 1)],
+                  dtype=data_type).reshape(tensor_in_sizes)
+    x2 = np.array([f * 1.0 for f in range(1, total_size_2 + 1)],
+                  dtype=data_type).reshape(filter_in_sizes)
+    with self.session() as sess:
+      if data_type == np.float32:
+        # TODO(b/64210055): Tolerance for TPU is high.
+        tolerance = 1e-2
+      else:
+        self.assertEqual(data_type, np.float64)
+        tolerance = 1e-8
+
+      t1 = array_ops.placeholder(shape=tensor_in_sizes, dtype=data_type)
+      t2 = array_ops.placeholder(shape=filter_in_sizes, dtype=data_type)
+
+      native_t1 = t1
+      strides = [1, stride, stride, 1]
+      dilations = [dilation, dilation]
+      if data_format == "NCHW":
+        # Transpose from NWHC input to NCHW
+        # Ex. [4, 5, 5, 48] to [4, 48, 5, 5]
+        native_t1 = array_ops.transpose(t1, [0, 3, 1, 2])
+        strides = [1, 1, stride, stride]
+
+      with self.test_scope():
+        conv_native = nn_impl.depthwise_conv2d(
+            native_t1,
+            t2,
+            strides=strides,
+            rate=dilations,
+            data_format=data_format,
+            padding=padding)
+
+      if data_format == "NCHW":
+        # Transpose back from NCHW to NHWC
+        conv_native = array_ops.transpose(conv_native, [0, 2, 3, 1])
+
+      with ops.device("CPU"):
+        # CPU only support NHWC format
+        strides = [1, stride, stride, 1]
+        conv_interface = nn_impl.depthwise_conv2d(
+            t1, t2, strides=strides, rate=dilations, padding=padding)
+
+      native_result = sess.run(conv_native, {t1: x1, t2: x2})
+      interface_result = sess.run(conv_interface, {t1: x1, t2: x2})
+
+    print("data_type:", data_type, "max diff = ",
+          np.amax(np.absolute(native_result - interface_result)))
+    self.assertAllClose(
+        np.ravel(native_result), np.ravel(interface_result), rtol=tolerance)
+
+  def testDilationDepthwiseConv2DWith(self):
+    for index, (input_size, filter_size, _, stride, dilation,
+                padding) in enumerate(ConfigsWithDilationsToTest()):
+      print("Testing DilationDepthwiseConv2D,", index, "th config:", input_size,
+            "*", filter_size, "stride:", stride, "dilation: ", dilation,
+            "padding:", padding)
+      for data_type in self.float_types:
+        # TODO(phawkins): the reference implementation only supports float32.
+        if data_type == np.float32:
+          self._VerifyValuesWithDilation(input_size, filter_size, stride,
+                                         dilation, padding, data_type)
+
+  def testDilationDepthwiseConv2DWithFormat(self):
+    for index, (input_size, filter_size, _, stride, dilation,
+                padding) in enumerate(ConfigsWithDilationsToTest()):
+      print("Testing DilationDepthwiseConv2DFormat,", index, "th config:",
+            input_size, "*", filter_size, "stride:", stride, "dilation:",
+            dilation, "padding:", padding)
+      for data_type in self.float_types:
+        # TODO(phawkins): the reference implementation only supports float32.
+        if data_type == np.float32:
+          self._VerifyValuesWithDilation(
+              input_size,
+              filter_size,
+              stride,
+              dilation,
+              padding,
+              data_type,
+              data_format="NCHW")
+
   def _CompareBackpropInput(self, input_sizes, filter_sizes, output_sizes,
                             stride, padding):
     x1 = np.random.rand(*filter_sizes).astype(np.float32)
@@ -420,5 +559,139 @@ class DepthwiseConv2DTest(xla_test.XLATestCase):
           padding,
           data_format="NCHW")
 
+  def _CompareBackpropInputWithDilation(self, input_sizes, filter_sizes,
+                                        output_sizes, stride, dilation,
+                                        padding):
+    x1 = np.random.rand(*filter_sizes).astype(np.float32)
+    x2 = np.random.rand(*output_sizes).astype(np.float32)
+
+    def _GetVal(use_xla):
+      with self.session():
+        t1 = array_ops.placeholder(np.float32, shape=filter_sizes)
+        t2 = array_ops.placeholder(np.float32, shape=output_sizes)
+        if use_xla:
+          with self.test_scope():
+            t0 = constant_op.constant(input_sizes, shape=[len(input_sizes)])
+            backprop = nn_ops.depthwise_conv2d_native_backprop_input(
+                t0,
+                t1,
+                t2,
+                strides=[1, stride, stride, 1],
+                dilations=[1, dilation, dilation, 1],
+                padding=padding)
+        else:
+          # TODO(wangtao): figure out gradient with stride > 1.
+          # depthwise_conv2d_native_backprop_input on CPU doesn't support
+          # dilation.
+          t3 = array_ops.space_to_batch(
+              t2, block_size=dilation, paddings=[[0, 0], [0, 0]])
+          input_sizes_transform = [
+              input_sizes[0] * dilation * dilation, input_sizes[1] // dilation,
+              input_sizes[2] // dilation, input_sizes[3]
+          ]
+          t0 = constant_op.constant(
+              input_sizes_transform, shape=[len(input_sizes)])
+          backprop_naive = nn_ops.depthwise_conv2d_native_backprop_input(
+              t0, t1, t3, strides=[1, stride, stride, 1], padding=padding)
+          backprop = array_ops.batch_to_space(
+              backprop_naive, [[0, 0], [0, 0]], block_size=dilation)
+
+        ret = backprop.eval({t1: x1, t2: x2})
+        self.assertShapeEqual(ret, backprop)
+        return ret
+
+    gpu_value = _GetVal(use_xla=True)
+    cpu_value = _GetVal(use_xla=False)
+
+    # TODO (b/64210055): Tolerance for TPU is high.
+    self.assertAllClose(cpu_value, gpu_value, rtol=1e-2, atol=1e-3)
+
+  def testDilationDepthwiseConv2DInputGradWithCompare(self):
+    for index, (input_size, filter_size, output_size, stride, dilation,
+                padding) in enumerate(ConfigsWithDilationsToTest()):
+      print("Testing DilationDepthwiseConv2DInputGradWithDilationCompare,",
+            index, "th config:", input_size, "*", filter_size, "stride:",
+            stride, "dilation:", dilation, "padding:", padding)
+      # TODO(wangtao): implement CPU grad computation with stride > 1.
+      if stride == 1:
+        self._CompareBackpropInputWithDilation(input_size, filter_size,
+                                               output_size, stride, dilation,
+                                               padding)
+
+  def _CompareBackpropFilterWithDilation(self,
+                                         input_sizes,
+                                         filter_sizes,
+                                         output_sizes,
+                                         stride,
+                                         dilation,
+                                         padding,
+                                         data_format="NHWC"):
+    x0 = np.random.rand(*input_sizes).astype(np.float32)
+    x2 = np.random.rand(*output_sizes).astype(np.float32)
+
+    def _GetVal(use_xla):
+      with self.session():
+        t0 = array_ops.placeholder(np.float32, shape=input_sizes)
+        t1 = constant_op.constant(filter_sizes, shape=[len(filter_sizes)])
+        t2 = array_ops.placeholder(np.float32, shape=output_sizes)
+        native_t0 = t0
+        native_t2 = t2
+        strides = [1, stride, stride, 1]
+        dilations = [1, dilation, dilation, 1]
+
+        if use_xla:
+          if data_format == "NCHW":
+            # Transpose from NWHC input to NCHW
+            # Ex. [4, 5, 5, 48] to [4, 48, 5, 5]
+            native_t0 = array_ops.transpose(t0, [0, 3, 1, 2])
+            native_t2 = array_ops.transpose(t2, [0, 3, 1, 2])
+            strides = [1, 1, stride, stride]
+            dilations = [1, 1, dilation, dilation]
+          with self.test_scope():
+            backprop = nn_ops.depthwise_conv2d_native_backprop_filter(
+                native_t0,
+                t1,
+                native_t2,
+                strides=strides,
+                padding=padding,
+                dilations=dilations,
+                data_format=data_format)
+        else:
+          # For CPU, the format NCHW is not supported. Therefore we always use
+          # NHWC here.
+          # depthwise_conv2d_native_backprop_filter on CPU doesn't support
+          # dilation.
+          native_t3 = array_ops.space_to_batch(
+              native_t2, block_size=dilation, paddings=[[0, 0], [0, 0]])
+          native_t0_transform = array_ops.space_to_batch(
+              native_t0, block_size=dilation, paddings=[[0, 0], [0, 0]])
+          backprop = nn_ops.depthwise_conv2d_native_backprop_filter(
+              native_t0_transform,
+              t1,
+              native_t3,
+              strides=strides,
+              padding=padding)
+        ret = backprop.eval({t0: x0, t2: x2})
+        self.assertShapeEqual(ret, backprop)
+        return ret
+
+    gpu_value = _GetVal(use_xla=True)
+    cpu_value = _GetVal(use_xla=False)
+    # TODO(b/64210055): Tolerance for TPU is high.
+    self.assertAllClose(cpu_value, gpu_value, rtol=1e-3, atol=1e-4)
+
+  def testDilationDepthwiseConv2DFilterGradCompare(self):
+    for index, (input_size, filter_size, output_size, stride, dilation,
+                padding) in enumerate(ConfigsWithDilationsToTest()):
+      print("Testing DilationDepthwiseConv2DFilterGradCompare,", index,
+            "th config:", input_size, "*", filter_size, "producing output",
+            output_size, "stride:", stride, "dilation:", dilation, "padding:",
+            padding)
+      if stride == 1:
+        # TODO(wangtao): implement CPU grad computation with stride > 1.
+        self._CompareBackpropFilterWithDilation(input_size, filter_size,
+                                                output_size, stride, dilation,
+                                                padding)
+
 if __name__ == "__main__":
   test.main()

tensorflow/core/kernels/conv_grad_filter_ops.cc

@@ -408,11 +408,15 @@ class Conv2DCustomBackpropFilterOp : public OpKernel {
                 errors::InvalidArgument(
                     "Current implementation does not yet support "
                     "dilations in the batch and depth dimensions."));
-    // TODO(yangzihao): Add a CPU implementation for dilated convolution.
+    if (std::is_same<Device, CPUDevice>::value ||
-    OP_REQUIRES(context, (dilations_[1] == 1 && dilations_[2] == 1),
+        std::is_same<Device, GPUDevice>::value) {
-                errors::InvalidArgument(
+      // TODO(yangzihao): Add a CPU implementation for dilated convolution.
-                    "Current libxsmm and customized CPU implementations do "
+      OP_REQUIRES(context, (dilations_[1] == 1 && dilations_[2] == 1),
-                    "not yet support dilation rates larger than 1."));
+                  errors::InvalidArgument(
+                      "Current libxsmm and customized CPU implementations do "
+                      "not yet support dilation rates larger than 1."));
+      dilations_ = {1, 1, 1, 1};
+    }
   }
 
   void Compute(OpKernelContext* context) override {
@@ -434,8 +438,8 @@ class Conv2DCustomBackpropFilterOp : public OpKernel {
         context,
         ConvBackpropComputeDimensionsV2(
             "Conv2DCustomBackpropFilter", /*num_spatial_dims=*/2, input.shape(),
-            filter_shape, out_backprop.shape(), /*dilations=*/{1, 1, 1, 1},
+            filter_shape, out_backprop.shape(), dilations_, strides_, padding_,
-            strides_, padding_, explicit_paddings_, data_format_, &dims));
+            explicit_paddings_, data_format_, &dims));
 
     Tensor* filter_backprop;
     OP_REQUIRES_OK(context,

tensorflow/python/ops/nn_grad.py

@@ -628,15 +628,17 @@ def _DepthwiseConv2dNativeGrad(op, grad):
           array_ops.shape(op.inputs[0]),
           op.inputs[1],
           grad,
-          op.get_attr("strides"),
+          dilations=op.get_attr("dilations"),
-          op.get_attr("padding"),
+          strides=op.get_attr("strides"),
+          padding=op.get_attr("padding"),
           data_format=op.get_attr("data_format")),
       nn_ops.depthwise_conv2d_native_backprop_filter(
           op.inputs[0],
           array_ops.shape(op.inputs[1]),
           grad,
-          op.get_attr("strides"),
+          dilations=op.get_attr("dilations"),
-          op.get_attr("padding"),
+          strides=op.get_attr("strides"),
+          padding=op.get_attr("padding"),
           data_format=op.get_attr("data_format"))
   ]
 

tensorflow/python/ops/nn_impl.py

@@ -715,6 +715,22 @@ def zero_fraction(value, name=None):
     return array_ops.identity(zero_fraction_float32, "fraction")
 
 
+# copybara:strip_begin
+# TODO(b/138808492): Remove code inside copybara
+# to make TPU code and CPU code consistent.
+def _enclosing_tpu_context():
+  # pylint: disable=protected-access
+  context = ops.get_default_graph()._get_control_flow_context()
+  # pylint: enable=protected-access
+  while context is not None and not isinstance(
+      context, control_flow_ops.XLAControlFlowContext):
+    context = context.outer_context
+  return context
+
+
+# copybara:strip_end
+
+
 # pylint: disable=redefined-builtin
 @tf_export(v1=["nn.depthwise_conv2d"])
 def depthwise_conv2d(input,
@@ -774,6 +790,25 @@ def depthwise_conv2d(input,
     if rate is None:
       rate = [1, 1]
 
+    # copybara:strip_begin
+    # TODO(b/138808492): Remove code inside copybara
+    # to make TPU code and CPU code consistent.
+    # Use depthwise_conv2d_native if executing on TPU.
+    if _enclosing_tpu_context() is not None:
+      if data_format == "NCHW":
+        dilations = [1, 1, rate[0], rate[1]]
+      else:
+        dilations = [1, rate[0], rate[1], 1]
+      return nn_ops.depthwise_conv2d_native(
+          input=input,
+          filter=filter,
+          strides=strides,
+          padding=padding,
+          data_format=data_format,
+          dilations=dilations,
+          name=name)
+    # copybara:strip_end
+
     def op(input_converted, _, padding):
       return nn_ops.depthwise_conv2d_native(
           input=input_converted,

tensorflow/python/ops/nn_ops.py

@@ -33,6 +33,7 @@ from tensorflow.python.framework import tensor_shape
 from tensorflow.python.framework import tensor_util
 from tensorflow.python.ops import array_ops
 from tensorflow.python.ops import check_ops
+from tensorflow.python.ops import control_flow_ops
 from tensorflow.python.ops import gen_nn_ops
 from tensorflow.python.ops import math_ops
 from tensorflow.python.ops import random_ops
@@ -919,6 +920,22 @@ convolution_v2.__doc__ = deprecation.rewrite_argument_docstring(
     "filter", "filters")
 
 
+# copybara:strip_begin
+# TODO(b/138808492): Remove code inside copybara
+# to make TPU code and CPU code consistent.
+def _enclosing_tpu_context():
+  # pylint: disable=protected-access
+  run_context = ops.get_default_graph()._get_control_flow_context()
+  # pylint: enable=protected-access
+  while run_context is not None and not isinstance(
+      run_context, control_flow_ops.XLAControlFlowContext):
+    run_context = run_context.outer_context
+  return run_context
+
+
+# copybara:strip_end
+
+
 def convolution_internal(
     input,  # pylint: disable=redefined-builtin
     filters,
@@ -958,8 +975,14 @@ def convolution_internal(
 
     conv_ops = {1: conv1d, 2: gen_nn_ops.conv2d, 3: gen_nn_ops.conv3d}
 
-    if all(i == 1 for i in dilations):
+    # copybara:strip_begin
-      # fast path if no dilation as gradient only supported on GPU for dilations
+    # TODO(b/138808492): Remove code inside copybara
+    # to make TPU code and CPU code consistent.
+    if _enclosing_tpu_context() is not None or all(i == 1 for i in dilations):
+      # fast path for TPU or if no dilation as gradient only supported on GPU
+      # for dilations
+    # copybara:strip_end
+    # copybara:insert if all(i == 1 for i in dilations):
       op = conv_ops[n]
       return op(
           input,
@@ -1056,7 +1079,9 @@ class Convolution(object):
     self.filter_shape = filter_shape
     self.data_format = data_format
     self.strides = strides
+    self.padding = padding
     self.name = name
+    self.dilation_rate = dilation_rate
     self.conv_op = _WithSpaceToBatch(
         input_shape,
         dilation_rate=dilation_rate,
@@ -1076,7 +1101,23 @@ class Convolution(object):
         name=self.name)
 
   def __call__(self, inp, filter):  # pylint: disable=redefined-builtin
-    return self.conv_op(inp, filter)
+    # copybara:strip_begin
+    # TODO(b/138808492): Remove code inside copybara
+    # to make TPU code and CPU code consistent.
+    # TPU convolution supports dilations greater than 1.
+    if _enclosing_tpu_context() is not None:
+      return convolution_internal(
+          inp,
+          filter,
+          strides=self.strides,
+          padding=self.padding,
+          data_format=self.data_format,
+          dilations=self.dilation_rate,
+          name=self.name)
+    else:
+      return self.conv_op(inp, filter)
+    # copybara:strip_end
+    # copybara:insert return self.conv_op(inp, filter)
 
 
 @tf_export(v1=["nn.pool"])