add softmax backward rule

paddle/fluid/distributed/auto_parallel/spmd_rules/softmax_spmd_rule.cc

@@ -20,10 +20,10 @@ namespace auto_parallel {
 
 using phi::distributed::auto_parallel::str_join;
 
-// step0: verify input args based on softmax logic
 std::pair<std::vector<TensorDistAttr>, std::vector<TensorDistAttr>>
 SoftmaxSPMDRule::InferForward(const std::vector<DistTensorSpec>& input_specs,
                               const paddle::framework::AttributeMap& attrs) {
+  // step0: verify input args based on softmax logic
   auto input_specs_size = input_specs.size();
   PADDLE_ENFORCE_EQ(
       input_specs_size,
@@ -94,7 +94,7 @@ SoftmaxSPMDRule::InferForward(const std::vector<DistTensorSpec>& input_specs,
   TensorDistAttr x_dist_attr_dst = CopyTensorDistAttrForOutput(x_dist_attr_src);
   x_dist_attr_dst.set_dims_mapping(x_dims_mapping);
 
-  VLOG(4) << "EmbeddingSPMDRule InferForward: "
+  VLOG(4) << "SoftmaxSPMDRule InferForward: "
           << "Einsum notation: [" << x_axes << " --> " << out_axes << "]. "
           << std::endl
           << "X shape: [" << str_join(x_shape) << "], src_dims_mapping: ["
@@ -106,11 +106,72 @@ SoftmaxSPMDRule::InferForward(const std::vector<DistTensorSpec>& input_specs,
 }
 
 std::pair<std::vector<TensorDistAttr>, std::vector<TensorDistAttr>>
-SoftmaxSPMDRule::InferBackward(const std::vector<DistTensorSpec>& output_specs,
-                               const std::vector<DistTensorSpec>& input_specs,
+SoftmaxSPMDRule::InferBackward(const std::vector<DistTensorSpec>& input_specs,
+                               const std::vector<DistTensorSpec>& output_specs,
                                const paddle::framework::AttributeMap& attrs) {
-  PADDLE_THROW(phi::errors::Unimplemented(
-      "InferBackward of SoftmaxSPMDRule is NOT implemented yet."));
+  // step0: verify input args based on softmax logic
+  int64_t input_specs_size = input_specs.size();
+  int64_t output_specs_size = output_specs.size();
+  PADDLE_ENFORCE_EQ(
+      input_specs_size,
+      1,
+      phi::errors::InvalidArgument(
+          "The size of InputSpec of softmax should be 1, but got [%d].",
+          input_specs_size));
+  PADDLE_ENFORCE_EQ(
+      output_specs_size,
+      1,
+      phi::errors::InvalidArgument(
+          "The size of InputSpec of softmax should be 1, but got [%d].",
+          output_specs_size));
+  VerifySpecs(output_specs, "softmax_backward");
+
+  // step1: build Einsum Notation
+  std::vector<int64_t> x_shape = input_specs[0].shape();
+  int64_t x_ndim = input_specs[0].shape().size();
+
+  std::string alphabet = "abcdefghijklmnopqrstuvwxyz";
+  std::string x_axes = GetBroadcastAxes(x_ndim, x_ndim, alphabet);
+  std::string out_axes = x_axes;
+
+  int axis = ExtractAttr<int>("axis", attrs);
+  // normalize axis
+  if (axis < 0) {
+    axis = x_ndim + axis;
+  }
+
+  // sharding on softmax_axis is not supported now, so set
+  // the notation on softmax_axis to '1' so that we can set
+  // its dim mapping to -1
+  x_axes[axis] = '1';
+
+  // step2: Sharding Propogation
+  std::vector<int64_t> out_dims_mapping = output_specs[0].dims_mapping();
+  std::unordered_map<std::string, int64_t> axis_to_dim_map =
+      ShardingMergeForTensors({{out_axes, out_dims_mapping}});
+
+  // infer input's dims mapping.
+  std::vector<int64_t> x_dims_mapping =
+      GetDimsMappingForAxes(x_axes, axis_to_dim_map);
+  TensorDistAttr input_dist_attr(input_specs[0].dist_attr());
+  input_dist_attr.set_dims_mapping(x_dims_mapping);
+
+  // update output's dims mapping.
+  out_dims_mapping[axis] = -1;
+  TensorDistAttr output_dist_attr(output_specs[0].dist_attr());
+  output_dist_attr.set_dims_mapping(out_dims_mapping);
+
+  VLOG(4) << "SoftmaxSPMDRule InferBackward: "
+          << "softmax_axis: " << axis << std::endl
+          << "Einsum notation: [" << x_axes << " --> " << out_axes << "]. "
+          << std::endl
+          << "Output shape: [" << str_join(output_specs[0].shape())
+          << "], src_dims_mapping: ["
+          << str_join(output_specs[0].dims_mapping())
+          << "], dst_dims_mapping: [" << str_join(out_dims_mapping)
+          << "]; Input dims_mapping: [" << str_join(x_dims_mapping) << "]";
+
+  return {{input_dist_attr}, {output_dist_attr}};
 }
 
 }  // namespace auto_parallel

test/auto_parallel/spmd_rules/test_softmax_rule.py

@@ -33,6 +33,8 @@ def setUp(self):
         x_tensor_dist_attr.process_mesh = process_mesh
         self.x_dist_tensor_spec = DistTensorSpec(x_shape, x_tensor_dist_attr)
 
+        self.out_dist_tensor_spec = DistTensorSpec(self.x_dist_tensor_spec)
+
         self.attrs = {
             'axis': -1,
         }
@@ -99,6 +101,68 @@ def test_softmax_infer_forward(self):
         self.assertEqual(infered_input_dist_attrs[0].dims_mapping, [-1, -1, 0])
         self.assertEqual(infered_output_dist_attrs[0].dims_mapping, [-1, -1, 0])
 
+    def test_softmax_infer_backward(self):
+        # sharding on batch axis I
+        self.out_dist_tensor_spec.set_dims_mapping([1, -1, -1])
+        result_dist_attrs = self.rule1.infer_backward(
+            [self.x_dist_tensor_spec], [self.out_dist_tensor_spec], self.attrs
+        )
+        self.assertEqual(len(result_dist_attrs), 2)
+        infered_input_dist_attrs = result_dist_attrs[0]
+        infered_output_dist_attrs = result_dist_attrs[1]
+
+        self.assertEqual(len(infered_input_dist_attrs), 1)
+        self.assertEqual(len(infered_output_dist_attrs), 1)
+
+        self.assertEqual(infered_input_dist_attrs[0].dims_mapping, [1, -1, -1])
+        self.assertEqual(infered_output_dist_attrs[0].dims_mapping, [1, -1, -1])
+
+        # sharding on batch axis II
+        self.out_dist_tensor_spec.set_dims_mapping([-1, 1, -1])
+        result_dist_attrs = self.rule1.infer_backward(
+            [self.x_dist_tensor_spec], [self.out_dist_tensor_spec], self.attrs
+        )
+        infered_input_dist_attrs = result_dist_attrs[0]
+        infered_output_dist_attrs = result_dist_attrs[1]
+        self.assertEqual(infered_input_dist_attrs[0].dims_mapping, [-1, 1, -1])
+        self.assertEqual(infered_output_dist_attrs[0].dims_mapping, [-1, 1, -1])
+
+        # sharding on softmax_axis
+        self.out_dist_tensor_spec.set_dims_mapping([1, -1, 0])
+        result_dist_attrs = self.rule1.infer_backward(
+            [self.x_dist_tensor_spec], [self.out_dist_tensor_spec], self.attrs
+        )
+        infered_input_dist_attrs = result_dist_attrs[0]
+        infered_output_dist_attrs = result_dist_attrs[1]
+        self.assertEqual(infered_input_dist_attrs[0].dims_mapping, [1, -1, -1])
+        self.assertEqual(infered_output_dist_attrs[0].dims_mapping, [1, -1, -1])
+
+        # sharding on softmax_axis + axis = 1
+        self.attrs = {
+            'axis': 1,
+        }
+        self.out_dist_tensor_spec.set_dims_mapping([-1, 1, 0])
+        result_dist_attrs = self.rule1.infer_backward(
+            [self.x_dist_tensor_spec], [self.out_dist_tensor_spec], self.attrs
+        )
+        infered_input_dist_attrs = result_dist_attrs[0]
+        infered_output_dist_attrs = result_dist_attrs[1]
+        self.assertEqual(infered_input_dist_attrs[0].dims_mapping, [-1, -1, 0])
+        self.assertEqual(infered_output_dist_attrs[0].dims_mapping, [-1, -1, 0])
+
+        # sharding on softmax_axis + axis = -2
+        self.attrs = {
+            'axis': -2,
+        }
+        self.out_dist_tensor_spec.set_dims_mapping([-1, 1, 0])
+        result_dist_attrs = self.rule1.infer_backward(
+            [self.x_dist_tensor_spec], [self.out_dist_tensor_spec], self.attrs
+        )
+        infered_input_dist_attrs = result_dist_attrs[0]
+        infered_output_dist_attrs = result_dist_attrs[1]
+        self.assertEqual(infered_input_dist_attrs[0].dims_mapping, [-1, -1, 0])
+        self.assertEqual(infered_output_dist_attrs[0].dims_mapping, [-1, -1, 0])
+
 
 if __name__ == "__main__":
     unittest.main()