PaddlePaddle
diff --git a/‎paddle/fluid/pybind/auto_parallel_py.cc
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+9Lines changed: 9 additions & 0 deletions b/‎paddle/fluid/pybind/auto_parallel_py.cc
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+9Lines changed: 9 additions & 0 deletions
diff --git a/‎paddle/phi/infermeta/spmd_rules/rules.h
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+4Lines changed: 4 additions & 0 deletions b/‎paddle/phi/infermeta/spmd_rules/rules.h
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+4Lines changed: 4 additions & 0 deletions
diff --git a/‎python/paddle/distributed/auto_parallel/static/completion.py
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+123-43Lines changed: 123 additions & 43 deletions b/‎python/paddle/distributed/auto_parallel/static/completion.py
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+123-43Lines changed: 123 additions & 43 deletions
diff --git a/‎python/paddle/distributed/auto_parallel/static/dist_op.py
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+3-1Lines changed: 3 additions & 1 deletion b/‎python/paddle/distributed/auto_parallel/static/dist_op.py
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+3-1Lines changed: 3 additions & 1 deletion
diff --git a/‎python/paddle/distributed/auto_parallel/static/operators/__init__.py
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+3Lines changed: 3 additions & 0 deletions b/‎python/paddle/distributed/auto_parallel/static/operators/__init__.py
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+3Lines changed: 3 additions & 0 deletions
@@ -543,6 +543,15 @@ void BindAutoParallel(py::module *m) {
           py::arg("memo"))
       .def("__str__", &OperatorDistAttr::to_string);
 
+  m->def(
+      "contains_spmd_rule",
+      [](const std::string op_type) {
+        return phi::distributed::SpmdRuleFactory::Instance().ContainsSpmdRule(
+                   op_type) ||
+               SPMDRuleMap::Instance().Has(op_type);  // TODO(ljz): unify here
+      },
+      py::return_value_policy::reference);
+
   m->def(
       "get_spmd_rule",
       [](const std::string op_type) {
 
@@ -333,6 +333,10 @@ PD_REGISTER_SPMD_RULE(
     trunc,
     PD_INFER_SPMD(phi::distributed::ElementwiseUnaryInferSpmd),
     PD_INFER_SPMD(phi::distributed::ElementwiseUnaryInferSpmdReverse));
+PD_REGISTER_SPMD_RULE(
+    dropout,
+    PD_INFER_SPMD(phi::distributed::ElementwiseUnaryInferSpmd),
+    PD_INFER_SPMD(phi::distributed::ElementwiseUnaryInferSpmdReverse));
 
 // elementwise binary rule
 PD_REGISTER_SPMD_RULE(
 
@@ -14,23 +14,41 @@
 
 import copy
 import logging
+import os
 
-from paddle.base.core import get_spmd_rule  # noqa: F401
+from paddle.base.core import (  # noqa: F401
+    contains_spmd_rule,
+    get_phi_spmd_rule,
+    get_spmd_rule,
+)
+from paddle.base.log_helper import get_logger
 from paddle.distributed.fleet.meta_optimizers.common import OpRole
 from paddle.framework import core
 
 from ..process_mesh import ProcessMesh, compute_compatible_process_mesh
 from .dist_attribute import OperatorDistAttr, TensorDistAttr
 from .dist_context import _node_id
-from .operators import find_compatible_distributed_operator_impls
+from .operators import (
+    find_compatible_distributed_operator_impls,
+    find_distributed_operator_impl_container,
+)
 from .process_group import get_world_process_group
 from .utils import (
     __no_shape_var_type__,
-    get_logger,
     is_gradient_clip_op,
     is_naive_data_parallel,
 )
 
+_logger = get_logger(
+    __name__, logging.INFO, fmt='%(asctime)s-%(levelname)s: %(message)s'
+)
+__skip_dims_mapping_op__ = [
+    "create_py_reader",
+    "create_double_buffer_reader",
+    "while",
+    "read",
+]
+
 
 def compute_compatible_dim_mapping(dim_mapping_list):
     """Compute the compatible dim mapping given a list of dim mapping."""
@@ -105,12 +123,61 @@ def _validate_dims_mapping(dims_mapping, process_mesh):
     return True
 
 
+def _can_apply_infer_spmd_rule(dist_op):
+    enable = os.getenv("FLAGS_infer_spmd_enable", True)
+    if isinstance(enable, str):
+        enable = enable.lower()
+        enable = True if enable == 'true' else False
+    enable = bool(enable)
+
+    # TODO remove me. ops to be adapted: lookup_table_v2, reshape2, split, transpose2,
+    __adapted_ops__ = [
+        "matmul_v2",
+        "elementwise_div",
+        "gelu",
+        "fused_softmax_mask_upper_triangle",
+        "elementwise_add",
+        "elementwise_mul",
+        "assign",
+        "scale",
+        "dropout",
+        "reduce_sum",
+        "layer_norm",
+    ]
+    op_type = dist_op.serial_op.type
+    return enable and contains_spmd_rule(op_type) and op_type in __adapted_ops__
+
+
+def _update_op_dims_mapping_and_distoperatorimpl(
+    dist_op, original_op_dist_attr, changed
+):
+    dist_op_container = find_distributed_operator_impl_container(dist_op)
+    _logger.debug(
+        "Update Op [{}] using DistOpContainer [{}].".format(
+            dist_op.serial_op.type, dist_op_container.type
+        )
+    )
+    updated = dist_op_container.update_dims_mapping(dist_op)
+    changed = updated or changed
+    # TODO(ljz) remove the below code once we introduce general reshard to replace specifc distopimpls
+    reverted = dist_op_container.mapping_to_dist_operator_impl(
+        dist_op, original_op_dist_attr
+    )
+    _logger.debug(
+        "Op [{}] use dist op impl [{}] idx [{}].".format(
+            dist_op.serial_op.type,
+            dist_op.dist_attr.impl_type,
+            dist_op.dist_attr.impl_idx,
+        )
+    )
+    return changed and not (reverted)
+
+
 class Completer:
     def __init__(self, dist_context):
         assert dist_context is not None
         self._dist_context = dist_context
         self._has_prepared = False
-        self._logger = get_logger(logging.INFO, "Completer")
 
     def _update_tensor_node_dims_mapping(self, tensor_node, fwd=True):
         changed = False
@@ -205,22 +272,20 @@ def _update_tensor_node_dims_mapping(self, tensor_node, fwd=True):
 
     def _update_op_node_dims_mapping(self, op_node, fwd=True):
         changed = False
+        op_desc = op_node.op()
+
+        # step0: skip corner cases
         if (not op_node.is_op()) or (op_node.op() is None):
             return False
         # Skip reader op
-        op_desc = op_node.op()
-        if (
-            op_desc.type() == "create_py_reader"
-            or op_desc.type() == "create_double_buffer_reader"
-            or op_desc.type() == "while"
-            or op_desc.type() == "read"
-        ):
+        if op_desc.type() in __skip_dims_mapping_op__:
             return False
+
         dist_op = self._dist_context.get_dist_op_for_graph(op_node)
         op_dist_attr = dist_op.dist_attr
         original_op_dist_attr = copy.deepcopy(op_dist_attr)
-        # step 1: merge the dims mappings of in
-        # dist_op with corresponding tensors
+
+        # step 1: merge the dims mappings from tensor nodes to op nodes
         if fwd:
             node_list = op_node.inputs
         else:
@@ -277,38 +342,53 @@ def _update_op_node_dims_mapping(self, op_node, fwd=True):
                         )
                     changed = True
 
-        # step 2: infer distributed attributes in dist_op
-        # Find the most compatible implementations from the distributed operator
-        op_dist_impls = find_compatible_distributed_operator_impls(
-            dist_op, fwd=True
-        )
-        if op_dist_impls is not None:
-            not_compatible = True
-            backup_op_dist_attr = copy.deepcopy(op_dist_attr)
-            backup_changed = changed
-            for op_dist_impl in op_dist_impls:
-                dim_changed = op_dist_impl.update_dims_mapping(dist_op)
-                if dim_changed:
-                    changed = True
-                if (
-                    op_dist_impl.is_auto_compatible(dist_op)
-                    and dist_op.validate_dist_attr()
-                ):
-                    op_dist_attr.impl_type = op_dist_impl.type
-                    op_dist_attr.impl_idx = op_dist_impl.idx
-                    not_compatible = False
-                    break
-                else:
-                    dist_op.dist_attr = backup_op_dist_attr
-                    changed = backup_changed
-            if not_compatible:
+        # step 2: Infer & Update dims mapping of op node using SPMD Rule.
+        if _can_apply_infer_spmd_rule(dist_op):
+            _logger.debug(
+                "Op [{}] update dims mapping using New InferSPMD Rule.".format(
+                    dist_op.serial_op.type
+                )
+            )
+            return _update_op_dims_mapping_and_distoperatorimpl(
+                dist_op, original_op_dist_attr, changed
+            )
+        else:
+            _logger.debug(
+                "Op [{}] update dims mapping using Original DistOp Rule.".format(
+                    dist_op.serial_op.type
+                )
+            )
+            # update_op_dims_mapping_v1()
+            op_dist_impls = find_compatible_distributed_operator_impls(
+                dist_op, fwd=True
+            )
+            if op_dist_impls is not None:
+                not_compatible = True
+                backup_op_dist_attr = copy.deepcopy(op_dist_attr)
+                backup_changed = changed
+                for op_dist_impl in op_dist_impls:
+                    dim_changed = op_dist_impl.update_dims_mapping(dist_op)
+                    if dim_changed:
+                        changed = True
+                    if (
+                        op_dist_impl.is_auto_compatible(dist_op)
+                        and dist_op.validate_dist_attr()
+                    ):
+                        op_dist_attr.impl_type = op_dist_impl.type
+                        op_dist_attr.impl_idx = op_dist_impl.idx
+                        not_compatible = False
+                        break
+                    else:
+                        dist_op.dist_attr = backup_op_dist_attr
+                        changed = backup_changed
+                if not_compatible:
+                    dist_op.dist_attr = original_op_dist_attr
+                    changed = False
+            else:
                 dist_op.dist_attr = original_op_dist_attr
                 changed = False
-        else:
-            dist_op.dist_attr = original_op_dist_attr
-            changed = False
 
-        return changed
+            return changed
 
     def _update_dims_mapping_between_graphs(self):
         changed = False
@@ -900,7 +980,7 @@ def complete_forward_annotation(self, serial_main_program=None):
             # Copy the corresponding distributed attribute from graph to serial_main_program
             self._dist_context.copy_dist_attr_from_graph_to_program()
         else:
-            self._logger.info("Default distributed attributed will be set.")
+            _logger.info("Default distributed attributed will be set.")
             self._dist_context.initialize(with_graph=False)
             # A fast and special completion for data parallel
             self._update_dist_attr_for_dp()
 
@@ -58,8 +58,10 @@ def dist_attr(self, dist_attr):
     def get_serial_input(self, name):
         if self._serial_op.type == "create_py_reader":
             tensor = None
-        else:
+        elif self._serial_op.block._find_var_recursive(name) is not None:
             tensor = self._serial_op.block._var_recursive(name)
+        else:
+            tensor = None
         return tensor
 
     def get_serial_output(self, name):
 
@@ -17,6 +17,8 @@
 from .common import register_distributed_operator_impl_container
 from .common import register_distributed_operator_impl
 from .common import find_compatible_distributed_operator_impls
+from .common import find_distributed_operator_impl_container
+
 from . import dist_embedding
 from . import dist_matmul
 from . import dist_reshape
@@ -39,3 +41,4 @@
 from . import dist_scale
 from . import dist_dropout
 from . import dist_flash_attn
+from . import dist_layer_norm