Adapt auto search (#37490)

* adapt auto search

* adapt auto search

* fix matmulv2 compatible

* del debug

paddle/fluid/framework/distributed_strategy.proto

@@ -273,6 +273,7 @@ message DistributedStrategy {
   optional bool fuse_grad_merge = 34 [ default = false ];
   optional bool semi_auto = 35 [ default = false ];
   optional bool adam_d2sum = 36 [ default = true ];
+  optional bool auto_search = 37 [ default = false ];
 
   optional RecomputeConfig recompute_configs = 101;
   optional AMPConfig amp_configs = 102;

python/paddle/distributed/auto_parallel/completion.py

@@ -715,6 +715,27 @@ def complete_backward_annotation(auto_parallel_main_prog, dist_context=None):
             grad_op_dist_attr.process_mesh = forward_op_process_mesh
 
             # var 
+            for input_name in grad_op.input_arg_names:
+                input_var = vars[input_name]
+                ref_dims_mapping = None
+                if "@GRAD" in input_name:
+                    forward_name = _get_forward_varname_from_grad_varname(
+                        input_name)
+                    ref_dims_mapping = forward_op_dist_attr.get_output_dims_mapping(
+                        forward_name)
+                else:
+                    if forward_op_dist_attr.get_input_dims_mapping(input_name):
+                        ref_dims_mapping = forward_op_dist_attr.get_input_dims_mapping(
+                            input_name)
+                    else:
+                        ref_dims_mapping = forward_op_dist_attr.get_output_dims_mapping(
+                            input_name)
+
+                assert ref_dims_mapping is not None, "[{}] 's dims mapping is NONE".format(
+                    input_var.name)
+                grad_op_dist_attr.set_input_dims_mapping(input_name,
+                                                         ref_dims_mapping)
+
             for output_name in grad_op.desc.output_names():
                 assert len(grad_op.desc.output(output_name)) in [0, 1]
                 if _is_grad_var_name(output_name):
@@ -726,41 +747,25 @@ def complete_backward_annotation(auto_parallel_main_prog, dist_context=None):
                     ]
                     input_name = "X"
                 assert input_name in forward_op.desc.input_names(
-                ), "var [{}] in op [{}]'s output but coulf not find [{}] in its forward op".format(
+                ), "var [{}] in op [{}]'s output but could not find [{}] in its forward op".format(
                     output_name, grad_op.type, input_name)
                 if len(grad_op.desc.output(output_name)) == 1:
-                    assert len(forward_op.desc.input(input_name)) == 1
-                    input_var = vars[forward_op.desc.input(input_name)[0]]
-                    input_var_dist_attr = dist_context.get_tensor_dist_attr_for_program(
-                        input_var)
-                    assert input_var_dist_attr is not None, "[{}] has not dist attribute".format(
-                        input_var.name)
-                    ref_dims_mapping = input_var_dist_attr.dims_mapping
-
                     # tensor dist attr
                     output_var = vars[grad_op.desc.output(output_name)[0]]
+                    forward_name = _get_forward_varname_from_grad_varname(
+                        output_var.name)
+                    ref_dims_mapping = forward_op_dist_attr.get_input_dims_mapping(
+                        forward_name)
+
                     output_var_dist_attr = TensorDistributedAttribute()
                     output_var_dist_attr.dims_mapping = ref_dims_mapping
                     output_var_dist_attr.process_mesh = forward_op_process_mesh
                     dist_context.set_tensor_dist_attr_for_program(
                         output_var, output_var_dist_attr)
 
-                    # op dist attr
                     grad_op_dist_attr.set_output_dims_mapping(output_var.name,
                                                               ref_dims_mapping)
 
-            for input_name in grad_op.input_arg_names:
-                input_var = vars[input_name]
-                input_var_dist_attr = dist_context.get_tensor_dist_attr_for_program(
-                    input_var)
-                assert input_var_dist_attr is not None, "[{}] has not dist attribute".format(
-                    input_var.name)
-                ref_dims_mapping = input_var_dist_attr.dims_mapping
-                assert ref_dims_mapping is not None, "[{}] 's dims mapping is NONE".format(
-                    input_var.name)
-                grad_op_dist_attr.set_input_dims_mapping(input_name,
-                                                         ref_dims_mapping)
-
             dist_context.set_op_dist_attr_for_program(grad_op,
                                                       grad_op_dist_attr)
 
@@ -828,13 +833,7 @@ def complete_update_annotation(auto_parallel_main_prog, dist_context):
 
                 param_dist_attr = dist_context.get_tensor_dist_attr_for_program(
                     param)
-                grad_dist_attr = dist_context.get_tensor_dist_attr_for_program(
-                    grad_var)
-
                 assert param_dist_attr is not None
-                assert grad_dist_attr is not None
-                assert param_dist_attr.dims_mapping == grad_dist_attr.dims_mapping
-
                 ref_process_mesh = dist_context.get_tensor_dist_attr_for_program(
                     param).process_mesh
                 assert ref_process_mesh is not None

python/paddle/distributed/auto_parallel/dist_context.py

@@ -335,6 +335,17 @@ class DistributedContext:
                         dist_op.serial_op.type, dist_tensor.dist_attr)
         return True
 
+    def __deepcopy__(self, memo):
+        cls = self.__class__
+        result = cls.__new__(cls)
+        memo[id(self)] = result
+        for k, v in self.__dict__.items():
+            if k == "_serial_program" or k == "_serial_graph":
+                setattr(result, k, v)
+            else:
+                setattr(result, k, copy.deepcopy(v, memo))
+        return result
+
 
 class DistributedOperatorContext:
     """
@@ -352,6 +363,17 @@ class DistributedOperatorContext:
         self.gradopidx2opidx = {}
         self.already_init_sync_vars = set()
 
+    def __deepcopy__(self, memo):
+        cls = self.__class__
+        result = cls.__new__(cls)
+        memo[id(self)] = result
+        for k, v in self.__dict__.items():
+            if k == "_dst_main_program" or k == "_dst_startup_program" or k == "_cur_src_op":
+                setattr(result, k, v)
+            else:
+                setattr(result, k, copy.deepcopy(v, memo))
+        return result
+
     def set_dst_main_program(self, prog):
         self._dst_main_program = prog
 

python/paddle/distributed/auto_parallel/dist_op.py

@@ -219,6 +219,17 @@ class DistributedOperator:
 
         return str
 
+    def __deepcopy__(self, memo):
+        cls = self.__class__
+        result = cls.__new__(cls)
+        memo[id(self)] = result
+        for k, v in self.__dict__.items():
+            if k == "_serial_op" or k == "_serial_inputs" or k == "_serial_outputs":
+                setattr(result, k, v)
+            else:
+                setattr(result, k, copy.deepcopy(v, memo))
+        return result
+
 
 class DistributedModule:
     def __init__(self, serial_module, dist_attr=None):

python/paddle/distributed/auto_parallel/dist_tensor.py

@@ -66,6 +66,17 @@ class DistributedTensor:
                 return False
         return True
 
+    def __deepcopy__(self, memo):
+        cls = self.__class__
+        result = cls.__new__(cls)
+        memo[id(self)] = result
+        for k, v in self.__dict__.items():
+            if k == "_serial_tensor":
+                setattr(result, k, v)
+            else:
+                setattr(result, k, copy.deepcopy(v, memo))
+        return result
+
     def __str__(self):
         str = "{{tensor name: {}, tensor id: {}".format(
             self.serial_tensor.desc.name(), self.serial_tensor.desc.id())

python/paddle/distributed/auto_parallel/operators/common.py

@@ -111,37 +111,27 @@ def find_best_compatible_distributed_operator_impl(name, dist_op, fwd=True):
     return best_compatible_impl, idx
 
 
-def copy_distributed_attr_for_var(dist_context, dst_var, src_var):
-    """
-    copy src var's dist_attr to dst var
-    """
-    dist_attr = dist_context.get_tensor_dist_attr_for_program(src_var)
-    dist_context.set_tensor_dist_attr_for_program(dst_var, dist_attr)
-
+def infer_shape(block, src_var, src_var_dist_attr, op_input_dist_attr):
+    var_shape = block.var(src_var.name).shape
+    var_topoloy = src_var_dist_attr.process_mesh.topology
+    var_dims_mapping = src_var_dist_attr.dims_mapping
+
+    complete_shape = []
+    for idx, shape in enumerate(var_shape):
+        if var_dims_mapping[idx] == -1:
+            complete_shape.append(shape)
+        else:
+            new_shape = shape * var_topoloy[var_dims_mapping[idx]]
+            complete_shape.append(new_shape)
+
+    exact_shape = []
+    input_topology = op_input_dist_attr.process_mesh.topology
+    input_dims_mapping = op_input_dist_attr.dims_mapping
+    for idx, shape in enumerate(complete_shape):
+        if input_dims_mapping[idx] == -1:
+            exact_shape.append(shape)
+        else:
+            new_shape = shape // input_topology[input_dims_mapping[idx]]
+            exact_shape.append(new_shape)
 
-def copy_distributed_attr_for_dist_op(dist_context, dist_op, dst_block,
-                                      src_op_dist_attr):
-    """
-    copy src op's dist_attr to dst dist op
-    """
-    from ..dist_attribute import OperatorDistributedAttribute
-    # need check dist op attr and its inputs and outputs
-
-    op_dist_attr = OperatorDistributedAttribute()
-    op_dist_attr.process_mesh = src_op_dist_attr.process_mesh
-    op_dist_attr.impl_idx = src_op_dist_attr.impl_idx
-
-    for input_varname in dist_op.desc.input_arg_names():
-        input_var = dst_block.var(input_varname)
-        tensor_dist_attr = dist_context.get_tensor_dist_attr_for_program(
-            input_var)
-        op_dist_attr.set_input_dist_attr(input_varname, tensor_dist_attr)
-
-    for output_varname in dist_op.desc.output_arg_names():
-        output_var = dst_block.var(output_varname)
-        tensor_dist_attr = dist_context.get_tensor_dist_attr_for_program(
-            output_var)
-        op_dist_attr.set_output_dist_attr(output_varname, tensor_dist_attr)
-
-    dist_context.set_op_dist_attr_for_program(dist_op, op_dist_attr)
-    op_dist_attr = dist_context.get_op_dist_attr_for_program(dist_op)
+    return exact_shape

python/paddle/distributed/auto_parallel/operators/dist_embedding.py

@@ -12,12 +12,11 @@
 # See the License for the specific language governing permissions and
 # limitations under the License
 
+from .common import infer_shape
 from .common import DistributedOperatorImplContainer
 from .common import DistributedOperatorImpl
 from .common import register_distributed_operator_impl_container
 from .common import register_distributed_operator_impl
-from .common import copy_distributed_attr_for_var
-from .common import copy_distributed_attr_for_dist_op
 from ..utils import is_dim_shard
 from ..utils import is_dim_replicate
 from ..utils import is_valid_list_index
@@ -172,6 +171,14 @@ class DistributedEmbeddingImpl(DistributedOperatorImpl):
         check_variable_and_dtype(Ids_var, 'input', ['int32', 'int64'],
                                  'c_embedding')
 
+        # infer new var shape with op dist attr
+        out_tensor_dist_attr = ctx.get_tensor_dist_attr_for_program(Out_var)
+        assert out_tensor_dist_attr is not None
+        out_var_dist_attr = op_dist_attr.get_output_dist_attr(Out_var.name)
+        assert out_var_dist_attr is not None
+        ref_shape = infer_shape(main_block, Out_var, out_tensor_dist_attr,
+                                out_var_dist_attr)
+
         intermediate_var_0 = main_block.create_var(
             name=unique_name.generate_with_ignorable_key(".".join(
                 ["c_embedding", 'tmp'])),
@@ -180,9 +187,9 @@ class DistributedEmbeddingImpl(DistributedOperatorImpl):
             type=core.VarDesc.VarType.LOD_TENSOR,
             persistable=False,
             stop_gradient=Out_var.stop_gradient)
-
-        # copy Out_var's dist_attr to intermediate_var_0's dist_attr
-        copy_distributed_attr_for_var(ctx, intermediate_var_0, Out_var)
+        # set intermediate_var_0's dist_attr with Out_var's dist_attr
+        ctx.set_tensor_dist_attr_for_program(intermediate_var_0,
+                                             out_var_dist_attr)
 
         check_variable_and_dtype(
             Out_var, 'tensor',
@@ -195,6 +202,8 @@ class DistributedEmbeddingImpl(DistributedOperatorImpl):
                     'W': [Weight_var]},
             outputs={'Out': [intermediate_var_0]},
             attrs={"start_index": relative_idx})
+        if intermediate_var_0.shape != ref_shape:
+            intermediate_var_0.desc.set_shape(ref_shape)
 
         # use_model_parallel
         c_allreduce_sum_op = main_block.append_op(
@@ -206,12 +215,46 @@ class DistributedEmbeddingImpl(DistributedOperatorImpl):
                 'use_calc_stream': True,
                 'use_model_parallel': True,
             })
-
-        # copy serial op's dist_attr to dist op's dist_attr
-        copy_distributed_attr_for_dist_op(ctx, c_embedding_op, main_block,
+        if Out_var.shape != ref_shape:
+            Out_var.desc.set_shape(ref_shape)
+
+        # set dist op's dist_attr with serial op's dist_attr
+        # matmulv2
+        embedding_op_dist_attr = OperatorDistributedAttribute()
+        embedding_op_dist_attr.process_mesh = op_dist_attr.process_mesh
+        embedding_op_dist_attr.impl_idx = op_dist_attr.impl_idx
+        for input_varname in c_embedding_op.desc.input_arg_names():
+            input_dist_attr = op_dist_attr.get_input_dist_attr(input_varname)
+            assert input_dist_attr is not None, "dist_attr is {}".format(
+                op_dist_attr)
+            embedding_op_dist_attr.set_input_dist_attr(input_varname,
+                                                       input_dist_attr)
+        output_varname = c_embedding_op.desc.output_arg_names()[0]
+        output_dist_attr = op_dist_attr.get_output_dist_attr(Out_var.name)
+        assert output_dist_attr is not None, "dist_attr is {}".format(
             op_dist_attr)
-        copy_distributed_attr_for_dist_op(ctx, c_allreduce_sum_op, main_block,
+        embedding_op_dist_attr.set_output_dist_attr(output_varname,
+                                                    output_dist_attr)
+        ctx.set_op_dist_attr_for_program(c_embedding_op, embedding_op_dist_attr)
+
+        # allreduce
+        allreduce_op_dist_attr = OperatorDistributedAttribute()
+        allreduce_op_dist_attr.process_mesh = op_dist_attr.process_mesh
+        allreduce_op_dist_attr.impl_idx = op_dist_attr.impl_idx
+        for input_varname in c_allreduce_sum_op.desc.input_arg_names():
+            input_var = main_block.var(input_varname)
+            tensor_dist_attr = ctx.get_tensor_dist_attr_for_program(input_var)
+            assert tensor_dist_attr is not None
+            allreduce_op_dist_attr.set_input_dist_attr(input_varname,
+                                                       tensor_dist_attr)
+        for output_varname in c_allreduce_sum_op.desc.output_arg_names():
+            output_dist_attr = op_dist_attr.get_output_dist_attr(output_varname)
+            assert output_dist_attr is not None, "dist_attr is {}".format(
                 op_dist_attr)
+            allreduce_op_dist_attr.set_output_dist_attr(output_varname,
+                                                        output_dist_attr)
+        ctx.set_op_dist_attr_for_program(c_allreduce_sum_op,
+                                         allreduce_op_dist_attr)
 
         # param initialization sync
         assert Weight_var.name not in dist_op_context.already_init_sync_vars

python/paddle/distributed/auto_parallel/parallelizer.py

@@ -12,7 +12,9 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
+import logging
 import paddle
+from paddle.distributed.utils import get_logger
 from paddle.distributed.fleet import cloud_utils
 import paddle.fluid.core as core
 from .dist_context import DistributedContext
@@ -22,7 +24,11 @@ from .completion import complete_annotation, complete_backward_annotation
 from .partitioner import Partitioner
 from .process_group import get_all_process_groups
 from .utils import make_data_unshard
+from .utils import set_grad_var_shape
 from .reshard import reshard
+# from .auto_search import auto_search
+
+_logger = get_logger(logging.INFO)
 
 
 class AutoParallelizer:
@@ -59,9 +65,19 @@ class AutoParallelizer:
         assert startup_program is not None
         main_program = loss.block.program
 
+        if self._dist_strategy.auto_search:
+            # auto search
+            _logger.info("Start search dist attr.")
+            # self._dist_context, _ = auto_search(main_program, startup_program,
+            #                                     loss, self._optimizer)
+            # completed_main_program = main_program
+            raise NotImplementedError("Auto search has not implemented")
+        else:
             # Annotation completion
+            _logger.info("Start annotation dist attr.")
             completed_main_program = complete_annotation(main_program,
                                                          self._dist_context)
+
         # Logical partition 
         rank = paddle.distributed.get_rank()
         partitioner = Partitioner(self._dist_strategy, self._dist_context, rank)
@@ -74,13 +90,8 @@ class AutoParallelizer:
             self._optimizer, dist_params_grads, partitioned_main_prog,
             partitioned_startup_prog)
 
-        # Traverse different rank programs and traverse each op of them,
-        # instantiate communication by process_mapping.
-        all_process_groups = get_all_process_groups()
-        for process_group in all_process_groups:
-            if rank not in process_group._ranks:
-                continue
-            process_group.instantiate()
+        # set the grad var shape
+        set_grad_var_shape(partitioned_main_prog, self._dist_context)
 
         # The last step: remove all distributed attributes to be compatiable
         # with inference.
@@ -91,6 +102,14 @@ class AutoParallelizer:
         reshard(partitioned_main_prog, partitioned_startup_prog, rank,
                 self._dist_context)
 
+        # Traverse different rank programs and traverse each op of them,
+        # instantiate communication by process_mapping.
+        all_process_groups = get_all_process_groups()
+        for process_group in all_process_groups:
+            if rank not in process_group._ranks:
+                continue
+            process_group.instantiate()
+
         # Copy distributed info to the default context
         set_default_distributed_context(self._dist_context)
 

python/paddle/distributed/auto_parallel/utils.py

@@ -981,3 +981,58 @@ def _get_split_indices(complete_shape, dims_mapping, process_shape,
             complete_shape))
     split_indices_list = [sorted(x) for x in split_indices_list]
     return split_indices_list
+
+
+def set_grad_var_shape(program, dist_context):
+    from .operators.common import infer_shape
+    from paddle.distributed.fleet.meta_optimizers.common import OpRole
+
+    block = program.global_block()
+    vars = block.vars
+    for op in block.ops:
+        if op.type == "sum":
+            continue
+        if int(op.attr('op_role')) == int(OpRole.Backward):
+            op_dist_attr = dist_context.get_op_dist_attr_for_program(op)
+            assert op_dist_attr is not None
+
+            for var_name in op.output_arg_names:
+                assert "@GRAD" in var_name
+                forward_var_name = var_name[:var_name.find("@GRAD")]
+                if op.type == "c_allreduce_sum" or op.type == "c_identity" or op.type == "scale":
+                    forward_var_name = op.input_arg_names[0]
+
+                need_set_shape_list = [
+                    "reshape2_grad", "softmax_with_cross_entropy_grad",
+                    "transpose2_grad", "softmax_grad", "cross_entropy_grad2",
+                    "dropout_grad"
+                ]
+                forward_list = [
+                    "reshape2", "softmax_with_cross_entropy", "transpose2",
+                    "softmax", "cross_entropy2", "dropout"
+                ]
+                if op.type in need_set_shape_list:
+                    for forward_op in block.ops:
+                        assert int(forward_op.attr('op_role')) != int(
+                            OpRole.Backward)
+                        idx = need_set_shape_list.index(op.type)
+                        forward_op_name = forward_list[idx]
+                        if forward_op.type == forward_op_name and forward_var_name in forward_op.input_arg_names:
+                            op_dist_attr = dist_context.get_op_dist_attr_for_program(
+                                forward_op)
+                            break
+
+                forward_input_dist_attr = op_dist_attr.get_input_dist_attr(
+                    forward_var_name)
+                assert forward_input_dist_attr is not None, f"{forward_var_name}"
+                forward_var = vars[forward_var_name]
+                forward_var_dist_attr = dist_context.get_tensor_dist_attr_for_program(
+                    forward_var)
+                assert forward_var_dist_attr is not None
+                grad_var = vars[var_name]
+                ref_shape = infer_shape(block, forward_var,
+                                        forward_var_dist_attr,
+                                        forward_input_dist_attr)
+
+                if list(grad_var.shape) != ref_shape:
+                    grad_var.desc.set_shape(ref_shape)

python/paddle/distributed/fleet/base/distributed_strategy.py

@@ -1631,6 +1631,29 @@ class DistributedStrategy(object):
         else:
             print("WARNING: semi-auto should have value of bool type")
 
+    @property
+    def auto_search(self):
+        """
+        Indicating whether we are using auto-search parallel function
+        For details, please reference the following code example
+        Default Value: False
+        Examples:
+          .. code-block:: python
+            import paddle
+            paddle.enable_static()
+            import paddle.distributed.fleet as fleet
+            strategy = fleet.DistributedStrategy()
+            strategy.auto_search = True
+        """
+        return self.strategy.auto_search
+
+    @auto_search.setter
+    def auto_search(self, flag):
+        if isinstance(flag, bool):
+            self.strategy.auto_search = flag
+        else:
+            print("WARNING: auto-search should have value of bool type")
+
     @property
     def cudnn_exhaustive_search(self):
         """