add update func of auto search (#37867)

* add update func of auto search

* update unitest

python/paddle/distributed/auto_parallel/utils.py

@@ -1036,3 +1036,139 @@ def set_grad_var_shape(program, dist_context):
 
                 if list(grad_var.shape) != ref_shape:
                     grad_var.desc.set_shape(ref_shape)
+
+
+def update_op_dims_mapping_by_default_dist_impl(dist_op):
+    changed = False
+    op_dist_attr = dist_op.dist_attr
+    op_desc = dist_op.serial_op.desc
+    # The following statement will be replaced by a more elegent way
+    if op_desc.type() == "shape" or op_desc.type() == "slice":
+        return False
+    output_names = op_desc.output_names()
+    xshape_arg_names = []
+    if "XShape" in output_names:
+        xshape_arg_names = op_desc.output("XShape")
+    batch_dim_mappings = []
+    for arg_name in op_desc.input_arg_names():
+        serial_tensor = dist_op.get_serial_input(arg_name)
+        if serial_tensor.is_parameter:
+            continue
+        dims_mapping = op_dist_attr.get_input_dims_mapping(arg_name)
+        if len(dims_mapping) > 1:
+            for idx, mapping in enumerate(dims_mapping[1:]):
+                assert mapping == -1, \
+                    "{} only the batch dimension (0-dim) can be sharded, but the dimension {} is sharded by {} part."\
+                        .format(op_desc.type(), idx, mapping)
+        batch_dim_mappings.append(dims_mapping[0])
+    for arg_name in op_desc.output_arg_names():
+        serial_tensor = dist_op.get_serial_output(arg_name)
+        if serial_tensor.is_parameter:
+            continue
+        dims_mapping = op_dist_attr.get_output_dims_mapping(arg_name)
+        if arg_name not in xshape_arg_names:
+            if len(dims_mapping) > 1:
+                for idx, mapping in enumerate(dims_mapping[1:]):
+                    assert mapping == -1, \
+                        "{} only the batch dimension (0-dim) can be sharded, but the dimension {} is sharded by {} part."\
+                            .format(op_desc.type(), idx, mapping)
+            batch_dim_mappings.append(dims_mapping[0])
+        else:
+            assert dims_mapping[0] == -1, \
+                "{} only the batch dimension (1-dim) of XShape can be sharded, but the dimension 0 is sharded by {} part."\
+                    .format(op_desc.type(), mapping)
+            if len(dims_mapping) > 2:
+                for idx, mapping in enumerate(dims_mapping[2:]):
+                    assert mapping == -1, \
+                        "{} only the batch dimension (1-dim) of XShape can be sharded, but the dimension {} is sharded by {} part."\
+                            .format(op_desc.type(), idx, mapping)
+            batch_dim_mappings.append(dims_mapping[1])
+
+    compatible_dim_mapping = compute_compatible_dim_mapping(batch_dim_mappings)
+    assert compatible_dim_mapping is not None, "There is no compatible dim mapping."
+    for arg_name in op_desc.input_arg_names():
+        serial_tensor = dist_op.get_serial_input(arg_name)
+        if serial_tensor.is_parameter:
+            continue
+        dims_mapping = op_dist_attr.get_input_dims_mapping(arg_name)
+        if compatible_dim_mapping != dims_mapping[0]:
+            dims_mapping[0] = compatible_dim_mapping
+            changed = True
+    for arg_name in op_desc.output_arg_names():
+        serial_tensor = dist_op.get_serial_output(arg_name)
+        if serial_tensor.is_parameter:
+            continue
+        dims_mapping = op_dist_attr.get_output_dims_mapping(arg_name)
+        if arg_name not in xshape_arg_names:
+            if compatible_dim_mapping != dims_mapping[0]:
+                dims_mapping[0] = compatible_dim_mapping
+                changed = True
+        else:
+            if compatible_dim_mapping != dims_mapping[1]:
+                dims_mapping[1] = compatible_dim_mapping
+                changed = True
+
+    return changed
+
+
+def update_op_dims_mapping_by_elementwise_like_dist_impl(dist_op):
+    changed = False
+    op_dist_attr = dist_op.dist_attr
+    op_desc = dist_op.serial_op.desc
+    input_arg_names = op_desc.input_arg_names()
+    input_dims_mapping_dict = {}
+    input_dims_mapping_lens = {}
+    max_dims_mapping_len = -1
+    for arg_name in input_arg_names:
+        dims_mapping = op_dist_attr.get_input_dims_mapping(arg_name)
+        if max_dims_mapping_len < len(dims_mapping):
+            max_dims_mapping_len = len(dims_mapping)
+        input_dims_mapping_dict[arg_name] = dims_mapping
+        input_dims_mapping_lens[arg_name] = len(dims_mapping)
+
+    dims_mapping_list = []
+    for arg_name in input_arg_names:
+        if input_dims_mapping_lens[arg_name] < max_dims_mapping_len:
+            new_dims_mapping = [-1 for _ in range(max_dims_mapping_len)]
+            for i in range(input_dims_mapping_lens[arg_name]):
+                new_idx = (max_dims_mapping_len -
+                           input_dims_mapping_lens[arg_name]) + i
+                new_dims_mapping[new_idx] = input_dims_mapping_dict[arg_name][i]
+            dims_mapping_list.append(new_dims_mapping)
+        else:
+            dims_mapping_list.append(input_dims_mapping_dict[arg_name])
+    output_arg_names = op_desc.output_arg_names()
+    for arg_name in output_arg_names:
+        dims_mapping = op_dist_attr.get_output_dims_mapping(arg_name)
+        assert len(dims_mapping) == max_dims_mapping_len
+        dims_mapping_list.append(dims_mapping)
+
+    compatible_dims_mapping = compute_compatible_dims_mapping(dims_mapping_list)
+    assert compatible_dims_mapping is not None, "There is no compatible dim mapping."
+
+    for arg_name in input_arg_names:
+        if input_dims_mapping_lens[arg_name] < max_dims_mapping_len:
+            new_dims_mapping = [
+                -1 for _ in range(input_dims_mapping_lens[arg_name])
+            ]
+            for i in range(input_dims_mapping_lens[arg_name]):
+                new_idx = (max_dims_mapping_len -
+                           input_dims_mapping_lens[arg_name]) + i
+                new_dims_mapping[i] = compatible_dims_mapping[new_idx]
+            if new_dims_mapping != input_dims_mapping_dict[arg_name]:
+                op_dist_attr.set_input_dims_mapping(arg_name, new_dims_mapping)
+                changed = True
+        else:
+            if compatible_dims_mapping != input_dims_mapping_dict[arg_name]:
+                op_dist_attr.set_input_dims_mapping(arg_name,
+                                                    compatible_dims_mapping)
+                changed = True
+
+    for arg_name in output_arg_names:
+        dims_mapping = op_dist_attr.get_output_dims_mapping(arg_name)
+        if compatible_dims_mapping != dims_mapping:
+            op_dist_attr.set_output_dims_mapping(arg_name,
+                                                 compatible_dims_mapping)
+            changed = True
+
+    return changed

python/paddle/fluid/tests/unittests/CMakeLists.txt

@@ -92,6 +92,7 @@ list(APPEND MIXED_DIST_TEST_OPS test_fleet_auto)
 list(APPEND MIXED_DIST_TEST_OPS test_fleet_static_mp_layers)
 list(APPEND MIXED_DIST_TEST_OPS test_auto_parallel_partitioner)
 list(APPEND MIXED_DIST_TEST_OPS test_auto_parallel_partitioner_gpt)
+list(APPEND MIXED_DIST_TEST_OPS test_auto_parallel_searcher)
 list(APPEND MIXED_DIST_TEST_OPS test_auto_parallel_reshard)
 list(APPEND MIXED_DIST_TEST_OPS test_auto_parallel_reshard_serial)
 list(APPEND MIXED_DIST_TEST_OPS test_auto_parallel_reshard_mppp)
@@ -257,6 +258,7 @@ if ((NOT WITH_GPU) AND (NOT WITH_ROCM))
     LIST(REMOVE_ITEM TEST_OPS test_parallel_margin_cross_entropy)
     LIST(REMOVE_ITEM TEST_OPS test_auto_parallel_partitioner)
     LIST(REMOVE_ITEM TEST_OPS test_auto_parallel_partitioner_gpt)
+    LIST(REMOVE_ITEM TEST_OPS test_auto_parallel_searcher)
     LIST(REMOVE_ITEM TEST_OPS test_auto_parallel_reshard)
     LIST(REMOVE_ITEM TEST_OPS test_auto_parallel_reshard_serial)
     LIST(REMOVE_ITEM TEST_OPS test_auto_parallel_reshard_mppp)
@@ -643,6 +645,7 @@ if(WITH_DISTRIBUTE)
             py_test_modules(test_fleet_lamb_meta_optimizer MODULES test_fleet_lamb_meta_optimizer ENVS ${dist_ENVS})
             py_test_modules(test_auto_parallel_partitioner MODULES test_auto_parallel_partitioner ENVS ${dist_ENVS})
             py_test_modules(test_auto_parallel_partitioner_gpt MODULES test_auto_parallel_partitioner_gpt ENVS ${dist_ENVS})
+            py_test_modules(test_auto_parallel_searcher MODULES test_auto_parallel_searcher ENVS ${dist_ENVS})
             py_test_modules(test_auto_parallel_reshard MODULES test_auto_parallel_reshard ENVS ${dist_ENVS})
             py_test_modules(test_auto_parallel_reshard_serial MODULES test_auto_parallel_reshard_serial ENVS ${dist_ENVS})
             py_test_modules(test_auto_parallel_reshard_mppp MODULES test_auto_parallel_reshard_mppp ENVS ${dist_ENVS})

python/paddle/fluid/tests/unittests/test_auto_parallel_searcher.py

+# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from __future__ import print_function
+
+# import os
+# import copy
+# import json
+import unittest
+
+import paddle
+import paddle.nn as nn
+import paddle.static as static
+import paddle.nn.functional as F
+import paddle.utils as utils
+# from paddle.distributed import fleet
+import paddle.distributed.auto_parallel as auto
+# from paddle.distributed.auto_parallel.cluster import Cluster
+# from paddle.distributed.auto_parallel.utils import SerialProgramInfo
+# from paddle.distributed.auto_parallel.searcher import Checker, Enumerater
+from paddle.distributed.auto_parallel.dist_context import DistributedContext
+# from paddle.distributed.auto_parallel.utils import get_all_distributed_main_program
+from paddle.distributed.auto_parallel.dist_attribute import TensorDistributedAttribute
+from paddle.distributed.auto_parallel.dist_attribute import OperatorDistributedAttribute
+from paddle.distributed.auto_parallel.utils import update_op_dims_mapping_by_default_dist_impl
+from paddle.distributed.auto_parallel.utils import update_op_dims_mapping_by_elementwise_like_dist_impl
+
+paddle.enable_static()
+
+
+class MLPLayer(nn.Layer):
+    def __init__(self,
+                 hidden_size=1024,
+                 intermediate_size=4 * 1024,
+                 initializer_range=0.02):
+        super(MLPLayer, self).__init__()
+        d_model = hidden_size
+        dim_feedforward = intermediate_size
+        weight_attr = paddle.ParamAttr(initializer=nn.initializer.Normal(
+            mean=0.0, std=initializer_range))
+        bias_attr = None
+
+        self.linear0 = nn.Linear(
+            d_model, dim_feedforward, weight_attr, bias_attr=bias_attr)
+        self.linear1 = nn.Linear(
+            dim_feedforward, d_model, weight_attr, bias_attr=bias_attr)
+        self.norm = nn.LayerNorm(d_model, epsilon=1e-5)
+
+    def forward(self, input):
+        out = self.norm(input)
+        out = self.linear0(out)
+        out = F.gelu(out, approximate=True)
+        out = self.linear1(out)
+        out = paddle.unsqueeze(out, axis=0)
+        out = paddle.reshape(out, [4, 1024])
+        return out
+
+
+def mlp_forward(train_program, start_program):
+    with static.program_guard(train_program,
+                              start_program), utils.unique_name.guard():
+        batch_size = 4
+        hidden_size = 1024
+        sequence_len = 512
+        input = static.data(
+            name="input", shape=[batch_size, hidden_size], dtype='float32')
+        label = static.data(
+            name="label", shape=[batch_size, 1], dtype='float32')
+        loss_func = paddle.nn.CrossEntropyLoss(reduction="none")
+        mlp = MLPLayer(
+            hidden_size=hidden_size,
+            intermediate_size=4 * hidden_size,
+            initializer_range=0.02)
+
+        predict = mlp(input)
+        error_cost = loss_func(predict, label)
+        loss = paddle.mean(error_cost)
+
+    return loss, train_program, start_program
+
+
+def set_default_dist_attr(program, dist_context, process_mesh):
+    ops = program.global_block().ops
+    vars = program.global_block().vars
+    for op in ops:
+        op_dist_attr = OperatorDistributedAttribute()
+        op_dist_attr.process_mesh = process_mesh
+        for var_name in op.input_arg_names:
+            tensor_dist_attr = TensorDistributedAttribute()
+            tensor_dist_attr.process_mesh = process_mesh
+            tensor_dist_attr.dims_mapping = [-1 for i in vars[var_name].shape]
+            dist_context.set_tensor_dist_attr_for_program(vars[var_name],
+                                                          tensor_dist_attr)
+            op_dist_attr.set_input_dims_mapping(var_name,
+                                                tensor_dist_attr.dims_mapping)
+
+        for var_name in op.output_arg_names:
+            tensor_dist_attr = TensorDistributedAttribute()
+            tensor_dist_attr.process_mesh = process_mesh
+            tensor_dist_attr.dims_mapping = [-1 for i in vars[var_name].shape]
+            dist_context.set_tensor_dist_attr_for_program(vars[var_name],
+                                                          tensor_dist_attr)
+            op_dist_attr.set_output_dims_mapping(var_name,
+                                                 tensor_dist_attr.dims_mapping)
+        dist_context.set_op_dist_attr_for_program(op, op_dist_attr)
+
+    dist_context.add_process_mesh(process_mesh)
+
+
+class TestMLPSearcher(unittest.TestCase):
+    def test_update(self):
+        train_program = paddle.static.Program()
+        startup_program = paddle.static.Program()
+        _, train_program, startup_program = mlp_forward(train_program,
+                                                        startup_program)
+        global_process_mesh = auto.ProcessMesh(mesh=[0, 1])
+        dist_context = DistributedContext()
+        set_default_dist_attr(train_program, dist_context, global_process_mesh)
+        ops = train_program.global_block().ops
+        vars = train_program.global_block().vars
+        from paddle.distributed.auto_parallel.operators.common import get_distributed_operator_impl_container
+        from paddle.distributed.auto_parallel.completion import is_elementwise_like_op
+        from paddle.distributed.auto_parallel.dist_op import DistributedOperator
+
+        for op in ops:
+            dist_op_impl_container = get_distributed_operator_impl_container(
+                op.type)
+            if dist_op_impl_container is None:
+                op_dist_attr = dist_context.get_op_dist_attr_for_program(op)
+                dist_op = DistributedOperator(op, op_dist_attr)
+                if is_elementwise_like_op(op.type):
+                    changed = update_op_dims_mapping_by_elementwise_like_dist_impl(
+                        dist_op)
+                    self.assertFalse(changed)
+
+                    dist_op.dist_attr.set_output_dims_mapping(
+                        op.output_arg_names[0], [0] + [
+                            -1
+                            for i in range(
+                                1, len(vars[op.output_arg_names[0]].shape))
+                        ])
+                    try:
+                        changed = update_op_dims_mapping_by_elementwise_like_dist_impl(
+                            dist_op)
+                    except:
+                        continue
+                    self.assertTrue(changed)
+                else:
+                    changed = update_op_dims_mapping_by_default_dist_impl(
+                        dist_op)
+                    self.assertFalse(changed)
+
+                    dist_op.dist_attr.set_output_dims_mapping(
+                        op.output_arg_names[0], [0] + [
+                            -1
+                            for i in range(
+                                1, len(vars[op.output_arg_names[0]].shape))
+                        ])
+                    try:
+                        changed = update_op_dims_mapping_by_default_dist_impl(
+                            dist_op)
+                    except:
+                        continue
+                    self.assertTrue(changed)
+
+
+if __name__ == "__main__":
+    unittest.main()