Add auto parallel cost model and unittests (#36363)

* Add auto parallel cost model and unittests

* Fixed code styles.

* Fixed bugs and codes style

* fixed typo

* Improved code style: object encapsulation.

* Fixed codes.

* Refractored estimate_cost

* Fixed typo

python/paddle/distributed/auto_parallel/__init__.py

@@ -21,5 +21,6 @@ from .interface import ProcessMesh  # noqa: F401
 from .completion import complete_annotation  # noqa: F401
 from .completion import complete_backward_annotation  # noqa: F401
 from .reshard import reshard  # noqa: F401
+from .cost_model import estimate_cost
 
 __all__ = []

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

@@ -91,6 +91,7 @@ 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)
 list(APPEND MIXED_DIST_TEST_OPS test_auto_parallel_reshard_dpmppp)
+list(APPEND MIXED_DIST_TEST_OPS test_auto_parallel_cost_model)
 foreach(TEST_OP ${MIXED_DIST_TEST_OPS})
   list(REMOVE_ITEM TEST_OPS ${TEST_OP})
 endforeach()
@@ -234,6 +235,7 @@ if ((NOT WITH_GPU) AND (NOT WITH_ROCM))
     LIST(REMOVE_ITEM TEST_OPS test_auto_parallel_reshard_serial)
     LIST(REMOVE_ITEM TEST_OPS test_auto_parallel_reshard_mppp)
     LIST(REMOVE_ITEM TEST_OPS test_auto_parallel_reshard_dpmppp)
+    LIST(REMOVE_ITEM TEST_OPS test_auto_parallel_cost_model)
     LIST(REMOVE_ITEM TEST_OPS test_auto_parallel_data_unshard)
 elseif(WITH_GPU)
     if (${CUDNN_VERSION} VERSION_LESS 7100)
@@ -608,6 +610,7 @@ if(WITH_DISTRIBUTE)
             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})
             py_test_modules(test_auto_parallel_reshard_dpmppp MODULES test_auto_parallel_reshard_dpmppp ENVS ${dist_ENVS})
+            py_test_modules(test_auto_parallel_cost_model MODULES test_auto_parallel_cost_model ENVS ${dist_ENVS})
         endif(NOT WIN32)
     endif(NOT APPLE)
     if(WITH_DGC)

python/paddle/fluid/tests/unittests/test_auto_parallel_cost_model.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 unittest
+
+import paddle
+import paddle.nn as nn
+import paddle.static as static
+import paddle.nn.functional as F
+import paddle.utils as utils
+import paddle.distributed.auto_parallel as auto
+from paddle.distributed.auto_parallel.context import DistributedContext
+from paddle.distributed import fleet
+from paddle.distributed.auto_parallel.partitioner import Partitioner
+from paddle.distributed.auto_parallel.completion import complete_backward_annotation
+from paddle.distributed.auto_parallel.reshard import reshard
+from paddle.distributed.auto_parallel.cost_model import estimate_cost
+import paddle.fluid.core as core
+
+paddle.enable_static()
+_global_parallel_strategy = "dp_mp_pp"
+ROOT_MESH = auto.ProcessMesh([[[0, 1], [4, 5]], [[2, 3], [6, 7]]])
+_global_process_mesh = auto.ProcessMesh(
+    [[[0, 1], [4, 5]], [[2, 3], [6, 7]]], parent=ROOT_MESH)
+PP_MESH_0 = auto.ProcessMesh([[0, 1], [4, 5]], parent=ROOT_MESH)
+PP_MESH_1 = auto.ProcessMesh([[2, 3], [6, 7]], parent=ROOT_MESH)
+NUM_RANKS = 8
+STAGE_0_CNT = 5
+STAGE_1_CNT = 10
+pp_cfg = [[0, 1, 4, 5], [2, 3, 6, 7]]
+
+device = "gpu" if core.is_compiled_with_cuda() else "cpu"
+
+
+class MLPLayer(nn.Layer):
+    def __init__(self,
+                 hidden_size=256,
+                 intermediate_size=4 * 256,
+                 initializer_range=0.02,
+                 is_distributed=True):
+        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)
+
+        self.is_distributed = is_distributed
+
+    def forward(self, input):
+        if self.is_distributed:
+            auto.shard_tensor(
+                self.linear0.weight, PP_MESH_0, dim_mapping=[-1, 1])
+            auto.shard_tensor(
+                self.linear1.weight, PP_MESH_1, dim_mapping=[1, -1])
+
+        out = self.norm(input)
+        out = self.linear0(out)
+        out = F.gelu(out, approximate=True)
+        out = self.linear1(out)
+
+        return out
+
+
+def get_single_node_data():
+    train_program = paddle.static.Program()
+    startup_program = paddle.static.Program()
+
+    loss, train_program, startup_program = mlp_forward(
+        train_program, startup_program, is_distributed=False)
+
+    cost_model = core.CostModel()
+    cost_data = cost_model.profile_measure(train_program, startup_program,
+                                           device, ["time"])
+
+    op_name2cost = [{}, {}]
+    for idx, op in enumerate(train_program.blocks[0].ops):
+        if idx <= STAGE_0_CNT:
+            op_name2cost[0][op.type] = cost_data.get_op_time_ms(idx)
+        elif idx <= STAGE_1_CNT:
+            op_name2cost[1][op.type] = cost_data.get_op_time_ms(idx)
+    return op_name2cost
+
+
+def mlp_forward(train_program, start_program, is_distributed=True):
+    with static.program_guard(train_program,
+                              start_program), utils.unique_name.guard():
+        batch_size = 4
+        hidden_size = 256
+        sequence_len = 128
+        if is_distributed:
+            input = static.data(
+                name="input", shape=[batch_size, hidden_size], dtype='float32')
+            label = static.data(
+                name="label", shape=[batch_size, 1], dtype='float32')
+        else:
+            input = paddle.ones(
+                name="input", shape=[batch_size, hidden_size], dtype='float32')
+            label = paddle.ones(
+                name="label", shape=[batch_size, 1], dtype='float32')
+
+        if is_distributed:
+            auto.shard_tensor(input, PP_MESH_0, dim_mapping=[0, -1])
+            auto.shard_tensor(label, PP_MESH_1, dim_mapping=[0, -1])
+
+        mlp = MLPLayer(
+            hidden_size=hidden_size,
+            intermediate_size=4 * hidden_size,
+            initializer_range=0.02,
+            is_distributed=is_distributed)
+
+        predict = mlp(input)
+        error_cost = paddle.nn.functional.square_error_cost(predict, label)
+        loss = paddle.mean(error_cost)
+
+    return loss, train_program, start_program
+
+
+def get_dist_prog(train_program, startup_program, dist_context, rank_id):
+    global _global_process_mesh
+    dist_context.set_process_mesh(_global_process_mesh)
+    loss, train_program, startup_program = mlp_forward(train_program,
+                                                       startup_program)
+
+    # auto completion
+    complete_train_program = auto.complete_annotation(train_program,
+                                                      dist_context)
+
+    dist_strategy = fleet.DistributedStrategy()
+    dist_main_prog = []
+    dist_startup_prog = []
+    for rank_id in range(NUM_RANKS):
+        partitioner = Partitioner(dist_strategy, dist_context, rank_id)
+        # logical partition
+        auto_parallel_main_prog, auto_parallel_startup_prog = partitioner.transpile_forward(
+            complete_train_program, startup_program)
+        dist_params_grads = partitioner.apply_backward(
+            loss, complete_train_program, startup_program,
+            auto_parallel_main_prog, auto_parallel_startup_prog)
+        optimizer = paddle.fluid.optimizer.AdamOptimizer()
+        opt_ops = partitioner.apply_optimize(optimizer, dist_params_grads,
+                                             auto_parallel_main_prog,
+                                             auto_parallel_startup_prog)
+        dist_main_prog.append(auto_parallel_main_prog)
+        dist_startup_prog.append(auto_parallel_startup_prog)
+    return dist_main_prog, dist_startup_prog
+
+
+def check_runtime_estimation(cost):
+    return cost.runtime > 0
+
+
+def check_memory_estimation(cost):
+    for i in range(NUM_RANKS):
+        if cost.static_mem[i] <= 0 or cost.peak_mem[i] <= 0:
+            return False
+        if cost.static_mem[i] > cost.peak_mem[i]:
+            return False
+    return True
+
+
+def check_empty_program_runtime(cost):
+    return cost.runtime == 0
+
+
+def check_empty_program_memory(cost):
+    for mem in cost.peak_mem:
+        if mem > 0:
+            return False
+    for mem in cost.static_mem:
+        if mem > 0:
+            return False
+    return True
+
+
+class TestCostModel(unittest.TestCase):
+    def test_empty_program_cost_model(self):
+        empty_program = paddle.static.Program()
+        startup_program = paddle.static.Program()
+        standalone_cost_data = [{}]
+        empty_pp_cfg = None
+        cluster = None
+        cost = estimate_cost(
+            [empty_program],
+            cluster=cluster,
+            pipeline_config=empty_pp_cfg,
+            standalone_cost_data=standalone_cost_data,
+            batch_size=1)
+
+        self.assertTrue(check_empty_program_runtime(cost))
+        self.assertTrue(check_empty_program_memory(cost))
+
+    def test_auto_parallel_cost_model(self):
+        train_program = paddle.static.Program()
+        startup_program = paddle.static.Program()
+        dist_context = DistributedContext()
+        standalone_cost_data = get_single_node_data()
+        distributed_program, dist_startup_prog = get_dist_prog(
+            train_program, startup_program, dist_context, 0)
+        for rank_id in range(NUM_RANKS):
+            complete_backward_annotation(distributed_program[rank_id],
+                                         dist_context)
+            reshard(distributed_program[rank_id], dist_startup_prog[rank_id],
+                    rank_id, dist_context)
+        cluster = None
+        cost = estimate_cost(
+            distributed_program,
+            cluster=cluster,
+            pipeline_config=pp_cfg,
+            standalone_cost_data=standalone_cost_data,
+            batch_size=4)
+        self.assertTrue(check_runtime_estimation(cost))
+        self.assertTrue(check_memory_estimation(cost))
+
+
+if __name__ == "__main__":
+    unittest.main()