[Auto Parallel]Add pattern for auto search (#48316)

* add pattern for auto search

* add unittest

python/paddle/distributed/auto_parallel/graph.py

@@ -123,6 +123,8 @@ class Graph:
         else:
             self._nodes[node_id].attrs.update(attrs)
 
+        return self._nodes[node_id]
+
     def add_edge(self, src_id, tgt_id, **attrs):
         # add nodes
         if src_id is None:
@@ -140,6 +142,7 @@ class Graph:
         # add the edge
         edge = Edge(src_id, tgt_id, **attrs)
         self._adjs[src_id][tgt_id] = edge
+        return edge
 
     def __len__(self):
         return len(self._nodes)

python/paddle/distributed/auto_parallel/tuner/rule_based_tuner.py

@@ -12,6 +12,143 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
+from abc import ABC, abstractmethod
+
+from ..graph import Graph
+
+
+_PATTERNS = {}
+
+
+def register_pattern(cls):
+    """Register pattern for rule-based tuner."""
+    name = cls.name
+
+    def register(name):
+        global _PATTERNS
+        _PATTERNS[name] = cls()
+
+    register(name)
+
+    return cls
+
+
+def convert_to_graph(ops, block):
+    """Convert ops to graph."""
+    graph = Graph()
+    graph.attrs["var_to_id"] = {}  # {var_name: node_id}
+    graph.attrs["id_to_var"] = {}  # {node_id: var_name}
+    graph.attrs["op_to_id"] = {}  # {op_id: node_id}
+    graph.attrs["id_to_op"] = {}  # {node_id: op_id}
+
+    node_id = -1
+    for op in ops:
+        attrs = op.all_attrs()
+        attrs["type"] = op.type
+        node_id += 1
+
+        # create op node
+        op_node = graph.add_node(node_id, **attrs)
+        graph.attrs["op_to_id"][op.desc.id()] = op_node.id
+        graph.attrs["id_to_op"][op_node.id] = op.desc.id()
+        for input_name in op.input_names:
+            for var_name in op.input(input_name):
+                if var_name not in graph.attrs["var_to_id"]:
+                    # create var node
+                    node_id += 1
+                    var_node = graph.add_node(node_id)
+                    var = block._var_recursive(var_name)
+                    if var.is_parameter:
+                        var_node.attrs["type"] = "param"
+                    else:
+                        var_node.attrs["type"] = "var"
+                    graph.attrs["var_to_id"][var_name] = var_node.id
+                    graph.attrs["id_to_var"][var_node.id] = var_name
+                else:
+                    var_node_id = graph.attrs["var_to_id"][var_name]
+                    var_node = graph._nodes[var_node_id]
+
+                # create edge that input -> op
+                input_edge = graph.add_edge(var_node.id, op_node.id)
+                input_edge.attrs["input_name"] = input_name
+
+            for output_name in op.output_names:
+                for var_name in op.output(output_name):
+                    if var_name not in graph.attrs["var_to_id"]:
+                        # create var node
+                        node_id += 1
+                        var_node = graph.add_node(node_id)
+                        var = block._var_recursive(var_name)
+                        if var.is_parameter:
+                            var_node.attrs["type"] = "param"
+                        else:
+                            var_node.attrs["type"] = "var"
+                        graph.attrs["var_to_id"][var_name] = var_node.id
+                        graph.attrs["id_to_var"][var_node.id] = var_name
+                    else:
+                        var_node_id = graph.attrs["var_to_id"][var_name]
+                        var_node = graph._nodes[var_node_id]
+
+                    # create edge that op -> output
+                    output_edge = graph.add_edge(op_node.id, var_node.id)
+                    output_edge.attrs["output_name"] = output_name
+
+    return graph
+
+
+class BasePattern(ABC):
+    name = "base"
+
+    def __init__(self):
+        self.graph = None
+        self.build()
+
+    @abstractmethod
+    def build(self):
+        pass
+
+
+@register_pattern
+class QKVPattern(BasePattern):
+    name = "qkv"
+
+    def __init__(self):
+        super().__init__()
+
+    def build(self):
+        self.graph = Graph()
+
+        query = self.graph.add_node(0, **{"type": "var"})
+
+        q_weight = self.graph.add_node(1, **{"dim": 2, "type": "param"})
+        k_weight = self.graph.add_node(2, **{"dim": 2, "type": "param"})
+        v_weight = self.graph.add_node(3, **{"dim": 2, "type": "param"})
+
+        q_matmul = self.graph.add_node(4, **{"type": "matmul_v2"})
+        k_matmul = self.graph.add_node(5, **{"type": "matmul_v2"})
+        v_matmul = self.graph.add_node(6, **{"type": "matmul_v2"})
+
+        q_x = self.graph.add_edge(0, 4, **{"input_name": "X"})
+        k_x = self.graph.add_edge(0, 5, **{"input_name": "X"})
+        v_x = self.graph.add_edge(0, 6, **{"input_name": "X"})
+        q_y = self.graph.add_edge(1, 4, **{"input_name": "Y"})
+        k_y = self.graph.add_edge(2, 5, **{"input_name": "Y"})
+        v_y = self.graph.add_edge(3, 6, **{"input_name": "Y"})
+
+        q = self.graph.add_node(7, **{"type": "var"})
+        k = self.graph.add_node(8, **{"type": "var"})
+        v = self.graph.add_node(9, **{"type": "var"})
+
+        q_out = self.graph.add_edge(7, 4, **{"output_name": "Out"})
+        k_out = self.graph.add_edge(8, 5, **{"output_name": "Out"})
+        v_out = self.graph.add_edge(9, 6, **{"output_name": "Out"})
+
+        # Pattern
+        self.graph.attrs["shard_tensor"] = [
+            (1, 2, 3),
+            [[-1, 0], [-1, 1]],
+        ]  # 2-tuple such as (tensor_id, patterns)
+
 
 class OperatorGroupUtil:
     common_starts = ["layer_norm", "matmul_v2", "matmul"]

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

@@ -119,5 +119,5 @@ if(WITH_DISTRIBUTE AND WITH_GPU)
   py_test_modules(test_engine_api_error MODULES test_engine_api_error)
   py_test_modules(test_fp16_assign MODULES test_fp16_assign)
   py_test_modules(test_group_operators MODULES test_group_operators)
-
+  py_test_modules(test_pattern MODULES test_pattern)
 endif()

python/paddle/fluid/tests/unittests/auto_parallel/test_pattern.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.
+
+import sys
+import unittest
+import numpy as np
+
+import paddle
+import paddle.static as static
+
+sys.path.append("..")
+import auto_parallel_gpt_model as modeling
+from auto_parallel_gpt_model import (
+    GPTModel,
+    GPTForPretraining,
+    GPTPretrainingCriterion,
+)
+
+
+def get_gpt_model(
+    train_program, start_program, place, batch_size, sequence_len, vocab_size
+):
+    with static.program_guard(train_program, start_program):
+        tokens = paddle.static.data(
+            name="tokens", shape=[batch_size, sequence_len], dtype='int64'
+        )
+        position_ids = paddle.static.data(
+            name="position_ids", shape=[batch_size, sequence_len], dtype='int64'
+        )
+        attention_mask = paddle.static.data(
+            name="attention_mask",
+            shape=[batch_size, 1, sequence_len, sequence_len],
+            dtype='float32',
+        )
+        labels = paddle.static.data(
+            name="labels", shape=[batch_size, sequence_len], dtype='int64'
+        )
+        loss_mask = paddle.static.data(
+            name="loss_mask", shape=[batch_size, sequence_len], dtype='float32'
+        )
+
+        gpt = GPTModel(
+            vocab_size=1000,
+            hidden_size=64,
+            num_hidden_layers=2,
+            num_attention_heads=8,
+            intermediate_size=256,
+            hidden_act="gelu",
+            hidden_dropout_prob=0.0,
+            attention_probs_dropout_prob=0.0,
+            max_position_embeddings=1024,
+            type_vocab_size=1,
+            initializer_range=0.02,
+            pad_token_id=0,
+            eos_token_id=7,
+            bos_token_id=0,
+            eol_token_id=3,
+        )
+
+        model = GPTForPretraining(
+            gpt, vocab_size=1000, hidden_size=64, initializer_range=0.02
+        )
+        preds = model(tokens, position_ids, attention_mask)
+        criterion = GPTPretrainingCriterion()
+        loss = criterion(preds, labels, loss_mask)
+
+    def gen_data():
+        np.random.seed(2021)
+        tokens = []
+        position_ids = []
+        attention_mask = []
+        labels = []
+        loss_mask = []
+        for _ in range(batch_size):
+            tokens.append(np.random.randint(vocab_size, size=sequence_len))
+            position_ids.append(np.arange(sequence_len))
+            attention_mask.append([np.tril(np.ones(sequence_len))])
+            labels.append(np.random.randint(vocab_size, size=sequence_len))
+            loss_mask.append(np.ones(sequence_len))
+
+        return tokens, position_ids, attention_mask, labels, loss_mask
+
+    return train_program, start_program, loss, gen_data
+
+
+class TestGroupOperators(unittest.TestCase):
+    def test_gpt(self):
+        modeling.init_global()
+        train_program = static.Program()
+        start_program = static.Program()
+        place = paddle.set_device("gpu")
+        batch_size = 8
+        sequence_len = 512
+        vocab_size = 1000
+        train_program, start_program, loss, gen_data = get_gpt_model(
+            train_program,
+            start_program,
+            place,
+            batch_size,
+            sequence_len,
+            vocab_size,
+        )
+        from paddle.distributed.auto_parallel.tuner.rule_based_tuner import (
+            RuleBasedTuner,
+            convert_to_graph,
+            _PATTERNS,
+        )
+        from paddle.distributed.auto_parallel.dist_context import (
+            DistributedContext,
+        )
+
+        dist_context = DistributedContext()
+        tuner = RuleBasedTuner(dist_context)
+        layers = tuner.group_operators(train_program.global_block().ops)
+        layer = layers[0]
+        graph = convert_to_graph(layer, train_program.global_block())
+        print(graph)
+        print("qkv: ", _PATTERNS["qkv"].graph)
+
+
+if __name__ == "__main__":
+    unittest.main()