add group operators (#48208)

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

+# Copyright (c) 2022 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.
+
+
+class OperatorGroupUtil:
+    common_starts = ["layer_norm", "matmul_v2", "matmul"]
+
+    @staticmethod
+    def get_ranks(seq):
+        """Get rank array of the given seq by doubled algorithm."""
+        ordered_seq = sorted(list(set(seq)))
+        item_to_rank = {item: idx for idx, item in enumerate(ordered_seq)}
+        inter_ranks = [item_to_rank[item] for item in seq]
+
+        length = len(inter_ranks)
+        power = 0
+        interval = 2**power
+        while interval < length:
+            for idx, item in enumerate(inter_ranks):
+                if idx + interval >= length:
+                    inter_ranks[idx] = [item, -1]
+                else:
+                    inter_ranks[idx] = [item, inter_ranks[idx + interval]]
+
+            tmp = []
+            for item in inter_ranks:
+                if item not in tmp:
+                    tmp.append(item)
+            tmp.sort(key=lambda x: (x[0], x[1]))
+            item_to_rank = {}
+            for idx, val in enumerate(tmp):
+                key = ",".join(str(item) for item in val)
+                item_to_rank[key] = idx
+
+            inter_ranks = [
+                item_to_rank[",".join(str(val) for val in item)]
+                for item in inter_ranks
+            ]
+            power += 1
+            interval = 2**power
+
+        return inter_ranks
+
+    @staticmethod
+    def get_suffixes(ranks):
+        """Get suffix array by the given rank array."""
+        suffixes = [0 for idx in range(len(ranks))]
+        for idx, item in enumerate(ranks):
+            suffixes[item] = idx
+        return suffixes
+
+    @staticmethod
+    def get_heights(suffixes, seq):
+        """Get height array by the suffix array and seq"""
+        heights = [-1 for i in range(len(suffixes))]
+        for i in range(1, len(seq)):
+            x = seq[suffixes[i - 1] :]
+            y = seq[suffixes[i] :]
+            max_len = len(x) if len(x) > len(y) else len(y)
+            same_count = 0
+            for j in range(max_len):
+                if j >= len(x) or j >= len(y):
+                    break
+                else:
+                    if x[j] == y[j]:
+                        same_count += 1
+                    else:
+                        break
+            heights[i] = same_count
+
+        return heights
+
+    @staticmethod
+    def get_longest_repeated_sub_seq(suffixes, heights, seq):
+        """Get longest repeated sub sequence by suffix array algorithm."""
+        length = len(seq)
+        if length <= 1:
+            return None
+        k = length // 2
+        height_groups = []
+        longest_sub_seq = None
+        longest_sub_seqs = []
+
+        while k >= 2:
+            height_group = []
+            for i in range(1, len(heights)):
+                if heights[i] >= k:
+                    if i == 1:
+                        height_group.append(0)
+                    height_group.append(i)
+                else:
+                    if i == 1:
+                        height_groups.append([0])
+                        height_group = [i]
+                    else:
+                        height_groups.append(height_group)
+                        height_group = [i]
+
+            if height_group:
+                height_groups.append(height_group)
+
+            for height_group in height_groups:
+                suffix_group = []
+                index_group = []
+                for idx in height_group:
+                    suffix_group.append(idx)
+                    index_group.append(suffixes[idx])
+
+                max_index = max(index_group)
+                min_index = min(index_group)
+                if max_index - min_index >= k:
+                    longest_sub_seq = seq[min_index : min_index + k]
+                    if longest_sub_seq[0] in OperatorGroupUtil.common_starts:
+                        return longest_sub_seq
+            if longest_sub_seq is not None:
+                return longest_sub_seq
+
+            k -= 1
+            height_groups = []
+
+        return longest_sub_seq
+
+    @staticmethod
+    def get_decomposed_sub_seq(seq):
+        """Get decomposed sub seq s by seq S such as s * R = S."""
+        if not seq:
+            return seq
+
+        decomposed_sub_seq = seq
+        seq_len = len(seq)
+        if seq_len == 1:
+            return decomposed_sub_seq
+        else:
+            for interval in range(2, seq_len + 1):
+                if seq_len % interval == 0:
+                    repeated_times = seq_len // interval
+                    decomposed_sub_seq = seq[0:interval]
+                    decomposed = True
+                    for j in range(1, repeated_times + 1):
+                        sub_seq = seq[interval * (j - 1) : interval * j]
+                        if sub_seq != decomposed_sub_seq:
+                            decomposed = False
+                            break
+                    if decomposed:
+                        return decomposed_sub_seq
+
+        return decomposed_sub_seq
+
+    @staticmethod
+    def replace_by_decomposed_seq(sub_seq, seq):
+        """Replace seq by sub seq."""
+        if not sub_seq:
+            return seq
+
+        result = []
+        sub_seq_len = len(sub_seq)
+        i = 0
+        while i < len(seq):
+            if seq[i : i + sub_seq_len] == sub_seq:
+                result.append(seq[i : i + sub_seq_len])
+                i += sub_seq_len
+            else:
+                result.append(seq[i])
+                i += 1
+
+        return result
+
+    @staticmethod
+    def stop_replace(seq):
+        for item in seq:
+            if not isinstance(item, list):
+                return False
+        return True
+
+
+class RuleBasedTuner:
+    def __init__(self, dist_context, mode="train"):
+        self._dist_context = dist_context
+        self._mode = mode
+
+    def group_operators(self, ops):
+        """
+        Group operators to layers.
+
+        Args:
+            ops (list): A operator list.
+
+        Returns:
+            List: The list contains the list of operators which belong to the same layer.
+        """
+        seq = [op.type for op in ops]
+
+        while not OperatorGroupUtil.stop_replace(seq):
+            to_replace_seq = []
+            to_replace_idxes = []
+            has_append = False
+            for idx, item in enumerate(seq):
+                if not isinstance(item, list):
+                    has_append = True
+                    to_replace_seq.append(item)
+                    to_replace_idxes.append(idx)
+                elif isinstance(seq, list) and not has_append:
+                    continue
+                elif isinstance(seq, list) and has_append:
+                    break
+
+            ranks = OperatorGroupUtil.get_ranks(to_replace_seq)
+            suffixes = OperatorGroupUtil.get_suffixes(ranks)
+            heights = OperatorGroupUtil.get_heights(suffixes, to_replace_seq)
+            longest_sub_seq = OperatorGroupUtil.get_longest_repeated_sub_seq(
+                suffixes, heights, to_replace_seq
+            )
+            has_merged = False
+            if longest_sub_seq is None:
+                for i in range(to_replace_idxes[-1] + 1, len(seq)):
+                    if isinstance(seq[i], list):
+                        seq[i] = to_replace_seq + seq[i]
+                        has_merged = True
+                        break
+                if not has_merged:
+                    for i in range(to_replace_idxes[0] - 1, -1, -1):
+                        if isinstance(seq[i], list):
+                            seq[i].extend(to_replace_seq)
+                            has_merged = True
+                            break
+                if not has_merged:
+                    seq = [to_replace_seq]
+                    break
+
+            decomposed_sub_seq = OperatorGroupUtil.get_decomposed_sub_seq(
+                longest_sub_seq
+            )
+            to_replace_seq = OperatorGroupUtil.replace_by_decomposed_seq(
+                decomposed_sub_seq, to_replace_seq
+            )
+            result = seq[: to_replace_idxes[0]]
+            if not has_merged:
+                result.extend(to_replace_seq)
+            result.extend(seq[to_replace_idxes[-1] + 1 :])
+            seq = result
+
+        layers = []
+        idx = 0
+        for groups in seq:
+            layer = []
+            for op in groups:
+                layer.append(ops[idx])
+                idx += 1
+            layers.append(layer)
+
+        return layers

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

@@ -118,5 +118,6 @@ if(WITH_DISTRIBUTE AND WITH_GPU)
                   test_conditional_block_reshard)
   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)
 
 endif()

python/paddle/fluid/tests/unittests/auto_parallel/test_group_operators.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,
+        )
+        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)
+        op_types = []
+        for layer in layers:
+            tmp = []
+            for op in layer:
+                tmp.append(op.type)
+            op_types.append(tmp)
+
+
+if __name__ == "__main__":
+    unittest.main()