[HybridParallel] Support segment for PipelineParallel (#34529)

* add layer segment

* add segement for transformer

* add utest

python/paddle/distributed/fleet/meta_parallel/parallel_layers/pp_layers.py

@@ -13,34 +13,14 @@
 # limitations under the License.
 import math
 import paddle
+import re
 from paddle.fluid.dygraph.layers import Layer
 from ...utils.log_util import logger, layer_to_str
 from functools import partial
 
 __all__ = []
 
 
-class SegmentLayers(object):
-    def __init__(self, layers_desc, num_parts, method="uniform"):
-        self._layers_desc = layers_desc
-        self.method = method
-        self.num_parts = num_parts
-        self.num_items = len(layers_desc)
-        assert self.num_items >= self.num_parts, "layer number should be greater than number of segments"
-
-    def do_segment(self):
-        if self.method == "uniform":
-            return self.uniform(self.num_items, self.num_parts)
-
-    def uniform(self, num_items, num_parts):
-        result = [0 for _ in range(num_parts + 1)]
-        part_size = math.floor(num_items / num_parts)
-        for i in range(num_parts):
-            result[i] = int(min(part_size * i, num_items))
-        result[num_parts] = num_items
-        return result
-
-
 class LayerDesc(object):
     def __init__(self, layer_func, *inputs, **kwargs):
         self.layer_func = layer_func
@@ -73,6 +53,75 @@ def __init__(self,
         self.shared_weight_attr = shared_weight_attr
 
 
+class SegmentLayers(object):
+    def __init__(self, layers_desc, num_parts, method="uniform"):
+        self._layers_desc = layers_desc
+        self.method = method
+        self.num_parts = num_parts
+        self.num_items = len(layers_desc)
+        assert self.num_items >= self.num_parts, "layer number should be greater than number of segments"
+
+    def do_segment(self):
+        if self.method == "uniform":
+            return self.uniform(self.num_items, self.num_parts)
+
+        elif self.method.startswith('layer:'):
+            # Divide equally according to the specified layer
+            layername = self.method.split(':')[1]
+            weights = [0] * len(self._layers_desc)
+            weight_idxs = self._gen_layer_weight(layername)
+            for idx in weight_idxs:
+                weights[idx] = 1
+
+            assert sum(
+                weights
+            ) % self.num_parts == 0, "number of layers ({}) should be divided by part number({})".format(
+                sum(weights), self.num_parts)
+            part_size = sum(weights) // self.num_parts
+            result = [0 for _ in range(self.num_parts + 1)]
+
+            memory_counter = 0
+            result_idx = 1
+            for idx, weight in enumerate(weights):
+                memory_counter += weight
+                if memory_counter == part_size:
+                    result[result_idx] = idx + 1
+                    result_idx += 1
+                    memory_counter = 0
+            result[self.num_parts] = len(weights)
+            return result
+
+    def _gen_layer_weight(self, layername):
+        weight_idxs = []
+        regex = re.compile(layername, re.IGNORECASE)
+        for idx, layer in enumerate(self._layers_desc):
+            name = None
+            if isinstance(layer, Layer):
+                name = layer.__class__.__name__
+            elif isinstance(layer, LayerDesc):
+                name = layer.layer_func.__name__
+            else:
+                try:
+                    name = layer.__name__
+                except AttributeError:
+                    # it is not error
+                    continue
+            if regex.search(name):
+                weight_idxs.append(idx)
+
+        assert len(
+            weight_idxs) > 0, "weight_idxs' length should be greater than 0"
+        return weight_idxs
+
+    def uniform(self, num_items, num_parts):
+        result = [0 for _ in range(num_parts + 1)]
+        part_size = math.floor(num_items / num_parts)
+        for i in range(num_parts):
+            result[i] = int(min(part_size * i, num_items))
+        result[num_parts] = num_items
+        return result
+
+
 class PipelineLayer(Layer):
     def __init__(self,
                  layers,
@@ -205,6 +254,9 @@ def _segment_network(self, seg_method):
             self._layers_desc, num_parts=self._num_stages, method=seg_method)
         self.segment_parts = seg.do_segment()
 
+        logger.info("segment result:" + ", ".join(
+            str(arg) for arg in self.segment_parts))
+
         self._start_pos = self.segment_parts[self._stage_id]
         self._end_pos = self.segment_parts[self._stage_id + 1]
 

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

@@ -121,13 +121,16 @@ def __init__(self, topology):
         self.descs = []
         self.descs.append(LayerDesc(EmbeddingPipe))
 
-        for x in range(5):
+        for x in range(6):
             self.descs.append(LayerDesc(TransformerNetPipe))
 
         self.descs.append(lambda x: x[0])
 
         super().__init__(
-            layers=self.descs, loss_fn=CriterionPipe(), topology=topology)
+            layers=self.descs,
+            loss_fn=CriterionPipe(),
+            topology=topology,
+            seg_method="layer:TransformerNetPipe")
 
 
 class TestDistPPTraning(unittest.TestCase):