【PIR Dist Op Reg No.4 and No.26】 reg global_scatter and limit_by_capa…

…city (#62579) * feat(pir): reg global_scatter and limit_by_capacity * feat(pir): reg global_scatter and limit_by_capacity * feat(pir): reg global_scatter and limit_by_capacity * feat(pir): reg global_scatter and limit_by_capacity * feat(pir): reg global_scatter and limit_by_capacity * feat(pir): reg global_scatter and limit_by_capacity * feat(pir): reg global_scatter and limit_by_capacity
PaddlePaddle · Mar 20, 2024 · 4024e45 · 4024e45
1 parent 7def47f
commit 4024e45
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diff --git a/paddle/fluid/operators/limit_by_capacity_op.cc b/paddle/fluid/operators/limit_by_capacity_op.cc
@@ -71,7 +71,7 @@ class LimitByCapacityOpMaker : public framework::OpProtoAndCheckerMaker {
     AddInput("capacity", "(Tensor) The input capacity.");
     AddOutput("Out",
               "(Tensor) The output tensor expert count limit by capacity.");
-    AddAttr<int>("n_worker", "（int), The number of works.");
+    AddAttr<int>("n_worker", "(int), The number of works.");
     AddComment(
         R"DOC(limit_by_capacity Operator.limit expert count by capacity.)DOC");
   }

diff --git a/paddle/fluid/pir/dialect/op_generator/ops_api_gen.py b/paddle/fluid/pir/dialect/op_generator/ops_api_gen.py
@@ -189,6 +189,8 @@
     'partial_allgather_',
     'nop',
     'nop_',
+    'limit_by_capacity',
+    'global_scatter',
 ]
 
 

diff --git a/paddle/fluid/pir/dialect/operator/ir/ops.yaml b/paddle/fluid/pir/dialect/operator/ir/ops.yaml
@@ -840,6 +840,15 @@
   kernel:
     func: get_tensor_from_selected_rows {selected_rows -> dense}
 
+- op : global_scatter
+  args : (Tensor x, Tensor local_count, Tensor global_count, int ring_id=0, bool use_calc_stream=false)
+  output : Tensor(out)
+  infer_meta :
+    func : GlobalScatterInferMeta
+  kernel :
+    func : global_scatter
+    data_type : x
+
 - op : greater_equal
   args : (Tensor x, Tensor y)
   output : Tensor(out)
@@ -919,6 +928,15 @@
   inplace: (x -> out)
   interfaces : paddle::dialect::InferSymbolicShapeInterface
 
+- op : limit_by_capacity
+  args : (Tensor expert_count, Tensor capacity, int n_worker)
+  output : Tensor(out)
+  infer_meta :
+    func : LimitByCapacityInferMeta
+  kernel :
+    func : limit_by_capacity
+    data_type : expert_count
+
 - op : linspace
   args : (Tensor start, Tensor stop, Tensor number, DataType dtype, Place place)
   output : Tensor(out)

diff --git a/paddle/phi/api/yaml/op_compat.yaml b/paddle/phi/api/yaml/op_compat.yaml
@@ -1614,6 +1614,12 @@
   attrs :
     {pre_nms_top_n : pre_nms_topN, post_nms_top_n : post_nms_topN}
 
+- op : global_scatter
+  inputs :
+    {x : X}
+  outputs :
+    out : Out
+
 - op : grad_add
   inputs :
     {x : X, y : Y}
@@ -3769,6 +3775,10 @@
   outputs :
     {param_out: ParamOut, velocity_out: VelocityOut, master_param_out: MasterParamOut}
 
+- op: limit_by_capacity
+  outputs :
+    out : Out
+
 - op: lod_array_length
   inputs :
     {x: X}

diff --git a/paddle/phi/infermeta/binary.cc b/paddle/phi/infermeta/binary.cc
@@ -2167,6 +2167,15 @@ void KronInferMeta(const MetaTensor& x, const MetaTensor& y, MetaTensor* out) {
   out->set_dtype(x.dtype());
 }
 
+void LimitByCapacityInferMeta(const MetaTensor& expert_count,
+                              const MetaTensor& capacity,
+                              int n_worker,
+                              MetaTensor* out) {
+  out->share_dims(expert_count);
+  out->share_lod(expert_count);
+  out->set_dtype(expert_count.dtype());
+}
+
 void LogLossInferMeta(const MetaTensor& input,
                       const MetaTensor& label,
                       float epsilon,

diff --git a/paddle/phi/infermeta/binary.h b/paddle/phi/infermeta/binary.h
@@ -380,6 +380,11 @@ void IndexAddInferMeta(const MetaTensor& x,
 
 void KronInferMeta(const MetaTensor& x, const MetaTensor& y, MetaTensor* out);
 
+void LimitByCapacityInferMeta(const MetaTensor& expert_count,
+                              const MetaTensor& capacity,
+                              int n_worker,
+                              MetaTensor* out);
+
 void LogicalBinaryInferMeta(const MetaTensor& x,
                             const MetaTensor& y,
                             MetaTensor* out);

diff --git a/paddle/phi/infermeta/ternary.cc b/paddle/phi/infermeta/ternary.cc
@@ -461,6 +461,33 @@ void InstanceNormInferMeta(const MetaTensor& x,
   }
 }
 
+void GlobalScatterInferMeta(const MetaTensor& x,
+                            const MetaTensor& local_count,
+                            const MetaTensor& global_count,
+                            int ring_id,
+                            bool use_calc_stream,
+                            MetaTensor* out) {
+  PADDLE_ENFORCE_GE(
+      ring_id,
+      0,
+      phi::errors::InvalidArgument(
+          "The ring_id (%d) for global scatter op must be non-negative.",
+          ring_id));
+  auto input_dims = x.dims();
+  auto ndim_input = input_dims.size();
+  // dim check
+  PADDLE_ENFORCE_EQ(
+      ndim_input,
+      2,
+      phi::errors::InvalidArgument("The input tensor's dimension must be 2. "
+                                   "But received input's dimension = %d.",
+                                   ndim_input));
+
+  phi::DDim out_dims = common::make_ddim({-1, -1});
+  out->set_dims(out_dims);
+  out->set_dtype(x.dtype());
+}
+
 void GroupNormInferMeta(const MetaTensor& x,
                         const MetaTensor& scale,
                         const MetaTensor& bias,

diff --git a/paddle/phi/infermeta/ternary.h b/paddle/phi/infermeta/ternary.h
@@ -103,6 +103,13 @@ void InstanceNormInferMeta(const MetaTensor& x,
                            MetaTensor* saved_variance,
                            MetaConfig config = MetaConfig());
 
+void GlobalScatterInferMeta(const MetaTensor& x,
+                            const MetaTensor& local_count,
+                            const MetaTensor& global_count,
+                            int ring_id,
+                            bool use_calc_stream,
+                            MetaTensor* out);
+
 void GroupNormInferMeta(const MetaTensor& x,
                         const MetaTensor& scale,
                         const MetaTensor& bias,

diff --git a/test/ir/pir/translator/CMakeLists.txt b/test/ir/pir/translator/CMakeLists.txt
@@ -26,6 +26,8 @@ list(APPEND DISTRIBUTED_OP_TRANSLATOR_TEST test_partial_recv_translator)
 list(APPEND DISTRIBUTED_OP_TRANSLATOR_TEST
      test_prune_gate_by_capacity_translator)
 list(APPEND DISTRIBUTED_OP_TRANSLATOR_TEST test_random_routing_translator)
+list(APPEND DISTRIBUTED_OP_TRANSLATOR_TEST test_limit_by_capacity_translator)
+list(APPEND DISTRIBUTED_OP_TRANSLATOR_TEST test_global_scatter_translator)
 
 if(NOT WITH_DISTRIBUTE)
   list(REMOVE_ITEM TEST_INTERP_CASES ${DISTRIBUTED_OP_TRANSLATOR_TEST})

diff --git a/test/ir/pir/translator/test_global_scatter_translator.py b/test/ir/pir/translator/test_global_scatter_translator.py
@@ -0,0 +1,50 @@
+# Copyright (c) 2024 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 unittest
+
+import test_op_translator
+
+import paddle
+from paddle.base.layer_helper import LayerHelper
+
+
+class TestDistributedLookupTableOpTranslator(
+    test_op_translator.TestOpTranslator
+):
+    def append_op(self):
+        self.op_type = "global_scatter"
+        x = paddle.ones(shape=(4, 8), dtype='float32')
+        local_count = paddle.to_tensor([0, 1], dtype='int64')
+        global_count = paddle.to_tensor([0, 1], dtype='int64')
+        out = paddle.ones(shape=(2, 8), dtype='float32')
+        attrs = {'ring_id': 0, 'use_calc_stream': False}
+        helper = LayerHelper(self.op_type)
+        helper.append_op(
+            type=self.op_type,
+            inputs={
+                "X": x,
+                "local_count": local_count,
+                "global_count": global_count,
+            },
+            outputs={"Out": out},
+            attrs=attrs,
+        )
+
+    def test_translator(self):
+        self.check()
+
+
+if __name__ == "__main__":
+    unittest.main()
diff --git a/test/ir/pir/translator/test_limit_by_capacity_translator.py b/test/ir/pir/translator/test_limit_by_capacity_translator.py
@@ -0,0 +1,47 @@
+# Copyright (c) 2024 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 unittest
+
+import test_op_translator
+
+import paddle
+from paddle.base.layer_helper import LayerHelper
+
+
+class TestDistributedLookupTableOpTranslator(
+    test_op_translator.TestOpTranslator
+):
+    def append_op(self):
+        self.op_type = "limit_by_capacity"
+        expert_count = paddle.ones(shape=(8 * 8192,), dtype='int64')
+        capacity = paddle.ones(shape=(8,), dtype='int64')
+        out = paddle.ones(shape=(8,), dtype='int64')
+        attrs = {
+            'n_worker': 8192,
+        }
+        helper = LayerHelper(self.op_type)
+        helper.append_op(
+            type=self.op_type,
+            inputs={"expert_count": expert_count, "capacity": capacity},
+            outputs={"Out": out},
+            attrs=attrs,
+        )
+
+    def test_translator(self):
+        self.check()
+
+
+if __name__ == "__main__":
+    unittest.main()