!258 add_minimum_distributed_op

Merge pull request !258 from lichen/add_minimum_distributed_op

mindspore/ccsrc/parallel/dynamic_creator.h

@@ -114,6 +114,7 @@ REGISTER(DropoutDoMaskInfo);
 REGISTER(ReshapeInfo);
 REGISTER(FloorDivInfo);
 REGISTER(MaximumInfo);
+REGISTER(MinimumInfo);
 REGISTER(CastInfo);
 REGISTER(GreaterInfo);
 REGISTER(SparseSoftmaxCrossEntropyWithLogitsInfo);

mindspore/ccsrc/parallel/ops_info/comparison_function_info.h

@@ -50,6 +50,14 @@ class MaximumInfo : public ArithmeticBase {
       : ArithmeticBase(name, inputs_shape, outputs_shape, attrs) {}
   ~MaximumInfo() override = default;
 };
+
+class MinimumInfo : public ArithmeticBase {
+ public:
+  MinimumInfo(const std::string& name, const Shapes& inputs_shape, const Shapes& outputs_shape,
+              const PrimitiveAttrs& attrs)
+      : ArithmeticBase(name, inputs_shape, outputs_shape, attrs) {}
+  ~MinimumInfo() override = default;
+};
 }  // namespace parallel
 }  // namespace mindspore
 

mindspore/ccsrc/parallel/ops_info/ops_utils.h

@@ -186,6 +186,7 @@ constexpr char LOG[] = "Log";
 constexpr char SIGMOID[] = "Sigmoid";
 constexpr char POW[] = "Pow";
 constexpr char MAXIMUM[] = "Maximum";
+constexpr char MINIMUM[] = "Minimum";
 constexpr char EQUAL[] = "Equal";
 constexpr char NOT_EQUAL[] = "NotEqual";
 constexpr char LOGICALNOT[] = "LogicalNot";

mindspore/ccsrc/parallel/step_auto_parallel.cc

@@ -93,6 +93,7 @@ std::vector<std::string> splittable_op_ = {MATMUL,
                                            SIGMOID,
                                            POW,
                                            MAXIMUM,
+                                           MINIMUM,
                                            EQUAL,
                                            NOT_EQUAL,
                                            LOGICALNOT,

tests/ut/python/parallel/test_comparison_function_info.py

@@ -54,11 +54,10 @@ def test_matmul_equal():
             out = self.equal(out, b)
             return out
 
-    context.set_auto_parallel_context(device_num=8, global_rank=0)
+    context.set_auto_parallel_context(device_num=8, global_rank=0, parallel_mode="semi_auto_parallel")
     strategy1 = ((2, 2), (2, 2))
     strategy2 = ((4, 2), (4, 2))
     net = GradWrap(NetWithLoss(Net(strategy1, strategy2)))
-    context.set_auto_parallel_context(parallel_mode="semi_auto_parallel")
 
     x = Tensor(np.ones([128, 32]), dtype=ms.float32)
     y = Tensor(np.ones([32, 64]), dtype=ms.float32)
@@ -78,11 +77,10 @@ def test_matmul_not_equal():
             out = self.notequal(out, b)
             return out
 
-    context.set_auto_parallel_context(device_num=8, global_rank=0)
+    context.set_auto_parallel_context(device_num=8, global_rank=0, parallel_mode="semi_auto_parallel")
     strategy1 = ((2, 2), (2, 2))
     strategy2 = ((4, 2), (4, 2))
     net = GradWrap(NetWithLoss(Net(strategy1, strategy2)))
-    context.set_auto_parallel_context(parallel_mode="semi_auto_parallel")
 
     x = Tensor(np.ones([128, 32]), dtype=ms.float32)
     y = Tensor(np.ones([32, 64]), dtype=ms.float32)
@@ -102,11 +100,10 @@ def test_matmul_not_equal_repeated_calculation():
             out = self.notequal(out, b)
             return out
 
-    context.set_auto_parallel_context(device_num=8, global_rank=0)
+    context.set_auto_parallel_context(device_num=8, global_rank=0, parallel_mode="semi_auto_parallel")
     strategy1 = ((2, 2), (2, 2))
     strategy2 = ((4, 1), (4, 1))
     net = GradWrap(NetWithLoss(Net(strategy1, strategy2)))
-    context.set_auto_parallel_context(parallel_mode="semi_auto_parallel")
 
     x = Tensor(np.ones([128, 32]), dtype=ms.float32)
     y = Tensor(np.ones([32, 64]), dtype=ms.float32)
@@ -126,11 +123,10 @@ def test_matmul_maximum():
             out = self.maximum(out, b)
             return out
 
-    context.set_auto_parallel_context(device_num=8, global_rank=0)
+    context.set_auto_parallel_context(device_num=8, global_rank=0, parallel_mode="semi_auto_parallel")
     strategy1 = ((2, 2), (2, 2))
     strategy2 = ((4, 2), (4, 2))
     net = GradWrap(NetWithLoss(Net(strategy1, strategy2)))
-    context.set_auto_parallel_context(parallel_mode="semi_auto_parallel")
 
     x = Tensor(np.ones([64, 32]), dtype=ms.float32)
     y = Tensor(np.ones([32, 64]), dtype=ms.float32)
@@ -150,11 +146,10 @@ def test_matmul_maximum_broadcast():
             out = self.maximum(out, b)
             return out
 
-    context.set_auto_parallel_context(device_num=8, global_rank=0)
+    context.set_auto_parallel_context(device_num=8, global_rank=0, parallel_mode="semi_auto_parallel")
     strategy1 = ((2, 2), (2, 2))
     strategy2 = ((4, 2), (2, ))
     net = GradWrap(NetWithLoss(Net(strategy1, strategy2)))
-    context.set_auto_parallel_context(parallel_mode="semi_auto_parallel")
 
     x = Tensor(np.ones([64, 32]), dtype=ms.float32)
     y = Tensor(np.ones([32, 64]), dtype=ms.float32)
@@ -174,13 +169,102 @@ def test_matmul_maximum_broadcast2():
             out = self.maximum(out, b)
             return out
 
-    context.set_auto_parallel_context(device_num=8, global_rank=0)
+    context.set_auto_parallel_context(device_num=8, global_rank=0, parallel_mode="semi_auto_parallel")
     strategy1 = ((2, 4), (4, 1))
     strategy2 = ((4, 1), (1, 2))
     net = GradWrap(NetWithLoss(Net(strategy1, strategy2)))
-    context.set_auto_parallel_context(parallel_mode="semi_auto_parallel")
 
     x = Tensor(np.ones([64, 32]), dtype=ms.float32)
     y = Tensor(np.ones([32, 1]), dtype=ms.float32)
     b = Tensor(np.ones([1, 64]), dtype=ms.float32)
-    _executor.compile(net, x, y, b)
+    _executor.compile(net, x, y, b)
+
+
+def test_matmul_minimum():
+    class Net(nn.Cell):
+        def __init__(self, strategy1, strategy2):
+            super().__init__()
+            self.matmul = P.MatMul().set_strategy(strategy1)
+            self.minimum = P.Minimum().set_strategy(strategy2)
+
+        def construct(self, x, y, b):
+            out = self.matmul(x, y)
+            out = self.minimum(out, b)
+            return out
+
+    context.set_auto_parallel_context(device_num=8, global_rank=0, parallel_mode="semi_auto_parallel")
+    strategy1 = ((2, 2), (2, 2))
+    strategy2 = ((4, 2), (4, 2))
+    net = GradWrap(NetWithLoss(Net(strategy1, strategy2)))
+
+    x = Tensor(np.ones([64, 32]), dtype=ms.float32)
+    y = Tensor(np.ones([32, 64]), dtype=ms.float32)
+    b = Tensor(np.ones([64, 64]), dtype=ms.float32)
+    _executor.compile(net, x, y, b)
+
+
+def test_matmul_minimum_broadcast():
+    class Net(nn.Cell):
+        def __init__(self, strategy1, strategy2):
+            super().__init__()
+            self.matmul = P.MatMul().set_strategy(strategy1)
+            self.minimum = P.Maximum().set_strategy(strategy2)
+
+        def construct(self, x, y, b):
+            out = self.matmul(x, y)
+            out = self.minimum(out, b)
+            return out
+
+    context.set_auto_parallel_context(device_num=8, global_rank=0, parallel_mode="semi_auto_parallel")
+    strategy1 = ((2, 2), (2, 2))
+    strategy2 = ((4, 2), (2, ))
+    net = GradWrap(NetWithLoss(Net(strategy1, strategy2)))
+
+    x = Tensor(np.ones([64, 32]), dtype=ms.float32)
+    y = Tensor(np.ones([32, 64]), dtype=ms.float32)
+    b = Tensor(np.ones([64]), dtype=ms.float32)
+    _executor.compile(net, x, y, b)
+
+
+def test_matmul_minimum_broadcast2():
+    class Net(nn.Cell):
+        def __init__(self, strategy1, strategy2):
+            super().__init__()
+            self.matmul = P.MatMul().set_strategy(strategy1)
+            self.minimum = P.Minimum().set_strategy(strategy2)
+
+        def construct(self, x, y, b):
+            out = self.matmul(x, y)
+            out = self.minimum(out, b)
+            return out
+
+    context.set_auto_parallel_context(device_num=8, global_rank=0, parallel_mode="semi_auto_parallel")
+    strategy1 = ((2, 4), (4, 1))
+    strategy2 = ((4, 1), (1, 2))
+    net = GradWrap(NetWithLoss(Net(strategy1, strategy2)))
+
+    x = Tensor(np.ones([64, 32]), dtype=ms.float32)
+    y = Tensor(np.ones([32, 1]), dtype=ms.float32)
+    b = Tensor(np.ones([1, 64]), dtype=ms.float32)
+    _executor.compile(net, x, y, b)
+
+
+def test_matmul_minimum_auto_parallel():
+    class Net(nn.Cell):
+        def __init__(self):
+            super().__init__()
+            self.matmul = P.MatMul()
+            self.minimum = P.Minimum()
+
+        def construct(self, x, y, b):
+            out = self.matmul(x, y)
+            out = self.minimum(out, b)
+            return out
+
+    context.set_auto_parallel_context(device_num=8, global_rank=0, parallel_mode="auto_parallel")
+    net = GradWrap(NetWithLoss(Net()))
+
+    x = Tensor(np.ones([64, 32]), dtype=ms.float32)
+    y = Tensor(np.ones([32, 1]), dtype=ms.float32)
+    b = Tensor(np.ones([1, 64]), dtype=ms.float32)
+    _executor.compile(net, x, y, b)