expand maximum_grad minimum_grad dropout_grad op

mindspore/_extends/graph_kernel/expanders/__init__.py

@@ -25,3 +25,6 @@ from .fused_adam import expand_fusedadam
 from .fused_adam_weight_decay import expand_fusedadamweightdecay
 from .reduce_mean import expand_reducemean
 from .tanh_grad import expand_tanhgrad
+from .maximum_grad import expand_maximumgrad
+from .minimum_grad import expand_minimumgrad
+from .dropout_grad import expand_dropoutgrad

mindspore/_extends/graph_kernel/expanders/dropout_grad.py

@@ -0,0 +1,44 @@
+# Copyright 2020 Huawei Technologies Co., Ltd
+#
+# 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.
+# ===========================================================================
+"""generate json desc for DropoutGrad"""
+from mindspore._extends.graph_kernel.model import model_builder as builder
+
+
+def expand_dropoutgrad(expand_info):
+    """DropoutGrad expander"""
+    # get op info.
+    dy_desc = expand_info['input_desc'][0]
+    mask_desc = expand_info['input_desc'][1]
+    keep_prob = None
+    for attr in expand_info['attr']:
+        if 'keep_prob' in attr:
+            keep_prob = attr['keep_prob']
+    if keep_prob is None:
+        raise RuntimeError("keep_prob does not exist in attrs.")
+    # generate a graph.
+    graph_builder = builder.GraphBuilder()
+    with graph_builder.graph_scope('main') as graph_scope:
+        # create tensor input.
+        input_dy = graph_builder.tensor(dy_desc['shape'], dy_desc['data_type'], dy_desc['format'])
+        input_mask = graph_builder.tensor(mask_desc['shape'], mask_desc['data_type'], mask_desc['format'])
+        graph_scope.set_input(input_dy, input_mask)
+        r_keep_prob = graph_builder.value(input_dy.dtype, 1.0 / keep_prob, "DefaultFormat")
+        # create op.
+        result = graph_builder.emit('Mul', [input_dy, r_keep_prob])
+        result = graph_builder.emit('Mul', [result, input_mask])
+        # set graph output.
+        graph_scope.set_output(result)
+    graph = graph_builder.get()[0]
+    return graph

mindspore/_extends/graph_kernel/expanders/maximum_grad.py

@@ -0,0 +1,58 @@
+# Copyright 2020 Huawei Technologies Co., Ltd
+#
+# 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.
+# ===========================================================================
+"""generate json desc for maximum_grad"""
+from mindspore._extends.graph_kernel.model import model_builder as builder
+
+
+def expand_maximumgrad(expand_info):
+    """MaximumGrad expander"""
+    # get op info.
+    input_desc_0 = expand_info['input_desc'][0]
+    input_desc_1 = expand_info['input_desc'][1]
+    input_desc_2 = expand_info['input_desc'][2]
+    attrs = expand_info['attr']
+    grad_x = None
+    grad_y = None
+    for item in attrs:
+        if 'grad_x' in item:
+            grad_x = item['grad_x']
+        if 'grad_y' in item:
+            grad_y = item['grad_y']
+    graph_builder = builder.GraphBuilder()
+
+    # generate a graph.
+    with graph_builder.graph_scope('main') as graph_scope:
+        # create tensor input.
+        input_x = graph_builder.tensor(input_desc_0['shape'], input_desc_0['data_type'], input_desc_0['format'])
+        input_y = graph_builder.tensor(input_desc_1['shape'], input_desc_1['data_type'], input_desc_1['format'])
+        input_dout = graph_builder.tensor(input_desc_2['shape'], input_desc_2['data_type'], input_desc_2['format'])
+        graph_scope.set_input(input_x, input_y, input_dout)
+        x_dtype = input_x.dtype
+        # cal result
+        ge_result = graph_builder.emit('GreaterEqual', [input_x, input_y])
+        ge_result = graph_builder.emit('Cast', [ge_result], attrs={'dst_type': x_dtype})
+        dx = graph_builder.emit('Mul', [ge_result, input_dout])
+        dy = graph_builder.emit('Sub', [input_dout, dx])
+
+        # set graph output according to grad_x and grad_y
+        if grad_x and grad_y:
+            graph_scope.set_output(dx, dy)
+        if grad_x and not grad_y:
+            graph_scope.set_output(dx)
+        if grad_y and not grad_x:
+            graph_scope.set_output(dy)
+
+    graph = graph_builder.get()[0]
+    return graph

mindspore/_extends/graph_kernel/expanders/minimum_grad.py

@@ -0,0 +1,58 @@
+# Copyright 2020 Huawei Technologies Co., Ltd
+#
+# 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.
+# ===========================================================================
+"""generate json desc for minimum_grad"""
+from mindspore._extends.graph_kernel.model import model_builder as builder
+
+
+def expand_minimumgrad(expand_info):
+    """MinimumGrad expander"""
+    # get op info.
+    input_desc_0 = expand_info['input_desc'][0]
+    input_desc_1 = expand_info['input_desc'][1]
+    input_desc_2 = expand_info['input_desc'][2]
+    attrs = expand_info['attr']
+    grad_x = None
+    grad_y = None
+    for item in attrs:
+        if 'grad_x' in item:
+            grad_x = item['grad_x']
+        if 'grad_y' in item:
+            grad_y = item['grad_y']
+    graph_builder = builder.GraphBuilder()
+    # generate a graph.
+    with graph_builder.graph_scope('main') as graph_scope:
+        # create tensor input.
+        input_x = graph_builder.tensor(input_desc_0['shape'], input_desc_0['data_type'], input_desc_0['format'])
+        input_y = graph_builder.tensor(input_desc_1['shape'], input_desc_1['data_type'], input_desc_1['format'])
+        input_dout = graph_builder.tensor(input_desc_2['shape'], input_desc_2['data_type'], input_desc_2['format'])
+        graph_scope.set_input(input_x, input_y, input_dout)
+        x_dtype = input_x.dtype
+
+        # cal result
+        le_result = graph_builder.emit('LessEqual', [input_x, input_y])
+        le_result = graph_builder.emit('Cast', [le_result], attrs={'dst_type': x_dtype})
+        dx = graph_builder.emit('Mul', [le_result, input_dout])
+        dy = graph_builder.emit('Sub', [input_dout, dx])
+
+        # set graph output according to grad_x and grad_y
+        if grad_x and grad_y:
+            graph_scope.set_output(dx, dy)
+        if grad_x and not grad_y:
+            graph_scope.set_output(dx)
+        if grad_y and not grad_x:
+            graph_scope.set_output(dy)
+
+    graph = graph_builder.get()[0]
+    return graph

mindspore/ccsrc/backend/optimizer/graph_kernel/graph_kernel_helper.cc

@@ -702,9 +702,9 @@ FuncGraphPtr JsonDescToAnf(const std::string &json_desc, const std::vector<AnfNo
 
 std::unordered_set<PrimitivePtr> GetExpandOps() {
   std::unordered_set<PrimitivePtr> expand_ops = {
-    prim::kPrimSquare,    prim::kPrimBiasAdd,   prim::kPrimBiasAddGrad,          prim::kPrimGelu,
-    prim::kPrimGeluGrad,  prim::kPrimFusedAdam, prim::kPrimFusedAdamWeightDecay, prim::kPrimTanhGrad,
-    prim::kPrimReduceMean};
+    prim::kPrimSquare,     prim::kPrimBiasAdd,     prim::kPrimBiasAddGrad,          prim::kPrimGelu,
+    prim::kPrimGeluGrad,   prim::kPrimFusedAdam,   prim::kPrimFusedAdamWeightDecay, prim::kPrimTanhGrad,
+    prim::kPrimReduceMean, prim::kPrimMaximumGrad, prim::kPrimMinimumGrad};
   return expand_ops;
 }
 

mindspore/core/base/core_ops.h

@@ -161,6 +161,7 @@ inline const PrimitivePtr kPrimLayerNormXBackprop = std::make_shared<Primitive>(
 inline const PrimitivePtr kPrimLayerNormBetaGammaBackprop = std::make_shared<Primitive>("LayerNormBetaGammaBackprop");
 inline const PrimitivePtr kPrimDropoutGenMask = std::make_shared<Primitive>("DropoutGenMask");
 inline const PrimitivePtr kPrimDropoutDoMask = std::make_shared<Primitive>("DropoutDoMask");
+inline const PrimitivePtr kPrimDropoutGrad = std::make_shared<Primitive>("DropoutGrad");
 inline const PrimitivePtr kPrimOneHot = std::make_shared<Primitive>("OneHot");
 inline const PrimitivePtr kPrimGelu = std::make_shared<Primitive>("Gelu");
 inline const PrimitivePtr kPrimGeluGrad = std::make_shared<Primitive>("GeluGrad");

tests/st/ops/graph_kernel/test_maximum_grad.py

@@ -0,0 +1,48 @@
+# Copyright 2020 Huawei Technologies Co., Ltd
+#
+# 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 numpy as np
+import pytest
+import mindspore.context as context
+from mindspore import Tensor
+from mindspore.nn import Cell
+import mindspore.ops.operations._grad_ops as G
+
+context.set_context(mode=context.GRAPH_MODE, enable_graph_kernel=True, device_target="GPU")
+
+
+class MaxmumGradNet(Cell):
+    def __init__(self):
+        super(MaxmumGradNet, self).__init__()
+        self.maximum_grad = G.MaximumGrad()
+
+    def construct(self, x, y, dy):
+        return self.maximum_grad(x, y, dy)
+
+
+@pytest.mark.level0
+@pytest.mark.platform_x86_gpu_training
+@pytest.mark.env_onecard
+def test_maximum_grad():
+    np.random.seed(0)
+    input_x = np.random.normal(0, 1, [2, 3]).astype(np.float32)
+    input_y = np.random.normal(0, 1, [2, 3]).astype(np.float32)
+    input_dout = np.maximum(input_x, input_y).astype(np.float32)
+    net = MaxmumGradNet()
+    result = net(Tensor(input_x), Tensor(input_y), Tensor(input_dout))
+    dx = input_dout * (input_x >= input_y)
+    dy = input_dout - dx
+    assert np.allclose(result[0].asnumpy(), dx, rtol=1.e-4, atol=1.e-8, equal_nan=True)
+    assert np.allclose(result[1].asnumpy(), dy, rtol=1.e-4, atol=1.e-8, equal_nan=True)

tests/st/ops/graph_kernel/test_minimum_grad.py

@@ -0,0 +1,48 @@
+# Copyright 2020 Huawei Technologies Co., Ltd
+#
+# 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 numpy as np
+import pytest
+import mindspore.context as context
+from mindspore import Tensor
+from mindspore.nn import Cell
+import mindspore.ops.operations._grad_ops as G
+
+context.set_context(mode=context.GRAPH_MODE, enable_graph_kernel=True, device_target="GPU")
+
+
+class MinmumGradNet(Cell):
+    def __init__(self):
+        super(MinmumGradNet, self).__init__()
+        self.minimum_grad = G.MinimumGrad()
+
+    def construct(self, x, y, dy):
+        return self.minimum_grad(x, y, dy)
+
+
+@pytest.mark.level0
+@pytest.mark.platform_x86_gpu_training
+@pytest.mark.env_onecard
+def test_minimum_grad():
+    np.random.seed(0)
+    input_x = np.random.normal(0, 1, [2, 3]).astype(np.float32)
+    input_y = np.random.normal(0, 1, [2, 3]).astype(np.float32)
+    input_dout = np.minimum(input_x, input_y).astype(np.float32)
+    net = MinmumGradNet()
+    result = net(Tensor(input_x), Tensor(input_y), Tensor(input_dout))
+    dx = input_dout * (input_x <= input_y)
+    dy = input_dout - dx
+    assert np.allclose(result[0].asnumpy(), dx, rtol=1.e-4, atol=1.e-8, equal_nan=True)
+    assert np.allclose(result[1].asnumpy(), dy, rtol=1.e-4, atol=1.e-8, equal_nan=True)