expand tanh_grad and reduce_mean, fix bug and add test_case in ci

mindspore/_extends/graph_kernel/expanders/__init__.py

@@ -23,3 +23,5 @@ from .bias_add import expand_biasadd
 from .bias_add_grad import expand_biasaddgrad
 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

mindspore/_extends/graph_kernel/expanders/reduce_mean.py

@@ -0,0 +1,65 @@
+# 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 reduce_mean"""
+from mindspore._extends.graph_kernel.model import model_builder as builder
+
+
+def expand_reducemean(expand_info):
+    """ReduceMean expander"""
+
+    # get op info.
+    input_desc = expand_info['input_desc'][0]
+    attrs = expand_info['attr']
+    axis = None
+    keep_dims = None
+    for item in attrs:
+        if 'axis' in item:
+            axis = item['axis']
+        if 'keep_dims' in item:
+            keep_dims = item['keep_dims']
+    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['shape'], input_desc['data_type'], input_desc['format'])
+        x_shape = input_x.shape
+        graph_scope.set_input(input_x)
+
+        # cal reduce_mean
+        # when axis = None, reduce axis are all
+        all_shape = 1.0
+        real_axis = []
+        if not axis:
+            for i, shape in enumerate(x_shape):
+                real_axis.append(i)
+                all_shape *= shape
+        else:
+            for idx in axis:
+                all_shape *= x_shape[idx]
+
+        all_shape_value = graph_builder.value(input_x.dtype, all_shape, input_x.data_format)
+
+        if not axis:
+            sum_x = graph_builder.emit('ReduceSum', [input_x], attrs={'reduce_axis': real_axis, 'keep_dims': keep_dims})
+        else:
+            sum_x = graph_builder.emit('ReduceSum', [input_x], attrs={'reduce_axis': axis, 'keep_dims': keep_dims})
+        result = graph_builder.emit('RealDiv', [sum_x, all_shape_value])
+
+        # set graph output.
+        graph_scope.set_output(result)
+
+    graph = graph_builder.get()[0]
+    return graph

mindspore/_extends/graph_kernel/expanders/tanh_grad.py

@@ -0,0 +1,47 @@
+# 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 tanh_grad"""
+from mindspore._extends.graph_kernel.model import model_builder as builder
+
+ONE = 1.0
+
+
+def expand_tanhgrad(expand_info):
+    """TanhGrad expander"""
+
+    # tanh_grad(y, dy) = dy * (1- y * y)
+    # get op info.
+    input_desc_0 = expand_info['input_desc'][0]
+    input_desc_1 = expand_info['input_desc'][1]
+    graph_builder = builder.GraphBuilder()
+
+    # generate a graph.
+    with graph_builder.graph_scope('main') as graph_scope:
+        # create tensor input.
+        input_y = graph_builder.tensor(input_desc_0['shape'], input_desc_0['data_type'], input_desc_0['format'])
+        input_dy = graph_builder.tensor(input_desc_1['shape'], input_desc_1['data_type'], input_desc_1['format'])
+        const_one = graph_builder.value(input_y.dtype, ONE, input_y.data_format)
+        graph_scope.set_input(input_y, input_dy)
+
+        # cal result
+        double_y = graph_builder.emit('Mul', [input_y, input_y])
+        one_sub_double_y = graph_builder.emit('Sub', [const_one, double_y])
+        result = graph_builder.emit('Mul', [input_dy, one_sub_double_y])
+
+        # set graph output.
+        graph_scope.set_output(result)
+
+    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::kPrimSquare,    prim::kPrimBiasAdd,   prim::kPrimBiasAddGrad,          prim::kPrimGelu,
+    prim::kPrimGeluGrad,  prim::kPrimFusedAdam, prim::kPrimFusedAdamWeightDecay, prim::kPrimTanhGrad,
+    prim::kPrimReduceMean};
   return expand_ops;
 }
 

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

@@ -29,12 +29,19 @@ bool BindValueToGraph::Run(const FuncGraphPtr &func_graph) {
   MS_EXCEPTION_IF_NULL(kernel_graph);
   auto &value_nodes = kernel_graph->graph_value_nodes();
   bool changed = false;
+  auto mng = func_graph->manager();
+  if (mng == nullptr) {
+    mng = Manage(func_graph, true);
+    func_graph->set_manager(mng);
+  }
   for (auto node : todos) {
     if (!GetValueNode<tensor::TensorPtr>(node)) {
       continue;
     }
     if (auto vptr = node->cast<ValueNodePtr>(); value_nodes.count(vptr) == 0) {
-      kernel_graph->AddValueNodeToGraph(vptr);
+      auto new_node = kernel_graph->NewValueNode(vptr);
+      mng->Replace(vptr, new_node);
+      kernel_graph->AddValueNodeToGraph(new_node);
       changed = true;
     }
   }

tests/st/ops/graph_kernel/test_fused_adam.py

@@ -0,0 +1,132 @@
+# 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
+import mindspore.nn as nn
+from mindspore import Tensor
+from mindspore.common.api import ms_function
+from mindspore.ops import operations as P
+from mindspore.ops import functional as F
+from mindspore.common import dtype as mstype
+from mindspore.common.parameter import Parameter
+
+context.set_context(mode=context.GRAPH_MODE, device_target="GPU", enable_graph_kernel=True)
+
+
+class Net(nn.Cell):
+    def __init__(self, decay_flag=True):
+        super(Net, self).__init__()
+        self.decay_flag = decay_flag
+        self.op_mul = P.Mul()
+        self.op_square = P.Square()
+        self.op_sqrt = P.Sqrt()
+        self.op_cast = P.Cast()
+        self.op_reshape = P.Reshape()
+        self.op_shape = P.Shape()
+        self.param = Parameter(Tensor(np.array([1, 3, 5]).astype(np.float32)), name='param')
+        self.m = Parameter(Tensor(np.array([0.11, 0.33, 0.55]).astype(np.float32)), name='m')
+        self.v = Parameter(Tensor(np.array([1.2, 3.4, 5.6]).astype(np.float32)), name='v')
+
+    @ms_function
+    def construct(self, beta1, beta2, one_sub_beta_1, one_sub_beta_2, gradient, eps, weight_decay_tensor, lr):
+        param_fp32 = self.op_cast(self.param, mstype.float32)
+        m_fp32 = self.op_cast(self.m, mstype.float32)
+        v_fp32 = self.op_cast(self.v, mstype.float32)
+        gradient_fp32 = self.op_cast(gradient, mstype.float32)
+
+        next_m = self.op_mul(beta1, m_fp32) + \
+            self.op_mul(self.op_cast(one_sub_beta_1, mstype.float32), gradient_fp32)
+        next_v = self.op_mul(beta2, v_fp32) + self.op_mul(self.op_cast(one_sub_beta_2,
+                                                                       mstype.float32), self.op_square(gradient_fp32))
+        update = next_m / (eps + self.op_sqrt(next_v))
+        if self.decay_flag:
+            update = self.op_mul(weight_decay_tensor, param_fp32) + update
+        update_with_lr = self.op_mul(lr, update)
+        next_param = param_fp32 - self.op_reshape(update_with_lr, self.op_shape(param_fp32))
+
+        depend_v = F.depend(next_param, F.assign(self.param, next_param))
+        depend_v = F.depend(depend_v, F.assign(self.m, next_m))
+        depend_v = F.depend(depend_v, F.assign(self.v, next_v))
+        return depend_v
+
+
+def CalFusedAdam(beta1, beta2, one_sub_beta_1, one_sub_beta_2, gradient, eps, weight_decay_tensor, lr, param, m, v,
+                 is_weight_decay=False):
+    m_expect = beta1 * m + one_sub_beta_1 * gradient
+    v_expect = beta2 * v + one_sub_beta_2 * gradient * gradient
+    update = m_expect / (np.sqrt(v_expect) + eps)
+    if is_weight_decay:
+        update += weight_decay_tensor * param
+    param_expect = param - lr * update
+    return param_expect, m_expect, v_expect
+
+
+@pytest.mark.level0
+@pytest.mark.platform_x86_gpu_training
+@pytest.mark.env_onecard
+def test_adam():
+    np.random.seed(0)
+    beta1 = np.array([0.9]).astype(np.float32)
+    beta2 = np.array([0.999]).astype(np.float32)
+    one_sub_beta_1 = (np.array([1.0]) - np.array([0.9])).astype(np.float32)
+    one_sub_beta_2 = (np.array([1.0]) - np.array([0.999])).astype(np.float32)
+    lr = np.array([0.012]).astype(np.float32)
+    eps = np.array([1e-6]).astype(np.float32)
+    weight_decay_tensor = np.array([0.021]).astype(np.float32)
+
+    gradient = np.array([0.01, 0.03, 0.05]).astype(np.float32)
+    m = np.array([0.11, 0.33, 0.55]).astype(np.float32)
+    v = np.array([1.2, 3.4, 5.6]).astype(np.float32)
+    param = np.array([1, 3, 5]).astype(np.float32)
+    is_weight_decay = False
+    opt = Net(is_weight_decay)
+    _ = opt(Tensor(beta1), Tensor(beta2), Tensor(one_sub_beta_1), Tensor(one_sub_beta_2), Tensor(gradient), Tensor(eps),
+            Tensor(weight_decay_tensor), Tensor(lr))
+    param_expect, m_expect, v_expect = CalFusedAdam(
+        beta1, beta2, one_sub_beta_1, one_sub_beta_2, gradient, eps, weight_decay_tensor, lr,
+        param, m, v, is_weight_decay)
+    assert np.allclose(opt.param.data.asnumpy(), param_expect, rtol=1.e-4, atol=1.e-8, equal_nan=True)
+    assert np.allclose(opt.m.data.asnumpy(), m_expect, rtol=1.e-4, atol=1.e-8, equal_nan=True)
+    assert np.allclose(opt.v.data.asnumpy(), v_expect, rtol=1.e-4, atol=1.e-8, equal_nan=True)
+
+
+def test_adam_weight_decay():
+    np.random.seed(0)
+    beta1 = np.array([0.9]).astype(np.float32)
+    beta2 = np.array([0.999]).astype(np.float32)
+    one_sub_beta_1 = (np.array([1.0]) - np.array([0.9])).astype(np.float32)
+    one_sub_beta_2 = (np.array([1.0]) - np.array([0.999])).astype(np.float32)
+    lr = np.array([0.012]).astype(np.float32)
+    eps = np.array([1e-6]).astype(np.float32)
+    weight_decay_tensor = np.array([0.021]).astype(np.float32)
+
+    gradient = np.array([0.01, 0.03, 0.05]).astype(np.float32)
+    m = np.array([0.11, 0.33, 0.55]).astype(np.float32)
+    v = np.array([1.2, 3.4, 5.6]).astype(np.float32)
+    param = np.array([1, 3, 5]).astype(np.float32)
+    is_weight_decay = True
+    opt = Net(is_weight_decay)
+    _ = opt(Tensor(beta1), Tensor(beta2), Tensor(one_sub_beta_1), Tensor(one_sub_beta_2), Tensor(gradient), Tensor(eps),
+            Tensor(weight_decay_tensor), Tensor(lr))
+    param_expect, m_expect, v_expect = CalFusedAdam(
+        beta1, beta2, one_sub_beta_1, one_sub_beta_2, gradient, eps, weight_decay_tensor, lr,
+        param, m, v, is_weight_decay)
+
+    assert np.allclose(opt.param.data.asnumpy(), param_expect, rtol=1.e-4, atol=1.e-8, equal_nan=True)
+    assert np.allclose(opt.m.data.asnumpy(), m_expect, rtol=1.e-4, atol=1.e-8, equal_nan=True)
+    assert np.allclose(opt.v.data.asnumpy(), v_expect, rtol=1.e-4, atol=1.e-8, equal_nan=True)

tests/st/ops/graph_kernel/test_reduce_mean.py

@@ -0,0 +1,45 @@
+# 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 as P
+
+context.set_context(mode=context.GRAPH_MODE, enable_graph_kernel=True, device_target="GPU")
+
+
+class Net(Cell):
+    def __init__(self):
+        super(Net, self).__init__()
+        self.reduce_mean = P.ReduceMean(keep_dims=False)
+
+    def construct(self, x):
+        return self.reduce_mean(x)
+
+
+@pytest.mark.level0
+@pytest.mark.platform_x86_gpu_training
+@pytest.mark.env_onecard
+def test_reduce_mean():
+    np.random.seed(0)
+    input_x = np.random.normal(0, 1, [2, 3, 4, 3]).astype(np.float32)
+    expect = np.mean(input_x, keepdims=False)
+    net = Net()
+    result = net(Tensor(input_x))
+    res = np.allclose(expect, result.asnumpy(), rtol=1.e-4, atol=1.e-7, equal_nan=True)
+    assert res

tests/st/ops/graph_kernel/test_tanh_grad.py

@@ -0,0 +1,46 @@
+# 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 TanhGradNet(Cell):
+    def __init__(self):
+        super(TanhGradNet, self).__init__()
+        self.tanh_grad = G.TanhGrad()
+
+    def construct(self, y, dy):
+        return self.tanh_grad(y, dy)
+
+
+@pytest.mark.level0
+@pytest.mark.platform_x86_gpu_training
+@pytest.mark.env_onecard
+def test_tanh_grad():
+    np.random.seed(0)
+    input_y = np.random.normal(0, 1, [2, 3, 4, 3]).astype(np.float32)
+    input_dy = np.random.normal(0, 1, [2, 3, 4, 3]).astype(np.float32)
+    net = TanhGradNet()
+    result = net(Tensor(input_y), Tensor(input_dy))
+    expect = input_dy * (1.0 - input_y * input_y)
+    res = np.allclose(expect, result.asnumpy(), rtol=1.e-4, atol=1.e-7, equal_nan=True)
+    assert res