!13476 add export bgcf mindir test

From: @changzherui
Reviewed-by: @zhunaipan,@kingxian
Signed-off-by: @kingxian

tests/st/export_and_load/test_bgcf.py

@@ -0,0 +1,218 @@
+# Copyright 2021 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.
+# ============================================================================
+"""Architecture"""
+import os
+import numpy as np
+import pytest
+
+import mindspore.nn as nn
+from mindspore import Parameter, Tensor
+from mindspore.ops import operations as P
+from mindspore.common import dtype as mstype
+from mindspore.common.initializer import initializer
+from mindspore.train.serialization import export
+
+
+class MeanConv(nn.Cell):
+    def __init__(self,
+                 feature_in_dim,
+                 feature_out_dim,
+                 activation,
+                 dropout=0.2):
+        super(MeanConv, self).__init__()
+        self.out_weight = Parameter(
+            initializer("XavierUniform", [feature_in_dim * 2, feature_out_dim], dtype=mstype.float32))
+        if activation == "tanh":
+            self.act = P.Tanh()
+        elif activation == "relu":
+            self.act = P.ReLU()
+        else:
+            raise ValueError("activation should be tanh or relu")
+        self.cast = P.Cast()
+        self.matmul = P.MatMul()
+        self.concat = P.Concat(axis=1)
+        self.reduce_mean = P.ReduceMean(keep_dims=False)
+        self.dropout = nn.Dropout(keep_prob=1 - dropout)
+
+    def construct(self, self_feature, neigh_feature):
+        neigh_matrix = self.reduce_mean(neigh_feature, 1)
+        neigh_matrix = self.dropout(neigh_matrix)
+        output = self.concat((self_feature, neigh_matrix))
+        output = self.act(self.matmul(output, self.out_weight))
+        return output
+
+
+class AttenConv(nn.Cell):
+    def __init__(self,
+                 feature_in_dim,
+                 feature_out_dim,
+                 dropout=0.2):
+        super(AttenConv, self).__init__()
+        self.out_weight = Parameter(
+            initializer("XavierUniform", [feature_in_dim * 2, feature_out_dim], dtype=mstype.float32))
+        self.cast = P.Cast()
+        self.squeeze = P.Squeeze(1)
+        self.concat = P.Concat(axis=1)
+        self.expanddims = P.ExpandDims()
+        self.softmax = P.Softmax(axis=-1)
+        self.matmul = P.MatMul()
+        self.matmul_3 = P.BatchMatMul()
+        self.matmul_t = P.BatchMatMul(transpose_b=True)
+        self.dropout = nn.Dropout(keep_prob=1 - dropout)
+
+    def construct(self, self_feature, neigh_feature):
+        query = self.expanddims(self_feature, 1)
+        neigh_matrix = self.dropout(neigh_feature)
+        score = self.matmul_t(query, neigh_matrix)
+        score = self.softmax(score)
+        atten_agg = self.matmul_3(score, neigh_matrix)
+        atten_agg = self.squeeze(atten_agg)
+        output = self.matmul(self.concat((atten_agg, self_feature)), self.out_weight)
+        return output
+
+
+class BGCF(nn.Cell):
+    def __init__(self,
+                 dataset_argv,
+                 architect_argv,
+                 activation,
+                 neigh_drop_rate,
+                 num_user,
+                 num_item,
+                 input_dim):
+        super(BGCF, self).__init__()
+        self.user_embed = Parameter(initializer("XavierUniform", [num_user, input_dim], dtype=mstype.float32))
+        self.item_embed = Parameter(initializer("XavierUniform", [num_item, input_dim], dtype=mstype.float32))
+        self.cast = P.Cast()
+        self.tanh = P.Tanh()
+        self.shape = P.Shape()
+        self.split = P.Split(0, 2)
+        self.gather = P.Gather()
+        self.reshape = P.Reshape()
+        self.concat_0 = P.Concat(0)
+        self.concat_1 = P.Concat(1)
+        (self.input_dim, self.num_user, self.num_item) = dataset_argv
+        self.layer_dim = architect_argv
+        self.gnew_agg_mean = MeanConv(self.input_dim, self.layer_dim,
+                                      activation=activation, dropout=neigh_drop_rate[1])
+        self.gnew_agg_mean.to_float(mstype.float16)
+        self.gnew_agg_user = AttenConv(self.input_dim, self.layer_dim, dropout=neigh_drop_rate[2])
+        self.gnew_agg_user.to_float(mstype.float16)
+        self.gnew_agg_item = AttenConv(self.input_dim, self.layer_dim, dropout=neigh_drop_rate[2])
+        self.gnew_agg_item.to_float(mstype.float16)
+        self.user_feature_dim = self.input_dim
+        self.item_feature_dim = self.input_dim
+        self.final_weight = Parameter(
+            initializer("XavierUniform", [self.input_dim * 3, self.input_dim * 3], dtype=mstype.float32))
+        self.raw_agg_funcs_user = MeanConv(self.input_dim, self.layer_dim,
+                                           activation=activation, dropout=neigh_drop_rate[0])
+        self.raw_agg_funcs_user.to_float(mstype.float16)
+        self.raw_agg_funcs_item = MeanConv(self.input_dim, self.layer_dim,
+                                           activation=activation, dropout=neigh_drop_rate[0])
+        self.raw_agg_funcs_item.to_float(mstype.float16)
+
+    def construct(self,
+                  u_id,
+                  pos_item_id,
+                  neg_item_id,
+                  pos_users,
+                  pos_items,
+                  u_group_nodes,
+                  u_neighs,
+                  u_gnew_neighs,
+                  i_group_nodes,
+                  i_neighs,
+                  i_gnew_neighs,
+                  neg_group_nodes,
+                  neg_neighs,
+                  neg_gnew_neighs,
+                  neg_item_num):
+        all_user_embed = self.gather(self.user_embed, self.concat_0((u_id, pos_users)), 0)
+        u_self_matrix_at_layers = self.gather(self.user_embed, u_group_nodes, 0)
+        u_neigh_matrix_at_layers = self.gather(self.item_embed, u_neighs, 0)
+        u_output_mean = self.raw_agg_funcs_user(u_self_matrix_at_layers, u_neigh_matrix_at_layers)
+        u_gnew_neighs_matrix = self.gather(self.item_embed, u_gnew_neighs, 0)
+        u_output_from_gnew_mean = self.gnew_agg_mean(u_self_matrix_at_layers, u_gnew_neighs_matrix)
+        u_output_from_gnew_att = self.gnew_agg_user(u_self_matrix_at_layers,
+                                                    self.concat_1((u_neigh_matrix_at_layers, u_gnew_neighs_matrix)))
+        u_output = self.concat_1((u_output_mean, u_output_from_gnew_mean, u_output_from_gnew_att))
+        all_user_rep = self.tanh(u_output)
+        all_pos_item_embed = self.gather(self.item_embed, self.concat_0((pos_item_id, pos_items)), 0)
+        i_self_matrix_at_layers = self.gather(self.item_embed, i_group_nodes, 0)
+        i_neigh_matrix_at_layers = self.gather(self.user_embed, i_neighs, 0)
+        i_output_mean = self.raw_agg_funcs_item(i_self_matrix_at_layers, i_neigh_matrix_at_layers)
+        i_gnew_neighs_matrix = self.gather(self.user_embed, i_gnew_neighs, 0)
+        i_output_from_gnew_mean = self.gnew_agg_mean(i_self_matrix_at_layers, i_gnew_neighs_matrix)
+        i_output_from_gnew_att = self.gnew_agg_item(i_self_matrix_at_layers,
+                                                    self.concat_1((i_neigh_matrix_at_layers, i_gnew_neighs_matrix)))
+        i_output = self.concat_1((i_output_mean, i_output_from_gnew_mean, i_output_from_gnew_att))
+        all_pos_item_rep = self.tanh(i_output)
+        neg_item_embed = self.gather(self.item_embed, neg_item_id, 0)
+        neg_self_matrix_at_layers = self.gather(self.item_embed, neg_group_nodes, 0)
+        neg_neigh_matrix_at_layers = self.gather(self.user_embed, neg_neighs, 0)
+        neg_output_mean = self.raw_agg_funcs_item(neg_self_matrix_at_layers, neg_neigh_matrix_at_layers)
+        neg_gnew_neighs_matrix = self.gather(self.user_embed, neg_gnew_neighs, 0)
+        neg_output_from_gnew_mean = self.gnew_agg_mean(neg_self_matrix_at_layers, neg_gnew_neighs_matrix)
+        neg_output_from_gnew_att = self.gnew_agg_item(neg_self_matrix_at_layers,
+                                                      self.concat_1(
+                                                          (neg_neigh_matrix_at_layers, neg_gnew_neighs_matrix)))
+        neg_output = self.concat_1((neg_output_mean, neg_output_from_gnew_mean, neg_output_from_gnew_att))
+        neg_output = self.tanh(neg_output)
+        neg_output_shape = self.shape(neg_output)
+        neg_item_rep = self.reshape(neg_output,
+                                    (self.shape(neg_item_embed)[0], neg_item_num, neg_output_shape[-1]))
+
+        return all_user_embed, all_user_rep, all_pos_item_embed, all_pos_item_rep, neg_item_embed, neg_item_rep
+
+
+class ForwardBGCF(nn.Cell):
+    def __init__(self,
+                 network):
+        super(ForwardBGCF, self).__init__()
+        self.network = network
+
+    def construct(self, users, items, neg_items, u_neighs, u_gnew_neighs, i_neighs, i_gnew_neighs):
+        _, user_rep, _, item_rep, _, _, = self.network(users, items, neg_items, users, items, users,
+                                                       u_neighs, u_gnew_neighs, items, i_neighs, i_gnew_neighs,
+                                                       items, i_neighs, i_gnew_neighs, 1)
+        return user_rep, item_rep
+
+@pytest.mark.level0
+@pytest.mark.platform_x86_ascend_training
+@pytest.mark.platform_arm_ascend_training
+@pytest.mark.env_onecard
+def test_export_bgcf():
+    num_user, num_item = 7068, 3570
+    network = BGCF([64, num_user, num_item], 64, "tanh",
+                   [0.0, 0.0, 0.0], num_user, num_item, 64)
+
+    forward_net = ForwardBGCF(network)
+    users = Tensor(np.zeros([num_user,]).astype(np.int32))
+    items = Tensor(np.zeros([num_item,]).astype(np.int32))
+    neg_items = Tensor(np.zeros([num_item, 1]).astype(np.int32))
+    u_test_neighs = Tensor(np.zeros([num_user, 40]).astype(np.int32))
+    u_test_gnew_neighs = Tensor(np.zeros([num_user, 20]).astype(np.int32))
+    i_test_neighs = Tensor(np.zeros([num_item, 40]).astype(np.int32))
+    i_test_gnew_neighs = Tensor(np.zeros([num_item, 20]).astype(np.int32))
+    input_data = [users, items, neg_items, u_test_neighs, u_test_gnew_neighs, i_test_neighs, i_test_gnew_neighs]
+    file_name = "bgcf"
+    export(forward_net, *input_data, file_name=file_name, file_format="MINDIR")
+    mindir_file = file_name + ".mindir"
+    assert os.path.exists(mindir_file)
+    os.remove(mindir_file)
+    export(forward_net, *input_data, file_name=file_name, file_format="AIR")
+    air_file = file_name + ".air"
+    assert os.path.exists(air_file)
+    os.remove(air_file)