From 975441164820d3fe161e53536a4a89612244c98b Mon Sep 17 00:00:00 2001 From: chenzhiyang <1792266893@qq.com> Date: Thu, 12 Sep 2024 12:05:59 +0000 Subject: [PATCH 1/2] migrate static.nn.xx to nn.XX --- distributed/CE_API/case/dist_CountFilterEntry.py | 10 +++------- distributed/CE_API/case/dist_ProbabilityEntry.py | 10 +++------- distributed/CE_API/case/dist_ShowClickEntry.py | 12 ++++-------- distributed/CE_API/case/dist_fleet_qat_init.py | 7 +++++-- 4 files changed, 15 insertions(+), 24 deletions(-) diff --git a/distributed/CE_API/case/dist_CountFilterEntry.py b/distributed/CE_API/case/dist_CountFilterEntry.py index 3726cad776..64aae5ccff 100644 --- a/distributed/CE_API/case/dist_CountFilterEntry.py +++ b/distributed/CE_API/case/dist_CountFilterEntry.py @@ -33,13 +33,9 @@ def test_CountFilterEntry(): input = paddle.static.data(name="ins", shape=[1], dtype="int64") - emb = paddle.static.nn.sparse_embedding( - input=input, - size=[sparse_feature_dim, embedding_size], - is_test=False, - entry=entry, - param_attr=paddle.ParamAttr(name="SparseFeatFactors", initializer=paddle.nn.initializer.Uniform()), - ) + Emb = paddle.nn.Embedding(num_embeddings=sparse_feature_dim, embedding_dim=embedding_size, sparse=True, + weight_attr=paddle.ParamAttr(name="SparseFeatFactors", initializer=paddle.nn.initializer.Uniform())) + emb = Emb(input) print(emb) print("test_CountFilterEntry ... ok") diff --git a/distributed/CE_API/case/dist_ProbabilityEntry.py b/distributed/CE_API/case/dist_ProbabilityEntry.py index 1ab1ce9ea5..c3b8fed9b8 100644 --- a/distributed/CE_API/case/dist_ProbabilityEntry.py +++ b/distributed/CE_API/case/dist_ProbabilityEntry.py @@ -33,13 +33,9 @@ def test_ProbabilityEntry(): input = paddle.static.data(name="ins", shape=[1], dtype="int64") - emb = paddle.static.nn.sparse_embedding( - input=input, - size=[sparse_feature_dim, embedding_size], - is_test=False, - entry=entry, - param_attr=paddle.ParamAttr(name="SparseFeatFactors", initializer=paddle.nn.initializer.Uniform()), - ) + Emb = paddle.nn.Embedding(num_embeddings=sparse_feature_dim, embedding_dim=embedding_size, sparse=True, + weight_attr=paddle.ParamAttr(name="SparseFeatFactors", initializer=paddle.nn.initializer.Uniform())) + emb = Emb(input) print(emb) print("test_ProbabilityEntry ... ok") diff --git a/distributed/CE_API/case/dist_ShowClickEntry.py b/distributed/CE_API/case/dist_ShowClickEntry.py index 824c887518..cc0c63ca07 100644 --- a/distributed/CE_API/case/dist_ShowClickEntry.py +++ b/distributed/CE_API/case/dist_ShowClickEntry.py @@ -36,14 +36,10 @@ def test_ShowClickEntry(): entry = paddle.distributed.ShowClickEntry("show", "click") - emb = paddle.static.nn.sparse_embedding( - input=input, - size=[sparse_feature_dim, embedding_size], - is_test=False, - entry=entry, - param_attr=paddle.ParamAttr(name="SparseFeatFactors", initializer=paddle.nn.initializer.Uniform()), - ) - assert emb.shape == (64,) + Emb = paddle.nn.Embedding(num_embeddings=sparse_feature_dim, embedding_dim=embedding_size, sparse=True, + weight_attr=paddle.ParamAttr(name="SparseFeatFactors", initializer=paddle.nn.initializer.Uniform())) + emb = Emb(input) + assert emb.shape == [1,64] print("test_ShowClickEntry ... ok") diff --git a/distributed/CE_API/case/dist_fleet_qat_init.py b/distributed/CE_API/case/dist_fleet_qat_init.py index b5f5b9fce9..b0256dba10 100644 --- a/distributed/CE_API/case/dist_fleet_qat_init.py +++ b/distributed/CE_API/case/dist_fleet_qat_init.py @@ -37,8 +37,11 @@ def run_example_code(): exe = paddle.static.Executor(place) # 1. Define the train program data = paddle.static.data(name="X", shape=[None, 1, 28, 28], dtype="float32") - conv2d = paddle.static.nn.conv2d(input=data, num_filters=6, filter_size=3) - bn = paddle.static.nn.batch_norm(input=conv2d, act="relu") + # DEL: conv2d = paddle.static.nn.conv2d(input=data, num_filters=6, filter_size=3) + Conv = paddle.nn.Conv2D(in_channels=1, out_channels=6, kernel_size=3) + conv2d = Conv(data) + # DEL: bn = paddle.static.nn.batch_norm(input=conv2d, act="relu") + bn = paddle.nn.BatchNorm2D(1)(conv2d) pool = F.max_pool2d(bn, kernel_size=2, stride=2) hidden = paddle.static.nn.fc(pool, size=10) loss = paddle.mean(hidden) From 8f2b335d60af6c2e07a2881175a238c284f452f5 Mon Sep 17 00:00:00 2001 From: chenzhiyang <1792266893@qq.com> Date: Thu, 12 Sep 2024 12:08:31 +0000 Subject: [PATCH 2/2] migrate static.nn.xx to nn.XX --- distributed/CE_API/case/dist_fleet_qat_init.py | 2 -- 1 file changed, 2 deletions(-) diff --git a/distributed/CE_API/case/dist_fleet_qat_init.py b/distributed/CE_API/case/dist_fleet_qat_init.py index b0256dba10..ecab555ea8 100644 --- a/distributed/CE_API/case/dist_fleet_qat_init.py +++ b/distributed/CE_API/case/dist_fleet_qat_init.py @@ -37,10 +37,8 @@ def run_example_code(): exe = paddle.static.Executor(place) # 1. Define the train program data = paddle.static.data(name="X", shape=[None, 1, 28, 28], dtype="float32") - # DEL: conv2d = paddle.static.nn.conv2d(input=data, num_filters=6, filter_size=3) Conv = paddle.nn.Conv2D(in_channels=1, out_channels=6, kernel_size=3) conv2d = Conv(data) - # DEL: bn = paddle.static.nn.batch_norm(input=conv2d, act="relu") bn = paddle.nn.BatchNorm2D(1)(conv2d) pool = F.max_pool2d(bn, kernel_size=2, stride=2) hidden = paddle.static.nn.fc(pool, size=10)