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shenweichen · Apr 4, 2021 · ea6bc38 · ea6bc38
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diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml
@@ -18,7 +18,7 @@ jobs:
     strategy:
       matrix:
         python-version: [3.6,3.7]
-        torch-version: [1.1.0,1.2.0,1.3.0,1.4.0,1.5.0,1.6.0,1.7.0]
+        torch-version: [1.1.0,1.2.0,1.3.0,1.4.0,1.5.0,1.6.0,1.7.0,1.8.1]
 
 #        exclude:
 #          - python-version: 3.5

diff --git a/README.md b/README.md
@@ -84,6 +84,11 @@ Let's [**Get Started!**](https://deepctr-torch.readthedocs.io/en/latest/Quick-St
          <a href="https://github.com/shenweichen">Shen Weichen</a> 
         <p>Core Dev<br> Zhejiang Unversity <br> <br>  </p>
       </td>
+      <td>
+         <a href="https://github.com/zanshuxun"><img width="70" height="70" src="https://github.com/zanshuxun.png?s=40" alt="pic"></a><br>
+         <a href="https://github.com/zanshuxun">Zan Shuxun</a>
+        <p>Core Dev<br> Beijing University <br> of  Posts and <br> Telecommunications</p>
+      </td>
       <td>
          <a href="https://github.com/weberrr"><img width="70" height="70" src="https://github.com/weberrr.png?s=40" alt="pic"></a><br>
          <a href="https://github.com/weberrr">Wang Ze</a> 
@@ -94,11 +99,6 @@ Let's [**Get Started!**](https://deepctr-torch.readthedocs.io/en/latest/Quick-St
          <a href="https://github.com/wutongzhang">Zhang Wutong</a>
          <p>Core Dev<br> Beijing University <br> of  Posts and <br> Telecommunications</p>
       </td>
-      <td>
-         <a href="https://github.com/zanshuxun"><img width="70" height="70" src="https://github.com/zanshuxun.png?s=40" alt="pic"></a><br>
-         <a href="https://github.com/zanshuxun">Zan Shuxun</a>
-        <p>Core Dev<br> Beijing University <br> of  Posts and <br> Telecommunications</p>
-      </td>
       <td>
          <a href="https://github.com/ZhangYuef"><img width="70" height="70" src="https://github.com/ZhangYuef.png?s=40" alt="pic"></a><br>
          <a href="https://github.com/ZhangYuef">Zhang Yuefeng</a>

diff --git a/deepctr_torch/__init__.py b/deepctr_torch/__init__.py
@@ -2,5 +2,5 @@
 from . import models
 from .utils import check_version
 
-__version__ = '0.2.5'
+__version__ = '0.2.6'
 check_version(__version__)
diff --git a/deepctr_torch/layers/activation.py b/deepctr_torch/layers/activation.py
@@ -12,7 +12,7 @@ class Dice(nn.Module):
 
     Output shape:
         - Same shape as input.
-    
+
     References
         - [Zhou G, Zhu X, Song C, et al. Deep interest network for click-through rate prediction[C]//Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. ACM, 2018: 1059-1068.](https://arxiv.org/pdf/1706.06978.pdf)
         - https://github.com/zhougr1993/DeepInterestNetwork, https://github.com/fanoping/DIN-pytorch

diff --git a/deepctr_torch/layers/interaction.py b/deepctr_torch/layers/interaction.py
@@ -126,7 +126,7 @@ def __init__(self, filed_size, embedding_size, bilinear_type="interaction", seed
             self.bilinear = nn.Linear(
                 embedding_size, embedding_size, bias=False)
         elif self.bilinear_type == "each":
-            for i in range(filed_size):
+            for _ in range(filed_size):
                 self.bilinear.append(
                     nn.Linear(embedding_size, embedding_size, bias=False))
         elif self.bilinear_type == "interaction":

diff --git a/deepctr_torch/models/autoint.py b/deepctr_torch/models/autoint.py
@@ -34,7 +34,7 @@ class AutoInt(BaseModel):
     :param device: str, ``"cpu"`` or ``"cuda:0"``
     :param gpus: list of int or torch.device for multiple gpus. If None, run on `device`. `gpus[0]` should be the same gpu with `device`.
     :return: A PyTorch model instance.
-    
+
     """
 
     def __init__(self, linear_feature_columns, dnn_feature_columns, att_layer_num=3, att_embedding_size=8, att_head_num=2,

diff --git a/deepctr_torch/models/basemodel.py b/deepctr_torch/models/basemodel.py
@@ -234,7 +234,7 @@ def fit(self, x=None, y=None, batch_size=None, epochs=1, verbose=1, initial_epoc
             train_result = {}
             try:
                 with tqdm(enumerate(train_loader), disable=verbose != 1) as t:
-                    for index, (x_train, y_train) in t:
+                    for _, (x_train, y_train) in t:
                         x = x_train.to(self.device).float()
                         y = y_train.to(self.device).float()
 
@@ -333,7 +333,7 @@ def predict(self, x, batch_size=256):
 
         pred_ans = []
         with torch.no_grad():
-            for index, x_test in enumerate(test_loader):
+            for _, x_test in enumerate(test_loader):
                 x = x_test[0].to(self.device).float()
 
                 y_pred = model(x).cpu().data.numpy()  # .squeeze()

diff --git a/deepctr_torch/models/dcn.py b/deepctr_torch/models/dcn.py
@@ -38,7 +38,7 @@ class DCN(BaseModel):
     :param device: str, ``"cpu"`` or ``"cuda:0"``
     :param gpus: list of int or torch.device for multiple gpus. If None, run on `device`. `gpus[0]` should be the same gpu with `device`.
     :return: A PyTorch model instance.
-    
+
     """
 
     def __init__(self, linear_feature_columns, dnn_feature_columns, cross_num=2, cross_parameterization='vector',

diff --git a/deepctr_torch/models/dcnmix.py b/deepctr_torch/models/dcnmix.py
@@ -38,7 +38,7 @@ class DCNMix(BaseModel):
     :param device: str, ``"cpu"`` or ``"cuda:0"``
     :param gpus: list of int or torch.device for multiple gpus. If None, run on `device`. `gpus[0]` should be the same gpu with `device`.
     :return: A PyTorch model instance.
-    
+
     """
 
     def __init__(self, linear_feature_columns,

diff --git a/deepctr_torch/models/deepfm.py b/deepctr_torch/models/deepfm.py
@@ -32,7 +32,7 @@ class DeepFM(BaseModel):
     :param device: str, ``"cpu"`` or ``"cuda:0"``
     :param gpus: list of int or torch.device for multiple gpus. If None, run on `device`. `gpus[0]` should be the same gpu with `device`.
     :return: A PyTorch model instance.
-    
+
     """
 
     def __init__(self,

diff --git a/deepctr_torch/models/difm.py b/deepctr_torch/models/difm.py
@@ -3,7 +3,7 @@
 Author:
     zanshuxun, [email protected]
 Reference:
-    Lu W, Yu Y, Chang Y, et al. A Dual Input-aware Factorization Machine for CTR Prediction[C]//IJCAI. 2020: 3139-3145.
+    [1] Lu W, Yu Y, Chang Y, et al. A Dual Input-aware Factorization Machine for CTR Prediction[C]//IJCAI. 2020: 3139-3145.(https://www.ijcai.org/Proceedings/2020/0434.pdf)
 """
 import torch
 import torch.nn as nn
@@ -29,9 +29,9 @@ class DIFM(BaseModel):
     :param dnn_use_bn: bool. Whether use BatchNormalization before activation or not in DNN
     :param task: str, ``"binary"`` for  binary logloss or  ``"regression"`` for regression loss
     :param device: str, ``"cpu"`` or ``"cuda:0"``
-    :param gpus: list of int or torch.device for multiple gpus. If None, run on `device`. `gpus[0]` should be the same gpu with `device`.
+    :param gpus: list of int or torch.device for multiple gpus. If None, run on ``device`` . ``gpus[0]`` should be the same gpu with ``device`` .
     :return: A PyTorch model instance.
-    
+
     """
 
     def __init__(self,
@@ -79,7 +79,7 @@ def __init__(self,
         self.to(device)
 
     def forward(self, X):
-        sparse_embedding_list, dense_value_list = self.input_from_feature_columns(X, self.dnn_feature_columns,
+        sparse_embedding_list, _ = self.input_from_feature_columns(X, self.dnn_feature_columns,
                                                                                   self.embedding_dict)
         if not len(sparse_embedding_list) > 0:
             raise ValueError("there are no sparse features")

diff --git a/deepctr_torch/models/fibinet.py b/deepctr_torch/models/fibinet.py
@@ -33,7 +33,7 @@ class FiBiNET(BaseModel):
     :param device: str, ``"cpu"`` or ``"cuda:0"``
     :param gpus: list of int or torch.device for multiple gpus. If None, run on `device`. `gpus[0]` should be the same gpu with `device`.
     :return: A PyTorch model instance.
-    
+
     """
 
     def __init__(self, linear_feature_columns, dnn_feature_columns, bilinear_type='interaction',

diff --git a/deepctr_torch/models/ifm.py b/deepctr_torch/models/ifm.py
@@ -3,7 +3,7 @@
 Author:
     zanshuxun, [email protected]
 Reference:
-    Yu Y, Wang Z, Yuan B. An Input-aware Factorization Machine for Sparse Prediction[C]//IJCAI. 2019: 1466-1472.
+    [1] Yu Y, Wang Z, Yuan B. An Input-aware Factorization Machine for Sparse Prediction[C]//IJCAI. 2019: 1466-1472.(https://www.ijcai.org/Proceedings/2019/0203.pdf)
 """
 import torch
 import torch.nn as nn
@@ -29,9 +29,9 @@ class IFM(BaseModel):
     :param dnn_use_bn: bool. Whether use BatchNormalization before activation or not in DNN
     :param task: str, ``"binary"`` for  binary logloss or  ``"regression"`` for regression loss
     :param device: str, ``"cpu"`` or ``"cuda:0"``
-    :param gpus: list of int or torch.device for multiple gpus. If None, run on `device`. `gpus[0]` should be the same gpu with `device`.
+    :param gpus: list of int or torch.device for multiple gpus. If None, run on ``device`` .  ``gpus[0]``  should be the same gpu with ``device`` .
     :return: A PyTorch model instance.
-    
+
     """
 
     def __init__(self,

diff --git a/deepctr_torch/models/mlr.py b/deepctr_torch/models/mlr.py
@@ -28,7 +28,7 @@ class MLR(BaseModel):
     :param device: str, ``"cpu"`` or ``"cuda:0"``
     :param gpus: list of int or torch.device for multiple gpus. If None, run on `device`. `gpus[0]` should be the same gpu with `device`.
     :return: A PyTorch model instance.
-    
+
     """
 
     def __init__(self, region_feature_columns, base_feature_columns=None, bias_feature_columns=None,

diff --git a/deepctr_torch/models/nfm.py b/deepctr_torch/models/nfm.py
@@ -31,7 +31,7 @@ class NFM(BaseModel):
     :param device: str, ``"cpu"`` or ``"cuda:0"``
     :param gpus: list of int or torch.device for multiple gpus. If None, run on `device`. `gpus[0]` should be the same gpu with `device`.
     :return: A PyTorch model instance.
-    
+
     """
 
     def __init__(self,

diff --git a/deepctr_torch/models/onn.py b/deepctr_torch/models/onn.py
@@ -52,7 +52,7 @@ class ONN(BaseModel):
     :param device: str, ``"cpu"`` or ``"cuda:0"``
     :param gpus: list of int or torch.device for multiple gpus. If None, run on `device`. `gpus[0]` should be the same gpu with `device`.
     :return: A PyTorch model instance.
-    
+
     """
 
     def __init__(self, linear_feature_columns, dnn_feature_columns,

diff --git a/deepctr_torch/models/pnn.py b/deepctr_torch/models/pnn.py
@@ -32,7 +32,7 @@ class PNN(BaseModel):
     :param device: str, ``"cpu"`` or ``"cuda:0"``
     :param gpus: list of int or torch.device for multiple gpus. If None, run on `device`. `gpus[0]` should be the same gpu with `device`.
     :return: A PyTorch model instance.
-    
+
     """
 
     def __init__(self, dnn_feature_columns, dnn_hidden_units=(128, 128), l2_reg_embedding=1e-5, l2_reg_dnn=0,

diff --git a/deepctr_torch/models/wdl.py b/deepctr_torch/models/wdl.py
@@ -30,7 +30,7 @@ class WDL(BaseModel):
     :param device: str, ``"cpu"`` or ``"cuda:0"``
     :param gpus: list of int or torch.device for multiple gpus. If None, run on `device`. `gpus[0]` should be the same gpu with `device`.
     :return: A PyTorch model instance.
-    
+
     """
 
     def __init__(self,

diff --git a/deepctr_torch/models/xdeepfm.py b/deepctr_torch/models/xdeepfm.py
@@ -36,7 +36,7 @@ class xDeepFM(BaseModel):
     :param device: str, ``"cpu"`` or ``"cuda:0"``
     :param gpus: list of int or torch.device for multiple gpus. If None, run on `device`. `gpus[0]` should be the same gpu with `device`.
     :return: A PyTorch model instance.
-    
+
     """
 
     def __init__(self, linear_feature_columns, dnn_feature_columns, dnn_hidden_units=(256, 256),

diff --git a/docs/pics/DIFM.png b/docs/pics/DIFM.png
diff --git a/docs/pics/IFM.png b/docs/pics/IFM.png
diff --git a/docs/source/FAQ.md b/docs/source/FAQ.md
@@ -75,5 +75,5 @@ model = DeepFM(...,device=device)
 ## 5. How to run the demo with multiple GPUs ?
 
 ```python
-    model = DeepFM(..., device=device, gpus=[0, 1])
+model = DeepFM(..., device=device, gpus=[0, 1])
 ```
diff --git a/docs/source/Features.md b/docs/source/Features.md
@@ -241,6 +241,27 @@ Feature Importance and Bilinear feature Interaction NETwork is proposed to dynam
 
 [Huang T, Zhang Z, Zhang J. FiBiNET: Combining Feature Importance and Bilinear feature Interaction for Click-Through Rate Prediction[J]. arXiv preprint arXiv:1905.09433, 2019.](https://arxiv.org/pdf/1905.09433.pdf)
 
+### IFM(Input-aware Factorization Machine)
+
+Input-aware Factorization Machine (IFM) learns a unique input-aware factor for the same feature in different instances via a neural network.
+
+[**IFM Model API**](./deepctr_torch.models.ifm.html)  
+
+![IFM](../pics/IFM.png)
+
+[Yu Y, Wang Z, Yuan B. An Input-aware Factorization Machine for Sparse Prediction[C]//IJCAI. 2019: 1466-1472.](https://www.ijcai.org/Proceedings/2019/0203.pdf)
+
+### DIFM(Dual Input-aware Factorization Machine)
+
+Dual Inputaware Factorization Machines (DIFM) can adaptively reweight the original feature representations at the bit-wise and vector-wise levels simultaneously.Furthermore, DIFMs strategically integrate various components including Multi-Head Self-Attention, Residual Networks and DNNs into a unified end-to-end model.
+
+[**DFM Model API**](./deepctr_torch.models.difm.html)  
+
+![DIFM](../pics/DIFM.png)
+
+[Lu W, Yu Y, Chang Y, et al. A Dual Input-aware Factorization Machine for CTR Prediction[C]//IJCAI. 2020: 3139-3145.](https://www.ijcai.org/Proceedings/2020/0434.pdf)
+
+
 
 ## Layers
 

diff --git a/docs/source/History.md b/docs/source/History.md
@@ -1,4 +1,5 @@
 # History
+- 04/04/2021 : [v0.2.6](https://github.com/shenweichen/DeepCTR-Torch/releases/tag/v0.2.6) released.Add add [IFM](./Features.html#ifm-input-aware-factorization-machine) and [DIFM](./Features.html#difm-dual-input-aware-factorization-machine);Support multi-gpus running([example](./FAQ.html#how-to-run-the-demo-with-multiple-gpus)).
 - 02/12/2021 : [v0.2.5](https://github.com/shenweichen/DeepCTR-Torch/releases/tag/v0.2.5) released.Fix bug in DCN-M.
 - 12/05/2020 : [v0.2.4](https://github.com/shenweichen/DeepCTR-Torch/releases/tag/v0.2.4) released.Imporve compatibility & fix issues.Add History callback.([example](https://deepctr-torch.readthedocs.io/en/latest/FAQ.html#set-learning-rate-and-use-earlystopping)).
 - 10/18/2020 : [v0.2.3](https://github.com/shenweichen/DeepCTR-Torch/releases/tag/v0.2.3) released.Add [DCN-M](./Features.html#dcn-deep-cross-network)&[DCN-Mix](./Features.html#dcn-mix-improved-deep-cross-network-with-mix-of-experts-and-matrix-kernel).Add EarlyStopping and ModelCheckpoint callbacks([example](https://deepctr-torch.readthedocs.io/en/latest/FAQ.html#set-learning-rate-and-use-earlystopping)).

diff --git a/docs/source/Models.rst b/docs/source/Models.rst
@@ -21,3 +21,5 @@ DeepCTR-Torch Models API
    ONN<deepctr_torch.models.onn>
    FGCNN<deepctr_torch.models.fgcnn>
    FiBiNET<deepctr_torch.models.fibinet>
+   IFM<deepctr_torch.models.ifm>
+   DIFM<deepctr_torch.models.difm>
diff --git a/docs/source/conf.py b/docs/source/conf.py
@@ -26,7 +26,7 @@
 # The short X.Y version
 version = ''
 # The full version, including alpha/beta/rc tags
-release = '0.2.5'
+release = '0.2.6'
 
 
 # -- General configuration ---------------------------------------------------

diff --git a/docs/source/deepctr_torch.models.difm.rst b/docs/source/deepctr_torch.models.difm.rst
@@ -0,0 +1,7 @@
+deepctr\_torch.models.difm module
+================================
+
+.. automodule:: deepctr_torch.models.difm
+    :members:
+    :no-undoc-members:
+    :no-show-inheritance:
diff --git a/docs/source/deepctr_torch.models.ifm.rst b/docs/source/deepctr_torch.models.ifm.rst
@@ -0,0 +1,7 @@
+deepctr\_torch.models.ifm module
+================================
+
+.. automodule:: deepctr_torch.models.ifm
+    :members:
+    :no-undoc-members:
+    :no-show-inheritance:
diff --git a/docs/source/deepctr_torch.models.rst b/docs/source/deepctr_torch.models.rst
@@ -10,6 +10,7 @@ Submodules
    deepctr_torch.models.autoint
    deepctr_torch.models.basemodel
    deepctr_torch.models.dcn
+   deepctr_torch.models.dcnmix
    deepctr_torch.models.deepfm
    deepctr_torch.models.fibinet
    deepctr_torch.models.mlr
@@ -20,6 +21,8 @@ Submodules
    deepctr_torch.models.xdeepfm
    deepctr_torch.models.din
    deepctr_torch.models.dien
+   deepctr_torch.models.ifm
+   deepctr_torch.models.difm
 
 Module contents
 ---------------

diff --git a/docs/source/index.rst b/docs/source/index.rst
@@ -34,13 +34,12 @@ You can read the latest code at https://github.com/shenweichen/DeepCTR-Torch and
 
 News
 -----
+04/04/2021 : Add `IFM <./Features.html#ifm-input-aware-factorization-machine>`_ and `DIFM <./Features.html#difm-dual-input-aware-factorization-machine>`_ . Support multi-gpus running(`example <./FAQ.html#how-to-run-the-demo-with-multiple-gpus>`_). `Changelog <https://github.com/shenweichen/DeepCTR-Torch/releases/tag/v0.2.6>`_
+
 02/12/2021 : Fix bug in DCN-M. `Changelog <https://github.com/shenweichen/DeepCTR-Torch/releases/tag/v0.2.4>`_
 
 12/05/2020 : Imporve compatibility & fix issues.Add History callback(`example <https://deepctr-torch.readthedocs.io/en/latest/FAQ.html#set-learning-rate-and-use-earlystopping>`_). `Changelog <https://github.com/shenweichen/DeepCTR-Torch/releases/tag/v0.2.3>`_
 
-10/18/2020 : Add `DCN-M <./Features.html#dcn-deep-cross-network>`_ and `DCN-Mix <./Features.html#dcn-mix-improved-deep-cross-network-with-mix-of-experts-and-matrix-kernel>`_ . Add EarlyStopping and ModelCheckpoint callbacks(`example <https://deepctr-torch.readthedocs.io/en/latest/FAQ.html#set-learning-rate-and-use-earlystopping>`_). `Changelog <https://github.com/shenweichen/DeepCTR-Torch/releases/tag/v0.2.3>`_
-
-
 DisscussionGroup
 -----------------------
 

diff --git a/setup.py b/setup.py
@@ -9,7 +9,7 @@
 
 setuptools.setup(
     name="deepctr-torch",
-    version="0.2.5",
+    version="0.2.6",
     author="Weichen Shen",
     author_email="[email protected]",
     description="Easy-to-use,Modular and Extendible package of deep learning based CTR(Click Through Rate) prediction models with PyTorch",

diff --git a/tests/utils.py b/tests/utils.py
@@ -70,7 +70,7 @@ def layer_test(layer_cls, kwargs = {}, input_shape=None,
                input_dtype=torch.float32, input_data=None, expected_output=None, 
                expected_output_shape=None, expected_output_dtype=None, fixed_batch_size=False):
     '''check layer is valid or not
-    
+
     :param layer_cls:
     :param input_shape:
     :param input_dtype: