Cherry pick uniform sparsity (#956)

* [cherry-pick][Unstructured_prune] add local_sparsity (#916)

* [cherry-pick][unstructured_pruner] add local_sparsity args in demo (#920)

* [Unstructured_prune] add local_sparsity demo

demo/dygraph/unstructured_pruning/train.py

@@ -50,6 +50,7 @@ add_arg('pruning_steps', int, 100,        "How many times you want to increase y
 add_arg('initial_ratio',    float, 0.15,         "The initial pruning ratio used at the start of pruning stage. Default: 0.15")
 add_arg('pruning_strategy', str, 'base',         "Which training strategy to use in pruning, we only support base and gmp for now. Default: base")
 add_arg('prune_params_type', str, None,           "Which kind of params should be pruned, we only support None (all but norms) and conv1x1_only for now. Default: None")
+add_arg('local_sparsity', bool, False,            "Whether to prune all the parameter matrix at the same ratio or not. Default: False")
 # yapf: enable
 
 
@@ -96,12 +97,14 @@ def create_unstructured_pruner(model, args, configs=None):
             mode=args.pruning_mode,
             ratio=args.ratio,
             threshold=args.threshold,
-            prune_params_type=args.prune_params_type)
+            prune_params_type=args.prune_params_type,
+            local_sparsity=args.local_sparsity)
     else:
         return GMPUnstructuredPruner(
             model,
             ratio=args.ratio,
             prune_params_type=args.prune_params_type,
+            local_sparsity=args.local_sparsity,
             configs=configs)
 
 
@@ -270,7 +273,6 @@ def compress(args):
                 train_reader_cost = 0.0
                 train_run_cost = 0.0
                 total_samples = 0
-
             reader_start = time.time()
 
     for i in range(args.last_epoch + 1, args.num_epochs):

demo/unstructured_prune/train.py

@@ -49,6 +49,7 @@ add_arg('tunning_epochs',   int, 60,             "The epoch numbers used to tune
 add_arg('pruning_steps',    int, 120,        "How many times you want to increase your ratio during training. Default: 120")
 add_arg('initial_ratio',    float, 0.15,         "The initial pruning ratio used at the start of pruning stage. Default: 0.15")
 add_arg('prune_params_type', str, None,           "Which kind of params should be pruned, we only support None (all but norms) and conv1x1_only for now. Default: None")
+add_arg('local_sparsity', bool, False,            "Whether to prune all the parameter matrix at the same ratio or not. Default: False")
 # yapf: enable
 
 model_list = models.__all__
@@ -96,13 +97,15 @@ def create_unstructured_pruner(train_program, args, place, configs):
             ratio=args.ratio,
             threshold=args.threshold,
             prune_params_type=args.prune_params_type,
-            place=place)
+            place=place,
+            local_sparsity=args.local_sparsity)
     else:
         return GMPUnstructuredPruner(
             train_program,
             ratio=args.ratio,
             prune_params_type=args.prune_params_type,
             place=place,
+            local_sparsity=args.local_sparsity,
             configs=configs)
 
 

docs/zh_cn/api_cn/dygraph/pruners/unstructured_pruner.rst

@@ -4,7 +4,7 @@
 UnstructuredPruner
 ----------
 
-.. py:class:: paddleslim.UnstructuredPruner(model, mode, threshold=0.01, ratio=0.55, prune_params_type=None, skip_params_func=None)
+.. py:class:: paddleslim.UnstructuredPruner(model, mode, threshold=0.01, ratio=0.55, prune_params_type=None, skip_params_func=None, local_sparsity=False)
 
 `源代码 <https://github.com/PaddlePaddle/PaddleSlim/blob/develop/paddleslim/dygraph/prune/unstructured_pruner.py>`_
 
@@ -44,6 +44,8 @@ UnstructuredPruner
 
 ..
 
+- **local_sparsity(bool)** - 剪裁比例（ratio）应用的范围：local_sparsity 开启时意味着每个参与剪裁的参数矩阵稀疏度均为 'ratio'， 关闭时表示只保证模型整体稀疏度达到'ratio'，但是每个参数矩阵的稀疏度可能存在差异。
+
 **返回：** 一个UnstructuredPruner类的实例。
 
 **示例代码：**
@@ -203,7 +205,7 @@ GMPUnstructuredPruner
 
 `源代码 <https://github.com/PaddlePaddle/PaddleSlim/blob/develop/paddleslim/dygraph/prune/unstructured_pruner.py>`_
 
-.. py:class:: paddleslim.GMPUnstructuredPruner(model, ratio=0.55, prune_params_type=None, skip_params_func=None, configs=None)
+.. py:class:: paddleslim.GMPUnstructuredPruner(model, ratio=0.55, prune_params_type=None, skip_params_func=None, local_sparsity=False, configs=None)
 
 该类是UnstructuredPruner的一个子类，通过覆盖step()方法，优化了训练策略，使稀疏化训练更易恢复到稠密模型精度。其他方法均继承自父类。
 
@@ -213,6 +215,7 @@ GMPUnstructuredPruner
 - **ratio(float)** - 稀疏化比例期望，只有在 mode=='ratio' 时才会生效。
 - **prune_params_type(str)** - 用以指定哪些类型的参数参与稀疏。目前只支持None和"conv1x1_only"两个选项，后者表示只稀疏化1x1卷积。而前者表示稀疏化除了归一化层的参数。
 - **skip_params_func(function)** - 一个指向function的指针，该function定义了哪些参数不应该被剪裁，默认（None）时代表所有归一化层参数不参与剪裁。
+- **local_sparsity(bool)** - 剪裁比例（ratio）应用的范围：local_sparsity 开启时意味着每个参与剪裁的参数矩阵稀疏度均为 'ratio'， 关闭时表示只保证模型整体稀疏度达到'ratio'，但是每个参数矩阵的稀疏度可能存在差异。
 - **configs(Dict)** - 传入额外的训练超参用以指导GMP训练过程。各参数介绍如下：
 
 .. code-block:: python

docs/zh_cn/api_cn/static/prune/unstructured_prune_api.rst

@@ -4,7 +4,7 @@
 UnstrucuturedPruner
 ----------
 
-.. py:class:: paddleslim.prune.UnstructuredPruner(program, mode, ratio=0.55, threshold=1e-2, scope=None, place=None, prune_params_type, skip_params_func=None)
+.. py:class:: paddleslim.prune.UnstructuredPruner(program, mode, ratio=0.55, threshold=1e-2, scope=None, place=None, prune_params_type, skip_params_func=None, local_sparsity=False)
 
 `源代码 <https://github.com/PaddlePaddle/PaddleSlim/blob/develop/paddleslim/prune/unstructured_pruner.py>`_
 
@@ -43,6 +43,7 @@ UnstrucuturedPruner
 
 ..
 
+- **local_sparsity(bool)** - 剪裁比例（ratio）应用的范围：local_sparsity 开启时意味着每个参与剪裁的参数矩阵稀疏度均为 'ratio'， 关闭时表示只保证模型整体稀疏度达到'ratio'，但是每个参数矩阵的稀疏度可能存在差异。
 
 **返回：** 一个UnstructuredPruner类的实例
 
@@ -280,7 +281,7 @@ UnstrucuturedPruner
 GMPUnstrucuturedPruner
 ----------
 
-.. py:class:: paddleslim.prune.GMPUnstructuredPruner(program, ratio=0.55, scope=None, place=None, prune_params_type=None, skip_params_func=None, configs=None)
+.. py:class:: paddleslim.prune.GMPUnstructuredPruner(program, ratio=0.55, scope=None, place=None, prune_params_type=None, skip_params_func=None, local_sparsity=False, configs=None)
 
 `源代码 <https://github.com/PaddlePaddle/PaddleSlim/blob/develop/paddleslim/prune/unstructured_pruner.py>`_
 
@@ -294,6 +295,7 @@ GMPUnstrucuturedPruner
 - **place(CPUPlace|CUDAPlace)** - 模型执行的设备，类型为CPUPlace或者CUDAPlace，默认（None）时代表CPUPlace。
 - **prune_params_type(String)** - 用以指定哪些类型的参数参与稀疏。目前只支持None和"conv1x1_only"两个选项，后者表示只稀疏化1x1卷积。而前者表示稀疏化除了归一化的参数。
 - **skip_params_func(function)** - 一个指向function的指针，该function定义了哪些参数不应该被剪裁，默认（None）时代表所有归一化层参数不参与剪裁。
+- **local_sparsity(bool)** - 剪裁比例（ratio）应用的范围：local_sparsity 开启时意味着每个参与剪裁的参数矩阵稀疏度均为 'ratio'， 关闭时表示只保证模型整体稀疏度达到'ratio'，但是每个参数矩阵的稀疏度可能存在差异。
 - **configs(Dict)** - 传入额外的训练超参用以指导GMP训练过程。具体描述如下：
 
 .. code-block:: python

paddleslim/dygraph/prune/unstructured_pruner.py

@@ -24,6 +24,7 @@ class UnstructuredPruner():
       - ratio(float): The parameters whose absolute values are in the smaller part decided by the ratio will be zeros. Default: 0.55
       - prune_params_type(str): The argument to control which type of ops will be pruned. Currently we only support None (all but norms) or conv1x1_only as input. It acts as a straightforward call to conv1x1 pruning.  Default: None
       - skip_params_func(function): The function used to select the parameters which should be skipped when performing pruning. Default: normalization-related params. 
+      - local_sparsity(bool): Whether to enable local sparsity. Local sparsity means all the weight matrices have the same sparsity. And the global sparsity only ensures the whole model's sparsity is equal to the passed-in 'ratio'. Default: False
     """
 
     def __init__(self,
@@ -32,14 +33,19 @@ class UnstructuredPruner():
                  threshold=0.01,
                  ratio=0.55,
                  prune_params_type=None,
-                 skip_params_func=None):
+                 skip_params_func=None,
+                 local_sparsity=False):
         assert mode in ('ratio', 'threshold'
                         ), "mode must be selected from 'ratio' and 'threshold'"
         assert prune_params_type is None or prune_params_type == 'conv1x1_only', "prune_params_type only supports None or conv1x1_only for now."
+        if local_sparsity:
+            assert mode == 'ratio', "We don't support local_sparsity==True and mode=='threshold' at the same time, please change the inputs accordingly."
         self.model = model
         self.mode = mode
         self.threshold = threshold
         self.ratio = ratio
+        self.local_sparsity = local_sparsity
+        self.thresholds = {}
 
         # Prority: passed-in skip_params_func > prune_params_type (conv1x1_only) > built-in _get_skip_params
         if skip_params_func is not None:
@@ -91,11 +97,19 @@ class UnstructuredPruner():
                     continue
                 t_param = param.value().get_tensor()
                 v_param = np.array(t_param)
-                params_flatten.append(v_param.flatten())
-        params_flatten = np.concatenate(params_flatten, axis=0)
-        total_length = params_flatten.size
-        self.threshold = np.sort(np.abs(params_flatten))[max(
-            0, round(self.ratio * total_length) - 1)].item()
+                if self.local_sparsity:
+                    flatten_v_param = v_param.flatten()
+                    cur_length = flatten_v_param.size
+                    cur_threshold = np.sort(np.abs(flatten_v_param))[max(
+                        0, round(self.ratio * cur_length) - 1)].item()
+                    self.thresholds[param.name] = cur_threshold
+                else:
+                    params_flatten.append(v_param.flatten())
+        if not self.local_sparsity:
+            params_flatten = np.concatenate(params_flatten, axis=0)
+            total_length = params_flatten.size
+            self.threshold = np.sort(np.abs(params_flatten))[max(
+                0, round(self.ratio * total_length) - 1)].item()
 
     def _update_masks(self):
         for name, sub_layer in self.model.named_sublayers():
@@ -105,7 +119,11 @@ class UnstructuredPruner():
                 if param.name in self.skip_params:
                     continue
                 mask = self.masks.get(param.name)
-                bool_tmp = (paddle.abs(param) >= self.threshold)
+                if self.local_sparsity:
+                    bool_tmp = (
+                        paddle.abs(param) >= self.thresholds[param.name])
+                else:
+                    bool_tmp = (paddle.abs(param) >= self.threshold)
                 paddle.assign(bool_tmp, output=mask)
 
     def summarize_weights(self, model, ratio=0.1):
@@ -248,6 +266,7 @@ class GMPUnstructuredPruner(UnstructuredPruner):
       - ratio(float): The parameters whose absolute values are in the smaller part decided by the ratio will be zeros. Default: 0.55
       - prune_params_type(str): The argument to control which type of ops will be pruned. Currently we only support None (all but norms) or conv1x1_only as input. It acts as a straightforward call to conv1x1 pruning.  Default: None
       - skip_params_func(function): The function used to select the parameters which should be skipped when performing pruning. Default: normalization-related params. 
+      - local_sparsity(bool): Whether to enable local sparsity. Local sparsity means all the weight matrices have the same sparsity. And the global sparsity only ensures the whole model's sparsity is equal to the passed-in 'ratio'. Default: False
       - configs(Dict): The dictionary contains all the configs for GMP pruner. Default: None
 
         .. code-block:: python
@@ -268,11 +287,13 @@ class GMPUnstructuredPruner(UnstructuredPruner):
                  ratio=0.55,
                  prune_params_type=None,
                  skip_params_func=None,
+                 local_sparsity=False,
                  configs=None):
 
         assert configs is not None, "Configs must be passed in for GMP pruner."
         super(GMPUnstructuredPruner, self).__init__(
-            model, 'ratio', 0.0, ratio, prune_params_type, skip_params_func)
+            model, 'ratio', 0.0, ratio, prune_params_type, skip_params_func,
+            local_sparsity)
         self.stable_iterations = configs.get('stable_iterations')
         self.pruning_iterations = configs.get('pruning_iterations')
         self.tunning_iterations = configs.get('tunning_iterations')

paddleslim/prune/unstructured_pruner.py

@@ -20,6 +20,7 @@ class UnstructuredPruner():
       - place(CPUPlace | CUDAPlace): The device place used to execute model. None means CPUPlace. Default: None.
       - prune_params_type(str): The argument to control which type of ops will be pruned. Currently we only support None (all but norms) or conv1x1_only as input. It acts as a straightforward call to conv1x1 pruning.  Default: None
       - skip_params_func(function): The function used to select the parameters which should be skipped when performing pruning. Default: normalization-related params. Default: None
+      - local_sparsity(bool): Whether to enable local sparsity. Local sparsity means all the weight matrices have the same sparsity. And the global sparsity only ensures the whole model's sparsity is equal to the passed-in 'ratio'. Default: False
     """
 
     def __init__(self,
@@ -30,15 +31,19 @@ class UnstructuredPruner():
                  scope=None,
                  place=None,
                  prune_params_type=None,
-                 skip_params_func=None):
+                 skip_params_func=None,
+                 local_sparsity=False):
         self.mode = mode
         self.ratio = ratio
         self.threshold = threshold
+        self.local_sparsity = local_sparsity
+        self.thresholds = {}
         assert self.mode in [
             'ratio', 'threshold'
         ], "mode must be selected from 'ratio' and 'threshold'"
         assert prune_params_type is None or prune_params_type == 'conv1x1_only', "prune_params_type only supports None or conv1x1_only for now."
-
+        if self.local_sparsity:
+            assert self.mode == 'ratio', "We don't support local_sparsity==True and mode=='threshold' at the same time, please change the inputs accordingly."
         self.scope = paddle.static.global_scope() if scope == None else scope
         self.place = paddle.static.cpu_places()[0] if place is None else place
 
@@ -156,14 +161,20 @@ class UnstructuredPruner():
                 continue
             t_param = self.scope.find_var(param).get_tensor()
             v_param = np.array(t_param)
-            params_flatten.append(v_param.flatten())
-        params_flatten = np.concatenate(params_flatten, axis=0)
-        self.threshold = self._partition_sort(params_flatten)
+            if self.local_sparsity:
+                cur_threshold = self._partition_sort(v_param.flatten())
+                self.thresholds[param] = cur_threshold
+            else:
+                params_flatten.append(v_param.flatten())
+        if not self.local_sparsity:
+            params_flatten = np.concatenate(params_flatten, axis=0)
+            self.threshold = self._partition_sort(params_flatten)
 
     def _partition_sort(self, params):
         total_len = len(params)
         params_zeros = params[params == 0]
         params_nonzeros = params[params != 0]
+        if len(params_nonzeros) == 0: return 0
         new_ratio = max((self.ratio * total_len - len(params_zeros)),
                         0) / len(params_nonzeros)
         return np.sort(np.abs(params_nonzeros))[max(
@@ -177,7 +188,10 @@ class UnstructuredPruner():
             t_param = self.scope.find_var(param).get_tensor()
             t_mask = self.scope.find_var(mask_name).get_tensor()
             v_param = np.array(t_param)
-            v_param[np.abs(v_param) < self.threshold] = 0
+            if self.local_sparsity:
+                v_param[np.abs(v_param) < self.thresholds[param]] = 0
+            else:
+                v_param[np.abs(v_param) < self.threshold] = 0
             v_mask = (v_param != 0).astype(v_param.dtype)
             t_mask.set(v_mask, self.place)
 
@@ -240,6 +254,7 @@ class UnstructuredPruner():
             if 'norm' in op.type() and 'grad' not in op.type():
                 for input in op.all_inputs():
                     skip_params.add(input.name())
+        print(skip_params)
         return skip_params
 
     def _get_skip_params_conv1x1(self, program):
@@ -296,6 +311,7 @@ class GMPUnstructuredPruner(UnstructuredPruner):
       - place(CPUPlace | CUDAPlace): The device place used to execute model. None means CPUPlace. Default: None.
       - prune_params_type(str): The argument to control which type of ops will be pruned. Currently we only support None (all but norms) or conv1x1_only as input. It acts as a straightforward call to conv1x1 pruning.  Default: None
       - skip_params_func(function): The function used to select the parameters which should be skipped when performing pruning. Default: normalization-related params. Default: None
+      - local_sparsity(bool): Whether to enable local sparsity. Local sparsity means all the weight matrices have the same sparsity. And the global sparsity only ensures the whole model's sparsity is equal to the passed-in 'ratio'. Default: False
       - configs(Dict): The dictionary contains all the configs for GMP pruner. Default: None. The detailed description is as below:
         
         .. code-block:: python
@@ -317,12 +333,13 @@ class GMPUnstructuredPruner(UnstructuredPruner):
                  place=None,
                  prune_params_type=None,
                  skip_params_func=None,
+                 local_sparsity=False,
                  configs=None):
         assert configs is not None, "Please pass in a valid config dictionary."
 
         super(GMPUnstructuredPruner, self).__init__(
             program, 'ratio', ratio, 0.0, scope, place, prune_params_type,
-            skip_params_func)
+            skip_params_func, local_sparsity)
         self.stable_iterations = configs.get('stable_iterations')
         self.pruning_iterations = configs.get('pruning_iterations')
         self.tunning_iterations = configs.get('tunning_iterations')

tests/dygraph/test_unstructured_prune.py

@@ -16,12 +16,14 @@ class TestUnstructuredPruner(unittest.TestCase):
     def _gen_model(self):
         self.net = mobilenet_v1(num_classes=10, pretrained=False)
         self.net_conv1x1 = mobilenet_v1(num_classes=10, pretrained=False)
-        self.pruner = UnstructuredPruner(self.net, mode='ratio', ratio=0.55)
+        self.pruner = UnstructuredPruner(
+            self.net, mode='ratio', ratio=0.55, local_sparsity=True)
         self.pruner_conv1x1 = UnstructuredPruner(
             self.net_conv1x1,
             mode='ratio',
             ratio=0.55,
-            prune_params_type='conv1x1_only')
+            prune_params_type='conv1x1_only',
+            local_sparsity=False)
 
     def test_prune(self):
         ori_sparsity = UnstructuredPruner.total_sparse(self.net)

tests/test_unstructured_pruner.py

@@ -42,13 +42,18 @@ class TestUnstructuredPruner(StaticCase):
         exe.run(self.startup_program, scope=self.scope)
 
         self.pruner = UnstructuredPruner(
-            self.main_program, 'ratio', scope=self.scope, place=place)
+            self.main_program,
+            'ratio',
+            scope=self.scope,
+            place=place,
+            local_sparsity=True)
         self.pruner_conv1x1 = UnstructuredPruner(
             self.main_program,
             'ratio',
             scope=self.scope,
             place=place,
-            prune_params_type='conv1x1_only')
+            prune_params_type='conv1x1_only',
+            local_sparsity=False)
 
     def test_unstructured_prune(self):
         for param in self.main_program.global_block().all_parameters():