Plt Support torch test, test=model

framework/e2e/PaddleLT_new/engine/paddle_eval.py

@@ -17,14 +17,16 @@
 
 from pltools.res_save import save_tensor
 
+from strategy.ordered_dict import OrderedDictProcess
+
 
 class LayerEval(object):
     """
     构建Layer评估的通用类
     """
 
     # def __init__(self, testing, layerfile, device_id):
-    def __init__(self, testing, layerfile, device_place_id, upstream_net):
+    def __init__(self, testing, layerfile, device_place_id, upstream_net, orderdict_usage="None"):
         """
         初始化
         """
@@ -37,6 +39,7 @@ def __init__(self, testing, layerfile, device_place_id, upstream_net):
 
         self.testing = testing
         self.upstream_net = upstream_net
+        self.orderdict_usage = orderdict_usage
         self.return_net_instance = self.testing.get("return_net_instance", "False")
         self.model_dtype = self.testing.get("model_dtype")
         paddle.set_default_dtype(self.model_dtype)
@@ -65,6 +68,8 @@ def _net_instant(self):
             net = self.upstream_net
         else:
             net = BuildLayer(layerfile=self.layerfile).get_layer()
+        if self.orderdict_usage != "None":
+            net = OrderedDictProcess(net=net, layerfile=self.layerfile, orderdict_usage=self.orderdict_usage).process()
         return net
 
     def _net_input_and_spec(self):

framework/e2e/PaddleLT_new/engine/paddle_eval_bm.py

@@ -24,7 +24,7 @@ class LayerEvalBM(object):
     """
 
     # def __init__(self, testing, layerfile, device_id):
-    def __init__(self, testing, layerfile, device_place_id, upstream_net):
+    def __init__(self, testing, layerfile, device_place_id, upstream_net, orderdict_usage="None"):
         """
         初始化
         """

framework/e2e/PaddleLT_new/engine/paddle_export.py

@@ -20,7 +20,7 @@ class LayerExport(object):
     构建Layer导出的通用类
     """
 
-    def __init__(self, testing, layerfile, device_place_id, upstream_net):
+    def __init__(self, testing, layerfile, device_place_id, upstream_net, orderdict_usage="None"):
         """
         初始化
         """

framework/e2e/PaddleLT_new/engine/paddle_infer.py

@@ -22,7 +22,7 @@ class LayerInfer(object):
     构建Layer预测的通用类
     """
 
-    def __init__(self, testing, layerfile, device_place_id, upstream_net):
+    def __init__(self, testing, layerfile, device_place_id, upstream_net, orderdict_usage="None"):
         """
         初始化
         """

framework/e2e/PaddleLT_new/engine/paddle_train.py

@@ -24,7 +24,7 @@ class LayerTrain(object):
     """
 
     # def __init__(self, testing, layerfile, device_id):
-    def __init__(self, testing, layerfile, device_place_id, upstream_net):
+    def __init__(self, testing, layerfile, device_place_id, upstream_net, orderdict_usage="None"):
         """
         初始化
         """

framework/e2e/PaddleLT_new/engine/paddle_train_bm.py

@@ -26,7 +26,7 @@ class LayerTrainBM(object):
     """
 
     # def __init__(self, testing, layerfile, device_id):
-    def __init__(self, testing, layerfile, device_place_id, upstream_net):
+    def __init__(self, testing, layerfile, device_place_id, upstream_net, orderdict_usage="None"):
         """
         初始化
         """

framework/e2e/PaddleLT_new/engine/torch_engine_map.py

@@ -7,12 +7,13 @@
 """
 
 from engine.torch_eval import TorchLayerEval
-from engine.torch_eval_bm import TorchLayerEvalBM
+
+# from engine.torch_eval_bm import TorchLayerEvalBM
 
 # from interpreter.testing_trans import TrainTrans, EvalTrans
 
 
 torch_engine_map = {
-    "dy_eval": TorchLayerEval,
-    "dy_eval_perf": TorchLayerEvalBM,  # 动态图评估性能
+    "torch_dy_eval": TorchLayerEval,
+    # "torch_dy_eval_perf": TorchLayerEvalBM,  # 动态图评估性能
 }

framework/e2e/PaddleLT_new/engine/torch_eval.py

@@ -6,49 +6,68 @@
 eval 方法
 """
 import os
+import traceback
 import numpy as np
 import torch
 from engine.torch_xtools import reset
 from generator.builder_layer import BuildLayer
 from generator.builder_data import BuildData
 
+from strategy.ordered_dict import OrderedDictProcess
+
 
 class TorchLayerEval(object):
     """
     构建Layer评估的通用类
     """
 
     # def __init__(self, testing, layerfile, device_id):
-    def __init__(self, testing, layerfile):
+    def __init__(self, testing, layerfile, device_place_id, upstream_net, orderdict_usage="None"):
         """
         初始化
         """
         self.seed = 33
         reset(self.seed)
 
         self.device = os.environ.get("PLT_SET_DEVICE")
-        torch.device(str(self.device))
+        torch.device(f"cuda:{device_place_id}")
 
         self.testing = testing
+        self.upstream_net = upstream_net
+        self.orderdict_usage = orderdict_usage
+        self.return_net_instance = self.testing.get("return_net_instance", "False")
         self.model_dtype = self.testing.get("model_dtype")
-
         # torch.set_default_dtype(self.model_dtype) # torch不支持字符串dtype, 测试框架暂时没兼容
 
         self.layerfile = layerfile
-        self.data = BuildData(layerfile=self.layerfile).get_single_data()
+        # self.data = BuildData(layerfile=self.layerfile).get_single_data()
+
+    def _net_input(self):
+        """get input"""
+        reset(self.seed)
+        data = BuildData(layerfile=self.layerfile).get_single_data(framework="torch")
+        return data
 
     def _net_instant(self):
-        """get net and data"""
+        """get net"""
         reset(self.seed)
-        net = BuildLayer(layerfile=self.layerfile).get_layer()
+        if self.upstream_net:
+            net = self.upstream_net
+        else:
+            net = BuildLayer(layerfile=self.layerfile).get_layer()
+        if self.orderdict_usage != "None":
+            net = OrderedDictProcess(net=net, layerfile=self.layerfile, orderdict_usage=self.orderdict_usage).process()
         return net
 
-    def dy_eval(self):
+    def torch_dy_eval(self):
         """dygraph eval"""
         net = self._net_instant()
-        # net.eval()
-        logit = net(*self.data)
-        return {"logit": logit}
+        net.eval()
+        logit = net(*self._net_input())
+        if self.return_net_instance == "True":
+            return {"res": {"logit": logit}, "net": net}
+        else:
+            return {"res": {"logit": logit}, "net": None}
 
     # def dy2st_eval(self):
     #     """dy2st eval"""

framework/e2e/PaddleLT_new/generator/builder_data.py

@@ -9,7 +9,7 @@
 import itertools
 import numpy as np
 
-if os.environ.get("FRAMEWORK") == "paddle":
+if "paddle" in os.environ.get("FRAMEWORK"):
     import paddle
     import diy
     import layerApicase
@@ -21,9 +21,10 @@
     elif os.environ.get("USE_PADDLE_MODEL", "None") == "PaddleNLP":
         import layerNLPcase
         import paddlenlp
-elif os.environ.get("FRAMEWORK") == "torch":
+
+if "torch" in os.environ.get("FRAMEWORK"):
     import torch
-    import layerTorchcase
+    import torch_case
 
 import pltools.np_tool as tool
 
@@ -36,7 +37,7 @@ def __init__(self, layerfile):
         self.layerfile = layerfile
         self.layer_module = eval(self.layerfile)
 
-    def get_single_data(self):
+    def get_single_data(self, framework="paddle"):
         """get data"""
         if hasattr(self.layer_module, "create_numpy_inputs"):
             # dataname = self.layerfile + ".create_numpy_inputs()"
@@ -46,24 +47,24 @@ def get_single_data(self):
                 if isinstance(i, (tuple, list)):  # 为了适配list输入的模型子图
                     tmp = []
                     for j in i:
-                        if os.environ.get("FRAMEWORK") == "paddle":
+                        if framework == "paddle":
                             if j.dtype == np.int64 or j.dtype == np.int32:
                                 tmp.append(paddle.to_tensor(j, stop_gradient=True))
                             else:
                                 tmp.append(paddle.to_tensor(j, stop_gradient=False))
-                        elif os.environ.get("FRAMEWORK") == "torch":
+                        elif framework == "torch":
                             if j.dtype == np.int64 or j.dtype == np.int32:
                                 tmp.append(torch.tensor(j, requires_grad=False))
                             else:
                                 tmp.append(torch.tensor(j, requires_grad=True))
                     data.append(tmp)
                 elif isinstance(i, np.ndarray):
-                    if os.environ.get("FRAMEWORK") == "paddle":
+                    if framework == "paddle":
                         if i.dtype == np.int64 or i.dtype == np.int32:
                             data.append(paddle.to_tensor(i, stop_gradient=True))
                         else:
                             data.append(paddle.to_tensor(i, stop_gradient=False))
-                    elif os.environ.get("FRAMEWORK") == "torch":
+                    elif framework == "torch":
                         if i.dtype == np.int64 or i.dtype == np.int32:
                             data.append(torch.tensor(i, requires_grad=False))
                         else:

framework/e2e/PaddleLT_new/generator/builder_layer.py

@@ -7,7 +7,8 @@
 
 import os
 
-if os.environ.get("FRAMEWORK") == "paddle":
+# if os.environ.get("FRAMEWORK") == "paddle":
+if "paddle" in os.environ.get("FRAMEWORK"):
     import paddle
     import diy
     import layerApicase
@@ -21,9 +22,10 @@
         import paddlenlp
 
         os.system("cd /root/.paddlenlp && rm -rf models")
-elif os.environ.get("FRAMEWORK") == "torch":
+
+if "torch" in os.environ.get("FRAMEWORK"):
     import torch
-    import layerTorchcase
+    import torch_case
 
 
 class BuildLayer(object):

framework/e2e/PaddleLT_new/layerApicase/__init__.py

@@ -1,3 +1,5 @@
 import layerApicase.perf_monitor
 import layerApicase.nn_sublayer
+import layerApicase.nn_extreme_size
 import layerApicase.math_sublayer
+import layerApicase.math_extreme_size

framework/e2e/PaddleLT_new/layerApicase/math_extreme_size/__init__.py

@@ -0,0 +1,15 @@
+import os
+import glob
+
+# 获取当前文件所在目录
+current_dir = os.path.dirname(__file__)
+
+# 获取当前目录下所有的 .py 文件路径
+py_files = glob.glob(os.path.join(current_dir, "*.py"))
+
+# 动态导入所有 .py 文件
+for py_file in py_files:
+    # 获取文件名（不含扩展名）
+    module_name = os.path.basename(py_file)[:-3]
+    # 导入模块
+    __import__('layerApicase.math_extreme_size.' + module_name, globals(), locals(), [])

framework/e2e/PaddleLT_new/layerApicase/math_extreme_size/abs_giant_size_func.py

@@ -0,0 +1,46 @@
+import numpy as np
+import paddle
+
+
+class LayerCase(paddle.nn.Layer):
+    """
+    case名称: abs_base
+    api简介: 求绝对值
+    """
+
+    def __init__(self):
+        super(LayerCase, self).__init__()
+
+    def forward(self, x, ):
+        """
+        forward
+        """
+
+        paddle.seed(33)
+        np.random.seed(33)
+        out = paddle.abs(x,  )
+        return out
+
+
+
+def create_inputspec(): 
+    inputspec = ( 
+        paddle.static.InputSpec(shape=(-1, -1, -1, -1, -1), dtype=paddle.float32, stop_gradient=False), 
+    )
+    return inputspec
+
+def create_tensor_inputs():
+    """
+    paddle tensor
+    """
+    inputs = (paddle.to_tensor(-1 + (1 - -1) * np.random.random([1024, 256, 128, 100, 2]).astype('float32'), dtype='float32', stop_gradient=False), )
+    return inputs
+
+
+def create_numpy_inputs():
+    """
+    numpy array
+    """
+    inputs = (-1 + (1 - -1) * np.random.random([1024, 256, 128, 100, 2]).astype('float32'), )
+    return inputs
+

framework/e2e/PaddleLT_new/layerApicase/nn_extreme_size/Conv2D_giant_size_class.py

@@ -0,0 +1,47 @@
+import numpy as np
+import paddle
+
+
+class LayerCase(paddle.nn.Layer):
+    """
+    case名称: Conv2D_giant_size_class
+    api简介: 2维卷积
+    """
+
+    def __init__(self):
+        super(LayerCase, self).__init__()
+        self.func = paddle.nn.Conv2D(kernel_size=[3, 3], in_channels=256, out_channels=1, )
+
+    def forward(self, data, ):
+        """
+        forward
+        """
+
+        paddle.seed(33)
+        np.random.seed(33)
+        out = self.func(data, )
+        return out
+
+
+
+def create_inputspec(): 
+    inputspec = ( 
+        paddle.static.InputSpec(shape=(-1, 256, -1, -1), dtype=paddle.float32, stop_gradient=False), 
+    )
+    return inputspec
+
+def create_tensor_inputs():
+    """
+    paddle tensor
+    """
+    inputs = (paddle.to_tensor(-1 + (1 - -1) * np.random.random([1024, 256, 128, 200]).astype('float32'), dtype='float32', stop_gradient=False), )
+    return inputs
+
+
+def create_numpy_inputs():
+    """
+    numpy array
+    """
+    inputs = (-1 + (1 - -1) * np.random.random([1024, 256, 128, 200]).astype('float32'), )
+    return inputs
+

framework/e2e/PaddleLT_new/layerApicase/nn_extreme_size/__init__.py

@@ -0,0 +1,15 @@
+import os
+import glob
+
+# 获取当前文件所在目录
+current_dir = os.path.dirname(__file__)
+
+# 获取当前目录下所有的 .py 文件路径
+py_files = glob.glob(os.path.join(current_dir, "*.py"))
+
+# 动态导入所有 .py 文件
+for py_file in py_files:
+    # 获取文件名（不含扩展名）
+    module_name = os.path.basename(py_file)[:-3]
+    # 导入模块
+    __import__('layerApicase.nn_extreme_size.' + module_name, globals(), locals(), [])