Cp/fix

paddlenlp/trainer/trainer.py

@@ -1771,16 +1771,8 @@ def _wrap_model(self, model, training=True):
         in_sep_parallel_mode = self.args.sep_parallel_degree > 1
 
         # Multi-gpu training
-        if (
-            self.args.world_size > 1
-            and not self.args.use_hybrid_parallel
-            or not (
-                in_pipeline_parallel_mode
-                or in_sharding_parallel_mode
-                or in_tensor_parallel_mode
-                or in_sep_parallel_mode
-            )
-        ):
+        if self.args.world_size > 1 and (not self.args.use_hybrid_parallel):
+            # MOE use DDP to broadcaset parameters.
             model = paddle.DataParallel(model)
             # Distributed training (should be after fp16 initialization)
 

paddlenlp/trainer/training_args.py

@@ -1406,7 +1406,7 @@ def is_segment_parallel_supported():
             if world_size > 1:
                 if not paddle.distributed.parallel.parallel_helper._is_parallel_ctx_initialized():
                     if self.unified_checkpoint:
-                        self.use_hybrid_parallel = True
+                        # DP use hybrid group
                         strategy = fleet.DistributedStrategy()
                         fleet.init(is_collective=True, strategy=strategy)
                     else:

paddlenlp/utils/safetensors.py

@@ -157,16 +157,16 @@ def __getitem__(self, index):
 
         out_start, out_stop, out_step = copy.deepcopy((self.start, self.stop, self.step))
         for i, (start, stop, step, slice_) in enumerate(zip(self.start, self.stop, self.step, index)):
-            out_start[i] = slice_.start or 0
-            out_step[i] = slice_.step or 1
-            out_stop[i] = slice_.stop or stop - start
+            out_start[i] = slice_.start if slice_.start is not None else 0
+            out_step[i] = slice_.step if slice_.step is not None else 1
+            out_stop[i] = slice_.stop if slice_.stop is not None else stop - start
             out_stop[i] = min(stop, out_stop[i])
 
         target_shape = []
-        for x, y, z in zip(out_start, out_stop, out_step):
+        for x, y, z, sli in zip(out_start, out_stop, out_step, index):
             assert z == 1, "only support step = 1"
-            if y - x > 1:
-                target_shape.append(int(y - x))
+            if y - x > 1 or sli.step is None:
+                target_shape.append(max(int(y - x), 0))
 
         if len(target_shape) == 0:
             if self.shape == [1]:

pyproject.toml

@@ -10,7 +10,7 @@ exclude = ['.flake8']
 
 [tool.pytest.ini_options]
 minversion = "6.0"
-addopts = "-ra -q --ignore model_zoo/gpt-3/"
+addopts = "-ra -q --dist loadgroup"
 pythonpath = ["."]
 testpaths = [
     "tests/data",
@@ -28,7 +28,7 @@ testpaths = [
     "tests/prompt",
     # "tests/taskflow",  TODO (paddle 2.5.1 breaks this test suite, debug later)
     "tests/utils",
-    "model_zoo",
+    # "model_zoo",
 ]
 python_files = [
     "test.py",

tests/trainer/test_lora_unified_checkpoint.py

@@ -149,7 +149,7 @@ def __test__(cls):
 
     def setUp(self):
         """
-        1. update runfrist and rerun to run defined different config
+        1. update runfirst and rerun to run defined different config
         2. update need_allclose to True if you want to check the result
         3. update rtol to the relative value you want to check
         """
@@ -169,7 +169,7 @@ def setUp(self):
 
         self.run_lora_file = "llm/finetune_generation.py"
 
-    def runfrist(self, train_args):
+    def runfirst(self, train_args):
         self.run_n1c8(self.run_lora_file, **train_args)
 
     def rerun(self, train_args):
@@ -181,7 +181,7 @@ def testTP4PP2(self):
         remove_ckpt(lora_arguments["output_dir"])
 
         train_args = self.configs["TP4PP2"]
-        self.runfrist(train_args)
+        self.runfirst(train_args)
         self.rerun(train_args)
 
         if self.need_allclose:
@@ -196,7 +196,7 @@ def testTP2Sharding4(self):
         remove_ckpt(lora_arguments["output_dir"])
 
         train_args = self.configs["TP2Sharding4"]
-        self.runfrist(train_args)
+        self.runfirst(train_args)
         self.rerun(train_args)
 
         if self.need_allclose:
@@ -213,7 +213,7 @@ def testTP8(self):
         remove_ckpt(lora_arguments["output_dir"])
 
         train_args = self.configs["TP8"]
-        self.runfrist(train_args)
+        self.runfirst(train_args)
         self.rerun(train_args)
 
         if self.need_allclose:
@@ -227,7 +227,7 @@ def testTP4DP2(self):
         remove_ckpt(lora_arguments["output_dir"])
 
         train_args = self.configs["TP4DP2"]
-        self.runfrist(train_args)
+        self.runfirst(train_args)
         self.rerun(train_args)
 
         if self.need_allclose:
@@ -242,7 +242,7 @@ def testTP4Sharding2(self):
         remove_ckpt(lora_arguments["output_dir"])
 
         train_args = self.configs["TP4Sharding2"]
-        self.runfrist(train_args)
+        self.runfirst(train_args)
         self.rerun(train_args)
 
         if self.need_allclose:
@@ -257,7 +257,7 @@ def testTP2PP4(self):
         remove_ckpt(lora_arguments["output_dir"])
 
         train_args = self.configs["TP2PP4"]
-        self.runfrist(train_args)
+        self.runfirst(train_args)
         self.rerun(train_args)
 
         if self.need_allclose:
@@ -272,7 +272,7 @@ def testPP8(self):
         remove_ckpt(lora_arguments["output_dir"])
 
         train_args = self.configs["PP8"]
-        self.runfrist(train_args)
+        self.runfirst(train_args)
         self.rerun(train_args)
 
         if self.need_allclose:
@@ -287,7 +287,7 @@ def testPP4DP2(self):
         remove_ckpt(lora_arguments["output_dir"])
 
         train_args = self.configs["PP4DP2"]
-        self.runfrist(train_args)
+        self.runfirst(train_args)
         self.rerun(train_args)
 
         if self.need_allclose:
@@ -302,7 +302,7 @@ def testPP4Sharding2(self):
         remove_ckpt(lora_arguments["output_dir"])
 
         train_args = self.configs["PP4Sharding2"]
-        self.runfrist(train_args)
+        self.runfirst(train_args)
         self.rerun(train_args)
 
         if self.need_allclose:
@@ -317,7 +317,7 @@ def testSharding8S1(self):
         remove_ckpt(lora_arguments["output_dir"])
 
         train_args = self.configs["Sharding8S1"]
-        self.runfrist(train_args)
+        self.runfirst(train_args)
         self.rerun(train_args)
 
         if self.need_allclose:
@@ -332,7 +332,7 @@ def testSharding8S2(self):
         remove_ckpt(lora_arguments["output_dir"])
 
         train_args = self.configs["Sharding8S2"]
-        self.runfrist(train_args)
+        self.runfirst(train_args)
         self.rerun(train_args)
 
         if self.need_allclose:
@@ -347,7 +347,7 @@ def testSharding4S1DP2(self):
         remove_ckpt(lora_arguments["output_dir"])
 
         train_args = self.configs["Sharding4S1DP2"]
-        self.runfrist(train_args)
+        self.runfirst(train_args)
         self.rerun(train_args)
 
         if self.need_allclose:
@@ -362,7 +362,7 @@ def testSharding4S2DP2(self):
         remove_ckpt(lora_arguments["output_dir"])
 
         train_args = self.configs["Sharding4S2DP2"]
-        self.runfrist(train_args)
+        self.runfirst(train_args)
         self.rerun(train_args)
 
         if self.need_allclose:
@@ -377,7 +377,7 @@ def testSharding2S1DP4(self):
         remove_ckpt(lora_arguments["output_dir"])
 
         train_args = self.configs["Sharding2S1DP4"]
-        self.runfrist(train_args)
+        self.runfirst(train_args)
         self.rerun(train_args)
 
         if self.need_allclose:
@@ -392,7 +392,7 @@ def testSharding2S2DP4(self):
         remove_ckpt(lora_arguments["output_dir"])
 
         train_args = self.configs["Sharding2S2DP4"]
-        self.runfrist(train_args)
+        self.runfirst(train_args)
         self.rerun(train_args)
 
         if self.need_allclose:
@@ -407,7 +407,7 @@ def testDP8(self):
         remove_ckpt(lora_arguments["output_dir"])
 
         train_args = self.configs["DP8"]
-        self.runfrist(train_args)
+        self.runfirst(train_args)
         self.rerun(train_args)
 
         if self.need_allclose:
@@ -416,27 +416,29 @@ def testDP8(self):
             np.testing.assert_allclose(res[0], res[1], self.rtol)
 
 
+@pytest.mark.skipif(True, reason="Skip for None CE")
 class TestUnifiedCheckpointOnN2C4(TestUnifiedCheckpointBase):
     def setUp(self):
         super().setUp()
         self.need_allclose = True
         self.rtol = 1e-7
 
-    def runfrist(self, train_args):
+    def runfirst(self, train_args):
         self.run_n2c4(self.run_lora_file, **train_args)
 
     def rerun(self, train_args):
         self.run_n2c4(self.run_lora_file, **train_args)
 
 
+@pytest.mark.skipif(True, reason="Skip for None CE")
 class TestUnifiedCheckpointOnN1C8CheckpointCompatible(TestUnifiedCheckpointBase):
     def setUp(self):
         super().setUp()
 
         self.need_allclose = True
         self.rtol = 1e-7
 
-    def runfrist(self, train_args):
+    def runfirst(self, train_args):
         train_args["unified_checkpoint"] = 0
         self.run_n1c8(self.run_lora_file, **train_args)
 
@@ -445,14 +447,15 @@ def rerun(self, train_args):
         self.run_n1c8(self.run_lora_file, **train_args)
 
 
+@pytest.mark.skipif(True, reason="Skip for None CE")
 class TestPaddleCheckpointOnN1C8Reset(TestUnifiedCheckpointBase):
     def setUp(self):
         super().setUp()
 
         self.need_allclose = True
         self.rtol = 1e-7
 
-    def runfrist(self, train_args):
+    def runfirst(self, train_args):
         train_args["unified_checkpoint"] = 0
         self.run_n1c8(self.run_lora_file, **train_args)
 
@@ -469,7 +472,7 @@ def setUp(self):
         self.need_allclose = True
         self.rtol = 1e-7
 
-    def runfrist(self, train_args):
+    def runfirst(self, train_args):
         train_args["unified_checkpoint"] = 0
         self.run_n2c4(self.run_lora_file, **train_args)