[CodeStyle][Typos][E-[11-20]] Fix typos(enfore, entrys, `envirnme…

…nt`, `environnement`, `epoches`, `EPOCHES`, `epslion`, `eqaul`, `Errorr`, `exmaple`, `expection`, `excption`) (#70447)

_typos.toml

@@ -20,6 +20,7 @@ clen = 'clen'
 cll = 'cll'
 dout = "dout"
 eles = 'eles'
+entrys = 'entrys'
 grad = "grad"
 kinf = 'kinf'
 kow = 'kow'
@@ -97,18 +98,6 @@ doubel = 'doubel'
 dobule = 'dobule'
 Dowloading = 'Dowloading'
 downsteram = 'downsteram'
-enfore = 'enfore'
-entrys = 'entrys'
-envirnment = 'envirnment'
-environnement = 'environnement'
-epoches = 'epoches'
-EPOCHES = 'EPOCHES'
-epslion = 'epslion'
-eqaul = 'eqaul'
-Errorr = 'Errorr'
-exmaple = 'exmaple'
-expection = 'expection'
-excption = 'excption'
 execuate = 'execuate'
 exsit = 'exsit'
 exsits = 'exsits'

paddle/cinn/hlir/framework/pir/compilation_task.cc

@@ -93,7 +93,7 @@ void GroupCompilationContext::PrepareModuleBuilder() {
 /**
  * For functions belonging to different broadcast groups, int args and the name
  * of the tensor args may be variate, but the number of the tensor args should
- * be fixed. So we need to unify the tensor args and symbol args. For exmaple,
+ * be fixed. So we need to unify the tensor args and symbol args. For example,
  * func1(_var, _var_1, S4, S5); func2(_var, _var_2, S1) would be unified to
  * func1(_var, _var_1, S4, S5, S1); func2(_var, _var_2, S4, S5, S1).
  */

paddle/cinn/utils/multi_threading.cc

@@ -100,7 +100,7 @@ void parallel_run(const WorkerFuncType& fn,
   } catch (::common::EnforceNotMet& ex) {
     LOG(ERROR) << ex.error_str();
     PADDLE_THROW(
-        ::common::errors::Fatal("Parallel compile Paddle enfore error"));
+        ::common::errors::Fatal("Parallel compile Paddle enforce error"));
   } catch (const std::exception& e) {
     LOG(ERROR) << "Parallel compile error " << e.what();
     PADDLE_THROW(::common::errors::Fatal("Parallel compile std::exception"));

paddle/fluid/platform/device/ipu/ipu_utils.h

@@ -104,7 +104,7 @@ const popart::DataType OnnxDType2PopartType(const ONNXDataType type);
 const ONNXDataType VarType2OnnxDType(const VarType::Type type);
 // VarType::Type to String in Popart
 const std::string VarType2PopartStr(const VarType::Type type);
-// Get bool from envirnment varaible
+// Get bool from environment varaible
 const bool GetBoolEnv(const std::string& str);
 // Request number of ipus must be pow(2, n)
 const int RequestIpus(const int num_ipus);

paddle/phi/backends/gpu/rocm/miopen_helper.h

@@ -28,7 +28,7 @@ limitations under the License. */
 #include "paddle/phi/core/dense_tensor.h"
 #include "paddle/phi/core/enforce.h"
 
-// MIOPEN do not have epslion definition
+// MIOPEN do not have epsilon definition
 #define CUDNN_BN_MIN_EPSILON 1e-05
 
 COMMON_DECLARE_bool(cudnn_deterministic);

paddle/phi/infermeta/fusion.cc

@@ -4345,7 +4345,7 @@ void CrossAttentionXPUInferMeta(
                     common::errors::InvalidArgument(
                         "The dim of input_kv should be 3! But received ",
                         input_kv_dims.size()));
-  // sequece length of q and k/v  not requied to be eqaul
+  // sequece length of q and k/v  not required to be equal
   // but batch size and dim should be the same
   PADDLE_ENFORCE_EQ(
       input_q_dims[0],

python/paddle/distributed/auto_parallel/high_level_api.py

@@ -309,7 +309,7 @@ def to_distributed(
             >>> from paddle.distributed import to_distributed
             >>> from paddle.distributed.auto_parallel.high_level_api import ToDistributedConfig
 
-            >>> EPOCHES = 1
+            >>> EPOCHS = 1
             >>> VOCAB_SIZE = 8000
             >>> BATCH_NUM = 2
             >>> BATCH_SIZE = 4
@@ -670,7 +670,7 @@ def to_distributed(
             ...     config=dist_config,
             ... )
 
-            >>> for epoch in range(EPOCHES):
+            >>> for epoch in range(EPOCHS):
             ...     dist_model.train()
             ...     for i, data in enumerate(dist_loader()):
             ...         inputs, labels = data

python/paddle/distributed/fleet/launch_utils.py

@@ -508,7 +508,7 @@ def start_local_trainers(
             "PADDLE_WORLD_DEVICE_IDS": ",".join(res),
         }
 
-        # The following three environnement variables are used for auto mapping
+        # The following three environment variables are used for auto mapping
         if current_env.get("PADDLE_CLUSTER_TOPO_PATH", None) is not None:
             proc_env["PADDLE_CLUSTER_TOPO_PATH"] = current_env[
                 "PADDLE_CLUSTER_TOPO_PATH"

python/paddle/io/dataloader/batch_sampler.py

@@ -374,7 +374,7 @@ def set_epoch(self, epoch: int) -> None:
         as seeds of random numbers. By default, users may not set this, all
         replicas (workers) use a different random ordering for each epoch.
         If set same number at each epoch, this sampler will yield the same
-        ordering at all epoches.
+        ordering at all epochs.
 
         Arguments:
             epoch (int): Epoch number.

test/auto_parallel/hybrid_strategy/to_distributed_api_for_llama.py

@@ -24,7 +24,7 @@
 from paddle.distributed import to_distributed
 from paddle.distributed.auto_parallel.high_level_api import ToDistributedConfig
 
-EPOCHES = 1
+EPOCHS = 1
 VOCAB_SIZE = 8000
 BATCH_NUM = 2
 BATCH_SIZE = 4
@@ -627,7 +627,7 @@ def test_to_distributed_api(self):
             dist_config,
         )
 
-        for epoch in range(EPOCHES):
+        for epoch in range(EPOCHS):
             dist_model.train()
             for i, data in enumerate(dist_loader()):
                 inputs, labels = data

test/cpp/pir/core/paddle_fatal_test.cc

@@ -25,7 +25,7 @@ class FatalClass {
 
 void throw_exception_in_func() {
   FatalClass test_case;
-  PADDLE_THROW(::common::errors::External("throw excption in func"));
+  PADDLE_THROW(::common::errors::External("throw exception in func"));
 }
 
 void terminate_in_func() { FatalClass test_case; }

test/deprecated/legacy_test/test_data_norm_op_deprecated.py

@@ -24,7 +24,7 @@
 paddle.enable_static()
 
 
-class TestDataNormOpErrorr(unittest.TestCase):
+class TestDataNormOpError(unittest.TestCase):
     def test_errors(self):
         with program_guard(Program(), Program()):
             x2 = paddle.static.data(name='x2', shape=[-1, 3, 4], dtype="int32")

test/legacy_test/test_imperative_data_loader_exception.py

@@ -42,7 +42,7 @@ def test_not_capacity(self):
             ):
                 base.io.DataLoader.from_generator()
 
-    def test_single_process_with_thread_expection(self):
+    def test_single_process_with_thread_exception(self):
         def error_sample_genarator(batch_num):
             def __reader__():
                 for _ in range(batch_num):
@@ -60,13 +60,13 @@ def __reader__():
             exception = None
             try:
                 for _ in loader():
-                    print("test_single_process_with_thread_expection")
+                    print("test_single_process_with_thread_exception")
             except core.EnforceNotMet as ex:
                 self.assertIn("Blocking queue is killed", str(ex))
                 exception = ex
             self.assertIsNotNone(exception)
 
-    def test_multi_process_with_process_expection(self):
+    def test_multi_process_with_process_exception(self):
         def error_sample_genarator(batch_num):
             def __reader__():
                 for _ in range(batch_num):
@@ -84,7 +84,7 @@ def __reader__():
             exception = None
             try:
                 for _ in loader():
-                    print("test_multi_process_with_thread_expection")
+                    print("test_multi_process_with_thread_exception")
             except core.EnforceNotMet as ex:
                 exception = ex
             self.assertIsNotNone(exception)

test/legacy_test/test_imperative_deepcf.py

@@ -156,7 +156,7 @@ def setUp(self):
 
         self.batch_size = int(os.environ.get('BATCH_SIZE', 128))
         self.num_batches = int(os.environ.get('NUM_BATCHES', 5))
-        self.num_epoches = int(os.environ.get('NUM_EPOCHES', 1))
+        self.num_epochs = int(os.environ.get('NUM_EPOCHS', 1))
 
     def get_data(self):
         user_ids = []
@@ -277,7 +277,7 @@ def test_deefcf(self):
                 else base.CUDAPlace(0)
             )
             exe.run(startup)
-            for e in range(self.num_epoches):
+            for e in range(self.num_epochs):
                 sys.stderr.write(f'epoch {e}\n')
                 for slice in range(
                     0, self.batch_size * self.num_batches, self.batch_size
@@ -307,7 +307,7 @@ def test_deefcf(self):
 
             deepcf = DeepCF(num_users, num_items, matrix)
             adam = paddle.optimizer.Adam(0.01, parameters=deepcf.parameters())
-            for e in range(self.num_epoches):
+            for e in range(self.num_epochs):
                 sys.stderr.write(f'epoch {e}\n')
                 for slice in range(
                     0, self.batch_size * self.num_batches, self.batch_size
@@ -343,7 +343,7 @@ def test_deefcf(self):
             deepcf2 = DeepCF(num_users, num_items, matrix)
             adam2 = paddle.optimizer.Adam(0.01, parameters=deepcf2.parameters())
             base.set_flags({'FLAGS_sort_sum_gradient': True})
-            for e in range(self.num_epoches):
+            for e in range(self.num_epochs):
                 sys.stderr.write(f'epoch {e}\n')
                 for slice in range(
                     0, self.batch_size * self.num_batches, self.batch_size
@@ -379,7 +379,7 @@ def test_deefcf(self):
             deepcf = DeepCF(num_users, num_items, matrix)
             adam = paddle.optimizer.Adam(0.01, parameters=deepcf.parameters())
 
-            for e in range(self.num_epoches):
+            for e in range(self.num_epochs):
                 sys.stderr.write(f'epoch {e}\n')
                 for slice in range(
                     0, self.batch_size * self.num_batches, self.batch_size