Merge pull request #6022 from qingqing01/nvprof

Small fix in profiler.py of fluid.

Author: qingqing01

python/paddle/v2/fluid/profiler.py

@@ -22,21 +22,21 @@ def cuda_profiler(output_file, output_mode=None, config=None):
     `output_file` with Key-Value pair format or Comma separated values format.
     The user can set the output mode by `output_mode` argument and set the
     counters/options for profiling by `config` argument. The default config
-    caontains 'gpustarttimestamp', 'gpustarttimestamp', 'gridsize3d',
-    'threadblocksize', 'streamid', 'enableonstart 0', 'conckerneltrace'.
+    is ['gpustarttimestamp', 'gpustarttimestamp', 'gridsize3d',
+    'threadblocksize', 'streamid', 'enableonstart 0', 'conckerneltrace'].
 
     Args:
         output_file (string) : The output file name, the result will be
             written into this file.
         output_mode (string) : The output mode has Key-Value pair format and
-            Comma separated values format. It should be 'kv' or 'csv'.
+            Comma separated values format. It should be 'kvp' or 'csv'.
         config (string) : The profiler options and counters can refer to
             "Compute Command Line Profiler User Guide".
     """
     if output_mode is None:
         output_mode = 'csv'
-    if output_mode not in ['kv', 'csv']:
-        raise ValueError("The output mode must be 'key-value' or 'csv'.")
+    if output_mode not in ['kvp', 'csv']:
+        raise ValueError("The output mode must be 'kvp' or 'csv'.")
     config = NVPROF_CONFIG if config is None else config
     core.nvprof_init(output_file, output_mode, config)
     # Enables profiler collection by the active CUDA profiling tool.

python/paddle/v2/fluid/tests/test_profiler.py

@@ -20,7 +20,7 @@ def test_nvprof(self):
 
         with profiler.cuda_profiler("cuda_profiler.txt", 'csv') as nvprof:
             for i in range(epoc):
-                input = np.random.random(dshape).astype("float32")
+                input = np.random.random(dshape).astype('float32')
                 exe.run(fluid.default_main_program(), feed={'data': input})