Merge pull request #227 from IntelPython/fix/npbench_mlp

Fix mlp numba parallel benchmark

Author: adarshyoga

dpbench/benchmarks/npbench/deep_learning/mlp/mlp_numba_np.py

@@ -18,8 +18,8 @@ def softmax(x):
     new_shape = (x.shape[0], 1)
     # tmp_max = np.max(x, axis=-1, keepdims=True)
     tmp_max = np.empty(new_shape, dtype=x.dtype)
-    for i in range(x.shape[1]):
-        tmp_max[:, 0] = np.max(x[:, i])
+    for i in range(x.shape[0]):
+        tmp_max[i, 0] = np.max(x[i])
     tmp_out = np.exp(x - tmp_max)
     # tmp_sum = np.sum(tmp_out, axis=-1, keepdims=True)
     tmp_sum = np.reshape(np.sum(tmp_out, axis=-1), new_shape)

dpbench/benchmarks/npbench/deep_learning/mlp/mlp_numba_npr.py

@@ -18,8 +18,8 @@ def softmax(x):
     new_shape = (x.shape[0], 1)
     # tmp_max = np.max(x, axis=-1, keepdims=True)
     tmp_max = np.empty(new_shape, dtype=x.dtype)
-    for i in nb.prange(x.shape[1]):
-        tmp_max[:, 0] = np.max(x[:, i])
+    for i in nb.prange(x.shape[0]):
+        tmp_max[i, 0] = np.max(x[i])
     tmp_out = np.exp(x - tmp_max)
     # tmp_sum = np.sum(tmp_out, axis=-1, keepdims=True)
     tmp_sum = np.reshape(np.sum(tmp_out, axis=-1), new_shape)