Add fp32 support for initialize

Author: ZzEeKkAa

dpbench/benchmarks/black_scholes/black_scholes_initialize.py

@@ -7,15 +7,15 @@ def initialize(nopt, seed, types_dict):
     import numpy as np
     import numpy.random as default_rng
 
-    dtype = types_dict["float"]
-    S0L = 10.0
-    S0H = 50.0
-    XL = 10.0
-    XH = 50.0
-    TL = 1.0
-    TH = 2.0
-    RISK_FREE = 0.1
-    VOLATILITY = 0.2
+    dtype: np.dtype = types_dict["float"]
+    S0L = dtype.type(10.0)
+    S0H = dtype.type(50.0)
+    XL = dtype.type(10.0)
+    XH = dtype.type(50.0)
+    TL = dtype.type(1.0)
+    TH = dtype.type(2.0)
+    RISK_FREE = dtype.type(0.1)
+    VOLATILITY = dtype.type(0.2)
 
     default_rng.seed(seed)
     price = default_rng.uniform(S0L, S0H, nopt).astype(dtype)

dpbench/benchmarks/dbscan/dbscan_initialize.py

@@ -64,8 +64,10 @@ class Params(NamedTuple):
         data_size, Params(eps=DEFAULT_EPS, minpts=DEFAULT_MINPTS)
     )
 
+    dtype: np.dtype = types_dict["float"]
+
     return (
-        X.flatten().astype(types_dict["float"]),
-        params.eps,
+        X.flatten().astype(dtype),
+        dtype.type(params.eps),
         params.minpts,
     )

dpbench/benchmarks/kmeans/kmeans_initialize.py

@@ -7,10 +7,10 @@ def initialize(npoints, niters, seed, ndims, ncentroids, types_dict):
     import numpy as np
     import numpy.random as default_rng
 
-    f_dtype = types_dict["float"]
-    i_dtype = types_dict["int"]
-    XL = 1.0
-    XH = 5.0
+    f_dtype: np.dtype = types_dict["float"]
+    i_dtype: np.dtype = types_dict["int"]
+    XL = f_dtype.type(1.0)
+    XH = f_dtype.type(5.0)
 
     default_rng.seed(seed)
 

dpbench/benchmarks/knn/knn_initialize.py

@@ -41,6 +41,6 @@ def _gen_test_data(test_size, data_dim, seed_test, dtype):
     )
     x_test = _gen_test_data(test_size, data_dim, seed_test, dtype)
     predictions = np.empty(test_size, types_dict["int"])
-    votes_to_classes = np.zeros((test_size, classes_num))
+    votes_to_classes = np.zeros((test_size, classes_num), dtype)
 
     return (x_train, y_train, x_test, predictions, votes_to_classes)

dpbench/benchmarks/rambo/rambo_initialize.py

@@ -19,4 +19,4 @@ def initialize(nevts, nout, types_dict):
             F1[i, j] = np.random.rand()
             Q1[i, j] = np.random.rand() * np.random.rand()
 
-    return (C1, F1, Q1, np.empty((nevts, nout, 4)))
+    return (C1, F1, Q1, np.empty((nevts, nout, 4), dtype))