Merge pull request #224 from IntelPython/feature/dtpye-precision-selection

Select dtype precision through configs

Author: adarshyoga

dpbench/benchmarks/black_scholes/black_scholes_initialize.py

@@ -3,11 +3,11 @@
 # SPDX-License-Identifier: Apache-2.0
 
 
-def initialize(nopt, seed):
+def initialize(nopt, seed, types_dict):
     import numpy as np
     import numpy.random as default_rng
 
-    dtype = np.float64
+    dtype = types_dict["float"]
     S0L = 10.0
     S0H = 50.0
     XL = 10.0

dpbench/benchmarks/dbscan/dbscan_initialize.py

@@ -3,7 +3,7 @@
 # SPDX-License-Identifier: Apache-2.0
 
 
-def initialize(n_samples, n_features, centers, seed):
+def initialize(n_samples, n_features, centers, seed, types_dict):
     from typing import NamedTuple
 
     import numpy as np
@@ -12,7 +12,6 @@ def initialize(n_samples, n_features, centers, seed):
 
     DEFAULT_EPS = 0.6
     DEFAULT_MINPTS = 20
-    dtype = np.float64
 
     class DataSize(NamedTuple):
         n_samples: int
@@ -66,7 +65,7 @@ class Params(NamedTuple):
     )
 
     return (
-        X.flatten().astype(dtype),
+        X.flatten().astype(types_dict["float"]),
         params.eps,
         params.minpts,
     )

dpbench/benchmarks/gpairs/gpairs_initialize.py

@@ -11,11 +11,11 @@ def _generate_rbins(dtype, nbins, rmax, rmin):
     return (rbins**2).astype(dtype)
 
 
-def initialize(nopt, seed, nbins, rmax, rmin):
+def initialize(nopt, seed, nbins, rmax, rmin, types_dict):
     import numpy.random as default_rng
 
     default_rng.seed(seed)
-    dtype = np.float32
+    dtype = types_dict["float"]
     x1 = np.random.randn(nopt).astype(dtype)
     y1 = np.random.randn(nopt).astype(dtype)
     z1 = np.random.randn(nopt).astype(dtype)

dpbench/benchmarks/kmeans/kmeans_initialize.py

@@ -3,20 +3,21 @@
 # SPDX-License-Identifier: Apache-2.0
 
 
-def initialize(npoints, niters, seed, ndims, ncentroids):
+def initialize(npoints, niters, seed, ndims, ncentroids, types_dict):
     import numpy as np
     import numpy.random as default_rng
 
-    dtype = np.float64
+    f_dtype = types_dict["float"]
+    i_dtype = types_dict["int"]
     XL = 1.0
     XH = 5.0
 
     default_rng.seed(seed)
 
-    arrayP = default_rng.uniform(XL, XH, (npoints, ndims)).astype(dtype)
-    arrayPclusters = np.ones(npoints, dtype=np.int64)
-    arrayC = np.ones((ncentroids, ndims), dtype=dtype)
-    arrayCsum = np.ones((ncentroids, ndims), dtype=dtype)
-    arrayCnumpoint = np.ones(ncentroids, dtype=np.int64)
+    arrayP = default_rng.uniform(XL, XH, (npoints, ndims)).astype(f_dtype)
+    arrayPclusters = np.ones(npoints, dtype=i_dtype)
+    arrayC = np.ones((ncentroids, ndims), dtype=f_dtype)
+    arrayCsum = np.ones((ncentroids, ndims), dtype=f_dtype)
+    arrayCnumpoint = np.ones(ncentroids, dtype=i_dtype)
 
     return (arrayP, arrayPclusters, arrayC, arrayCsum, arrayCnumpoint)

dpbench/benchmarks/knn/knn_initialize.py

@@ -4,12 +4,18 @@
 
 
 def initialize(
-    test_size, train_size, data_dim, classes_num, seed_test, seed_train
+    test_size,
+    train_size,
+    data_dim,
+    classes_num,
+    seed_test,
+    seed_train,
+    types_dict,
 ):
     import numpy as np
     import numpy.random as default_rng
 
-    dtype = np.float64
+    dtype = types_dict["float"]
 
     def _gen_data_x(ip_size, data_dim, seed, dtype):
         default_rng.seed(seed)
@@ -34,7 +40,7 @@ def _gen_test_data(test_size, data_dim, seed_test, dtype):
         train_size, data_dim, classes_num, seed_train, dtype
     )
     x_test = _gen_test_data(test_size, data_dim, seed_test, dtype)
-    predictions = np.empty(test_size, np.int64)
+    predictions = np.empty(test_size, types_dict["int"])
     votes_to_classes = np.zeros((test_size, classes_num))
 
     return (x_train, y_train, x_test, predictions, votes_to_classes)

dpbench/benchmarks/l2_norm/l2_norm_initialize.py

@@ -3,11 +3,11 @@
 # SPDX-License-Identifier: Apache-2.0
 
 
-def initialize(npoints, dims, seed):
+def initialize(npoints, dims, seed, types_dict):
     import numpy as np
     import numpy.random as default_rng
 
-    dtype = np.float64
+    dtype = types_dict["float"]
 
     default_rng.seed(seed)
 

dpbench/benchmarks/pairwise_distance/pairwise_distance_initialize.py

@@ -3,11 +3,11 @@
 # SPDX-License-Identifier: Apache-2.0
 
 
-def initialize(npoints, dims, seed):
+def initialize(npoints, dims, seed, types_dict):
     import numpy as np
     import numpy.random as default_rng
 
-    dtype = np.float64
+    dtype = types_dict["float"]
 
     default_rng.seed(seed)
 

dpbench/benchmarks/rambo/rambo_initialize.py

@@ -3,12 +3,14 @@
 # SPDX-License-Identifier: Apache-2.0
 
 
-def initialize(nevts, nout):
+def initialize(nevts, nout, types_dict):
     import numpy as np
 
-    C1 = np.empty((nevts, nout))
-    F1 = np.empty((nevts, nout))
-    Q1 = np.empty((nevts, nout))
+    dtype = types_dict["float"]
+
+    C1 = np.empty((nevts, nout), dtype=dtype)
+    F1 = np.empty((nevts, nout), dtype=dtype)
+    Q1 = np.empty((nevts, nout), dtype=dtype)
 
     np.random.seed(777)
     for i in range(nevts):

dpbench/config/benchmark.py

@@ -19,6 +19,8 @@ class Init:
     func_name: str = ""
     package_path: str = ""
     module_name: str = ""
+    types_dict_name: str = ""
+    precision: str = ""
     input_args: List[str] = field(default_factory=list)
     output_args: List[str] = field(default_factory=list)
 
@@ -28,10 +30,18 @@ def from_dict(obj: Any) -> "Init":
         _func_name = str(obj.get("func_name") or "")
         _package_path = str(obj.get("package_path") or "")
         _module_name = str(obj.get("module_name") or "")
+        _types_dict_name = str(obj.get("types_dict_name") or "")
+        _precision = str(obj.get("precision") or "")
         _input_args = obj.get("input_args")
         _output_args = obj.get("output_args")
         return Init(
-            _func_name, _package_path, _module_name, _input_args, _output_args
+            _func_name,
+            _package_path,
+            _module_name,
+            _types_dict_name,
+            _precision,
+            _input_args,
+            _output_args,
         )
 
     def __post_init__(self):

dpbench/config/config.py

@@ -18,3 +18,4 @@ class Config:
     frameworks: list[Framework]
     benchmarks: list[Benchmark]
     implementations: list[Implementation]
+    dtypes: dict[str, dict[str, str]]