Merge remote-tracking branch 'upstream/experimental' into experimental

Author: shivasankarka

README.MD

@@ -14,6 +14,8 @@
     <br />
     <!-- when we create docs -->
     <a href="https://github.com/Mojo-Numerics-and-Algorithms-group/NuMojo-Examples-and-Benchmarks/blob/main/docs/README.md"><strong>Explore the docs» </strong></a>
+    <br>
+    <a href="https://discord.com/channels/1149778565756366939/1149778566603620455"><strong>Check out our Discord» </strong></a>
     <br />
     <!-- <br /> -->
     <!-- <a href="">View Demo</a>

new_tests/README.md

@@ -0,0 +1,12 @@
+# Testing guide
+
+Inorder to ensure that all of our code works and that a new code doesn't break old code we need to instate a testing policy (which we can hopefully implement a CI/CD process for running).
+
+Due to the similiarity with NumPy, a Numpy equivelent comparison will be our method for confirming validity.
+
+The functions contained in utils_for_test.mojo `check` and `check_is_close` will be used to compare NuMojo results with NumPy equivelents.
+
+Each test must be in it's own def function and begin with the word test example `test_arange`. This allows the `mojo test` command to find it. A single function can cover multiple similiar tests but they should have unique strings to identify which check failed.
+
+
+

new_tests/test_array_creation.mojo

@@ -0,0 +1,68 @@
+import numojo as nm
+from time import now
+from python import Python, PythonObject
+from utils_for_test import check, check_is_close
+    
+def test_arange():
+    var np = Python.import_module("numpy")
+    check(nm.arange[nm.i64](0,100),np.arange(0,100,dtype=np.int64),"Arange is broken")
+    check(nm.arange[nm.f64](0,100),np.arange(0,100,dtype=np.float64),"Arange is broken")
+
+def test_linspace():
+    var np = Python.import_module("numpy")
+    check(nm.linspace[nm.f64](0,100),np.linspace(0,100,dtype=np.float64),"Linspace is broken")
+
+def test_logspace():
+    var np = Python.import_module("numpy")
+    check_is_close(nm.logspace[nm.f64](0,100,5),np.logspace(0,100,5,dtype=np.float64),"Logspace is broken")
+
+def test_geomspace():
+    var np = Python.import_module("numpy")
+    check_is_close(nm.geomspace[nm.f64](1,100,5),np.geomspace(1,100,5,dtype=np.float64),"Logspace is broken")
+
+def test_zeros():
+    var np = Python.import_module("numpy")
+    check(nm.zeros[nm.f64](10,10,10,10),np.zeros((10,10,10,10),dtype=np.float64),"Zeros is broken")
+
+def test_ones():
+    var np = Python.import_module("numpy")
+    check(nm.ones[nm.f64](10,10,10,10),np.ones((10,10,10,10),dtype=np.float64),"Ones is broken")
+
+def test_full():
+    var np = Python.import_module("numpy")
+    check(nm.full[nm.f64](10,10,10,10,fill_value=10),np.full((10,10,10,10),10,dtype=np.float64),"Full is broken")
+
+def test_identity():
+    var np = Python.import_module("numpy")
+    check(nm.identity[nm.i64](100),np.identity(100,dtype=np.int64),"Identity is broken")
+
+def test_eye():
+    var np = Python.import_module("numpy")
+    check(nm.eye[nm.i64](100,100),np.eye(100,100,dtype=np.int64),"Eye is broken")
+def main():
+    var np = Python.import_module("numpy")
+    var arr = nm.arange[nm.f64](0,100)
+    arr.reshape(10,10)
+    var np_arr = np.arange(0,100).reshape(10,10)
+    # Arange like flat arrays
+    # check(nm.arange[nm.i64](0,100),np.arange(0,100,dtype=np.int64),"Arange is broken")
+    # check(nm.linspace[nm.i64](0,100),np.linspace(0,100,dtype=np.float64),"Linspace is broken")
+    # check_is_close(nm.logspace[nm.i64](0,100,5),np.logspace(0,100,5,dtype=np.float64),"Logspace is broken")
+    # check_is_close(nm.geomspace[nm.i64](1,100,5),np.geomspace(1,100,5,dtype=np.float64),"Logspace is broken")
+    # print((arr@arr).to_numpy()-np.matmul(np_arr,np_arr))
+    print(nm.matmul_naive[nm.f64](arr,arr).to_numpy())#-np.matmul(np_arr,np_arr))
+    print(np.matmul(np_arr,np_arr))
+    # # Basic ND arrays
+    # print(nm.sin[nm.f64](nm.arange[nm.f64](0,15)))
+    # print( np.sin(np.arange(0,15, dtype=np.float64)))
+    # check(nm.zeros[nm.f64](10,10,10,10),np.zeros((10,10,10,10),dtype=np.float64),"Zeros is broken")
+    # check(nm.ones[nm.f64](10,10,10,10),np.ones((10,10,10,10),dtype=np.float64),"Ones is broken")
+    # check(nm.full[nm.f64](10,10,10,10,fill_value=10),np.full((10,10,10,10),10,dtype=np.float64),"Full is broken")
+    
+    # # 2d Linalg related arrays
+    
+    # check(nm.identity[nm.i64](100),np.identity(100,dtype=np.int64),"Identity is broken")
+    # check(nm.eye[nm.i64](100,100),np.eye(100,100,dtype=np.int64),"Eye is broken")
+    
+
+    

new_tests/test_math.mojo

@@ -0,0 +1,41 @@
+import numojo as nm
+from time import now
+from python import Python, PythonObject
+from utils_for_test import check, check_is_close
+
+
+def test_add_array():
+    var np = Python.import_module("numpy")
+    var arr = nm.arange[nm.f64](0,15)
+
+    check(nm.add[nm.f64](arr,5.0),np.arange(0,15)+5,"Add array + scalar")
+    check(nm.add[nm.f64](arr,arr),np.arange(0,15)+np.arange(0,15),"Add array + array")
+
+def test_add_array_par():
+    var np = Python.import_module("numpy")
+    var arr = nm.arange[nm.f64](0,500)
+
+    check(nm.add[nm.f64,backend=nm.math.math_funcs.VectorizedParallelizedNWorkers[6]](arr,5.0),np.arange(0,500)+5,"Add array + scalar")
+    check(nm.add[nm.f64,nm.math.math_funcs.VectorizedParallelizedNWorkers[6]](arr,arr),np.arange(0,500)+np.arange(0,500),"Add array + array")
+
+def test_sin():
+    var np = Python.import_module("numpy")
+    var arr = nm.arange[nm.f64](0,15)
+
+    check_is_close(nm.sin[nm.f64](arr),np.sin(np.arange(0,15)),"Add array + scalar")
+
+def test_sin_par():
+    var np = Python.import_module("numpy")
+    var arr = nm.arange[nm.f64](0,15)
+
+    check_is_close(nm.sin[nm.f64, backend=nm .math.math_funcs.VectorizedParallelizedNWorkers[6]](arr),np.sin(np.arange(0,15)),"Add array + scalar")
+
+def test_matmul():
+    var np = Python.import_module("numpy")
+    var arr = nm.arange[nm.f64](0,100)
+    arr.reshape(10,10)
+    var np_arr = np.arange(0,100).reshape(10,10)
+    check_is_close(arr@arr,np.matmul(np_arr,np_arr),"Dunder matmul is broken")
+    # The only matmul that currently works is par (__matmul__)
+    # check_is_close(nm.matmul_tiled_unrolled_parallelized(arr,arr),np.matmul(np_arr,np_arr),"TUP matmul is broken")
+

new_tests/utils_for_test.mojo

@@ -0,0 +1,12 @@
+from python import Python, PythonObject
+from testing.testing import assert_true
+import numojo as nm
+
+
+fn check(array:nm.NDArray,np_sol:PythonObject,st:String)raises:
+    var np = Python.import_module("numpy")
+    assert_true(np.all(np.equal(array.to_numpy(),np_sol)),st)
+
+fn check_is_close(array:nm.NDArray,np_sol:PythonObject,st:String)raises:
+    var np = Python.import_module("numpy")
+    assert_true(np.all(np.isclose(array.to_numpy(),np_sol)),st)