ENH: update random.binomial (#650)

* ENH: update random.binomial

Author: samir-nasibli

dpnp/backend/kernels/dpnp_krnl_random.cpp

@@ -108,27 +108,45 @@ INP_DLLEXPORT void dpnp_rng_beta_c(void* result, const _DataType a, const _DataT
 template <typename _DataType>
 void dpnp_rng_binomial_c(void* result, const int ntrial, const double p, const size_t size)
 {
+    if (result == nullptr)
+    {
+        return;
+    }
     if (!size)
     {
         return;
     }
     _DataType* result1 = reinterpret_cast<_DataType*>(result);
 
-    if (dpnp_queue_is_cpu_c())
+    if (ntrial == 0 || p == 0)
     {
-        mkl_rng::binomial<_DataType> distribution(ntrial, p);
-        // perform generation
-        auto event_out = mkl_rng::generate(distribution, DPNP_RNG_ENGINE, size, result1);
-        event_out.wait();
+        dpnp_zeros_c<_DataType>(result, size);
+    }
+    else if (p == 1)
+    {
+        _DataType* fill_value = reinterpret_cast<_DataType*>(dpnp_memory_alloc_c(sizeof(_DataType)));
+        fill_value[0] = static_cast<_DataType>(ntrial);
+        dpnp_initval_c<_DataType>(result, fill_value, size);
+        dpnp_memory_free_c(fill_value);
     }
     else
     {
-        int errcode = viRngBinomial(VSL_RNG_METHOD_BINOMIAL_BTPE, get_rng_stream(), size, result1, ntrial, p);
-        if (errcode != VSL_STATUS_OK)
+        if (dpnp_queue_is_cpu_c())
         {
-            throw std::runtime_error("DPNP RNG Error: dpnp_rng_binomial_c() failed.");
+            mkl_rng::binomial<_DataType> distribution(ntrial, p);
+            auto event_out = mkl_rng::generate(distribution, DPNP_RNG_ENGINE, size, result1);
+            event_out.wait();
+        }
+        else
+        {
+            int errcode = viRngBinomial(VSL_RNG_METHOD_BINOMIAL_BTPE, get_rng_stream(), size, result1, ntrial, p);
+            if (errcode != VSL_STATUS_OK)
+            {
+                throw std::runtime_error("DPNP RNG Error: dpnp_rng_binomial_c() failed.");
+            }
         }
     }
+    return;
 }
 
 template <typename _DataType>

dpnp/random/dpnp_algo_random.pyx

@@ -177,26 +177,19 @@ cpdef dparray dpnp_rng_binomial(int ntrial, double p, size):
     cdef DPNPFuncData kernel_data
     cdef fptr_dpnp_rng_binomial_c_1out_t func
 
-    if ntrial == 0 or p == 0.0:
-        result = dparray(size, dtype=dtype)
-        result.fill(0.0)
-    elif p == 1.0:
-        result = dparray(size, dtype=dtype)
-        result.fill(ntrial)
-    else:
-        # convert string type names (dparray.dtype) to C enum DPNPFuncType
-        param1_type = dpnp_dtype_to_DPNPFuncType(dtype)
+    # convert string type names (dparray.dtype) to C enum DPNPFuncType
+    param1_type = dpnp_dtype_to_DPNPFuncType(dtype)
 
-        # get the FPTR data structure
-        kernel_data = get_dpnp_function_ptr(DPNP_FN_RNG_BINOMIAL, param1_type, param1_type)
+    # get the FPTR data structure
+    kernel_data = get_dpnp_function_ptr(DPNP_FN_RNG_BINOMIAL, param1_type, param1_type)
 
-        result_type = dpnp_DPNPFuncType_to_dtype(< size_t > kernel_data.return_type)
-        # ceate result array with type given by FPTR data
-        result = dparray(size, dtype=result_type)
+    result_type = dpnp_DPNPFuncType_to_dtype(< size_t > kernel_data.return_type)
+    # ceate result array with type given by FPTR data
+    result = dparray(size, dtype=result_type)
 
-        func = <fptr_dpnp_rng_binomial_c_1out_t > kernel_data.ptr
-        # call FPTR function
-        func(result.get_data(), ntrial, p, result.size)
+    func = <fptr_dpnp_rng_binomial_c_1out_t > kernel_data.ptr
+    # call FPTR function
+    func(result.get_data(), ntrial, p, result.size)
 
     return result