Get rid of block size

dpnp/dpnp_iface_histograms.py

@@ -298,16 +298,12 @@ def histogram(a, bins=10, range=None, density=None, weights=None):
     else:
         ntype = weights.dtype
 
-    # We set a block size, as this allows us to iterate over chunks when
-    # computing histograms, to minimize memory usage.
-    block_size = 65536
-
     # The fast path uses bincount, but that only works for certain types
     # of weight
     # simple_weights = (
     #     weights is None or
-    #     np.can_cast(weights.dtype, np.double) or
-    #     np.can_cast(weights.dtype, complex)
+    #     dpnp.can_cast(weights.dtype, dpnp.double) or
+    #     dpnp.can_cast(weights.dtype, complex)
     # )
     # TODO: implement a fast path
     simple_weights = False
@@ -317,24 +313,19 @@ def histogram(a, bins=10, range=None, density=None, weights=None):
         pass
     else:
         # Compute via cumulative histogram
-        cum_n = dpnp.zeros_like(bin_edges, dtype=ntype)
         if weights is None:
-            for i in _range(0, len(a), block_size):
-                sa = dpnp.sort(a[i : i + block_size])
-                cum_n += _search_sorted_inclusive(sa, bin_edges)
+            sa = dpnp.sort(a)
+            cum_n = _search_sorted_inclusive(sa, bin_edges)
         else:
             zero = dpnp.zeros(
                 1, dtype=ntype, sycl_queue=a.sycl_queue, usm_type=a.usm_type
             )
-            for i in _range(0, len(a), block_size):
-                tmp_a = a[i : i + block_size]
-                tmp_w = weights[i : i + block_size]
-                sorting_index = dpnp.argsort(tmp_a)
-                sa = tmp_a[sorting_index]
-                sw = tmp_w[sorting_index]
-                cw = dpnp.concatenate((zero, sw.cumsum(dtype=ntype)))
-                bin_index = _search_sorted_inclusive(sa, bin_edges)
-                cum_n += cw[bin_index]
+            sorting_index = dpnp.argsort(a)
+            sa = a[sorting_index]
+            sw = weights[sorting_index]
+            cw = dpnp.concatenate((zero, sw.cumsum(dtype=ntype)))
+            bin_index = _search_sorted_inclusive(sa, bin_edges)
+            cum_n = cw[bin_index]
 
         n = dpnp.diff(cum_n)