[JAX SC] Vectorize ragged input array generation (~16x speedup).

This is particularly bad with lot of tables and larges batch size
wasting upto ~63% of the benchmark time in generation, this
vectorization brings it close to 10% now.

PiperOrigin-RevId: 836946285

Author: adityagupta1089

jax_tpu_embedding/sparsecore/lib/nn/tests/preprocess_input_benchmarks.py

@@ -134,17 +134,16 @@ def generate_samples_for_feature_spec(feature_specs, num_samples, ragged=False):
       all_features.append(features)
       all_feature_weights.append(feature_weights)
     else:
-      features = []
-      feature_weights = []
-      for _ in range(num_samples):
-        num_ids = np.random.randint(1, 32)
-        ids = np.random.randint(
-            table_spec.vocabulary_size,
-            size=(num_ids,),
-            dtype=np.int32,
-        )
-        features.append(ids)
-        feature_weights.append(np.ones((num_ids,), dtype=np.float32))
+      counts = np.random.randint(1, 32, size=num_samples)
+      total_ids = np.sum(counts)
+      ids_flat = np.random.randint(
+          table_spec.vocabulary_size,
+          size=(total_ids,),
+          dtype=np.int32,
+      )
+      split_indices = np.cumsum(counts)[:-1]
+      features = np.split(ids_flat, split_indices)
+      feature_weights = [np.ones((c,), dtype=np.float32) for c in counts]
       all_features.append(np.array(features, dtype=object))
       all_feature_weights.append(np.array(feature_weights, dtype=object))
   return all_features, all_feature_weights
@@ -160,18 +159,19 @@ def generate_sparse_coo_inputs_for_feature_spec(
 
   for feature_spec in feature_specs:
     table_spec = feature_spec.table_spec
-    indices_tensors = []
-    values_tensors = []
-    for i in range(num_samples):
-      num_ids = np.random.randint(1, 32)
-      for j in range(num_ids):
-        indices_tensors.append([i, j])
-      for _ in range(num_ids):
-        values_tensors.append(np.random.randint(table_spec.vocabulary_size))
-    all_indices_tensors.append(np.array(indices_tensors, dtype=np.int64))
-    all_values_tensors.append(np.array(values_tensors, dtype=np.int32))
+    counts = np.random.randint(1, 32, size=num_samples)
+    total_ids = np.sum(counts)
+    values = np.random.randint(
+        table_spec.vocabulary_size, size=total_ids, dtype=np.int32
+    )
+    row_indices = np.repeat(np.arange(num_samples), counts)
+    col_indices = np.concatenate([np.arange(c) for c in counts])
+    indices = np.stack([row_indices, col_indices], axis=1)
+
+    all_indices_tensors.append(indices.astype(np.int64))
+    all_values_tensors.append(values)
     all_dense_shape_tensors.append(
-        np.array([num_samples, vocab_size], dtype=np.int64)
+        np.array([num_samples, np.max(counts)], dtype=np.int64)
     )
   return all_indices_tensors, all_values_tensors, all_dense_shape_tensors