diff --git a/tensorflow/lite/build_def.bzl b/tensorflow/lite/build_def.bzl
index 59b1d9d2a0b..8255211d271 100644
--- a/tensorflow/lite/build_def.bzl
+++ b/tensorflow/lite/build_def.bzl
@@ -303,6 +303,7 @@ def generated_test_models():
         "transpose",
         "transpose_conv",
         "unpack",
+        "unroll_batch_matmul",
         "where",
         "zeros_like",
     ]
diff --git a/tensorflow/lite/kernels/fully_connected.cc b/tensorflow/lite/kernels/fully_connected.cc
index 63cca1cf542..a1eecb284ab 100644
--- a/tensorflow/lite/kernels/fully_connected.cc
+++ b/tensorflow/lite/kernels/fully_connected.cc
@@ -117,7 +117,7 @@ TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) {
   // Note that quantized inference requires that all tensors have their
   // parameters set. This is usually done during quantized training.
   TfLiteType data_type = input->type;
-  if (data_type != kTfLiteFloat32) {
+  if (data_type != kTfLiteFloat32 && data_type != kTfLiteInt32) {
     double real_multiplier = 0.0;
     TF_LITE_ENSURE_STATUS(GetQuantizedConvolutionMultipler(
         context, input, filter, bias, output, &real_multiplier));
diff --git a/tensorflow/lite/testing/generate_examples.py b/tensorflow/lite/testing/generate_examples.py
index eccef341a03..9b0f59f9da1 100644
--- a/tensorflow/lite/testing/generate_examples.py
+++ b/tensorflow/lite/testing/generate_examples.py
@@ -3470,6 +3470,32 @@ def make_logical_xor_tests(zip_path):
   return _make_logical_tests(tf.logical_xor)(zip_path)
 
 
+def make_unroll_batch_matmul_tests(zip_path):
+  """Make a set of tests to test unroll_batch_matmul."""
+
+  test_parameters = [{"dtype": [tf.float32], "shape": [[(2, 2, 3), (2, 3, 2)]]}]
+
+  def build_graph(parameters):
+    """Build the batch_matmul op testing graph."""
+    input_tensor1 = tf.placeholder(
+        dtype=parameters["dtype"], shape=parameters["shape"][0])
+    input_tensor2 = tf.placeholder(
+        dtype=parameters["dtype"], shape=parameters["shape"][1])
+    # Should be unrolled and replaced with fully_connected ops in the end.
+    out = tf.matmul(input_tensor1, input_tensor2)
+    return [input_tensor1, input_tensor2], [out]
+
+  def build_inputs(parameters, sess, inputs, outputs):
+    input_value1 = create_tensor_data(
+        parameters["dtype"], shape=parameters["shape"][0])
+    input_value2 = create_tensor_data(
+        parameters["dtype"], shape=parameters["shape"][1])
+    return [input_value1, input_value2], sess.run(
+        outputs, feed_dict=dict(zip(inputs, [input_value1, input_value2])))
+
+  make_zip_of_tests(zip_path, test_parameters, build_graph, build_inputs)
+
+
 # Toco binary path provided by the generate rule.
 bin_path = None
 
diff --git a/tensorflow/lite/toco/graph_transformations/unroll_batch_matmul.cc b/tensorflow/lite/toco/graph_transformations/unroll_batch_matmul.cc
index d59954fc740..41a735394d7 100644
--- a/tensorflow/lite/toco/graph_transformations/unroll_batch_matmul.cc
+++ b/tensorflow/lite/toco/graph_transformations/unroll_batch_matmul.cc
@@ -117,7 +117,8 @@ namespace toco {
     auto* slice_b_op = new SliceOperator;
     slice_b_op->inputs = {
         batch_op->inputs[1],
-        CreateInt32Array(model, batch_name + "/slice_b/slice/begin", {0, 0, 0}),
+        CreateInt32Array(model, batch_name + "/slice_b/slice/begin",
+                         {batch, 0, 0}),
         CreateInt32Array(
             model, batch_name + "/slice_b/slice/size",
             {1, input_array_b.shape().dims(1), input_array_b.shape().dims(2)}),