Add split_k qvm for long context (#1564)

* Add splitk qvm

* configurable splitk

* tuning

* remove extra instantiation

* remove refactor

* separate test

* cpu tolerance

python/tests/test_quantized.py

@@ -163,6 +163,31 @@ class TestQuantized(mlx_tests.MLXTestCase):
                 self.assertEqual(y_q.shape, y_hat.shape)
                 self.assertLess((y_q - y_hat).abs().max(), 1e-3)
 
+    def test_qvm_splitk(self):
+        key = mx.random.key(0)
+        k1, k2 = mx.random.split(key)
+        tests = product(
+            [128, 64, 32],  # group_size
+            [2, 4, 8],  # bits
+            [128],  # M
+            [16384],  # N
+            [1, 3],  # B
+        )
+        for group_size, bits, M, N, B in tests:
+            with self.subTest(shape=(B, M, N), group_size=group_size, bits=bits):
+                x_shape = (1, N) if B == 0 else (B, 1, N)
+                w_shape = (N, M) if B == 0 else (B, N, M)
+                x = mx.random.normal(shape=x_shape, key=k1)
+                w = mx.random.normal(shape=w_shape, key=k2)
+                w_q, scales, biases = mx.quantize(w, group_size, bits)
+                w_hat = mx.dequantize(w_q, scales, biases, group_size, bits)
+                y_q = mx.quantized_matmul(
+                    x, w_q, scales, biases, False, group_size, bits
+                )
+                y_hat = x @ w_hat
+                self.assertEqual(y_q.shape, y_hat.shape)
+                self.assertLess((y_q - y_hat).abs().max(), 2e-3)
+
     def test_throw(self):
         x = mx.random.normal(shape=(10, 512))
         w = mx.random.normal(shape=(32, 512))