feat: support 1D vectors in MatMul

closes #948

concrete/numpy/compile.py

@@ -536,7 +536,7 @@ def hack_offset_negative_inputs_to_lookup_tables(op_graph: OPGraph) -> None:
     # This does not update the TLU input values to allow for proper table generation.
     # Thankfully we are not supposed to touch the op_graph beyond that point
     for node in list((nx_graph := op_graph.graph).nodes):
-        if isinstance(node, GenericFunction):
+        if isinstance(node, GenericFunction) and node.op_kind == "TLU":
             ordered_preds_and_inputs = op_graph.get_ordered_preds_and_inputs_of(node)
             variable_input_indices = [
                 idx

concrete/numpy/np_mlir_converter.py

@@ -79,7 +79,7 @@ class NPMLIRConverter(OPGraphConverter):
             additional_conversion_info["tables"] = {
                 node: generate_deduplicated_tables(node, op_graph.get_ordered_preds(node))
                 for node in op_graph.graph.nodes()
-                if isinstance(node, GenericFunction)
+                if isinstance(node, GenericFunction) and node.op_kind == "TLU"
             }
 
         return additional_conversion_info

concrete/numpy/tracing.py

@@ -642,17 +642,45 @@ def _on_numpy_multiply(lhs, rhs):
     return lhs.__mul__(rhs)
 
 
-def _on_numpy_matmul(lhs, rhs):
+def _on_numpy_matmul(lhs: NPTracer, rhs: NPTracer):
     common_output_dtypes_and_shapes = get_numpy_function_output_dtype_and_shape_from_input_tracers(
         numpy.matmul, lhs, rhs
     )
     assert_true(len(common_output_dtypes_and_shapes) == 1)
 
+    output_shape = common_output_dtypes_and_shapes[0][1]
+
+    # TODO: https://github.com/zama-ai/concretefhe-internal/issues/1174
+    # remove all the reshape logic once matmul supports more combinations of arguments
+    if isinstance(lhs_output := lhs.output, TensorValue) and isinstance(
+        rhs_output := rhs.output, TensorValue
+    ):
+        # Manage non 2D cases
+        if lhs_output.ndim == 1 and rhs_output.ndim == 1:
+            lhs = lhs.reshape((1, lhs_output.shape[0]))
+            rhs = rhs.reshape((rhs_output.shape[0], 1))
+        elif lhs_output.ndim == 1:
+            # lhs is a vector, reshape to be 2D and give proper result
+            output_shape = lhs_output.shape
+            lhs = lhs.reshape((1, lhs_output.shape[0]))
+        elif rhs_output.ndim == 1:
+            # rhs is a vector, reshape to be 2D and give proper result
+            output_shape = rhs_output.shape
+            rhs = rhs.reshape((rhs_output.shape[0], 1))
+
     traced_computation = MatMul(
         [lhs.output, rhs.output],
         common_output_dtypes_and_shapes[0][0],
     )
-    return NPTracer([lhs, rhs], traced_computation, output_idx=0)
+
+    matmul_tracer = NPTracer([lhs, rhs], traced_computation, output_idx=0)
+
+    # Return the reshaped result if vector reshaping for 2D matmul happened
+    if matmul_tracer.shape != output_shape:
+        if output_shape == ():
+            return matmul_tracer[0, 0]
+        return matmul_tracer.reshape(output_shape)
+    return matmul_tracer
 
 
 NPTracer.UFUNC_ROUTING[numpy.add] = _on_numpy_add

tests/numpy/test_compile.py

@@ -1338,6 +1338,21 @@ def test_compile_and_run_constant_dot_correctness(
             (1, 2),
             (0, 8),
         ),
+        pytest.param(
+            (2,),
+            (2,),
+            (0, 8),
+        ),
+        pytest.param(
+            (5, 5),
+            (5,),
+            (0, 4),
+        ),
+        pytest.param(
+            (5,),
+            (5, 5),
+            (0, 4),
+        ),
     ],
 )
 def test_compile_and_run_matmul_correctness(