[Test] separate rtlsim and npysim tests for fclayer

tests/fpgadataflow/test_fpgadataflow_fclayer.py

@@ -115,7 +115,7 @@ def prepare_inputs(input_tensor, idt, wdt):
 @pytest.mark.parametrize("mw", [4])
 # HLS matrix height (output features)
 @pytest.mark.parametrize("mh", [4])
-def test_fpgadataflow_fclayer(idt, wdt, act, nf, sf, mw, mh):
+def test_fpgadataflow_fclayer_npysim(idt, wdt, act, nf, sf, mw, mh):
     if nf == -1:
         nf = mh
     if sf == -1:
@@ -175,6 +175,77 @@ def test_fpgadataflow_fclayer(idt, wdt, act, nf, sf, mw, mh):
     # execute model
     y_produced = oxe.execute_onnx(model, input_dict)["outp"]
     assert (y_produced.reshape(y_expected.shape) == y_expected).all(), "npysim failed"
+    model = model.transform(CleanUp())
+
+
+# activation: None or DataType
+@pytest.mark.parametrize("act", [None, DataType.BIPOLAR, DataType.INT2])
+# weight datatype
+@pytest.mark.parametrize("wdt", [DataType.BIPOLAR, DataType.INT2])
+# input datatype
+@pytest.mark.parametrize("idt", [DataType.BIPOLAR, DataType.INT2])
+# neuron folding, -1 is maximum possible
+@pytest.mark.parametrize("nf", [-1, 1])
+# synapse folding, -1 is maximum possible
+@pytest.mark.parametrize("sf", [-1, 1])
+# HLS matrix width (input features)
+@pytest.mark.parametrize("mw", [4])
+# HLS matrix height (output features)
+@pytest.mark.parametrize("mh", [4])
+def test_fpgadataflow_fclayer_rtlsim(idt, wdt, act, nf, sf, mw, mh):
+    if nf == -1:
+        nf = mh
+    if sf == -1:
+        sf = mw
+    pe = mh // nf
+    simd = mw // sf
+    assert mh % pe == 0
+    assert mw % sf == 0
+    # generate weights
+    W = gen_finn_dt_tensor(wdt, (mw, mh))
+    # generate input data
+    x = gen_finn_dt_tensor(idt, (1, mw))
+    if act is None:
+        # no activation, produce accumulators
+        T = None
+        tdt = None
+        if wdt == DataType.BIPOLAR and idt == DataType.BIPOLAR:
+            odt = DataType.UINT32
+        else:
+            odt = DataType.INT32
+    else:
+        odt = act
+        (min, max) = calculate_signed_dot_prod_range(idt, wdt, mw)
+        n_steps = act.get_num_possible_values() - 1
+        T = np.random.randint(min, max - 1, (mh, n_steps)).astype(np.float32)
+        # provide non-decreasing thresholds
+        T = np.sort(T, axis=1)
+        # generate thresholds for activation
+        if wdt == DataType.BIPOLAR and idt == DataType.BIPOLAR:
+            tdt = DataType.UINT32
+            # bias thresholds to be positive
+            T = np.ceil((T + mw) / 2)
+            assert (T >= 0).all()
+        else:
+            tdt = DataType.INT32
+    model = make_single_fclayer_modelwrapper(W, pe, simd, wdt, idt, odt, T, tdt)
+    # prepare input data
+    input_dict = prepare_inputs(x, idt, wdt)
+    if wdt == DataType.BIPOLAR and idt == DataType.BIPOLAR:
+        # convert inputs to binary and use xnorpopcountmatmul
+        y = xp.xnorpopcountmatmul((x + 1) / 2, (W + 1) / 2)
+    else:
+        y = np.matmul(x, W)
+    if T is not None:
+        y = multithreshold(y, T)
+        if act == DataType.BIPOLAR:
+            # binary to bipolar
+            y = 2 * y - 1
+        else:
+            # signed offset
+            y += act.min()
+    oshape = model.get_tensor_shape("outp")
+    y_expected = y.reshape(oshape)
     # TODO split up into several dependent tests -- need to check how this
     # works for parametrized tests...
     model = model.transform(SetSimMode("rtlsim"))