reduce cover factor to ensure overflow in tests

calad0i · Apr 26, 2024 · bb4f5ec · bb4f5ec
1 parent 2ba9ded
commit bb4f5ec
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Showing 4 changed files with 10 additions and 11 deletions.
diff --git a/test/test_syn_hlayers.py b/test/test_syn_hlayers.py
@@ -92,7 +92,7 @@ def custom_fn(x):
 @pytest.mark.parametrize("N", [1000])
 @pytest.mark.parametrize("rnd_strategy", ['auto', 'standard_round', 'floor'])
 @pytest.mark.parametrize("io_type", ['io_parallel', 'io_stream'])
-@pytest.mark.parametrize("cover_factor", [0.5, 1.0])
+@pytest.mark.parametrize("cover_factor", [0.25, 1.0])
 @pytest.mark.parametrize("aggressive", [True, False])
 @pytest.mark.parametrize("backend", ['vivado', 'vitis'])
 @pytest.mark.parametrize("seed", [42])
@@ -103,7 +103,10 @@ def test_syn_hlayers(layer, N: int, rnd_strategy: str, io_type: str, cover_facto
     data = get_data(N, 1, 1, seed)
 
     test_grad = N > 100
-    cond = None if 'softmax' not in layer else softmax_cond
+    cond = None
+    if 'softmax' in layer:
+        cond = softmax_cond
+        cover_factor = max(cover_factor, 0.5)  # Softmax table size is sensitive to range
 
     skip_sl_test = 'custom' in layer
     run_model_test(model,

diff --git a/test/test_syn_large.py b/test/test_syn_large.py
@@ -68,7 +68,7 @@ def get_data(N: int, sigma: float, max_scale: float, seed):
 @pytest.mark.parametrize("N", [50000, 10])
 @pytest.mark.parametrize("rnd_strategy", ['auto'])
 @pytest.mark.parametrize("io_type", ['io_parallel', 'io_stream'])
-@pytest.mark.parametrize("cover_factor", [0.49, 1.0])
+@pytest.mark.parametrize("cover_factor", [0.25, 1.0])
 @pytest.mark.parametrize("aggressive", [True, False])
 @pytest.mark.parametrize("backend", ['vivado', 'vitis'])
 @pytest.mark.parametrize("seed", [42])

diff --git a/test/test_syn_players.py b/test/test_syn_players.py
@@ -43,6 +43,8 @@ def create_model(layer: str, rnd_strategy: str, io_type: str):
         raise Exception(f'Please add test for {layer}')
 
     out = eval(layer)(_inp)
+    if 'maxpool' in layer.lower():
+        out = HQuantize()(out)
     model = keras.Model(inp, out)
 
     for layer in model.layers:
@@ -62,12 +64,6 @@ def get_data(N: int, sigma: float, max_scale: float, seed):
     return (a1 * a2).astype(np.float32)
 
 
-def parallel_avg_pool_cond(a, b):
-    close: np.ndarray = np.abs(a - b) < 1e-2
-
-    assert np.all(close), f"Keras-Proxy mismatch for approx avg pool: {np.sum(np.any(~close, axis=tuple(range(1,close.ndim))))} out of {a.shape[0]} samples are very different. Sample: {a[~close].ravel()[:5]} vs {b[~close].ravel()[:5]}"
-
-
 @pytest.mark.parametrize('layer',
                          [
                              "PConcatenate()",
@@ -89,7 +85,7 @@ def parallel_avg_pool_cond(a, b):
 @pytest.mark.parametrize("N", [1000])
 @pytest.mark.parametrize("rnd_strategy", ['auto', 'standard_round', 'floor'])
 @pytest.mark.parametrize("io_type", ['io_parallel', 'io_stream'])
-@pytest.mark.parametrize("cover_factor", [0.5, 1.0])
+@pytest.mark.parametrize("cover_factor", [0.25, 1.0])
 @pytest.mark.parametrize("aggressive", [True, False])
 @pytest.mark.parametrize("backend", ['vivado', 'vitis'])
 @pytest.mark.parametrize("seed", [42])

diff --git a/test/test_syn_small.py b/test/test_syn_small.py
@@ -55,7 +55,7 @@ def get_data(N: int, sigma: float, max_scale: float, seed):
 @pytest.mark.parametrize("N", [50000, 10])
 @pytest.mark.parametrize("rnd_strategy", ['auto', 'standard_round', 'floor'])
 @pytest.mark.parametrize("io_type", ['io_parallel', 'io_stream'])
-@pytest.mark.parametrize("cover_factor", [0.49, 1.0])
+@pytest.mark.parametrize("cover_factor", [0.25, 1.0])
 @pytest.mark.parametrize("aggressive", [True, False])
 @pytest.mark.parametrize("backend", ['vivado', 'vitis'])
 @pytest.mark.parametrize("seed", [114514, 42, 1919810])