Add precisoin bits to recurrent pytorch pytest (#1215)

* change default precision to fixed<32,16> throughout

* added rounding to the input values plus small fixes

Author: jmitrevs

test/pytest/test_recurrent_pytorch.py

@@ -38,12 +38,18 @@ def test_gru(backend, io_type):
     model.eval()
 
     X_input = torch.randn(1, 1, 10)
+    X_input = np.round(X_input * 2**16) * 2**-16  # make it exact ap_fixed<32,16>
     h0 = torch.randn(1, 1, 20)
+    h0 = np.round(h0 * 2**16) * 2**-16
 
     pytorch_prediction = model(torch.Tensor(X_input), torch.Tensor(h0)).detach().numpy()
 
     config = config_from_pytorch_model(
-        model, [(None, 1, 10), (None, 1, 20)], channels_last_conversion="off", transpose_outputs=False
+        model,
+        [(None, 1, 10), (None, 1, 20)],
+        channels_last_conversion="off",
+        transpose_outputs=False,
+        default_precision='fixed<32,16>',
     )
     output_dir = str(test_root_path / f'hls4mlprj_pytorch_api_gru_{backend}_{io_type}')
 
@@ -63,10 +69,13 @@ def test_gru_stream(backend, io_type):
     model.eval()
 
     X_input = torch.randn(1, 1, 10)
+    X_input = np.round(X_input * 2**16) * 2**-16  # make it exact ap_fixed<32,16>
 
     pytorch_prediction = model(torch.Tensor(X_input)).detach().numpy()
 
-    config = config_from_pytorch_model(model, (None, 1, 10), channels_last_conversion="off", transpose_outputs=False)
+    config = config_from_pytorch_model(
+        model, (None, 1, 10), channels_last_conversion="off", transpose_outputs=False, default_precision='fixed<32,16>'
+    )
     output_dir = str(test_root_path / f'hls4mlprj_pytorch_api_gru_{backend}_{io_type}')
 
     hls_model = convert_from_pytorch_model(model, hls_config=config, output_dir=output_dir, backend=backend, io_type=io_type)
@@ -105,13 +114,20 @@ def test_lstm(backend, io_type):
     model.eval()
 
     X_input = torch.randn(1, 1, 10)
+    X_input = np.round(X_input * 2**16) * 2**-16  # make it exact ap_fixed<32,16>
     h0 = torch.randn(1, 1, 20)
+    h0 = np.round(h0 * 2**16) * 2**-16
     c0 = torch.randn(1, 1, 20)
+    c0 = np.round(c0 * 2**16) * 2**-16
 
     pytorch_prediction = model(torch.Tensor(X_input), torch.Tensor(h0), torch.tensor(c0)).detach().numpy()
 
     config = config_from_pytorch_model(
-        model, [(None, 1, 10), (None, 1, 20), (None, 1, 20)], channels_last_conversion="off", transpose_outputs=False
+        model,
+        [(None, 1, 10), (None, 1, 20), (None, 1, 20)],
+        channels_last_conversion="off",
+        transpose_outputs=False,
+        default_precision='fixed<32,16>',
     )
     output_dir = str(test_root_path / f'hls4mlprj_pytorch_api_lstm_{backend}_{io_type}')
 
@@ -140,10 +156,13 @@ def test_lstm_stream(backend, io_type):
         model.eval()
 
         X_input = torch.randn(1, 1, 10)
+        X_input = np.round(X_input * 2**16) * 2**-16  # make it exact ap_fixed<32,16>
 
         pytorch_prediction = model(torch.Tensor(X_input)).detach().numpy()
 
-        config = config_from_pytorch_model(model, [(None, 1, 10)], channels_last_conversion="off", transpose_outputs=False)
+        config = config_from_pytorch_model(
+            model, [(None, 1, 10)], channels_last_conversion="off", transpose_outputs=False, default_precision='fixed<32,16>'
+        )
         output_dir = str(test_root_path / f'hls4mlprj_pytorch_api_lstm_{backend}_{io_type}')
 
         hls_model = convert_from_pytorch_model(
@@ -179,17 +198,26 @@ def test_rnn(backend, io_type):
         model.eval()
 
         X_input = torch.randn(1, 1, 10)
+        X_input = np.round(X_input * 2**16) * 2**-16  # make it exact ap_fixed<32,16>
         h0 = torch.zeros(1, 1, 20)
 
         pytorch_prediction = model(torch.Tensor(X_input), torch.Tensor(h0)).detach().numpy()
 
         config = config_from_pytorch_model(
-            model, [(1, 10), (1, 20)], channels_last_conversion="off", transpose_outputs=False
+            model,
+            [(1, 10), (1, 20)],
+            channels_last_conversion="off",
+            transpose_outputs=False,
+            default_precision='fixed<32,16>',
         )
         output_dir = str(test_root_path / f'hls4mlprj_pytorch_api_rnn_{backend}_{io_type}')
 
         hls_model = convert_from_pytorch_model(
-            model, hls_config=config, output_dir=output_dir, backend=backend, io_type=io_type
+            model,
+            hls_config=config,
+            output_dir=output_dir,
+            backend=backend,
+            io_type=io_type,
         )
 
         hls_model.compile()