Add test for concat with 16 bit (#1159)

model_compression_toolkit/target_platform_capabilities/tpc_models/imx500_tpc/v4/tpc_pytorch.py

@@ -85,7 +85,8 @@ def generate_pytorch_tpc(name: str, tp_model: tp.TargetPlatformModel):
                                                     topk,
                                                     squeeze,
                                                     MaxPool2d])
-        tp.OperationsSetToLayers("Default16BitInout", [torch.stack, torch.cat])
+        tp.OperationsSetToLayers("Default16BitInout",
+                                 [torch.stack, torch.cat, torch.concat, torch.concatenate])
 
         tp.OperationsSetToLayers("Conv", [Conv2d, ConvTranspose2d],
                                  attr_mapping=pytorch_linear_attr_mapping)

tests/keras_tests/feature_networks_tests/feature_networks/activation_16bit_test.py

@@ -40,6 +40,7 @@ def get_tpc(self):
     def create_networks(self):
         inputs = layers.Input(shape=self.get_input_shapes()[0][1:])
         x = tf.multiply(inputs, inputs)
+        x = tf.concat([x, x], axis=1)
         x = tf.add(x, np.ones((3,), dtype=np.float32))
         x1 = tf.subtract(x, np.ones((3,), dtype=np.float32))
         x = tf.multiply(x, x1)
@@ -49,9 +50,9 @@ def create_networks(self):
 
     def compare(self, quantized_model, float_model, input_x=None, quantization_info=None):
         mul1_act_quant = quantized_model.layers[3]
-        mul2_act_quant = quantized_model.layers[9]
+        mul2_act_quant = quantized_model.layers[11]
         self.unit_test.assertTrue(mul1_act_quant.activation_holder_quantizer.num_bits == 16,
-                                  "1st mul activation bits should be 16 bits because of following add node.")
+                                  "1st mul activation bits should be 16 bits because of following concat node.")
         self.unit_test.assertTrue(mul1_act_quant.activation_holder_quantizer.signed == True,
                                   "1st mul activation should be forced by TPC to be signed, even though activations as all positive.")
         self.unit_test.assertTrue(mul2_act_quant.activation_holder_quantizer.num_bits == 8,
@@ -77,6 +78,16 @@ def get_tpc(self):
     def get_resource_utilization(self):
         return mct.core.ResourceUtilization(activation_memory=200)
 
+    def create_networks(self):
+        inputs = layers.Input(shape=self.get_input_shapes()[0][1:])
+        x = tf.multiply(inputs, inputs)
+        x = tf.add(x, np.ones((3,), dtype=np.float32))
+        x1 = tf.subtract(x, np.ones((3,), dtype=np.float32))
+        x = tf.multiply(x, x1)
+        x = tf.keras.layers.Conv2D(3, 1)(x)
+        outputs = tf.divide(x, 2*np.ones((3,), dtype=np.float32))
+        return keras.Model(inputs=inputs, outputs=outputs)
+
     def compare(self, quantized_model, float_model, input_x=None, quantization_info=None):
         mul1_act_quant = quantized_model.layers[3]
         mul2_act_quant = quantized_model.layers[9]

tests/pytorch_tests/model_tests/feature_models/activation_16bit_test.py

@@ -26,15 +26,18 @@
 
 class Activation16BitNet(torch.nn.Module):
 
-    def __init__(self):
+    def __init__(self, use_concat=True):
         super().__init__()
+        self.use_concat = use_concat
         self.conv = torch.nn.Conv2d(3, 3, 1)
         self.register_buffer('add_const', torch.rand((3, 1, 1)))
         self.register_buffer('sub_const', torch.rand((3, 1, 1)))
         self.register_buffer('div_const', 2*torch.ones((3, 1, 1)))
 
     def forward(self, x):
         x = torch.mul(x, x)
+        if self.use_concat:
+            x = torch.concat([x, x], dim=2)
         x1 = torch.add(x, self.add_const)
         x = torch.sub(x, self.sub_const)
         x = torch.mul(x, x1)
@@ -60,7 +63,7 @@ def compare(self, quantized_model, float_model, input_x=None, quantization_info=
         mul1_act_quant = quantized_model.mul_activation_holder_quantizer
         mul2_act_quant = quantized_model.mul_1_activation_holder_quantizer
         self.unit_test.assertTrue(mul1_act_quant.activation_holder_quantizer.num_bits == 16,
-                                  "1st mul activation bits should be 16 bits because of following add node.")
+                                  "1st mul activation bits should be 16 bits because of following concat node.")
         self.unit_test.assertTrue(mul1_act_quant.activation_holder_quantizer.signed == True,
                                   "1st mul activation should be forced by TPC to be signed, even though activations as all positive.")
         self.unit_test.assertTrue(mul2_act_quant.activation_holder_quantizer.num_bits == 8,
@@ -90,6 +93,9 @@ def get_tpc(self):
     def get_resource_utilization(self):
         return mct.core.ResourceUtilization(activation_memory=200)
 
+    def create_networks(self):
+        return Activation16BitNet(use_concat=False)
+
     def compare(self, quantized_model, float_model, input_x=None, quantization_info=None):
         mul1_act_quant = quantized_model.mul_activation_holder_quantizer
         mul2_act_quant = quantized_model.mul_1_activation_holder_quantizer