jacobbohlin commented on a change in pull request #9707:
URL: https://github.com/apache/tvm/pull/9707#discussion_r772318222
##########
File path: tests/python/contrib/test_ethosu/test_codegen.py
##########
@@ -242,6 +242,93 @@ def representative_dataset():
infra.verify_source(compiled_models, accel_type)
[email protected]("weight_min, weight_max", [(0.0, 1e-11), (-1e10,
1e10)])
+def test_out_of_range_scaling(weight_min, weight_max):
+ ifm_shape = (1, 6, 6, 2)
+ dtype = "int8"
+ strides = (1, 1)
+ kernel_shape = (1, 1)
+ dilation = (1, 1)
+ padding = "SAME"
+ activation = "RELU"
+ accel_type = "ethos-u55-128"
+
+ def create_tflite_graph():
+ class Model(tf.Module):
+ @tf.function
+ def tf_function(self, x):
+ # Use tf.nn API to create the model
+ tf_strides = [1, strides[0], strides[1], 1]
+ weights = np.random.uniform(size=[kernel_shape[-1],
kernel_shape[1], 2, 2])
Review comment:
You are right, I fixed this and refactored to use Matthew's functions
##########
File path: tests/python/contrib/test_ethosu/test_codegen.py
##########
@@ -242,6 +242,93 @@ def representative_dataset():
infra.verify_source(compiled_models, accel_type)
[email protected]("weight_min, weight_max", [(0.0, 1e-11), (-1e10,
1e10)])
+def test_out_of_range_scaling(weight_min, weight_max):
+ ifm_shape = (1, 6, 6, 2)
+ dtype = "int8"
+ strides = (1, 1)
+ kernel_shape = (1, 1)
+ dilation = (1, 1)
+ padding = "SAME"
+ activation = "RELU"
+ accel_type = "ethos-u55-128"
+
+ def create_tflite_graph():
+ class Model(tf.Module):
+ @tf.function
+ def tf_function(self, x):
+ # Use tf.nn API to create the model
+ tf_strides = [1, strides[0], strides[1], 1]
+ weights = np.random.uniform(size=[kernel_shape[-1],
kernel_shape[1], 2, 2])
+ # Overwrite to force quantization that produces out of range
shift values
+ weights[0][0][0][0] = weight_min
+ weights[0][0][1][0] = weight_max
+ op = tf.nn.conv2d(
+ x,
+ filters=tf.constant(
+ weights,
+ dtype=tf.float32,
+ ),
+ strides=tf_strides,
+ padding=padding,
+ dilations=dilation,
+ )
+ if activation:
+ op = tf.nn.relu(op)
+ return op
+
+ model = Model()
+ concrete_func = model.tf_function.get_concrete_function(
+ tf.TensorSpec(ifm_shape, dtype=tf.float32)
+ )
+
+ # Convert the model
+ def representative_dataset():
+ for _ in range(100):
+ data = np.random.rand(*tuple(ifm_shape))
+ yield [data.astype(np.float32)]
+
+ converter =
tf.lite.TFLiteConverter.from_concrete_functions([concrete_func])
+ converter.optimizations = [tf.lite.Optimize.DEFAULT]
+ converter.representative_dataset = representative_dataset
+ converter.target_spec.supported_ops =
[tf.lite.OpsSet.TFLITE_BUILTINS_INT8]
+ converter.inference_input_type = tf.int8
+ converter.inference_output_type = tf.int8
+ tflite_model = converter.convert()
+ return tflite_model
+
+ tflite_graph = create_tflite_graph()
+ tflite_model = tflite.Model.Model.GetRootAsModel(tflite_graph, 0)
+
+ relay_module, params = relay.frontend.from_tflite(
+ tflite_model,
+ shape_dict={"input": ifm_shape},
+ dtype_dict={"input": dtype},
+ )
+ mod = partition_for_ethosu(relay_module, params)
+ print(mod)
Review comment:
Done
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