NicolaLancellotti commented on a change in pull request #9442:
URL: https://github.com/apache/tvm/pull/9442#discussion_r743017967
##########
File path: tests/python/contrib/test_ethosu/test_codegen.py
##########
@@ -343,5 +343,256 @@ def representative_dataset():
infra.verify_source(compiled_models, accel_type)
[email protected]("accel_type", ACCEL_TYPES)
[email protected]("operator_type", ["ADD", "SUB", "MUL", "MIN", "MAX"])
[email protected](
+ "ifm_shape, ifm2_shape",
+ [
+ ([1, 2, 3, 4], [1, 2, 3, 4]),
+ ([1, 2, 3, 4], [1, 1, 3, 1]),
+ ([1, 1, 3, 1], [1, 2, 3, 4]),
+ ],
+)
[email protected]("activation_function", ["NONE", "RELU"])
+def test_ethosu_binary_elementwise(
+ accel_type,
+ operator_type,
+ ifm_shape,
+ ifm2_shape,
+ activation_function,
+):
+ dtype = "int8"
+
+ def create_tflite_graph():
+ tf.config.run_functions_eagerly(True)
+
+ class Model(tf.Module):
+ @tf.function
+ def tf_function(self, lhs, rhs):
+ if operator_type == "ADD":
+ op = tf.math.add(lhs, rhs)
+ elif operator_type == "SUB":
+ op = tf.math.subtract(lhs, rhs)
+ elif operator_type == "MUL":
+ op = tf.math.multiply(lhs, rhs)
+ elif operator_type == "MIN":
+ op = tf.math.minimum(lhs, rhs)
+ elif operator_type == "MAX":
+ op = tf.math.maximum(lhs, rhs)
+ if activation_function == "RELU":
+ op = tf.nn.relu(op)
+ return op
+
+ model = Model()
+ concrete_func = model.tf_function.get_concrete_function(
+ tf.TensorSpec(ifm_shape, dtype=tf.float32),
tf.TensorSpec(ifm2_shape, dtype=tf.float32)
+ )
+
+ # Convert the model
+ def representative_dataset():
+ for _ in range(100):
+ data = np.random.rand(*tuple(ifm_shape))
+ data2 = np.random.rand(*tuple(ifm2_shape)) * 2
+ yield [data.astype(np.float32), data2.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)
+
+ mod, params = relay.frontend.from_tflite(
+ tflite_model,
+ shape_dict={"ifm": ifm_shape, "ifm2": ifm2_shape},
+ dtype_dict={"ifm": dtype, "ifm2": dtype},
+ )
+ mod = partition_for_ethosu(mod, params)
+
+ # Generate reference data
+ input_data, output_data = infra.generate_ref_data_tflite(tflite_graph)
+
+ compiled_models = infra.build_source(
+ mod,
+ input_data,
+ output_data,
+ accel_type,
+ output_tolerance=1 if operator_type == "MAX" else 0,
Review comment:
The Max operator with the RELU activation function is not bit accurate,
it needs a tolerance of 1.
I'll investigate and address this problem in a follow-up pr.
##########
File path: tests/python/contrib/test_ethosu/test_legalize.py
##########
@@ -558,5 +558,193 @@ def verify(ext_func):
verify(mod["tvmgen_default_ethosu_main_0"])
[email protected]("operator_type", ["ADD", "SUB", "MUL", "MIN", "MAX"])
[email protected](
+ "ifm_shape, ifm2_shape, reversed_operands",
+ [
+ ([1, 2, 3, 4], [1, 2, 3, 4], False),
+ ([1, 2, 3, 4], [1, 1, 3, 1], False),
+ ([1, 1, 3, 1], [1, 2, 3, 4], True),
+ ],
+)
[email protected]("activation_function", ["NONE", "RELU"])
+def test_tflite_binary_elemwise_legalize(
+ operator_type,
+ ifm_shape,
+ ifm2_shape,
+ reversed_operands,
+ activation_function,
+):
+ dtype = "int8"
+
+ def create_tflite_graph():
+ class Model(tf.Module):
+ @tf.function
+ def tf_function(self, x, y):
+ if operator_type == "ADD":
+ op = tf.math.add(x, y)
+ elif operator_type == "SUB":
+ op = tf.math.subtract(x, y)
+ elif operator_type == "MUL":
+ op = tf.math.multiply(x, y)
+ elif operator_type == "MIN":
+ op = tf.math.minimum(x, y)
+ elif operator_type == "MAX":
+ op = tf.math.maximum(x, y)
+ if activation_function == "RELU":
+ op = tf.nn.relu(op)
+ return op
+
+ model = Model()
+ concrete_func = model.tf_function.get_concrete_function(
+ tf.TensorSpec(ifm_shape, dtype=tf.float32),
tf.TensorSpec(ifm2_shape, dtype=tf.float32)
+ )
+
+ # Convert the model
+ def representative_dataset():
+ for _ in range(100):
+ data = np.random.rand(*tuple(ifm_shape))
+ data2 = np.random.rand(*tuple(ifm2_shape)) * 2
+ yield [data.astype(np.float32), data2.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
+
+ def verify(ext_func):
+ out_shape = ifm2_shape if reversed_operands else ifm_shape
+ shapes = [ifm_shape, ifm2_shape]
+ ifm_index, ifm2_index = (1, 0) if reversed_operands else (0, 1)
+ op = ext_func.body
+ assert list(op.args[0].checked_type.shape) == shapes[ifm_index]
+ assert list(op.args[1].checked_type.shape) == shapes[ifm2_index]
+ assert op.args[0].checked_type.dtype == dtype
+ assert list(op.checked_type.shape) == out_shape
+ assert op.checked_type.dtype == dtype
+ assert op.attrs.operator_type == operator_type
+ assert op.attrs.reversed_operands == reversed_operands
+ if activation_function == "RELU":
+ assert str(op.attrs.activation) == "CLIP"
+
+ if operator_type == "ADD":
+ rewriter = legalize.AddRewriter()
+ pattern_table = [
+ (
+ ethosu.AddParams.composite_name,
+ ethosu.qnn_add_pattern(),
+ lambda pat: ethosu.AddParams(pat).is_valid(),
+ ),
+ ]
+ elif operator_type == "SUB":
+ rewriter = legalize.SubRewriter()
+ pattern_table = [
+ (
+ ethosu.SubParams.composite_name,
+ ethosu.qnn_subtract_pattern(),
+ lambda pat: ethosu.SubParams(pat).is_valid(),
+ ),
+ ]
+ elif operator_type == "MUL":
+ rewriter = legalize.MulRewriter()
+ pattern_table = [
+ (
+ ethosu.MulParams.composite_name,
+ ethosu.qnn_mul_pattern(),
+ lambda pat: ethosu.MulParams(pat).is_valid(),
+ ),
+ ]
+ elif operator_type == "MIN":
+ rewriter = legalize.MinRewriter()
+ pattern_table = [
+ (
+ ethosu.MinParams.composite_name,
+ ethosu.minimum_pattern(),
+ lambda pat: ethosu.MinParams(pat).is_valid(),
+ ),
+ ]
+ elif operator_type == "MAX":
+ rewriter = legalize.MaxRewriter()
+ pattern_table = [
+ (
+ ethosu.MaxParams.composite_name,
+ ethosu.maximum_pattern(),
+ lambda pat: ethosu.MaxParams(pat).is_valid(),
+ ),
+ ]
+
+ tflite_graph = create_tflite_graph()
+ tflite_model = tflite.Model.Model.GetRootAsModel(tflite_graph, 0)
+
+ mod, _ = relay.frontend.from_tflite(
+ tflite_model,
+ shape_dict={"x": ifm_shape, "y": ifm2_shape},
+ dtype_dict={"x": dtype, "y": dtype},
+ )
+ mod = partition_ethosu_by_table(mod, pattern_table)
+
+ mod["tvmgen_default_ethosu_main_0"] = dataflow_pattern.rewrite(
+ rewriter, mod["tvmgen_default_ethosu_main_0"]
+ )
+ verify(mod["tvmgen_default_ethosu_main_0"])
+
+
[email protected](
+ "ifm_shape, ifm2_shape, reversed_operands",
+ [
+ ([1, 2, 3, 4], [1, 2, 3, 4], False),
+ ([1, 2, 3, 4], [1, 1, 3, 1], False),
+ ([1, 1, 3, 1], [1, 2, 3, 4], True),
+ ],
+)
+def test_ethosu_left_shift_binary_elemwise_legalize(ifm_shape, ifm2_shape,
reversed_operands):
+ dtype = "int32"
+ operator_type = "SHL"
+
+ def create_graph():
+ input1 = relay.var("x1", shape=ifm_shape, dtype=dtype)
+ input2 = relay.var("x2", shape=ifm2_shape, dtype=dtype)
+ c1 = relay.left_shift(input1, input2)
Review comment:
@ekalda Currently, all the NPU primitive operators are covered.
Because the NPU left shift operator does the "actual" left shift, we
legalize the Relay left shift operator.
Conversely, the NPU right shift operator is actually a rounding right shift
operator and so the Relay right shift operator cannot be legalized.
@lhutton1 The Relay right shift operator cannot be legalized to a rounding
right shift followed by an element-wise subtract of 1.
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