mbaret commented on a change in pull request #9442:
URL: https://github.com/apache/tvm/pull/9442#discussion_r743845777
##########
File path: python/tvm/relay/backend/contrib/ethosu/legalize.py
##########
@@ -423,11 +640,20 @@ class LegalizeEthosU:
def transform_module(
self, mod: tvm.ir.IRModule, ctx: tvm.ir.transform.PassContext
) -> tvm.ir.IRModule:
+ """This is the method that replace the operations with
hardware/codegen supported
Review comment:
that replaces
##########
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(
Review comment:
Not necessarily something to resolve in this patch, but I wonder if we
can abstract some of the boilerplate code here into a utility function.
##########
File path: python/tvm/relay/op/contrib/ethosu.py
##########
@@ -458,6 +458,316 @@ def qnn_avgpool2d_pattern() ->
tvm.relay.dataflow_pattern.DFPattern:
return pattern
+class BinaryElementwiseParams:
+ """
+ This class will parse a call to a ethosu.binary_elementwise composite
function
+ and extract the parameter information.
+ """
+
+ def __init__(self, func_body: Call, operator_type: str,
has_quantization_parameters: bool):
+ clip = None
+ if str(func_body.op) == "clip":
+ clip = func_body
+ binary_op = clip.args[0]
+ else:
+ binary_op = func_body
+
+ layout = "NHWC"
+
+ if has_quantization_parameters:
+ self.ifm = TensorParams(
+ binary_op.args[BinaryElementwiseArgs.ifm.value],
+ layout,
+ binary_op.args[BinaryElementwiseArgs.ifm_scale.value],
+ binary_op.args[BinaryElementwiseArgs.ifm_zero_point.value],
+ )
+ self.ifm2 = TensorParams(
+ binary_op.args[BinaryElementwiseArgs.ifm2.value],
+ layout,
+ binary_op.args[BinaryElementwiseArgs.ifm2_scale.value],
+ binary_op.args[BinaryElementwiseArgs.ifm2_zero_point.value],
+ )
+ self.ofm = TensorParams(
+ binary_op,
+ layout,
+ binary_op.args[BinaryElementwiseArgs.ofm_scale.value],
+ binary_op.args[BinaryElementwiseArgs.ofm_zero_point.value],
+ )
+ else:
+ self.ifm = TensorParams(
+ binary_op.args[BinaryElementwiseArgs.ifm.value],
+ layout,
+ )
+ self.ifm2 = TensorParams(
+ binary_op.args[BinaryElementwiseArgs.ifm2.value],
+ layout,
+ )
+ self.ofm = TensorParams(
+ binary_op,
+ layout,
+ )
+ self.activation = clip
+ self.operator_type = operator_type
+
+ def brodcastable(x, y):
Review comment:
broadcastable (or 'can_broadcast' might be better)
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