NicolaLancellotti commented on a change in pull request #9442:
URL: https://github.com/apache/tvm/pull/9442#discussion_r743013392
##########
File path: python/tvm/relay/op/contrib/ethosu.py
##########
@@ -458,6 +458,348 @@ 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):
+ for i in range(1, 4):
+ if x.shape[i] == y.shape[i] or y.shape[i] == 1:
+ continue
+ return False
+ return True
+
+ if brodcastable(self.ifm, self.ifm2):
+ self.reversed_operands = False
+ self.valid_broadcast = True
+ elif brodcastable(self.ifm2, self.ifm):
+ self.reversed_operands = True
+ self.ifm, self.ifm2 = self.ifm2, self.ifm
+ self.valid_broadcast = True
+ else:
+ self.valid_broadcast = False
+
+ def is_valid(self):
+ """
+ This function checks whether BinaryElementwise has compatible
attributes with the NPU
+ """
+ if np.dtype(self.ofm) == np.int32 and self.activation is not None:
+ return False
+ if len(self.ifm.shape) != 4 or len(self.ifm2.shape) != 4:
+ return False
+ if self.ifm.shape[0] != 1 or self.ifm2.shape[0] != 1:
+ return False
+ if not self.valid_broadcast:
+ return False
+ return True
+
+
+class AddParams(BinaryElementwiseParams):
+ """
+ This class will parse a call to a ethosu.binary_elementwise Add composite
function
+ and extract the parameter information.
+ """
+
+ composite_name = "ethosu.add"
+
+ def __init__(self, func_body: Call):
+ BinaryElementwiseParams.__init__(self, func_body, "ADD", True)
+
+ def is_valid(self):
+ """
+ This function checks whether Add has compatible attributes with the NPU
+ """
+ if not super().is_valid():
+ return False
+ if not check_valid_dtypes(
+ [self.ifm, self.ifm2, self.ofm], supported_dtypes=[np.uint8,
np.int8, np.int32]
+ ):
+ return False
+ return True
+
+
+def qnn_add_pattern() -> tvm.relay.dataflow_pattern.DFPattern:
+ """
+ This function creates the pattern for qnn.add with optional fused RELU
activation.
+ """
+ pattern = is_op("qnn.add")(
+ wildcard(),
+ wildcard(),
+ is_constant(),
+ is_constant(),
+ is_constant(),
+ is_constant(),
+ is_constant(),
+ is_constant(),
+ )
+ pattern = pattern.optional(is_op("clip"))
+ return pattern
+
+
+class SubParams(BinaryElementwiseParams):
+ """
+ This class will parse a call to a ethosu.binary_elementwise Sub composite
function
+ and extract the parameter information.
+ """
+
+ composite_name = "ethosu.sub"
+
+ def __init__(self, func_body: Call):
+ BinaryElementwiseParams.__init__(self, func_body, "SUB", True)
+
+ def is_valid(self):
+ """
+ This function checks whether Sub has compatible attributes with the NPU
+ """
+ if not super().is_valid():
+ return False
+ if not check_valid_dtypes(
+ [self.ifm, self.ifm2, self.ofm], supported_dtypes=[np.uint8,
np.int8, np.int32]
+ ):
+ return False
+ return True
+
+
+def qnn_subtract_pattern() -> tvm.relay.dataflow_pattern.DFPattern:
+ """
+ This function creates the pattern for qnn.subtract with optional fused
RELU activation.
+ """
+ pattern = is_op("qnn.subtract")(
+ wildcard(),
+ wildcard(),
+ is_constant(),
+ is_constant(),
+ is_constant(),
+ is_constant(),
+ is_constant(),
+ is_constant(),
+ )
+ pattern = pattern.optional(is_op("clip"))
+ return pattern
+
+
+class MulParams(BinaryElementwiseParams):
+ """
+ This class will parse a call to a ethosu.binary_elementwise Mul composite
function
+ and extract the parameter information.
+ """
+
+ composite_name = "ethosu.mul"
+
+ def __init__(self, func_body: Call):
+ BinaryElementwiseParams.__init__(self, func_body, "MUL", True)
+
+ def is_valid(self):
+ """
+ This function checks whether Mul has compatible attributes with the NPU
+ """
+ if not super().is_valid():
+ return False
+ if not check_valid_dtypes(
+ [self.ifm, self.ifm2, self.ofm], supported_dtypes=[np.uint8,
np.int8, np.int32]
+ ):
+ return False
+ return True
+
+
+def qnn_mul_pattern() -> tvm.relay.dataflow_pattern.DFPattern:
+ """
+ This function creates the pattern for qnn.mul with optional fused RELU
activation.
+ """
+ pattern = is_op("qnn.mul")(
+ wildcard(),
+ wildcard(),
+ is_constant(),
+ is_constant(),
+ is_constant(),
+ is_constant(),
+ is_constant(),
+ is_constant(),
+ )
+ pattern = pattern.optional(is_op("clip"))
+ return pattern
+
+
+class MinParams(BinaryElementwiseParams):
+ """
+ This class will parse a call to a ethosu.binary_elementwise Min composite
function
+ and extract the parameter information.
+ """
+
+ composite_name = "ethosu.min"
+
+ def __init__(self, func_body: Call):
+ BinaryElementwiseParams.__init__(self, func_body, "MIN", False)
+
+ def is_valid(self):
+ """
+ This function checks whether Min has compatible attributes with the NPU
+ """
+ if not super().is_valid():
+ return False
+ if self.ifm.dtype != self.ifm2.dtype:
+ return False
+ if not check_valid_dtypes(
+ [self.ifm, self.ifm2, self.ofm], supported_dtypes=[np.uint8,
np.int8]
+ ):
+ return False
+ return True
+
+
+def minimum_pattern() -> tvm.relay.dataflow_pattern.DFPattern:
+ """
+ This function creates the pattern for minimum with optional fused RELU
activation.
+ """
+ pattern = is_op("minimum")(wildcard(), wildcard())
+ pattern = pattern.optional(is_op("clip"))
+ return pattern
+
+
+class MaxParams(BinaryElementwiseParams):
+ """
+ This class will parse a call to a ethosu.binary_elementwise Max composite
function
+ and extract the parameter information.
+ """
+
+ composite_name = "ethosu.max"
+
+ def __init__(self, func_body: Call):
+ BinaryElementwiseParams.__init__(self, func_body, "MAX", False)
+
+ def is_valid(self):
+ """
+ This function checks whether Max has compatible attributes with the NPU
+ """
+ if not super().is_valid():
+ return False
+ if self.ifm.dtype != self.ifm2.dtype:
+ return False
+ if not check_valid_dtypes(
+ [self.ifm, self.ifm2, self.ofm], supported_dtypes=[np.uint8,
np.int8]
+ ):
+ return False
+ return True
+
+
+def maximum_pattern() -> tvm.relay.dataflow_pattern.DFPattern:
+ """
+ This function creates the pattern for maximum with optional fused RELU
activation.
+ """
+ pattern = is_op("maximum")(wildcard(), wildcard())
+ pattern = pattern.optional(is_op("clip"))
+ return pattern
+
+
+class ShrParams(BinaryElementwiseParams):
+ """
+ This class will parse a call to a ethosu.binary_elementwise Shr composite
function
+ and extract the parameter information.
+ """
+
+ composite_name = "ethosu.shr"
+
+ def __init__(self, func_body: Call):
+ BinaryElementwiseParams.__init__(self, func_body, "SHR", False)
+
+ def is_valid(self):
+ """
+ This function checks whether Shr has compatible attributes with the NPU
+ """
+ if not super().is_valid():
+ return False
+ if not check_valid_dtypes([self.ifm, self.ifm2],
supported_dtypes=[np.int32]):
+ return False
+ if not check_valid_dtypes([self.ofm], supported_dtypes=[np.uint8,
np.int8, np.int32]):
+ return False
+ return True
+
+
+def shr_pattern() -> tvm.relay.dataflow_pattern.DFPattern:
+ """
+ This function creates the pattern for right_shift.
+ """
+ pattern = is_op("right_shift")(wildcard(), wildcard())
Review comment:
Removed
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