NicolaLancellotti commented on a change in pull request #9384:
URL: https://github.com/apache/tvm/pull/9384#discussion_r740161363
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File path: python/tvm/relay/op/contrib/ethosu.py
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@@ -331,6 +332,133 @@ def qnn_depthwise_conv2d_pattern() ->
tvm.relay.dataflow_pattern.DFPattern:
return clip_or_req
+class MaxPool2DParams:
+ """
+ This class will parse a call to a ethosu.maxpool2d composite function
+ and extract the parameter information.
+ """
+
+ composite_name = "ethosu.maxpool2d"
+ # The hardware only supports padding upto the numbers as follows
+ padding_bounds = [127, 127, 128, 128]
+
+ def __init__(self, func_body: Call):
+ clip = None
+ if str(func_body.op) == "clip":
+ clip = func_body
+ pool_op = clip.args[0]
+ else:
+ pool_op = func_body
+
+ attrs = pool_op.attrs
+ self.ifm = TensorParams(pool_op.args[MaxPoolArgs.ifm.value],
attrs.layout)
+ self.ofm = TensorParams(pool_op, attrs.layout)
+ self.pool_shape = [int(i) for i in attrs.pool_size]
+ self.strides = attrs.strides
+ self.padding = attrs.padding
+ self.activation = clip
+ self.pooling_type = "MAX"
+
+ def is_valid(self):
+ """
+ This function checks whether MaxPool2D has compatible attributes with
the NPU
+ """
+ tensor_params = [self.ifm, self.ofm]
+ if not check_valid_dtypes(tensor_params):
+ return False
+ if self.ifm.dtype != self.ofm.dtype:
+ return False
+ if not check_strides(self.strides):
+ return False
+ if not check_batch_size(self.ifm):
+ return False
+ if not check_padding(self.padding, self.padding_bounds):
+ return False
+ # Check pool size
+ if (
+ len(self.pool_shape) != 2
+ or self.pool_shape[1] > 256
+ or self.pool_shape[0] * self.pool_shape[1] > 256 * 256
+ ):
+ return False
+ return True
Review comment:
I agree, I'll create a `check_pool_shape` function.
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