AndrewZhaoLuo commented on a change in pull request #9017:
URL: https://github.com/apache/tvm/pull/9017#discussion_r709416154
##########
File path: python/tvm/relay/frontend/onnx.py
##########
@@ -654,6 +676,40 @@ def _impl_v1(cls, inputs, attr, params):
)
+class QLinearGlobalAveragePool(OnnxOpConverter):
+ "Operator converter for QLinearGlobalAveragePool from Microsoft
onnxruntime contrib opset."
+
+ @classmethod
+ def _impl_v1(cls, inputs, attr, params):
+ rank = len(infer_shape(inputs[0]))
+
+ x_scale = get_scalar(inputs[1], params)
+ x_zero_point = get_scalar(inputs[2], params, dtype="int32")
+ y_scale = fold_constant(get_scalar(inputs[3], params))
+ y_zero_point = get_scalar(inputs[4], params, dtype="int32")
+
+ input_dtype = infer_type(inputs[0]).checked_type.dtype
+
+ # Onnxruntime documentation does not mention that this global avg_pool
should follow the
Review comment:
I'm fine with this for now but this should be a TODO since I believe the
actual implementation does not dq -> pool -> q
https://github.com/microsoft/onnxruntime/blob/master/onnxruntime/core/mlas/lib/qlgavgpool.cpp
##########
File path: python/tvm/relay/frontend/onnx.py
##########
@@ -351,6 +366,13 @@ class AveragePool(Pool):
name = "avg_pool"
+class QLinearAveragePool(Pool):
Review comment:
I think composition rather than subclassing would be a cleaner solution.
Right now all the code to handle quantization + not quantization are in the
same place which makes it a bit harder to read. Please separate it.
You can do something like refactor the Pool impl to a new class method like
_run_calculation(...) and call it from QLinearAveragePool
##########
File path: tests/python/frontend/onnx/test_forward.py
##########
@@ -3056,6 +3056,152 @@ def verify_global_pooling(x_shape, mode):
verify_global_pooling([4, 1, 2, 6, 4], mode)
+@tvm.testing.parametrize_targets
+def test_qlinear_average_pool(target, dev):
+ def verify_qlinear_average_pool(
+ x_shape, kernel_shape, strides, pads, out_shape, auto_pad="NOTSET"
+ ):
+ input_nodes = [
+ helper.make_tensor_value_info("X", TensorProto.FLOAT,
list(x_shape)),
+ ]
+
+ output_nodes = [
+ helper.make_tensor_value_info("Y", TensorProto.FLOAT,
list(out_shape)),
+ ]
+
+ input_names = ["X"]
+
+ node = helper.make_node(
+ "AveragePool",
Review comment:
Should these be QLinear Nodes?
##########
File path: python/tvm/relay/frontend/onnx.py
##########
@@ -3794,12 +3850,14 @@ def _get_convert_map(opset):
"Xor": Renamer("logical_xor"),
# defs/nn
"AveragePool": AveragePool.get_converter(opset),
+ "QLinearAveragePool": QLinearAveragePool.get_converter(opset),
Review comment:
There's a quantization section down below, you should move these there
##########
File path: tests/python/frontend/onnx/test_forward.py
##########
@@ -3056,6 +3056,152 @@ def verify_global_pooling(x_shape, mode):
verify_global_pooling([4, 1, 2, 6, 4], mode)
+@tvm.testing.parametrize_targets
+def test_qlinear_average_pool(target, dev):
+ def verify_qlinear_average_pool(
+ x_shape, kernel_shape, strides, pads, out_shape, auto_pad="NOTSET"
+ ):
+ input_nodes = [
+ helper.make_tensor_value_info("X", TensorProto.FLOAT,
list(x_shape)),
+ ]
+
+ output_nodes = [
+ helper.make_tensor_value_info("Y", TensorProto.FLOAT,
list(out_shape)),
+ ]
+
+ input_names = ["X"]
+
+ node = helper.make_node(
+ "AveragePool",
+ inputs=input_names,
+ outputs=["Y"],
+ kernel_shape=kernel_shape,
+ strides=strides,
+ )
+
+ if pads is None:
+ pad_attr = helper.make_attribute("auto_pad", auto_pad)
+ else:
+ pad_attr = helper.make_attribute("pads", pads)
+ node.attribute.append(pad_attr)
+
+ graph = helper.make_graph(
+ [node],
+ "qlinear_average_pool_test",
+ inputs=input_nodes,
+ outputs=output_nodes,
+ )
+
+ model = helper.make_model(graph,
producer_name="qlinear_average_pool_Test")
+ quantize_and_verify_with_ort(model, input_names, [x_shape], target,
dev)
+
+ # Pool1D
+ verify_qlinear_average_pool(
+ x_shape=[1, 1, 32],
+ kernel_shape=[3],
+ strides=[1],
+ pads=[1, 1],
+ out_shape=[1, 1, 32],
+ )
+ # Pool2D
+ verify_qlinear_average_pool(
+ x_shape=[1, 1, 32, 32],
+ kernel_shape=[3, 3],
+ strides=[1, 1],
+ pads=[1, 1, 1, 1],
+ out_shape=[1, 1, 32, 32],
+ )
+
+ # Pool1D with stride
+ verify_qlinear_average_pool(
+ x_shape=[1, 1, 32],
+ kernel_shape=[3],
+ strides=[2],
+ pads=[1, 1],
+ out_shape=[1, 1, 16],
+ )
+ # Pool2D with stride
+ verify_qlinear_average_pool(
+ x_shape=[1, 1, 32, 32],
+ kernel_shape=[3, 3],
+ strides=[2, 2],
+ pads=[1, 1, 1, 1],
+ out_shape=[1, 1, 16, 16],
+ )
+
+ # Pool1D with stride and autopadding
+ verify_qlinear_average_pool(
+ x_shape=[1, 1, 32],
+ kernel_shape=[3],
+ strides=[2],
+ pads=None,
+ out_shape=[1, 1, 16],
+ auto_pad="SAME_UPPER",
+ )
+ # Pool2D with stride and autopadding
+ verify_qlinear_average_pool(
+ x_shape=[1, 1, 32, 32],
+ kernel_shape=[3, 3],
+ strides=[2, 2],
+ pads=None,
+ out_shape=[1, 1, 16, 16],
+ auto_pad="SAME_UPPER",
+ )
+
+ # Pool3D with stride
+ verify_qlinear_average_pool(
+ x_shape=[1, 1, 32, 32, 32],
+ kernel_shape=[3, 3, 3],
+ strides=[2, 2, 2],
+ pads=[1, 1, 1, 1, 1, 1],
+ out_shape=[1, 1, 16, 16, 16],
+ )
+
+ # Pool3D with stride and autopadding
+ verify_qlinear_average_pool(
+ x_shape=[1, 1, 32, 32, 32],
+ kernel_shape=[3, 3, 3],
+ strides=[2, 2, 2],
+ pads=None,
+ out_shape=[1, 1, 16, 16, 16],
+ auto_pad="SAME_UPPER",
+ )
+
+
+@tvm.testing.parametrize_targets
+def test_qlinear_global_average_pool(target, dev):
+ def verify_qlinear_global_average_pool(x_shape):
+ out_shape = x_shape[:2] + [1] * (len(x_shape) - 2)
+
+ node_type = "GlobalAveragePool"
+
+ input_names = ["X"]
+
+ pool_node = helper.make_node(node_type, inputs=input_names,
outputs=["Y"])
Review comment:
Should these be QLinear Nodes?
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