From b69a759618c8c758545d9ef8fc6d67634d838665 Mon Sep 17 00:00:00 2001
From: Yaman Umuroglu <yamanu@xilinx.com>
Date: Mon, 11 Oct 2021 15:27:19 +0200
Subject: [PATCH 1/5] [Deps] update finn-base to get faster shape inference
 base ops

---
 docker/Dockerfile.finn | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/docker/Dockerfile.finn b/docker/Dockerfile.finn
index c7c418482d..802292cac3 100644
--- a/docker/Dockerfile.finn
+++ b/docker/Dockerfile.finn
@@ -86,7 +86,7 @@ RUN pip install -e git+https://github.com/fbcotter/dataset_loading.git@0.0.4#egg
 
 # git-based Python repo dependencies
 # these are installed in editable mode for easier co-development
-ARG FINN_BASE_COMMIT="d38426634f1cfbc5432a0e52c3f65c07fad12aa4"
+ARG FINN_BASE_COMMIT="9e8fc02683d272cc14309eeebcf14293dc5e4edd"
 ARG FINN_EXP_COMMIT="f82c0d9868bb88ea045dfadb28508d327d287221"
 ARG BREVITAS_COMMIT="462f86cdc60f9915baf13afd1676fb21da44c2ee"
 ARG PYVERILATOR_COMMIT="0c3eb9343500fc1352a02c020a736c8c2db47e8e"

From 1663022fb8508cf5fe4cf5b988b1361676167033 Mon Sep 17 00:00:00 2001
From: Yaman Umuroglu <yamanu@xilinx.com>
Date: Mon, 11 Oct 2021 15:41:28 +0200
Subject: [PATCH 2/5] [Refactor] use RandomNormal for faster/more compact shape
 inference

---
 src/finn/custom_op/fpgadataflow/addstreams_batch.py | 13 +++----------
 .../custom_op/fpgadataflow/channelwise_op_batch.py  | 12 +++---------
 .../fpgadataflow/convolutioninputgenerator.py       | 12 +++---------
 .../fpgadataflow/convolutioninputgenerator1d.py     | 13 +++----------
 src/finn/custom_op/fpgadataflow/downsampler.py      | 13 +++----------
 src/finn/custom_op/fpgadataflow/fmpadding_batch.py  | 13 +++----------
 .../custom_op/fpgadataflow/globalaccpool_batch.py   | 13 +++----------
 src/finn/custom_op/fpgadataflow/iodma.py            | 13 +++----------
 .../custom_op/fpgadataflow/labelselect_batch.py     | 12 +++---------
 src/finn/custom_op/fpgadataflow/pool_batch.py       | 13 +++----------
 .../streamingdatawidthconverter_batch.py            | 13 +++----------
 .../fpgadataflow/streamingfclayer_batch.py          | 13 +++----------
 src/finn/custom_op/fpgadataflow/streamingfifo.py    | 13 +++----------
 .../fpgadataflow/streamingmaxpool_batch.py          | 13 +++----------
 .../custom_op/fpgadataflow/thresholding_batch.py    | 13 +++----------
 src/finn/custom_op/fpgadataflow/upsampler.py        | 13 +++----------
 .../fpgadataflow/vector_vector_activate_batch.py    | 13 +++----------
 17 files changed, 51 insertions(+), 167 deletions(-)

diff --git a/src/finn/custom_op/fpgadataflow/addstreams_batch.py b/src/finn/custom_op/fpgadataflow/addstreams_batch.py
index 856f84fae0..aaf8687de1 100644
--- a/src/finn/custom_op/fpgadataflow/addstreams_batch.py
+++ b/src/finn/custom_op/fpgadataflow/addstreams_batch.py
@@ -29,7 +29,7 @@
 import numpy as np
 import os
 import warnings
-from onnx import TensorProto, helper
+from onnx import helper
 
 from finn.core.datatype import DataType
 from finn.custom_op.fpgadataflow.hlscustomop import HLSCustomOp
@@ -84,18 +84,11 @@ def make_shape_compatible_op(self, model):
         assert ishape == exp_ishape, "Unexpected input1 shape."
         ishape = tuple(model.get_tensor_shape(self.onnx_node.input[1]))
         assert ishape == exp_ishape, "Unexpected input2 shape."
-        # implement tensor with correct shape
-        values = np.random.randn(*oshape).astype(np.float32)
         return helper.make_node(
-            "Constant",
+            "RandomNormal",
             inputs=[],
             outputs=[self.onnx_node.output[0]],
-            value=helper.make_tensor(
-                name="const_tensor",
-                data_type=TensorProto.FLOAT,
-                dims=values.shape,
-                vals=values.flatten().astype(float),
-            ),
+            shape=list(oshape),
         )
 
     def infer_node_datatype(self, model):
diff --git a/src/finn/custom_op/fpgadataflow/channelwise_op_batch.py b/src/finn/custom_op/fpgadataflow/channelwise_op_batch.py
index 073d6620ac..b274bb8dc1 100644
--- a/src/finn/custom_op/fpgadataflow/channelwise_op_batch.py
+++ b/src/finn/custom_op/fpgadataflow/channelwise_op_batch.py
@@ -30,7 +30,7 @@
 import os
 import warnings
 from math import ceil
-from onnx import TensorProto, helper
+from onnx import helper
 
 from finn.core.datatype import DataType
 from finn.custom_op.fpgadataflow.hlscustomop import HLSCustomOp
@@ -125,17 +125,11 @@ def calc_tmem(self):
     def make_shape_compatible_op(self, model):
         oshape = self.get_normal_output_shape()
         # implement tensor with correct shape
-        values = np.random.randn(*oshape).astype(np.float32)
         return helper.make_node(
-            "Constant",
+            "RandomNormal",
             inputs=[],
             outputs=[self.onnx_node.output[0]],
-            value=helper.make_tensor(
-                name="const_tensor",
-                data_type=TensorProto.FLOAT,
-                dims=values.shape,
-                vals=values.flatten().astype(float),
-            ),
+            shape=list(oshape),
         )
 
     def infer_node_datatype(self, model):
diff --git a/src/finn/custom_op/fpgadataflow/convolutioninputgenerator.py b/src/finn/custom_op/fpgadataflow/convolutioninputgenerator.py
index 9ec7bc662d..8566ee035d 100644
--- a/src/finn/custom_op/fpgadataflow/convolutioninputgenerator.py
+++ b/src/finn/custom_op/fpgadataflow/convolutioninputgenerator.py
@@ -29,7 +29,7 @@
 import math
 import numpy as np
 import os
-from onnx import TensorProto, helper
+from onnx import helper
 
 from finn.core.datatype import DataType
 from finn.custom_op.fpgadataflow.hlscustomop import HLSCustomOp
@@ -148,17 +148,11 @@ def make_shape_compatible_op(self, model):
         ishape = tuple(model.get_tensor_shape(self.onnx_node.input[0]))
         assert ishape == exp_ishape, "Unexpect input shape for ConvInpGen."
         # implement tensor with correct shape
-        values = np.random.randn(*oshape).astype(np.float32)
         return helper.make_node(
-            "Constant",
+            "RandomNormal",
             inputs=[],
             outputs=[self.onnx_node.output[0]],
-            value=helper.make_tensor(
-                name="const_tensor",
-                data_type=TensorProto.FLOAT,
-                dims=values.shape,
-                vals=values.flatten().astype(float),
-            ),
+            shape=list(oshape),
         )
 
     def infer_node_datatype(self, model):
diff --git a/src/finn/custom_op/fpgadataflow/convolutioninputgenerator1d.py b/src/finn/custom_op/fpgadataflow/convolutioninputgenerator1d.py
index b428210acf..c76cb47d2f 100644
--- a/src/finn/custom_op/fpgadataflow/convolutioninputgenerator1d.py
+++ b/src/finn/custom_op/fpgadataflow/convolutioninputgenerator1d.py
@@ -29,7 +29,7 @@
 import math
 import numpy as np
 import os
-from onnx import TensorProto, helper
+from onnx import helper
 
 from finn.core.datatype import DataType
 from finn.custom_op.fpgadataflow.hlscustomop import HLSCustomOp
@@ -137,18 +137,11 @@ def make_shape_compatible_op(self, model):
         oshape = self.get_normal_output_shape()
         ishape = tuple(model.get_tensor_shape(self.onnx_node.input[0]))
         assert ishape == exp_ishape, "Unexpect input shape for ConvInpGen."
-        # implement tensor with correct shape
-        values = np.random.randn(*oshape).astype(np.float32)
         return helper.make_node(
-            "Constant",
+            "RandomNormal",
             inputs=[],
             outputs=[self.onnx_node.output[0]],
-            value=helper.make_tensor(
-                name="const_tensor",
-                data_type=TensorProto.FLOAT,
-                dims=values.shape,
-                vals=values.flatten().astype(float),
-            ),
+            shape=list(oshape),
         )
 
     def infer_node_datatype(self, model):
diff --git a/src/finn/custom_op/fpgadataflow/downsampler.py b/src/finn/custom_op/fpgadataflow/downsampler.py
index 2313ab28b4..de251a8486 100644
--- a/src/finn/custom_op/fpgadataflow/downsampler.py
+++ b/src/finn/custom_op/fpgadataflow/downsampler.py
@@ -1,7 +1,7 @@
 import numpy as np
 import os
 import warnings
-from onnx import TensorProto, helper
+from onnx import helper
 
 from finn.core.datatype import DataType
 from finn.custom_op.fpgadataflow.hlscustomop import HLSCustomOp
@@ -83,18 +83,11 @@ def make_shape_compatible_op(self, model):
         oshape = self.get_normal_output_shape()
         ishape = tuple(model.get_tensor_shape(self.onnx_node.input[0]))
         assert ishape == exp_ishape, "Unexpect input shape for DownSampler."
-        # implement tensor with correct shape
-        values = np.random.randn(*oshape).astype(np.float32)
         return helper.make_node(
-            "Constant",
+            "RandomNormal",
             inputs=[],
             outputs=[self.onnx_node.output[0]],
-            value=helper.make_tensor(
-                name="const_tensor",
-                data_type=TensorProto.FLOAT,
-                dims=values.shape,
-                vals=values.flatten().astype(float),
-            ),
+            shape=list(oshape),
         )
 
     def infer_node_datatype(self, model):
diff --git a/src/finn/custom_op/fpgadataflow/fmpadding_batch.py b/src/finn/custom_op/fpgadataflow/fmpadding_batch.py
index ca0b2f12ab..ec2a2b690f 100644
--- a/src/finn/custom_op/fpgadataflow/fmpadding_batch.py
+++ b/src/finn/custom_op/fpgadataflow/fmpadding_batch.py
@@ -1,7 +1,7 @@
 import numpy as np
 import os
 import warnings
-from onnx import TensorProto, helper
+from onnx import helper
 
 from finn.core.datatype import DataType
 from finn.custom_op.fpgadataflow.hlscustomop import HLSCustomOp
@@ -99,18 +99,11 @@ def make_shape_compatible_op(self, model):
         oshape = self.get_normal_output_shape()
         ishape = tuple(model.get_tensor_shape(self.onnx_node.input[0]))
         assert ishape == exp_ishape, "Unexpect input shape for SameResize."
-        # implement tensor with correct shape
-        values = np.random.randn(*oshape).astype(np.float32)
         return helper.make_node(
-            "Constant",
+            "RandomNormal",
             inputs=[],
             outputs=[self.onnx_node.output[0]],
-            value=helper.make_tensor(
-                name="const_tensor",
-                data_type=TensorProto.FLOAT,
-                dims=values.shape,
-                vals=values.flatten().astype(float),
-            ),
+            shape=list(oshape),
         )
 
     def infer_node_datatype(self, model):
diff --git a/src/finn/custom_op/fpgadataflow/globalaccpool_batch.py b/src/finn/custom_op/fpgadataflow/globalaccpool_batch.py
index eabdcf599d..d6f860db15 100644
--- a/src/finn/custom_op/fpgadataflow/globalaccpool_batch.py
+++ b/src/finn/custom_op/fpgadataflow/globalaccpool_batch.py
@@ -29,7 +29,7 @@
 import numpy as np
 import os
 import warnings
-from onnx import TensorProto, helper
+from onnx import helper
 
 from finn.core.datatype import DataType
 from finn.custom_op.fpgadataflow.hlscustomop import HLSCustomOp
@@ -95,18 +95,11 @@ def make_shape_compatible_op(self, model):
         oshape = self.get_normal_output_shape()
         ishape = tuple(model.get_tensor_shape(self.onnx_node.input[0]))
         assert ishape == exp_ishape, "Unexpected input shape."
-        # implement tensor with correct shape
-        values = np.random.randn(*oshape).astype(np.float32)
         return helper.make_node(
-            "Constant",
+            "RandomNormal",
             inputs=[],
             outputs=[self.onnx_node.output[0]],
-            value=helper.make_tensor(
-                name="const_tensor",
-                data_type=TensorProto.FLOAT,
-                dims=values.shape,
-                vals=values.flatten(),
-            ),
+            shape=list(oshape),
         )
 
     def infer_node_datatype(self, model):
diff --git a/src/finn/custom_op/fpgadataflow/iodma.py b/src/finn/custom_op/fpgadataflow/iodma.py
index 4fa74e35db..34842181c3 100644
--- a/src/finn/custom_op/fpgadataflow/iodma.py
+++ b/src/finn/custom_op/fpgadataflow/iodma.py
@@ -29,7 +29,7 @@
 import math
 import numpy as np
 import warnings
-from onnx import TensorProto, helper
+from onnx import helper
 
 from finn.core.datatype import DataType
 from finn.custom_op.fpgadataflow.hlscustomop import HLSCustomOp
@@ -146,18 +146,11 @@ def make_shape_compatible_op(self, model):
         oshape = self.get_normal_output_shape()
         ishape = tuple(model.get_tensor_shape(self.onnx_node.input[0]))
         assert ishape == exp_ishape, "Unexpected input shape."
-        # implement tensor with correct shape
-        values = np.random.randn(*oshape).astype(np.float32)
         return helper.make_node(
-            "Constant",
+            "RandomNormal",
             inputs=[],
             outputs=[self.onnx_node.output[0]],
-            value=helper.make_tensor(
-                name="const_tensor",
-                data_type=TensorProto.FLOAT,
-                dims=values.shape,
-                vals=values.flatten().astype(float),
-            ),
+            shape=list(oshape),
         )
 
     def infer_node_datatype(self, model):
diff --git a/src/finn/custom_op/fpgadataflow/labelselect_batch.py b/src/finn/custom_op/fpgadataflow/labelselect_batch.py
index d70d0f6a9b..4d34de8fcc 100644
--- a/src/finn/custom_op/fpgadataflow/labelselect_batch.py
+++ b/src/finn/custom_op/fpgadataflow/labelselect_batch.py
@@ -102,18 +102,12 @@ def make_shape_compatible_op(self, model):
         oshape = self.get_normal_output_shape()
         ishape = tuple(model.get_tensor_shape(self.onnx_node.input[0]))
         assert ishape == exp_ishape, "Unexpected input shape."
-        # implement tensor with correct shape
-        values = np.random.randn(*oshape).astype(np.int64)
         return helper.make_node(
-            "Constant",
+            "RandomNormal",
             inputs=[],
             outputs=[self.onnx_node.output[0]],
-            value=helper.make_tensor(
-                name="const_tensor",
-                data_type=TensorProto.INT64,
-                dims=values.shape,
-                vals=values.flatten(),
-            ),
+            shape=list(oshape),
+            dtype=TensorProto.INT64,
         )
 
     def infer_node_datatype(self, model):
diff --git a/src/finn/custom_op/fpgadataflow/pool_batch.py b/src/finn/custom_op/fpgadataflow/pool_batch.py
index cef964acd5..5bcc653597 100644
--- a/src/finn/custom_op/fpgadataflow/pool_batch.py
+++ b/src/finn/custom_op/fpgadataflow/pool_batch.py
@@ -28,7 +28,7 @@
 
 import numpy as np
 import os
-from onnx import TensorProto, helper
+from onnx import helper
 
 from finn.core.datatype import DataType
 from finn.custom_op.fpgadataflow.hlscustomop import HLSCustomOp
@@ -163,18 +163,11 @@ def make_shape_compatible_op(self, model):
         oshape = self.get_normal_output_shape()
         ishape = tuple(model.get_tensor_shape(self.onnx_node.input[0]))
         assert ishape == exp_ishape, "Unexpected input shape for Pool_Batch."
-        # implement tensor with correct shape
-        values = np.random.randn(*oshape).astype(np.float32)
         return helper.make_node(
-            "Constant",
+            "RandomNormal",
             inputs=[],
             outputs=[self.onnx_node.output[0]],
-            value=helper.make_tensor(
-                name="const_tensor",
-                data_type=TensorProto.FLOAT,
-                dims=values.shape,
-                vals=values.flatten().astype(float),
-            ),
+            shape=list(oshape),
         )
 
     def infer_node_datatype(self, model):
diff --git a/src/finn/custom_op/fpgadataflow/streamingdatawidthconverter_batch.py b/src/finn/custom_op/fpgadataflow/streamingdatawidthconverter_batch.py
index 67e3cd3654..aab87be52f 100644
--- a/src/finn/custom_op/fpgadataflow/streamingdatawidthconverter_batch.py
+++ b/src/finn/custom_op/fpgadataflow/streamingdatawidthconverter_batch.py
@@ -30,7 +30,7 @@
 import numpy as np
 import os
 import warnings
-from onnx import TensorProto, helper
+from onnx import helper
 
 from finn.core.datatype import DataType
 from finn.custom_op.fpgadataflow.hlscustomop import HLSCustomOp
@@ -165,18 +165,11 @@ def make_shape_compatible_op(self, model):
         oshape = self.get_normal_output_shape()
         ishape = tuple(model.get_tensor_shape(self.onnx_node.input[0]))
         assert ishape == tuple(exp_ishape), "Unexpect input shape for StreamingDWC."
-        # implement tensor with correct shape
-        values = np.random.randn(*oshape).astype(np.float32)
         return helper.make_node(
-            "Constant",
+            "RandomNormal",
             inputs=[],
             outputs=[self.onnx_node.output[0]],
-            value=helper.make_tensor(
-                name="const_tensor",
-                data_type=TensorProto.FLOAT,
-                dims=values.shape,
-                vals=values.flatten().astype(float),
-            ),
+            shape=list(oshape),
         )
 
     def infer_node_datatype(self, model):
diff --git a/src/finn/custom_op/fpgadataflow/streamingfclayer_batch.py b/src/finn/custom_op/fpgadataflow/streamingfclayer_batch.py
index 96594d4413..2f1097d712 100644
--- a/src/finn/custom_op/fpgadataflow/streamingfclayer_batch.py
+++ b/src/finn/custom_op/fpgadataflow/streamingfclayer_batch.py
@@ -31,7 +31,7 @@
 import os
 import textwrap
 import warnings
-from onnx import TensorProto, helper
+from onnx import helper
 
 from finn.core.datatype import DataType
 from finn.custom_op.fpgadataflow.hlscustomop import HLSCustomOp
@@ -151,18 +151,11 @@ def calc_tmem(self):
 
     def make_shape_compatible_op(self, model):
         oshape = self.get_normal_output_shape()
-        # implement tensor with correct shape
-        values = np.random.randn(*oshape).astype(np.float32)
         return helper.make_node(
-            "Constant",
+            "RandomNormal",
             inputs=[],
             outputs=[self.onnx_node.output[0]],
-            value=helper.make_tensor(
-                name="const_tensor",
-                data_type=TensorProto.FLOAT,
-                dims=values.shape,
-                vals=values.flatten().astype(float),
-            ),
+            shape=list(oshape),
         )
 
     def infer_node_datatype(self, model):
diff --git a/src/finn/custom_op/fpgadataflow/streamingfifo.py b/src/finn/custom_op/fpgadataflow/streamingfifo.py
index 9653d698f5..ce09d5565a 100644
--- a/src/finn/custom_op/fpgadataflow/streamingfifo.py
+++ b/src/finn/custom_op/fpgadataflow/streamingfifo.py
@@ -30,7 +30,7 @@
 import os
 import subprocess
 import warnings
-from onnx import TensorProto, helper
+from onnx import helper
 from shutil import copy
 
 from finn.core.datatype import DataType
@@ -78,18 +78,11 @@ def make_shape_compatible_op(self, model):
         oshape = self.get_normal_output_shape()
         ishape = tuple(model.get_tensor_shape(self.onnx_node.input[0]))
         assert ishape == tuple(exp_ishape), "Unexpect input shape for StreamingFIFO."
-        # implement tensor with correct shape
-        values = np.random.randn(*oshape).astype(np.float32)
         return helper.make_node(
-            "Constant",
+            "RandomNormal",
             inputs=[],
             outputs=[self.onnx_node.output[0]],
-            value=helper.make_tensor(
-                name="const_tensor",
-                data_type=TensorProto.FLOAT,
-                dims=values.shape,
-                vals=values.flatten().astype(float),
-            ),
+            shape=list(oshape),
         )
 
     def infer_node_datatype(self, model):
diff --git a/src/finn/custom_op/fpgadataflow/streamingmaxpool_batch.py b/src/finn/custom_op/fpgadataflow/streamingmaxpool_batch.py
index 19a42fe2d6..c0641ffb74 100644
--- a/src/finn/custom_op/fpgadataflow/streamingmaxpool_batch.py
+++ b/src/finn/custom_op/fpgadataflow/streamingmaxpool_batch.py
@@ -29,7 +29,7 @@
 import numpy as np
 import os
 import warnings
-from onnx import TensorProto, helper
+from onnx import helper
 
 from finn.core.datatype import DataType
 from finn.custom_op.fpgadataflow.hlscustomop import HLSCustomOp
@@ -140,18 +140,11 @@ def make_shape_compatible_op(self, model):
         oshape = self.get_normal_output_shape()
         ishape = tuple(model.get_tensor_shape(self.onnx_node.input[0]))
         assert ishape == exp_ishape, "Unexpect input shape for StreamingMaxPool."
-        # implement tensor with correct shape
-        values = np.random.randn(*oshape).astype(np.float32)
         return helper.make_node(
-            "Constant",
+            "RandomNormal",
             inputs=[],
             outputs=[self.onnx_node.output[0]],
-            value=helper.make_tensor(
-                name="const_tensor",
-                data_type=TensorProto.FLOAT,
-                dims=values.shape,
-                vals=values.flatten().astype(float),
-            ),
+            shape=list(oshape),
         )
 
     def infer_node_datatype(self, model):
diff --git a/src/finn/custom_op/fpgadataflow/thresholding_batch.py b/src/finn/custom_op/fpgadataflow/thresholding_batch.py
index 7fb7634dc2..df562f5450 100644
--- a/src/finn/custom_op/fpgadataflow/thresholding_batch.py
+++ b/src/finn/custom_op/fpgadataflow/thresholding_batch.py
@@ -31,7 +31,7 @@
 import textwrap
 import warnings
 from math import ceil, log2
-from onnx import TensorProto, helper
+from onnx import helper
 
 from finn.core.datatype import DataType
 from finn.custom_op.fpgadataflow.hlscustomop import HLSCustomOp
@@ -112,18 +112,11 @@ def calc_tmem(self):
 
     def make_shape_compatible_op(self, model):
         oshape = self.get_normal_output_shape()
-        # implement tensor with correct shape
-        values = np.random.randn(*oshape).astype(np.float32)
         return helper.make_node(
-            "Constant",
+            "RandomNormal",
             inputs=[],
             outputs=[self.onnx_node.output[0]],
-            value=helper.make_tensor(
-                name="const_tensor",
-                data_type=TensorProto.FLOAT,
-                dims=values.shape,
-                vals=values.flatten().astype(float),
-            ),
+            shape=list(oshape),
         )
 
     def infer_node_datatype(self, model):
diff --git a/src/finn/custom_op/fpgadataflow/upsampler.py b/src/finn/custom_op/fpgadataflow/upsampler.py
index e8aa09b1c0..fcdf9d7d2c 100644
--- a/src/finn/custom_op/fpgadataflow/upsampler.py
+++ b/src/finn/custom_op/fpgadataflow/upsampler.py
@@ -1,7 +1,7 @@
 import numpy as np
 import os
 import warnings
-from onnx import TensorProto, helper
+from onnx import helper
 
 from finn.core.datatype import DataType
 from finn.custom_op.fpgadataflow.hlscustomop import HLSCustomOp
@@ -69,18 +69,11 @@ def make_shape_compatible_op(self, model):
         assert (
             ishape == exp_ishape
         ), "Unexpect input shape for UpsampleNearestNeighbour_Batch."
-        # implement tensor with correct shape
-        values = np.random.randn(*oshape).astype(np.float32)
         return helper.make_node(
-            "Constant",
+            "RandomNormal",
             inputs=[],
             outputs=[self.onnx_node.output[0]],
-            value=helper.make_tensor(
-                name="const_tensor",
-                data_type=TensorProto.FLOAT,
-                dims=values.shape,
-                vals=values.flatten().astype(float),
-            ),
+            shape=list(oshape),
         )
 
     def infer_node_datatype(self, model):
diff --git a/src/finn/custom_op/fpgadataflow/vector_vector_activate_batch.py b/src/finn/custom_op/fpgadataflow/vector_vector_activate_batch.py
index 9fc133b9bc..d9e7e566e6 100644
--- a/src/finn/custom_op/fpgadataflow/vector_vector_activate_batch.py
+++ b/src/finn/custom_op/fpgadataflow/vector_vector_activate_batch.py
@@ -2,7 +2,7 @@
 import numpy as np
 import os
 import warnings
-from onnx import TensorProto, helper
+from onnx import helper
 
 from finn.core.datatype import DataType
 from finn.custom_op.fpgadataflow.hlscustomop import HLSCustomOp
@@ -129,18 +129,11 @@ def calc_tmem(self):
 
     def make_shape_compatible_op(self, model):
         oshape = self.get_normal_output_shape()
-        # implement tensor with correct shape
-        values = np.random.randn(*oshape).astype(np.float32)
         return helper.make_node(
-            "Constant",
+            "RandomNormal",
             inputs=[],
             outputs=[self.onnx_node.output[0]],
-            value=helper.make_tensor(
-                name="const_tensor",
-                data_type=TensorProto.FLOAT,
-                dims=values.shape,
-                vals=values.flatten().astype(float),
-            ),
+            shape=list(oshape),
         )
 
     def infer_node_datatype(self, model):

From f78200b9fc66a2687e4b0c75993d31a2fa78cac2 Mon Sep 17 00:00:00 2001
From: Yaman Umuroglu <yamanu@xilinx.com>
Date: Tue, 12 Oct 2021 22:17:47 +0200
Subject: [PATCH 3/5] [Deps] update finn-base

---
 docker/Dockerfile.finn | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/docker/Dockerfile.finn b/docker/Dockerfile.finn
index 802292cac3..10393fac4b 100644
--- a/docker/Dockerfile.finn
+++ b/docker/Dockerfile.finn
@@ -86,7 +86,7 @@ RUN pip install -e git+https://github.com/fbcotter/dataset_loading.git@0.0.4#egg
 
 # git-based Python repo dependencies
 # these are installed in editable mode for easier co-development
-ARG FINN_BASE_COMMIT="9e8fc02683d272cc14309eeebcf14293dc5e4edd"
+ARG FINN_BASE_COMMIT="db444c1936300f5594499f624c9b4ff1f287492b"
 ARG FINN_EXP_COMMIT="f82c0d9868bb88ea045dfadb28508d327d287221"
 ARG BREVITAS_COMMIT="462f86cdc60f9915baf13afd1676fb21da44c2ee"
 ARG PYVERILATOR_COMMIT="0c3eb9343500fc1352a02c020a736c8c2db47e8e"

From 2e04199971dd13177b4019123ed69eb0aad7fdc1 Mon Sep 17 00:00:00 2001
From: Yaman Umuroglu <yamanu@xilinx.com>
Date: Wed, 13 Oct 2021 10:35:29 +0200
Subject: [PATCH 4/5] [Deps] update finn-base

---
 docker/Dockerfile.finn | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/docker/Dockerfile.finn b/docker/Dockerfile.finn
index 10393fac4b..1572ba2872 100644
--- a/docker/Dockerfile.finn
+++ b/docker/Dockerfile.finn
@@ -86,7 +86,7 @@ RUN pip install -e git+https://github.com/fbcotter/dataset_loading.git@0.0.4#egg
 
 # git-based Python repo dependencies
 # these are installed in editable mode for easier co-development
-ARG FINN_BASE_COMMIT="db444c1936300f5594499f624c9b4ff1f287492b"
+ARG FINN_BASE_COMMIT="7c2603a95e90e4de2575020e575c24eab6a15889"
 ARG FINN_EXP_COMMIT="f82c0d9868bb88ea045dfadb28508d327d287221"
 ARG BREVITAS_COMMIT="462f86cdc60f9915baf13afd1676fb21da44c2ee"
 ARG PYVERILATOR_COMMIT="0c3eb9343500fc1352a02c020a736c8c2db47e8e"

From fe8f8768cc9211bb0709b024ec517566b487ba45 Mon Sep 17 00:00:00 2001
From: Yaman Umuroglu <yamanu@xilinx.com>
Date: Wed, 13 Oct 2021 10:37:31 +0200
Subject: [PATCH 5/5] [Refactor] switch to CustomOp base-class impl for shape
 inf

---
 src/finn/custom_op/fpgadataflow/addstreams_batch.py       | 8 +-------
 src/finn/custom_op/fpgadataflow/channelwise_op_batch.py   | 8 +-------
 .../custom_op/fpgadataflow/convolutioninputgenerator.py   | 8 +-------
 .../custom_op/fpgadataflow/convolutioninputgenerator1d.py | 8 +-------
 src/finn/custom_op/fpgadataflow/downsampler.py            | 8 +-------
 src/finn/custom_op/fpgadataflow/fmpadding_batch.py        | 8 +-------
 src/finn/custom_op/fpgadataflow/globalaccpool_batch.py    | 8 +-------
 src/finn/custom_op/fpgadataflow/iodma.py                  | 8 +-------
 src/finn/custom_op/fpgadataflow/labelselect_batch.py      | 4 +++-
 src/finn/custom_op/fpgadataflow/pool_batch.py             | 8 +-------
 .../fpgadataflow/streamingdatawidthconverter_batch.py     | 8 +-------
 src/finn/custom_op/fpgadataflow/streamingfclayer_batch.py | 8 +-------
 src/finn/custom_op/fpgadataflow/streamingfifo.py          | 8 +-------
 src/finn/custom_op/fpgadataflow/streamingmaxpool_batch.py | 8 +-------
 src/finn/custom_op/fpgadataflow/thresholding_batch.py     | 8 +-------
 src/finn/custom_op/fpgadataflow/upsampler.py              | 8 +-------
 .../fpgadataflow/vector_vector_activate_batch.py          | 8 +-------
 17 files changed, 19 insertions(+), 113 deletions(-)

diff --git a/src/finn/custom_op/fpgadataflow/addstreams_batch.py b/src/finn/custom_op/fpgadataflow/addstreams_batch.py
index aaf8687de1..fa80e47485 100644
--- a/src/finn/custom_op/fpgadataflow/addstreams_batch.py
+++ b/src/finn/custom_op/fpgadataflow/addstreams_batch.py
@@ -29,7 +29,6 @@
 import numpy as np
 import os
 import warnings
-from onnx import helper
 
 from finn.core.datatype import DataType
 from finn.custom_op.fpgadataflow.hlscustomop import HLSCustomOp
@@ -84,12 +83,7 @@ def make_shape_compatible_op(self, model):
         assert ishape == exp_ishape, "Unexpected input1 shape."
         ishape = tuple(model.get_tensor_shape(self.onnx_node.input[1]))
         assert ishape == exp_ishape, "Unexpected input2 shape."
-        return helper.make_node(
-            "RandomNormal",
-            inputs=[],
-            outputs=[self.onnx_node.output[0]],
-            shape=list(oshape),
-        )
+        return super().make_const_shape_op(oshape)
 
     def infer_node_datatype(self, model):
         node = self.onnx_node
diff --git a/src/finn/custom_op/fpgadataflow/channelwise_op_batch.py b/src/finn/custom_op/fpgadataflow/channelwise_op_batch.py
index 083ee894f4..4961f61482 100644
--- a/src/finn/custom_op/fpgadataflow/channelwise_op_batch.py
+++ b/src/finn/custom_op/fpgadataflow/channelwise_op_batch.py
@@ -30,7 +30,6 @@
 import os
 import warnings
 from math import ceil
-from onnx import helper
 
 from finn.core.datatype import DataType
 from finn.custom_op.fpgadataflow.hlscustomop import HLSCustomOp
@@ -125,12 +124,7 @@ def calc_tmem(self):
     def make_shape_compatible_op(self, model):
         oshape = self.get_normal_output_shape()
         # implement tensor with correct shape
-        return helper.make_node(
-            "RandomNormal",
-            inputs=[],
-            outputs=[self.onnx_node.output[0]],
-            shape=list(oshape),
-        )
+        return super().make_const_shape_op(oshape)
 
     def infer_node_datatype(self, model):
         node = self.onnx_node
diff --git a/src/finn/custom_op/fpgadataflow/convolutioninputgenerator.py b/src/finn/custom_op/fpgadataflow/convolutioninputgenerator.py
index def0b20b08..a401883684 100644
--- a/src/finn/custom_op/fpgadataflow/convolutioninputgenerator.py
+++ b/src/finn/custom_op/fpgadataflow/convolutioninputgenerator.py
@@ -29,7 +29,6 @@
 import math
 import numpy as np
 import os
-from onnx import helper
 
 from finn.core.datatype import DataType
 from finn.custom_op.fpgadataflow.hlscustomop import HLSCustomOp
@@ -148,12 +147,7 @@ def make_shape_compatible_op(self, model):
         ishape = tuple(model.get_tensor_shape(self.onnx_node.input[0]))
         assert ishape == exp_ishape, "Unexpect input shape for ConvInpGen."
         # implement tensor with correct shape
-        return helper.make_node(
-            "RandomNormal",
-            inputs=[],
-            outputs=[self.onnx_node.output[0]],
-            shape=list(oshape),
-        )
+        return super().make_const_shape_op(oshape)
 
     def infer_node_datatype(self, model):
         node = self.onnx_node
diff --git a/src/finn/custom_op/fpgadataflow/convolutioninputgenerator1d.py b/src/finn/custom_op/fpgadataflow/convolutioninputgenerator1d.py
index 6a81b91770..c4cf804126 100644
--- a/src/finn/custom_op/fpgadataflow/convolutioninputgenerator1d.py
+++ b/src/finn/custom_op/fpgadataflow/convolutioninputgenerator1d.py
@@ -29,7 +29,6 @@
 import math
 import numpy as np
 import os
-from onnx import helper
 
 from finn.core.datatype import DataType
 from finn.custom_op.fpgadataflow.hlscustomop import HLSCustomOp
@@ -137,12 +136,7 @@ def make_shape_compatible_op(self, model):
         oshape = self.get_normal_output_shape()
         ishape = tuple(model.get_tensor_shape(self.onnx_node.input[0]))
         assert ishape == exp_ishape, "Unexpect input shape for ConvInpGen."
-        return helper.make_node(
-            "RandomNormal",
-            inputs=[],
-            outputs=[self.onnx_node.output[0]],
-            shape=list(oshape),
-        )
+        return super().make_const_shape_op(oshape)
 
     def infer_node_datatype(self, model):
         node = self.onnx_node
diff --git a/src/finn/custom_op/fpgadataflow/downsampler.py b/src/finn/custom_op/fpgadataflow/downsampler.py
index 9dcbb5e144..6a0667f67d 100644
--- a/src/finn/custom_op/fpgadataflow/downsampler.py
+++ b/src/finn/custom_op/fpgadataflow/downsampler.py
@@ -1,7 +1,6 @@
 import numpy as np
 import os
 import warnings
-from onnx import helper
 
 from finn.core.datatype import DataType
 from finn.custom_op.fpgadataflow.hlscustomop import HLSCustomOp
@@ -83,12 +82,7 @@ def make_shape_compatible_op(self, model):
         oshape = self.get_normal_output_shape()
         ishape = tuple(model.get_tensor_shape(self.onnx_node.input[0]))
         assert ishape == exp_ishape, "Unexpect input shape for DownSampler."
-        return helper.make_node(
-            "RandomNormal",
-            inputs=[],
-            outputs=[self.onnx_node.output[0]],
-            shape=list(oshape),
-        )
+        return super().make_const_shape_op(oshape)
 
     def infer_node_datatype(self, model):
         node = self.onnx_node
diff --git a/src/finn/custom_op/fpgadataflow/fmpadding_batch.py b/src/finn/custom_op/fpgadataflow/fmpadding_batch.py
index dd76195669..f29ea431ff 100644
--- a/src/finn/custom_op/fpgadataflow/fmpadding_batch.py
+++ b/src/finn/custom_op/fpgadataflow/fmpadding_batch.py
@@ -1,7 +1,6 @@
 import numpy as np
 import os
 import warnings
-from onnx import helper
 
 from finn.core.datatype import DataType
 from finn.custom_op.fpgadataflow.hlscustomop import HLSCustomOp
@@ -99,12 +98,7 @@ def make_shape_compatible_op(self, model):
         oshape = self.get_normal_output_shape()
         ishape = tuple(model.get_tensor_shape(self.onnx_node.input[0]))
         assert ishape == exp_ishape, "Unexpect input shape for SameResize."
-        return helper.make_node(
-            "RandomNormal",
-            inputs=[],
-            outputs=[self.onnx_node.output[0]],
-            shape=list(oshape),
-        )
+        return super().make_const_shape_op(oshape)
 
     def infer_node_datatype(self, model):
         node = self.onnx_node
diff --git a/src/finn/custom_op/fpgadataflow/globalaccpool_batch.py b/src/finn/custom_op/fpgadataflow/globalaccpool_batch.py
index d6f860db15..6d4a55ee5c 100644
--- a/src/finn/custom_op/fpgadataflow/globalaccpool_batch.py
+++ b/src/finn/custom_op/fpgadataflow/globalaccpool_batch.py
@@ -29,7 +29,6 @@
 import numpy as np
 import os
 import warnings
-from onnx import helper
 
 from finn.core.datatype import DataType
 from finn.custom_op.fpgadataflow.hlscustomop import HLSCustomOp
@@ -95,12 +94,7 @@ def make_shape_compatible_op(self, model):
         oshape = self.get_normal_output_shape()
         ishape = tuple(model.get_tensor_shape(self.onnx_node.input[0]))
         assert ishape == exp_ishape, "Unexpected input shape."
-        return helper.make_node(
-            "RandomNormal",
-            inputs=[],
-            outputs=[self.onnx_node.output[0]],
-            shape=list(oshape),
-        )
+        return super().make_const_shape_op(oshape)
 
     def infer_node_datatype(self, model):
         node = self.onnx_node
diff --git a/src/finn/custom_op/fpgadataflow/iodma.py b/src/finn/custom_op/fpgadataflow/iodma.py
index 34842181c3..802c7e7851 100644
--- a/src/finn/custom_op/fpgadataflow/iodma.py
+++ b/src/finn/custom_op/fpgadataflow/iodma.py
@@ -29,7 +29,6 @@
 import math
 import numpy as np
 import warnings
-from onnx import helper
 
 from finn.core.datatype import DataType
 from finn.custom_op.fpgadataflow.hlscustomop import HLSCustomOp
@@ -146,12 +145,7 @@ def make_shape_compatible_op(self, model):
         oshape = self.get_normal_output_shape()
         ishape = tuple(model.get_tensor_shape(self.onnx_node.input[0]))
         assert ishape == exp_ishape, "Unexpected input shape."
-        return helper.make_node(
-            "RandomNormal",
-            inputs=[],
-            outputs=[self.onnx_node.output[0]],
-            shape=list(oshape),
-        )
+        return super().make_const_shape_op(oshape)
 
     def infer_node_datatype(self, model):
         node = self.onnx_node
diff --git a/src/finn/custom_op/fpgadataflow/labelselect_batch.py b/src/finn/custom_op/fpgadataflow/labelselect_batch.py
index 4d34de8fcc..1eb5962fdb 100644
--- a/src/finn/custom_op/fpgadataflow/labelselect_batch.py
+++ b/src/finn/custom_op/fpgadataflow/labelselect_batch.py
@@ -106,8 +106,10 @@ def make_shape_compatible_op(self, model):
             "RandomNormal",
             inputs=[],
             outputs=[self.onnx_node.output[0]],
-            shape=list(oshape),
+            mean=0.0,
+            scale=1.0,
             dtype=TensorProto.INT64,
+            shape=list(oshape),
         )
 
     def infer_node_datatype(self, model):
diff --git a/src/finn/custom_op/fpgadataflow/pool_batch.py b/src/finn/custom_op/fpgadataflow/pool_batch.py
index 332b400c0d..ba8a446f2c 100644
--- a/src/finn/custom_op/fpgadataflow/pool_batch.py
+++ b/src/finn/custom_op/fpgadataflow/pool_batch.py
@@ -28,7 +28,6 @@
 
 import numpy as np
 import os
-from onnx import helper
 
 from finn.core.datatype import DataType
 from finn.custom_op.fpgadataflow.hlscustomop import HLSCustomOp
@@ -163,12 +162,7 @@ def make_shape_compatible_op(self, model):
         oshape = self.get_normal_output_shape()
         ishape = tuple(model.get_tensor_shape(self.onnx_node.input[0]))
         assert ishape == exp_ishape, "Unexpected input shape for Pool_Batch."
-        return helper.make_node(
-            "RandomNormal",
-            inputs=[],
-            outputs=[self.onnx_node.output[0]],
-            shape=list(oshape),
-        )
+        return super().make_const_shape_op(oshape)
 
     def infer_node_datatype(self, model):
         node = self.onnx_node
diff --git a/src/finn/custom_op/fpgadataflow/streamingdatawidthconverter_batch.py b/src/finn/custom_op/fpgadataflow/streamingdatawidthconverter_batch.py
index aaf796b15b..1791706afa 100644
--- a/src/finn/custom_op/fpgadataflow/streamingdatawidthconverter_batch.py
+++ b/src/finn/custom_op/fpgadataflow/streamingdatawidthconverter_batch.py
@@ -30,7 +30,6 @@
 import numpy as np
 import os
 import warnings
-from onnx import helper
 
 from finn.core.datatype import DataType
 from finn.custom_op.fpgadataflow.hlscustomop import HLSCustomOp
@@ -165,12 +164,7 @@ def make_shape_compatible_op(self, model):
         oshape = self.get_normal_output_shape()
         ishape = tuple(model.get_tensor_shape(self.onnx_node.input[0]))
         assert ishape == tuple(exp_ishape), "Unexpect input shape for StreamingDWC."
-        return helper.make_node(
-            "RandomNormal",
-            inputs=[],
-            outputs=[self.onnx_node.output[0]],
-            shape=list(oshape),
-        )
+        return super().make_const_shape_op(oshape)
 
     def infer_node_datatype(self, model):
         node = self.onnx_node
diff --git a/src/finn/custom_op/fpgadataflow/streamingfclayer_batch.py b/src/finn/custom_op/fpgadataflow/streamingfclayer_batch.py
index ccc9780ff4..90abb66e66 100644
--- a/src/finn/custom_op/fpgadataflow/streamingfclayer_batch.py
+++ b/src/finn/custom_op/fpgadataflow/streamingfclayer_batch.py
@@ -31,7 +31,6 @@
 import os
 import textwrap
 import warnings
-from onnx import helper
 
 from finn.core.datatype import DataType
 from finn.custom_op.fpgadataflow.hlscustomop import HLSCustomOp
@@ -151,12 +150,7 @@ def calc_tmem(self):
 
     def make_shape_compatible_op(self, model):
         oshape = self.get_normal_output_shape()
-        return helper.make_node(
-            "RandomNormal",
-            inputs=[],
-            outputs=[self.onnx_node.output[0]],
-            shape=list(oshape),
-        )
+        return super().make_const_shape_op(oshape)
 
     def infer_node_datatype(self, model):
         node = self.onnx_node
diff --git a/src/finn/custom_op/fpgadataflow/streamingfifo.py b/src/finn/custom_op/fpgadataflow/streamingfifo.py
index 02d9a1915c..91f6ed5b8d 100644
--- a/src/finn/custom_op/fpgadataflow/streamingfifo.py
+++ b/src/finn/custom_op/fpgadataflow/streamingfifo.py
@@ -30,7 +30,6 @@
 import os
 import subprocess
 import warnings
-from onnx import helper
 from shutil import copy
 
 from finn.core.datatype import DataType
@@ -78,12 +77,7 @@ def make_shape_compatible_op(self, model):
         oshape = self.get_normal_output_shape()
         ishape = tuple(model.get_tensor_shape(self.onnx_node.input[0]))
         assert ishape == tuple(exp_ishape), "Unexpect input shape for StreamingFIFO."
-        return helper.make_node(
-            "RandomNormal",
-            inputs=[],
-            outputs=[self.onnx_node.output[0]],
-            shape=list(oshape),
-        )
+        return super().make_const_shape_op(oshape)
 
     def infer_node_datatype(self, model):
         node = self.onnx_node
diff --git a/src/finn/custom_op/fpgadataflow/streamingmaxpool_batch.py b/src/finn/custom_op/fpgadataflow/streamingmaxpool_batch.py
index 5574d67066..1e66a5c204 100644
--- a/src/finn/custom_op/fpgadataflow/streamingmaxpool_batch.py
+++ b/src/finn/custom_op/fpgadataflow/streamingmaxpool_batch.py
@@ -29,7 +29,6 @@
 import numpy as np
 import os
 import warnings
-from onnx import helper
 
 from finn.core.datatype import DataType
 from finn.custom_op.fpgadataflow.hlscustomop import HLSCustomOp
@@ -140,12 +139,7 @@ def make_shape_compatible_op(self, model):
         oshape = self.get_normal_output_shape()
         ishape = tuple(model.get_tensor_shape(self.onnx_node.input[0]))
         assert ishape == exp_ishape, "Unexpect input shape for StreamingMaxPool."
-        return helper.make_node(
-            "RandomNormal",
-            inputs=[],
-            outputs=[self.onnx_node.output[0]],
-            shape=list(oshape),
-        )
+        return super().make_const_shape_op(oshape)
 
     def infer_node_datatype(self, model):
         node = self.onnx_node
diff --git a/src/finn/custom_op/fpgadataflow/thresholding_batch.py b/src/finn/custom_op/fpgadataflow/thresholding_batch.py
index d5fa86abdd..610139f44e 100644
--- a/src/finn/custom_op/fpgadataflow/thresholding_batch.py
+++ b/src/finn/custom_op/fpgadataflow/thresholding_batch.py
@@ -31,7 +31,6 @@
 import textwrap
 import warnings
 from math import ceil, log2
-from onnx import helper
 
 from finn.core.datatype import DataType
 from finn.custom_op.fpgadataflow.hlscustomop import HLSCustomOp
@@ -112,12 +111,7 @@ def calc_tmem(self):
 
     def make_shape_compatible_op(self, model):
         oshape = self.get_normal_output_shape()
-        return helper.make_node(
-            "RandomNormal",
-            inputs=[],
-            outputs=[self.onnx_node.output[0]],
-            shape=list(oshape),
-        )
+        return super().make_const_shape_op(oshape)
 
     def infer_node_datatype(self, model):
         node = self.onnx_node
diff --git a/src/finn/custom_op/fpgadataflow/upsampler.py b/src/finn/custom_op/fpgadataflow/upsampler.py
index 60052b46a1..d5f809305b 100644
--- a/src/finn/custom_op/fpgadataflow/upsampler.py
+++ b/src/finn/custom_op/fpgadataflow/upsampler.py
@@ -1,7 +1,6 @@
 import numpy as np
 import os
 import warnings
-from onnx import helper
 
 from finn.core.datatype import DataType
 from finn.custom_op.fpgadataflow.hlscustomop import HLSCustomOp
@@ -69,12 +68,7 @@ def make_shape_compatible_op(self, model):
         assert (
             ishape == exp_ishape
         ), "Unexpect input shape for UpsampleNearestNeighbour_Batch."
-        return helper.make_node(
-            "RandomNormal",
-            inputs=[],
-            outputs=[self.onnx_node.output[0]],
-            shape=list(oshape),
-        )
+        return super().make_const_shape_op(oshape)
 
     def infer_node_datatype(self, model):
         node = self.onnx_node
diff --git a/src/finn/custom_op/fpgadataflow/vector_vector_activate_batch.py b/src/finn/custom_op/fpgadataflow/vector_vector_activate_batch.py
index 0ad03f37cd..c67eb0f21b 100644
--- a/src/finn/custom_op/fpgadataflow/vector_vector_activate_batch.py
+++ b/src/finn/custom_op/fpgadataflow/vector_vector_activate_batch.py
@@ -2,7 +2,6 @@
 import numpy as np
 import os
 import warnings
-from onnx import helper
 
 from finn.core.datatype import DataType
 from finn.custom_op.fpgadataflow.hlscustomop import HLSCustomOp
@@ -129,12 +128,7 @@ def calc_tmem(self):
 
     def make_shape_compatible_op(self, model):
         oshape = self.get_normal_output_shape()
-        return helper.make_node(
-            "RandomNormal",
-            inputs=[],
-            outputs=[self.onnx_node.output[0]],
-            shape=list(oshape),
-        )
+        return super().make_const_shape_op(oshape)
 
     def infer_node_datatype(self, model):
         node = self.onnx_node