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] [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 856f84fae..aaf8687de 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 @@ class AddStreams_Batch(HLSCustomOp):
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 073d6620a..b274bb8dc 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 numpy as np
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 @@ class ChannelwiseOp_Batch(HLSCustomOp):
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 9ec7bc662..8566ee035 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 @@ class ConvolutionInputGenerator(HLSCustomOp):
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 b428210ac..c76cb47d2 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 @@ class ConvolutionInputGenerator1D(HLSCustomOp):
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 2313ab28b..de251a848 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 @@ class DownSampler(HLSCustomOp):
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 ca0b2f12a..ec2a2b690 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 @@ class FMPadding_Batch(HLSCustomOp):
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 eabdcf599..d6f860db1 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 @@ class GlobalAccPool_Batch(HLSCustomOp):
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 4fa74e35d..34842181c 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 @@ class IODMA(HLSCustomOp):
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 d70d0f6a9..4d34de8fc 100644
--- a/src/finn/custom_op/fpgadataflow/labelselect_batch.py
+++ b/src/finn/custom_op/fpgadataflow/labelselect_batch.py
@@ -102,18 +102,12 @@ class LabelSelect_Batch(HLSCustomOp):
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 cef964acd..5bcc65359 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 @@ class Pool_Batch(HLSCustomOp):
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 67e3cd365..aab87be52 100644
--- a/src/finn/custom_op/fpgadataflow/streamingdatawidthconverter_batch.py
+++ b/src/finn/custom_op/fpgadataflow/streamingdatawidthconverter_batch.py
@@ -30,7 +30,7 @@ import math
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 @@ class StreamingDataWidthConverter_Batch(HLSCustomOp):
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 96594d441..2f1097d71 100644
--- a/src/finn/custom_op/fpgadataflow/streamingfclayer_batch.py
+++ b/src/finn/custom_op/fpgadataflow/streamingfclayer_batch.py
@@ -31,7 +31,7 @@ import numpy as np
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 @@ class StreamingFCLayer_Batch(HLSCustomOp):
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 9653d698f..ce09d5565 100644
--- a/src/finn/custom_op/fpgadataflow/streamingfifo.py
+++ b/src/finn/custom_op/fpgadataflow/streamingfifo.py
@@ -30,7 +30,7 @@ import numpy as np
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 @@ class StreamingFIFO(HLSCustomOp):
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 19a42fe2d..c0641ffb7 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 @@ class StreamingMaxPool_Batch(HLSCustomOp):
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 7fb7634dc..df562f545 100644
--- a/src/finn/custom_op/fpgadataflow/thresholding_batch.py
+++ b/src/finn/custom_op/fpgadataflow/thresholding_batch.py
@@ -31,7 +31,7 @@ import os
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 @@ class Thresholding_Batch(HLSCustomOp):
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 e8aa09b1c..fcdf9d7d2 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 @@ class UpsampleNearestNeighbour_Batch(HLSCustomOp):
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 9fc133b9b..d9e7e566e 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 math
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 @@ class Vector_Vector_Activate_Batch(HLSCustomOp):
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):
--
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