From 96179ebe99f0444a6df055bbeb3bd6e7180df939 Mon Sep 17 00:00:00 2001
From: Tobi-Alonso <tobi.alonso@gmail.com>
Date: Fri, 4 Sep 2020 18:04:55 +0100
Subject: [PATCH] [fpgadataflow] Fix tensor element bitwidth missmatch for
labelselect_batch and channelwise_op_batch
---
.../fpgadataflow/channelwise_op_batch.py | 65 ++++++++++++++++++-
.../fpgadataflow/labelselect_batch.py | 5 ++
2 files changed, 67 insertions(+), 3 deletions(-)
diff --git a/src/finn/custom_op/fpgadataflow/channelwise_op_batch.py b/src/finn/custom_op/fpgadataflow/channelwise_op_batch.py
index d8e74a4d1..88b55aaec 100644
--- a/src/finn/custom_op/fpgadataflow/channelwise_op_batch.py
+++ b/src/finn/custom_op/fpgadataflow/channelwise_op_batch.py
@@ -41,6 +41,8 @@ from finn.util.data_packing import (
)
from . import templates
+import warnings
+
# ONNX i/o tensor shape assumptions for channelwise ops:
# input 0 is the input tensor, shape (..., NumChannels)
# input 1 is the channelwise parameter tensor, shape (NumChannels, params_per_channel)
@@ -48,6 +50,37 @@ from . import templates
# the ... here can be any shape (representing groups of vectors)
+def get_smallest_possible(vals):
+ """Returns smallest (fewest bits) possible DataType that can represent
+ value. Prefers unsigned integers where possible."""
+ vals = np.array(vals)
+ for v in vals:
+ assert int(v) == v, "Error float value"
+
+ for k in DataType.__members__:
+ dt = DataType[k]
+
+ if dt in [DataType.BIPOLAR, DataType.TERNARY, DataType.FLOAT32]:
+ # not currently supported
+ continue
+
+ if (dt.min() <= vals).all() and (vals <= dt.max()).all():
+ return dt
+
+ warnings.warn(
+ """InferChannelwiseLinearLayer: Output values may not be
+ representable with supported data types.
+ Setting maximum width data type available.
+ This will lead to errors if there are no constrains on the input
+ """
+ )
+
+ if (0 <= vals).all():
+ return DataType.UINT64
+ else:
+ return DataType.INT64
+
+
class ChannelwiseOp_Batch(HLSCustomOp):
"""Class that corresponds to finn-hls Thresholding_Batch function.
It can implement a variety of channel-wise parametrized operations,
@@ -109,10 +142,36 @@ class ChannelwiseOp_Batch(HLSCustomOp):
def infer_node_datatype(self, model):
node = self.onnx_node
# check input datatype against property
- idt_name = self.get_input_datatype().name
+ idt = model.get_tensor_datatype(node.input[0])
+
exp_idt_name = self.get_nodeattr("inputDataType")
- assert exp_idt_name == idt_name, "Bad input DataType for ChannelwiseOp layer"
- # TODO: dynamically infer/update odt based on idt as done in ConvertToHLSLayers?
+ if exp_idt_name != idt.name:
+ func = self.get_nodeattr("Func")
+ assert func in ["add", "mul"], "Bad input DataType for ChannelwiseOp layer"
+
+ self.set_nodeattr("inputDataType", idt.name)
+ # update the func in ['add','mul'] cases
+
+ # get parameter ranges
+ param = model.get_initializer(node.input[1])
+ param_min = min(param.flatten())
+ param_max = max(param.flatten())
+
+ # set function and determine output data type
+ if func == "add":
+ out_min = idt.min() + param_min
+ out_max = idt.max() + param_max
+ odt = get_smallest_possible([out_min, out_max])
+ elif func == "mul":
+ possible_limits = []
+ possible_limits += [idt.min() * param_min]
+ possible_limits += [idt.min() * param_max]
+ possible_limits += [idt.max() * param_min]
+ possible_limits += [idt.max() * param_max]
+ odt = get_smallest_possible(possible_limits)
+
+ self.set_nodeattr("outputDataType", odt.name)
+
# set output datatype from property
odt = self.get_output_datatype()
model.set_tensor_datatype(node.output[0], odt)
diff --git a/src/finn/custom_op/fpgadataflow/labelselect_batch.py b/src/finn/custom_op/fpgadataflow/labelselect_batch.py
index f61fbf12d..c6598a30e 100644
--- a/src/finn/custom_op/fpgadataflow/labelselect_batch.py
+++ b/src/finn/custom_op/fpgadataflow/labelselect_batch.py
@@ -113,6 +113,11 @@ class LabelSelect_Batch(HLSCustomOp):
)
def infer_node_datatype(self, model):
+ node = self.onnx_node
+ # check input datatype against property
+ idt = model.get_tensor_datatype(node.input[0])
+ self.set_nodeattr("inputDataType", idt.name)
+
odt = self.get_output_datatype()
model.set_tensor_datatype(self.onnx_node.output[0], odt)
--
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