From 37e43b87bc3a87c57f72f32e4a92ea8700c89101 Mon Sep 17 00:00:00 2001
From: Yaman Umuroglu <maltanar@gmail.com>
Date: Mon, 2 Dec 2019 23:34:15 +0000
Subject: [PATCH] [CodeGen, Refactor] made dir and executable CustomOp
attributes
also move "if thresholds exist" logic into generate_threshold
---
src/finn/custom_op/fpgadataflow/__init__.py | 11 ++--
.../fpgadataflow/streamingfclayer_batch.py | 55 ++++++++++---------
.../fpgadataflow/streamingmaxpool.py | 4 +-
.../fpgadataflow/streamingmaxpool_batch.py | 4 +-
4 files changed, 40 insertions(+), 34 deletions(-)
diff --git a/src/finn/custom_op/fpgadataflow/__init__.py b/src/finn/custom_op/fpgadataflow/__init__.py
index e0cf22bcb..f9d2c6ba5 100644
--- a/src/finn/custom_op/fpgadataflow/__init__.py
+++ b/src/finn/custom_op/fpgadataflow/__init__.py
@@ -41,14 +41,13 @@ class HLSCustomOp(CustomOp):
self.code_gen_dir = util.get_by_name(onnx_node.attribute, "code_gen_dir")
self.executable_path = ""
+ def get_nodeattr_types(self):
+ return {"code_gen_dir": ("s", False, ""), "executable_path": ("s", False, "")}
+
def code_generation(self, model):
node = self.onnx_node
- if node.op_type == "StreamingFCLayer_Batch":
- self.generate_weights(model)
- try:
- self.generate_thresholds(model)
- except:
- pass
+ self.generate_weights(model)
+ self.generate_thresholds(model)
self.global_includes()
self.defines()
self.read_npy_data()
diff --git a/src/finn/custom_op/fpgadataflow/streamingfclayer_batch.py b/src/finn/custom_op/fpgadataflow/streamingfclayer_batch.py
index 0487b9a2b..3e06a3e51 100644
--- a/src/finn/custom_op/fpgadataflow/streamingfclayer_batch.py
+++ b/src/finn/custom_op/fpgadataflow/streamingfclayer_batch.py
@@ -15,7 +15,7 @@ class StreamingFCLayer_Batch(HLSCustomOp):
super().__init__(onnx_node)
def get_nodeattr_types(self):
- return {
+ my_attrs = {
"WMEM": ("i", True, 0),
"TMEM": ("i", True, 0),
"PE": ("i", True, 0),
@@ -29,6 +29,8 @@ class StreamingFCLayer_Batch(HLSCustomOp):
"weightDataType": ("s", True, ""),
"outputDataType": ("s", True, ""),
}
+ my_attrs.update(super().get_nodeattr_types())
+ return my_attrs
def make_shape_compatible_op(self):
pass
@@ -186,32 +188,33 @@ class StreamingFCLayer_Batch(HLSCustomOp):
def generate_thresholds(self, model):
thresholds = model.get_initializer(self.onnx_node.input[2])
- threshold_tensor = self.get_hls_compatible_threshold_tensor(thresholds)
- tdt = DataType.INT32
- # use UINT32 threshold export for bipolar times bipolar
- inp_is_bipolar = self.get_input_datatype() == DataType.BIPOLAR
- wt_is_bipolar = self.get_weight_datatype() == DataType.BIPOLAR
- if inp_is_bipolar and wt_is_bipolar:
- tdt = DataType.UINT32
- thresholds_hls_code = numpy_to_hls_code(
- threshold_tensor, tdt, "thresholds", False, True
- )
- # write thresholds into thresh.h
- f_thresh = open("{}/thresh.h".format(self.tmp_dir), "w")
- tdt_hls = tdt.get_hls_datatype_str()
- odt_hls = self.get_output_datatype().get_hls_datatype_str()
- f_thresh.write(
- "static ThresholdsActivation<{},{},{},{},{},{}> threshs = ".format(
- self.get_nodeattr("TMEM"),
- self.get_nodeattr("PE"),
- threshold_tensor.shape[-1],
- tdt_hls,
- odt_hls,
- self.get_nodeattr("ActVal"),
+ if thresholds is not None:
+ threshold_tensor = self.get_hls_compatible_threshold_tensor(thresholds)
+ tdt = DataType.INT32
+ # use UINT32 threshold export for bipolar times bipolar
+ inp_is_bipolar = self.get_input_datatype() == DataType.BIPOLAR
+ wt_is_bipolar = self.get_weight_datatype() == DataType.BIPOLAR
+ if inp_is_bipolar and wt_is_bipolar:
+ tdt = DataType.UINT32
+ thresholds_hls_code = numpy_to_hls_code(
+ threshold_tensor, tdt, "thresholds", False, True
)
- )
- f_thresh.write(thresholds_hls_code)
- f_thresh.close()
+ # write thresholds into thresh.h
+ f_thresh = open("{}/thresh.h".format(self.tmp_dir), "w")
+ tdt_hls = tdt.get_hls_datatype_str()
+ odt_hls = self.get_output_datatype().get_hls_datatype_str()
+ f_thresh.write(
+ "static ThresholdsActivation<{},{},{},{},{},{}> threshs = ".format(
+ self.get_nodeattr("TMEM"),
+ self.get_nodeattr("PE"),
+ threshold_tensor.shape[-1],
+ tdt_hls,
+ odt_hls,
+ self.get_nodeattr("ActVal"),
+ )
+ )
+ f_thresh.write(thresholds_hls_code)
+ f_thresh.close()
def execute_node(self, context, graph):
node = self.onnx_node
diff --git a/src/finn/custom_op/fpgadataflow/streamingmaxpool.py b/src/finn/custom_op/fpgadataflow/streamingmaxpool.py
index c0878ded1..ee35b476a 100644
--- a/src/finn/custom_op/fpgadataflow/streamingmaxpool.py
+++ b/src/finn/custom_op/fpgadataflow/streamingmaxpool.py
@@ -9,11 +9,13 @@ from finn.custom_op.fpgadataflow import HLSCustomOp
class StreamingMaxPool(HLSCustomOp):
def get_nodeattr_types(self):
- return {
+ my_attrs = {
"ImgDim": ("i", True, 0),
"PoolDim": ("i", True, 0),
"NumChannels": ("i", True, 0),
}
+ my_attrs.update(super().get_nodeattr_types())
+ return my_attrs
def make_shape_compatible_op(self):
pass
diff --git a/src/finn/custom_op/fpgadataflow/streamingmaxpool_batch.py b/src/finn/custom_op/fpgadataflow/streamingmaxpool_batch.py
index 3185e0502..e21ffeae1 100644
--- a/src/finn/custom_op/fpgadataflow/streamingmaxpool_batch.py
+++ b/src/finn/custom_op/fpgadataflow/streamingmaxpool_batch.py
@@ -9,11 +9,13 @@ from finn.custom_op.fpgadataflow import HLSCustomOp
class StreamingMaxPool_Batch(HLSCustomOp):
def get_nodeattr_types(self):
- return {
+ my_attrs = {
"ImgDim": ("i", True, 0),
"PoolDim": ("i", True, 0),
"NumChannels": ("i", True, 0),
}
+ my_attrs.update(super().get_nodeattr_types())
+ return my_attrs
def make_shape_compatible_op(self):
pass
--
GitLab