From b7d9003720ce7257337b929c313611a87e750c21 Mon Sep 17 00:00:00 2001
From: auphelia <jakobapk@web.de>
Date: Wed, 15 Jan 2020 14:52:30 +0000
Subject: [PATCH] [Code Generation - npysim] Added functionality that npysim is
only performed in execution of node when sim_mode = "npysim"
---
src/finn/custom_op/fpgadataflow/__init__.py | 23 ++++--
.../fpgadataflow/convolutioninputgenerator.py | 77 +++++++++++--------
.../test_fpgadataflow_convinputgenerator.py | 4 +-
3 files changed, 64 insertions(+), 40 deletions(-)
diff --git a/src/finn/custom_op/fpgadataflow/__init__.py b/src/finn/custom_op/fpgadataflow/__init__.py
index 362b7a64e..9905d7f1d 100644
--- a/src/finn/custom_op/fpgadataflow/__init__.py
+++ b/src/finn/custom_op/fpgadataflow/__init__.py
@@ -164,12 +164,23 @@ compilation transformations?
process_execute.communicate()
def execute_node(self, context, graph):
- # save input(s)
- self.dynamic_input_to_npy(context, 1)
- # execute the precompiled model
- self.exec_precompiled_singlenode_model()
- # load output npy file
- self.npy_to_dynamic_output(context)
+ mode = self.get_nodeattr("sim_mode")
+ if mode == "npysim":
+ # save input(s)
+ self.dynamic_input_to_npy(context, 1)
+ # execute the precompiled model
+ self.exec_precompiled_singlenode_model()
+ # load output npy file
+ self.npy_to_dynamic_output(context)
+ elif mode == "rtlsim":
+ pass
+ else:
+ raise Exception(
+ """Invalid value for attribute sim_mode! Is currently set to: {}
+ has to be set to one of the following value ("npysim", "rtlsim")""".format(
+ mode
+ )
+ )
def generate_params(self, model):
pass
diff --git a/src/finn/custom_op/fpgadataflow/convolutioninputgenerator.py b/src/finn/custom_op/fpgadataflow/convolutioninputgenerator.py
index 7f834cf5c..22139fc53 100644
--- a/src/finn/custom_op/fpgadataflow/convolutioninputgenerator.py
+++ b/src/finn/custom_op/fpgadataflow/convolutioninputgenerator.py
@@ -45,39 +45,50 @@ class ConvolutionInputGenerator(HLSCustomOp):
return self.get_nodeattr("SIMD") * self.get_nodeattr("Input_precision")
def execute_node(self, context, graph):
- node = self.onnx_node
- k = self.get_nodeattr("ConvKernelDim")
- ifm_dim = self.get_nodeattr("IFMDim")
- ifm_ch = self.get_nodeattr("IFMChannels")
- ofm_dim = self.get_nodeattr("OFMDim")
- out_pix = ofm_dim * ofm_dim
- idt = self.get_input_datatype()
- if idt == DataType.BIPOLAR:
- # use binary for bipolar storage
- idt = DataType.BINARY
-
- # TODO ensure codegen dir exists
- code_gen_dir = self.get_nodeattr("code_gen_dir_npysim")
- # create a npy file for input of the node
-
- inp = context[node.input[0]]
- assert str(inp.dtype) == "float32"
- assert inp.shape == (1, ifm_ch, ifm_dim, ifm_dim)
- reshaped_inp = inp.transpose(0, 2, 3, 1)
- np.save(os.path.join(code_gen_dir, "input_0.npy"), reshaped_inp)
- # execute the precompiled model
- super().exec_precompiled_singlenode_model()
- # load output npy file
- super().npy_to_dynamic_output(context)
- if self.get_output_datatype() == DataType.BIPOLAR:
- out = context[node.output[0]]
- out = 2 * out - 1
- context[node.output[0]] = out
- assert context[node.output[0]].shape == (1, out_pix, k * k, ifm_ch)
- # reshape output to have expected shape
- context[node.output[0]] = context[node.output[0]].reshape(
- 1, out_pix, k * k * ifm_ch
- )
+ mode = self.get_nodeattr("sim_mode")
+ if mode == "npysim":
+ node = self.onnx_node
+ k = self.get_nodeattr("ConvKernelDim")
+ ifm_dim = self.get_nodeattr("IFMDim")
+ ifm_ch = self.get_nodeattr("IFMChannels")
+ ofm_dim = self.get_nodeattr("OFMDim")
+ out_pix = ofm_dim * ofm_dim
+ idt = self.get_input_datatype()
+ if idt == DataType.BIPOLAR:
+ # use binary for bipolar storage
+ idt = DataType.BINARY
+
+ # TODO ensure codegen dir exists
+ code_gen_dir = self.get_nodeattr("code_gen_dir_npysim")
+ # create a npy file for input of the node
+
+ inp = context[node.input[0]]
+ assert str(inp.dtype) == "float32"
+ assert inp.shape == (1, ifm_ch, ifm_dim, ifm_dim)
+ reshaped_inp = inp.transpose(0, 2, 3, 1)
+ np.save(os.path.join(code_gen_dir, "input_0.npy"), reshaped_inp)
+ # execute the precompiled model
+ super().exec_precompiled_singlenode_model()
+ # load output npy file
+ super().npy_to_dynamic_output(context)
+ if self.get_output_datatype() == DataType.BIPOLAR:
+ out = context[node.output[0]]
+ out = 2 * out - 1
+ context[node.output[0]] = out
+ assert context[node.output[0]].shape == (1, out_pix, k * k, ifm_ch)
+ # reshape output to have expected shape
+ context[node.output[0]] = context[node.output[0]].reshape(
+ 1, out_pix, k * k * ifm_ch
+ )
+ elif mode == "rtlsim":
+ pass
+ else:
+ raise Exception(
+ """Invalid value for attribute sim_mode! Is currently set to: {}
+ has to be set to one of the following value ("npysim", "rtlsim")""".format(
+ mode
+ )
+ )
def global_includes(self):
self.code_gen_dict["$GLOBALS$"] = ['#include "slidingwindow.h"']
diff --git a/tests/fpgadataflow/test_fpgadataflow_convinputgenerator.py b/tests/fpgadataflow/test_fpgadataflow_convinputgenerator.py
index 450fdcd13..e32d8b765 100644
--- a/tests/fpgadataflow/test_fpgadataflow_convinputgenerator.py
+++ b/tests/fpgadataflow/test_fpgadataflow_convinputgenerator.py
@@ -12,6 +12,7 @@ from finn.transformation.fpgadataflow.codegen_ipgen import CodeGen_ipgen
from finn.transformation.fpgadataflow.codegen_npysim import CodeGen_npysim
from finn.transformation.fpgadataflow.compile import Compile
from finn.transformation.fpgadataflow.hlssynth_ipgen import HLSSynth_IPGen
+from finn.transformation.fpgadataflow.set_sim_mode import SetSimMode
from finn.transformation.general import GiveUniqueNodeNames
@@ -145,7 +146,7 @@ def test_fpgadataflow_slidingwindow(idt, k, ifm_dim, ifm_ch, stride):
model = make_single_slidingwindow_modelwrapper(
k, ifm_ch, ifm_dim, ofm_dim, simd, stride, idt
)
-
+ model = model.transform(SetSimMode("npysim"))
model = model.transform(CodeGen_npysim())
model = model.transform(Compile())
@@ -164,4 +165,5 @@ def test_fpgadataflow_slidingwindow(idt, k, ifm_dim, ifm_ch, stride):
model = model.transform(GiveUniqueNodeNames())
model = model.transform(CodeGen_ipgen("xc7z020clg400-1", 5))
model = model.transform(HLSSynth_IPGen())
+ model = model.transform(SetSimMode("rtlsim"))
model = model.transform(CleanUp())
--
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