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Commit ebdfdedd authored by auphelia's avatar auphelia
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[Code gen] Added missing commands to hlslib node files

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src/finn/custom_op/fpgadataflow/streamingfclayer_batch.py
+ 28
− 2
View file @ ebdfdedd
import os
import subprocess
import numpy as np
import tempfile as tmp
import numpy as np
import finn.core.utils as utils
from finn.backend.fpgadataflow.utils import numpy_to_hls_code
from finn.core.datatype import DataType
......@@ -22,11 +23,22 @@ class StreamingFCLayer_Batch(HLSCustomOp):
pass
def execute_node(self, node, context, graph):
# make temporary directory for generated files
self.tmp_dir = tmp.mkdtemp(prefix=str(node.op_type) + "_")
# create empty list for temporary files to enable the option
# to delete the files after the execution
temp_files = []
# get attributes for correct packing of weights and thresholds
self.get_attributes(node)
# create a npy file fore each input of the node (in_ind is input index)
in_ind = 0
temp_files = []
for inputs in node.input:
# it is assumed that the first input of the node is the data input
# the second input are the weights
# the third input are the thresholds
if in_ind == 0:
np.save(
os.path.join(self.tmp_dir, "input_{}.npy".format(in_ind)),
......@@ -36,12 +48,15 @@ class StreamingFCLayer_Batch(HLSCustomOp):
elif in_ind == 1:
weights = context[inputs]
self.WMEM = weights.shape[2]
# transpose and expand the weights to get the right shape
# for the code generation
weights = np.transpose(weights, (1, 2, 0))
weights = np.expand_dims(weights, 0)
weights = numpy_to_hls_code(
weights, DataType.BINARY, "weights", True, True
)
# write weights into params.h
f_weights = open("{}/params.h".format(self.tmp_dir), "w")
f_weights.write(
"static BinaryWeights<{},{},{}> weights = ".format(
......@@ -55,11 +70,15 @@ class StreamingFCLayer_Batch(HLSCustomOp):
else:
thresholds = context[inputs]
self.TMEM = thresholds.shape[0]
# transpose and expand the thresholds to get the right shape
# for the code generation
thresholds = np.transpose(thresholds, (1, 0, 2))
thresholds = np.expand_dims(thresholds, 0)
thresholds = numpy_to_hls_code(
thresholds, DataType.BINARY, "thresholds", True, True
)
# write weights into thresh.h
f_thresh = open("{}/thresh.h".format(self.tmp_dir), "w")
f_thresh.write(
"""static ThresholdsActivation<{},{},1,ap_uint<16>,
......@@ -73,7 +92,10 @@ class StreamingFCLayer_Batch(HLSCustomOp):
in_ind += 1
# code generation
self.code_generation(node)
# c++ compilation and execution flow
temp_files.append("{}/execute_{}.cpp".format(self.tmp_dir, node.op_type))
bash_compile = """g++ -o {}/execute_{} {}/execute_{}.cpp
/workspace/cnpy/cnpy.cpp -I/workspace/cnpy/
......@@ -88,6 +110,8 @@ class StreamingFCLayer_Batch(HLSCustomOp):
process_execute.communicate()
temp_files.append("{}/execute_{}".format(self.tmp_dir, node.op_type))
temp_files.append("{}/output.npy".format(self.tmp_dir))
# load output npy file
output = np.load("{}/output.npy".format(self.tmp_dir))
context[node.output[0]] = output
# deleting temporary files
......@@ -121,6 +145,8 @@ class StreamingFCLayer_Batch(HLSCustomOp):
]
def read_npy_data(self, node):
# c++ code to read out an npy file
# and put it in hls::stream in the correct order
self.code_gen_dict["$READNPYDATA$"] = []
self.code_gen_dict["$READNPYDATA$"].append(
"""cnpy::NpyArray arr0 = cnpy::npy_load("{}/input_0.npy");\n
......
src/finn/custom_op/fpgadataflow/streamingmaxpool.py
+ 23
− 8
View file @ ebdfdedd
import os
import numpy as np
import subprocess
import tempfile as tmp
import numpy as np
from finn.core.utils import get_by_name
from finn.custom_op.fpgadataflow import HLSCustomOp
......@@ -16,22 +16,34 @@ class StreamingMaxPool(HLSCustomOp):
pass
def execute_node(self, node, context, graph):
# make temporary directory for generated files
self.tmp_dir = tmp.mkdtemp(prefix=str(node.op_type) + "_")
in_ind = 0
# create empty list for temporary files to enable the option
# to delete the files after the execution
temp_files = []
# create a npy file fore each input of the node (in_ind is input index)
in_ind = 0
for inputs in node.input:
np.save(os.path.join(self.tmp_dir, "input_{}.npy".format(in_ind)),
context[inputs],)
np.save(
os.path.join(self.tmp_dir, "input_{}.npy".format(in_ind)),
context[inputs],
)
temp_files.append("{}/input_{}.npy".format(self.tmp_dir, in_ind))
in_ind += 1
# code generation
self.code_generation(node)
# c++ compilation and execution flow
temp_files.append("{}/execute_{}.cpp".format(self.tmp_dir, node.op_type))
bash_compile = """g++ -o {}/execute_{} {}/execute_{}.cpp
/workspace/cnpy/cnpy.cpp -I/workspace/cnpy/
-I/workspace/finn-hlslib -I/workspace/vivado-hlslib
--std=c++11 -lz""".format(
self.tmp_dir, node.op_type, self.tmp_dir, node.op_type
)
)
process_compile = subprocess.Popen(bash_compile.split(), stdout=subprocess.PIPE)
process_compile.communicate()
bash_execute = "{}/execute_{}".format(self.tmp_dir, node.op_type)
......@@ -39,13 +51,14 @@ class StreamingMaxPool(HLSCustomOp):
process_execute.communicate()
temp_files.append("{}/execute_{}".format(self.tmp_dir, node.op_type))
temp_files.append("{}/output.npy".format(self.tmp_dir))
output = np.load("{}/output.npy".format(self.tmp_dir))
# load output npy file
output = np.load("{}/output.npy".format(self.tmp_dir))
context[node.output[0]] = output
# deleting temporary files
#for temp_file in temp_files:
# for temp_file in temp_files:
# os.remove(temp_file)
def get_attributes(self, node):
self.ImgDim = get_by_name(node.attribute, "ImgDim").i
self.PoolDim = get_by_name(node.attribute, "PoolDim").i
......@@ -62,6 +75,8 @@ class StreamingMaxPool(HLSCustomOp):
]
def read_npy_data(self, node):
# c++ code to read out an npy file
# and put it in hls::stream in the correct order
self.code_gen_dict["$READNPYDATA$"] = []
input_ind = 0
input_file_names = []
......
src/finn/custom_op/fpgadataflow/streamingmaxpool_batch.py
+ 25
− 9
View file @ ebdfdedd
import os
import numpy as np
import subprocess
import tempfile as tmp
import numpy as np
from finn.core.utils import get_by_name
from finn.custom_op.fpgadataflow import HLSCustomOp
......@@ -14,24 +14,36 @@ class StreamingMaxPool_Batch(HLSCustomOp):
def infer_node_datatype(self, node, model):
pass
def execute_node(self, node, context, graph):
# make temporary directory for generated files
self.tmp_dir = tmp.mkdtemp(prefix=str(node.op_type) + "_")
in_ind = 0
# create empty list for temporary files to enable the option
# to delete the files after the execution
temp_files = []
# create a npy file fore each input of the node (in_ind is input index)
in_ind = 0
for inputs in node.input:
np.save(os.path.join(self.tmp_dir, "input_{}.npy".format(in_ind)),
context[inputs],)
np.save(
os.path.join(self.tmp_dir, "input_{}.npy".format(in_ind)),
context[inputs],
)
temp_files.append("{}/input_{}.npy".format(self.tmp_dir, in_ind))
in_ind += 1
# code generation
self.code_generation(node)
# c++ compilation and execution flow
temp_files.append("{}/execute_{}.cpp".format(self.tmp_dir, node.op_type))
bash_compile = """g++ -o {}/execute_{} {}/execute_{}.cpp
/workspace/cnpy/cnpy.cpp -I/workspace/cnpy/
-I/workspace/finn-hlslib -I/workspace/vivado-hlslib
--std=c++11 -lz""".format(
self.tmp_dir, node.op_type, self.tmp_dir, node.op_type
)
)
process_compile = subprocess.Popen(bash_compile.split(), stdout=subprocess.PIPE)
process_compile.communicate()
bash_execute = "{}/execute_{}".format(self.tmp_dir, node.op_type)
......@@ -39,10 +51,12 @@ class StreamingMaxPool_Batch(HLSCustomOp):
process_execute.communicate()
temp_files.append("{}/execute_{}".format(self.tmp_dir, node.op_type))
temp_files.append("{}/output.npy".format(self.tmp_dir))
output = np.load("{}/output.npy".format(self.tmp_dir))
# load output npy file
output = np.load("{}/output.npy".format(self.tmp_dir))
context[node.output[0]] = output
# deleting temporary files
#for temp_file in temp_files:
# for temp_file in temp_files:
# os.remove(temp_file)
def get_attributes(self, node):
......@@ -63,6 +77,8 @@ class StreamingMaxPool_Batch(HLSCustomOp):
]
def read_npy_data(self, node):
# c++ code to read out an npy file
# and put it in hls::stream in the correct order
self.code_gen_dict["$READNPYDATA$"] = []
input_ind = 0
input_file_names = []
......@@ -153,7 +169,7 @@ class StreamingMaxPool_Batch(HLSCustomOp):
self.code_gen_dict["$SAVEASCNPY$"] = [
"""cnpy::npy_save("{}/output.npy",&output_data_vector[0],
{{{},{},{}}},"w");""".format(
self.tmp_dir,
self.tmp_dir,
numReps,
self.NumChannels,
int(self.ImgDim / self.PoolDim),
......
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