From 88e4d89f7ddc80dd9b261048e05ed835a60a09d3 Mon Sep 17 00:00:00 2001
From: auphelia <jakobapk@web.de>
Date: Mon, 27 Apr 2020 17:53:29 +0100
Subject: [PATCH] [Trafo-templates] Restructure driver.py in order to make it
more generic
---
.../transformation/fpgadataflow/templates.py | 231 ++++++++++--------
1 file changed, 132 insertions(+), 99 deletions(-)
diff --git a/src/finn/transformation/fpgadataflow/templates.py b/src/finn/transformation/fpgadataflow/templates.py
index 20b1bd58e..6ff4a4f0b 100644
--- a/src/finn/transformation/fpgadataflow/templates.py
+++ b/src/finn/transformation/fpgadataflow/templates.py
@@ -97,109 +97,142 @@ from finn.util.data_packing import (
)
from finn.core.datatype import DataType
-def load_input(N):
- ishape_normal = $INPUT_SHAPE_NORMAL$
- # load desired input .npy file
- ibuf_normal = np.load("input.npy")
- # ensure that shape is as expected
- assert ibuf_normal.shape == ishape_normal
- return ibuf_normal
-
-def pack_input(ibuf_normal, N):
- # input FINN DataType
- idt = $INPUT_FINN_DATATYPE$
- ishape_folded = $INPUT_SHAPE_FOLDED$
- # convert to folded form
- ibuf_folded = ibuf_normal.reshape(ishape_folded)
- # pack the input buffer, reversing both SIMD dim and endianness
- ibuf_packed = finnpy_to_packed_bytearray(
- ibuf_folded, idt, reverse_endian=True, reverse_inner=True
- )
- return ibuf_packed
-
-def unpack_output(obuf_packed, N):
- # output FINN DataType
- odt = $OUTPUT_FINN_DATATYPE$
- oshape_folded = $OUTPUT_SHAPE_FOLDED$
- # unpack the packed output buffer from accelerator
- obuf_folded = packed_bytearray_to_finnpy(
- obuf_packed, odt, oshape_folded, reverse_endian=True, reverse_inner=True
- )
- return obuf_folded
-
-def save_output(obuf_folded, N):
- # convert to normal reshape and save
- oshape_normal = $OUTPUT_SHAPE_NORMAL$
- obuf_normal = obuf_folded.reshape(oshape_normal)
- np.save("output.npy", obuf_normal)
+class RemoteTest():
+ def __init__(
+ self,
+ exec_mode,
+ N,
+ bitfile="resizer.bit",
+ inputfile="input.npy",
+ outputfile="output.npy"):
+
+ self.exec_mode = exec_mode
+ self.N = N
+ self.inputfile = inputfile
+ self.outputfile = outputfile
+ self.ol = Overlay(bitfile)
+ self.dma = self.ol.axi_dma_0
+ self.ctrl_regs = self.ol.resize_accel_0
+ self.ishape_packed = $INPUT_SHAPE_PACKED$
+ self.oshape_packed = $OUTPUT_SHAPE_PACKED$
+ # AXI lite register offset for number of iterations
+ # used by TLastMarker to signal end of transmission for AXI CDMA
+ self.REG_OFFSET_NUM_ITERS = 0x10
+
+ def load_input(self):
+ N = self.N
+ ishape_normal = $INPUT_SHAPE_NORMAL$
+ # load desired input .npy file
+ ibuf_normal = np.load(self.inputfile)
+ # ensure that shape is as expected
+ assert ibuf_normal.shape == ishape_normal
+ return ibuf_normal
+
+ def pack_input(self, ibuf_normal):
+ N = self.N
+ # input FINN DataType
+ idt = $INPUT_FINN_DATATYPE$
+ ishape_folded = $INPUT_SHAPE_FOLDED$
+ # convert to folded form
+ ibuf_folded = ibuf_normal.reshape(ishape_folded)
+ # pack the input buffer, reversing both SIMD dim and endianness
+ ibuf_packed = finnpy_to_packed_bytearray(
+ ibuf_folded, idt, reverse_endian=True, reverse_inner=True
+ )
+ return ibuf_packed
+
+ def unpack_output(self, obuf_packed):
+ N = self.N
+ # output FINN DataType
+ odt = $OUTPUT_FINN_DATATYPE$
+ oshape_folded = $OUTPUT_SHAPE_FOLDED$
+ # unpack the packed output buffer from accelerator
+ obuf_folded = packed_bytearray_to_finnpy(
+ obuf_packed, odt, oshape_folded, reverse_endian=True, reverse_inner=True
+ )
+ return obuf_folded
+
+ def save_output(self, obuf_folded):
+ N = self.N
+ # convert to normal reshape and save
+ oshape_normal = $OUTPUT_SHAPE_NORMAL$
+ obuf_normal = obuf_folded.reshape(oshape_normal)
+ np.save(self.outputfile, obuf_normal)
+
+ def allocate_pynqbuffer(self, shape, data=None):
+ buf_device = allocate(shape=shape, dtype=np.uint8)
+
+ # if necessary copy the packed data into the PYNQ buffer
+ # TODO optimization: pack directly into the PYNQ buffer?
+ if data is not None:
+ np.copyto(buf_device, data)
+
+ return buf_device
+
+
+ def run_nw(self):
+ exec_mode = self.exec_mode
+ if exec_mode == "remote_pynq":
+ ibuf_normal = self.load_input()
+ ibuf_packed = self.pack_input(ibuf_normal)
+ elif exec_mode != "throughput_test":
+ raise Exception("Exec mode has to be set to remote_pynq or throughput_test")
+
+ # set up TLastMarker with correct num. samples
+ self.ctrl_regs.write(self.REG_OFFSET_NUM_ITERS, N)
+
+ # allocate a PYNQ buffer for the packed input buffer
+ if exec_mode == "remote_pynq":
+ ibuf_packed_device = self.allocate_pynqbuffer(self.ishape_packed, ibuf_packed)
+ else:
+ ibuf_packed_device = self.allocate_pynqbuffer(self.ishape_packed)
+
+ # allocate a PYNQ buffer for the returned packed output buffer
+ obuf_packed = self.allocate_pynqbuffer(self.oshape_packed)
+
+ if exec_mode == "throughput_test":
+ # measure runtime of network
+ start = time.time()
+ res={}
+
+ # set up the DMA and wait until all transfers complete
+ dma = self.dma
+ dma.sendchannel.transfer(ibuf_packed_device)
+ dma.recvchannel.transfer(obuf_packed)
+ dma.sendchannel.wait()
+ dma.recvchannel.wait()
+
+
+ if exec_mode == "throughput_test":
+ end = time.time()
+ runtime = end - start
+ res["runtime[ms]"] = runtime*1000
+ res["throughput[images/s]"] = N / runtime
+ res["DRAM_in_bandwidth[Mb/s]"] = np.prod(self.ishape_packed)*0.000001 / runtime
+ res["DRAM_out_bandwidth[Mb/s]"] = np.prod(self.oshape_packed)*0.000001 / runtime
+ file = open("nw_metrics.txt", "w")
+ file.write(str(res))
+ file.close()
+ else:
+ obuf_folded = self.unpack_output(obuf_packed)
+ self.save_output(obuf_folded)
+
if __name__ == "__main__":
- parser = argparse.ArgumentParser(description='Please select functional verification ("remote_pynq") or throughput test ("throughput_test")')
- parser.add_argument('exec_mode', help='metadata prop exec_mode as string')
+ parser = argparse.ArgumentParser(description='Set exec mode, batchsize N, bitfile name, inputfile name and outputfile name')
+ parser.add_argument('exec_mode', help='Please select functional verification ("remote_pynq") or throughput test ("throughput_test")')
+ parser.add_argument('N', help='number of samples for inference', type=int)
+ parser.add_argument('bitfile', default="resizer.bit")
+ parser.add_argument('inputfile', default="input.npy")
+ parser.add_argument('outputfile', default="output.npy")
args = parser.parse_args()
exec_mode = args.exec_mode
+ N = args.N
+ bitfile = args.bitfile
+ inputfile = args.inputfile
+ outputfile = args.outputfile
- bitfile_path = "resizer.bit"
- ol = Overlay(bitfile_path)
- dma=ol.axi_dma_0
- ctrl_regs=ol.resize_accel_0
- # AXI lite register offset for number of iterations
- # used by TLastMarker to signal end of transmission for AXI CDMA
- REG_OFFSET_NUM_ITERS = 0x10
-
- # number of samples for inference
- if exec_mode == "remote_pynq":
- N = 1
- elif exec_mode == "throughput_test":
- res={}
- N = 1000
- else:
- raise Exception("Exec mode has to be set to remote_pynq or throughput_test")
-
- # declare input/output types and shapes for the accelerator
- ishape_packed = $INPUT_SHAPE_PACKED$
- oshape_packed = $OUTPUT_SHAPE_PACKED$
-
- if exec_mode == "remote_pynq":
- ibuf_normal = load_input(N)
- ibuf_packed = pack_input(ibuf_normal, N)
- elif exec_mode == "throughput_test":
- ibuf_packed = np.asarray(np.random.uniform(low=0, high=1, size=tuple(ishape_packed)), dtype=np.uint8)
-
- # set up TLastMarker with correct num. samples
- ctrl_regs.write(REG_OFFSET_NUM_ITERS, N)
-
- # allocate a PYNQ buffer for the packed input buffer
- ibuf_packed_device = allocate(shape=ishape_packed, dtype=np.uint8)
- # copy the packed data into the PYNQ buffer
- # TODO optimization: pack directly into the PYNQ buffer?
- np.copyto(ibuf_packed_device, ibuf_packed)
-
- # allocate a PYNQ buffer for the returned packed output buffer
- obuf_packed = allocate(shape=oshape_packed, dtype=np.uint8)
-
- if exec_mode == "throughput_test":
- # measure runtime of network
- start = time.time()
-
- # set up the DMA and wait until all transfers complete
- dma.sendchannel.transfer(ibuf_packed_device)
- dma.recvchannel.transfer(obuf_packed)
- dma.sendchannel.wait()
- dma.recvchannel.wait()
-
-
- if exec_mode == "throughput_test":
- end = time.time()
- runtime = end - start
- res["runtime[ms]"] = runtime*1000
- res["throughput[images/s]"] = N / runtime
- file = open("nw_metrics.txt", "w")
- file.write(str(res))
- file.close()
-
- else:
- obuf_folded = unpack_output(obuf_packed, N)
- save_output(obuf_folded, N)
+ Test = RemoteTest(exec_mode, N, bitfile, inputfile, outputfile)
+ Test.run_nw()
"""
--
GitLab