From 7233107e7a40e858789b9e944948216bcc0fd83c Mon Sep 17 00:00:00 2001
From: Yaman Umuroglu <maltanar@gmail.com>
Date: Thu, 11 Aug 2022 21:56:36 +0200
Subject: [PATCH] [Test] flesh out new FIFO sizing test
---
.../test_fifosizing.py} | 91 ++++++++++++-------
1 file changed, 60 insertions(+), 31 deletions(-)
rename tests/{end2end/test_end2end_fifosizing.py => fpgadataflow/test_fifosizing.py} (56%)
diff --git a/tests/end2end/test_end2end_fifosizing.py b/tests/fpgadataflow/test_fifosizing.py
similarity index 56%
rename from tests/end2end/test_end2end_fifosizing.py
rename to tests/fpgadataflow/test_fifosizing.py
index 326626723..d93c5630d 100644
--- a/tests/end2end/test_end2end_fifosizing.py
+++ b/tests/fpgadataflow/test_fifosizing.py
@@ -28,76 +28,105 @@
import pkg_resources as pk
+import pytest
+
+import json
import numpy as np
+import shutil
from qonnx.custom_op.registry import getCustomOp
+from qonnx.transformation.general import GiveUniqueNodeNames
import finn.builder.build_dataflow as build
import finn.builder.build_dataflow_config as build_cfg
from finn.analysis.fpgadataflow.dataflow_performance import dataflow_performance
from finn.transformation.fpgadataflow.derive_characteristic import DeriveCharacteristic
+from finn.transformation.fpgadataflow.hlssynth_ip import HLSSynthIP
+from finn.transformation.fpgadataflow.insert_dwc import InsertDWC
+from finn.transformation.fpgadataflow.prepare_ip import PrepareIP
from finn.transformation.fpgadataflow.prepare_rtlsim import PrepareRTLSim
from finn.util.basic import make_build_dir
def custom_step_fifosize(model, cfg):
# TODO convert to NodeLocalTransformation
- def accumulate_char_fxn(chrc):
- p = len(chrc)
- ret = []
- for t in range(2 * p):
- if t == 0:
- ret.append(chrc[0])
- else:
- ret.append(ret[-1] + chrc[t % p])
- return ret
-
# TODO handle chrc for input and output nodes
all_act_tensors = [x.name for x in model.graph.value_info]
for tensor_nm in all_act_tensors:
# generate accumulated characteristic functions
- prod = getCustomOp(model.find_producer(tensor_nm))
+ prod = model.find_producer(tensor_nm)
+ cons = model.find_consumer(tensor_nm)
+ if prod is None or cons is None:
+ continue
+ prod = getCustomOp(prod)
+ period = prod.get_nodeattr("io_characteristic_period")
prod_chrc = prod.get_nodeattr("io_characteristic")
- prod_chrc = np.asarray(prod_chrc, dtype=np.uint8).reshape(2, -1)[1]
- prod_chrc = accumulate_char_fxn(prod_chrc)
- cons = getCustomOp(model.find_consumer(tensor_nm))
+ prod_chrc = np.asarray(prod_chrc).reshape(2, -1)[1]
+ cons = getCustomOp(cons)
cons_chrc = cons.get_nodeattr("io_characteristic")
- cons_chrc = np.asarray(cons_chrc, dtype=np.uint8).reshape(2, -1)[0]
- cons_chrc = accumulate_char_fxn(cons_chrc)
- # TODO find minimum phase shift
-
- for node in model.graph.node:
- inst = getCustomOp(node)
- chrc = inst.get_nodeattr("io_characteristic")
- chrc = np.asarray(chrc, dtype=np.uint8).reshape(2, -1)
-
+ cons_chrc = np.asarray(cons_chrc).reshape(2, -1)[0]
+ # find minimum phase shift satisfying the constraint
+ pshift_min = period
+ for pshift_cand in range(period):
+ pshift_condition = [
+ (prod_chrc[i + pshift_cand] >= cons_chrc[i])
+ for i in range(period - pshift_cand)
+ ]
+ if all(pshift_condition):
+ pshift_min = pshift_cand
+ break
+ fifo_depth = max(
+ [(prod_chrc[i + pshift_cand] - cons_chrc[i]) for i in range(pshift_min)]
+ )
+ prod.set_nodeattr("outFIFODepth", fifo_depth)
+ cons.set_nodeattr("inFIFODepth", fifo_depth)
return model
def custom_step_fifocharacterize(model, cfg):
+ model = model.transform(InsertDWC())
+ model = model.transform(GiveUniqueNodeNames())
+ model = model.transform(
+ PrepareIP(cfg._resolve_fpga_part(), cfg._resolve_hls_clk_period())
+ )
+ model = model.transform(HLSSynthIP())
model = model.transform(PrepareRTLSim())
period = model.analysis(dataflow_performance)["max_cycles"] + 10
model = model.transform(DeriveCharacteristic(period))
return model
-def test_end2end_fifosizing():
+@pytest.mark.slow
+@pytest.mark.vivado
+def test_fifosizing():
chkpt_name = pk.resource_filename("finn.qnn-data", "build_dataflow/model.onnx")
tmp_output_dir = make_build_dir("build_fifosizing_")
- # tmp_output_dir = "/tmp/finn_dev_maltanar/build_fifosizing_5mt0o6s_"
steps = build_cfg.default_build_dataflow_steps
- steps = steps[:10]
- steps.append(custom_step_fifocharacterize)
- # steps.append(custom_step_fifosize)
+ steps.insert(10, custom_step_fifocharacterize)
+ steps.insert(11, custom_step_fifosize)
cfg = build_cfg.DataflowBuildConfig(
output_dir=tmp_output_dir,
auto_fifo_depths=False,
target_fps=10000,
synth_clk_period_ns=10.0,
board="Pynq-Z1",
+ rtlsim_batch_size=100,
shell_flow_type=build_cfg.ShellFlowType.VIVADO_ZYNQ,
- generate_outputs=[],
+ generate_outputs=[
+ build_cfg.DataflowOutputType.ESTIMATE_REPORTS,
+ build_cfg.DataflowOutputType.STITCHED_IP,
+ build_cfg.DataflowOutputType.RTLSIM_PERFORMANCE,
+ ],
steps=steps,
- default_mem_mode=build_cfg.ComputeEngineMemMode.CONST,
- start_step="custom_step_fifocharacterize",
+ default_mem_mode=build_cfg.ComputeEngineMemMode.DECOUPLED,
)
build.build_dataflow_cfg(chkpt_name, cfg)
+ with open(tmp_output_dir + "/report/estimate_network_performance.json") as f:
+ est_data = json.load(f)
+ with open(tmp_output_dir + "/report/rtlsim_performance.json") as f:
+ sim_data = json.load(f)
+ assert (
+ float(sim_data["throughput[images/s]"])
+ / float(est_data["estimated_throughput_fps"])
+ > 0.9
+ )
+ shutil.rmtree(tmp_output_dir)
--
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