test_fpgadataflow_vvau.py 10.46 KiB
# Copyright (c) 2020, Xilinx
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import pytest
import numpy as np
from onnx import TensorProto, helper
from qonnx.core.datatype import DataType
from qonnx.core.modelwrapper import ModelWrapper
from qonnx.custom_op.general.multithreshold import multithreshold
from qonnx.custom_op.registry import getCustomOp
from qonnx.transformation.general import GiveUniqueNodeNames
from qonnx.transformation.infer_datatypes import InferDataTypes
from qonnx.transformation.infer_shapes import InferShapes
from qonnx.util.basic import gen_finn_dt_tensor, qonnx_make_model
import finn.core.onnx_exec as oxe
from finn.analysis.fpgadataflow.exp_cycles_per_layer import exp_cycles_per_layer
from finn.transformation.fpgadataflow.compile_cppsim import CompileCppSim
from finn.transformation.fpgadataflow.hlssynth_ip import HLSSynthIP
from finn.transformation.fpgadataflow.minimize_accumulator_width import (
MinimizeAccumulatorWidth,
)
from finn.transformation.fpgadataflow.prepare_cppsim import PrepareCppSim
from finn.transformation.fpgadataflow.prepare_ip import PrepareIP
from finn.transformation.fpgadataflow.prepare_rtlsim import PrepareRTLSim
from finn.transformation.fpgadataflow.set_exec_mode import SetExecMode
def _infer_sparse_weight_tensor(W_conv, k_h, k_w, channels):
W_sparse = np.zeros((channels, channels, k_h, k_w), dtype=np.float32)
for ch in range(channels):
W_sparse[ch][ch] = W_conv[ch][0]
W_conv = W_sparse.astype(np.float32)
W_matmul = W_conv.transpose(0, 2, 3, 1)
W_matmul = W_matmul.reshape(channels, channels * k_h * k_w)
W_matmul = W_matmul.T
return W_matmul
def _calculate_dot_prod_range(dt_a, dt_b, len):
"""Returns the (min,max) values a dot product between two (un)signed vectors of
types dt_a and dt_b of len elements can take."""
min_prod = 2**30
max_prod = -(2**30)
for a_val in [dt_a.min(), dt_a.max()]:
for b_val in [dt_b.min(), dt_b.max()]:
prod = a_val * b_val * len
if prod < min_prod:
min_prod = prod
if prod > max_prod:
max_prod = prod
return (min_prod, max_prod)
def _make_single_vvau_modelwrapper(
W,
pe,
simd,
k_h,
k_w,
channels,
dim_h,
dim_w,
wdt,
idt,
odt,
T=None,
tdt=None,
mem_mode="const",
):
in_shape = [1, dim_h, dim_w, k_h * k_w * channels] # [N, H, W, K*K*CH]
out_shape = [
1,
dim_h,
dim_w,
channels,
] # [N, H, W, OFM_CH] (OFM_CH=IFM_CH because depthwise convolution)
inp = helper.make_tensor_value_info("inp", TensorProto.FLOAT, in_shape)
outp = helper.make_tensor_value_info("outp", TensorProto.FLOAT, out_shape)
if T is not None:
no_act = 0
node_inp_list = ["inp", "weights", "thresh"]
if odt == DataType["BIPOLAR"]:
actval = 0
else:
actval = odt.min()
else:
no_act = 1
node_inp_list = ["inp", "weights"]
actval = 0
VVAU_node = helper.make_node(
"VectorVectorActivation",
node_inp_list,
["outp"],
domain="finn.custom_op.fpgadataflow",
backend="fpgadataflow",
PE=pe,
SIMD=simd,
Dim=[dim_h, dim_w],
Channels=channels,
Kernel=[k_h, k_w],
resType="lut",
ActVal=actval,
inputDataType=idt.name,
weightDataType=wdt.name,
outputDataType=odt.name,
noActivation=no_act,
mem_mode=mem_mode,
)
graph = helper.make_graph(
nodes=[VVAU_node], name="vvau_graph", inputs=[inp], outputs=[outp]
)
model = qonnx_make_model(graph, producer_name="vvau-model")
model = ModelWrapper(model)
model.set_tensor_datatype("inp", idt)
model.set_tensor_datatype("outp", odt)
model.set_tensor_datatype("weights", wdt)
model.set_initializer("weights", W)
model.set_tensor_shape("weights", (channels, 1, k_h, k_w))
if T is not None:
model.set_tensor_datatype("thresh", tdt)
model.set_initializer("thresh", T)
# Minimize accumulator width to obtain realistic HLS reports
model = model.transform(MinimizeAccumulatorWidth())
model = model.transform(InferShapes())
model = model.transform(InferDataTypes())
return model
def prepare_inputs(input_tensor):
return {"inp": input_tensor}
# input datatype
@pytest.mark.parametrize("idt", [DataType["BIPOLAR"], DataType["UINT4"]])
# weight datatype
@pytest.mark.parametrize("wdt", [DataType["BIPOLAR"], DataType["UINT4"]])
# activation: None or DataType
@pytest.mark.parametrize("act", [DataType["BIPOLAR"], DataType["UINT4"], None])
# PE
@pytest.mark.parametrize("pe", [1, 3, 6])
# SIMD
@pytest.mark.parametrize("simd", [1, 9])
# Input image shape
@pytest.mark.parametrize("dim_h", [10])
@pytest.mark.parametrize("dim_w", [10, 1])
# Kernel shape
@pytest.mark.parametrize("k_h", [3])
@pytest.mark.parametrize("k_w", [3, 1])
# Number of input and output channels
@pytest.mark.parametrize("channels", [3, 6])
# memory mode
@pytest.mark.parametrize("mem_mode", ["const", "decoupled"])
# execution mode
@pytest.mark.parametrize("exec_mode", ["cppsim", "rtlsim"])
@pytest.mark.fpgadataflow
@pytest.mark.slow
@pytest.mark.vivado
def test_fpgadataflow_vvau(
idt, wdt, act, pe, simd, dim_h, dim_w, k_h, k_w, channels, mem_mode, exec_mode
):
if dim_w == 1 and k_w != 1:
pytest.skip("1D image requires 1D kernel, skipping.")
if channels % pe != 0:
pytest.skip("Requirement Channels divisable by PE is violated.")
if (k_h * k_w) % simd != 0:
pytest.skip("Requirement kernel (k_h * k_w) divisable by SIMD is violated.")
# Generate weights in expected shape for ONNX and HLS node
W = gen_finn_dt_tensor(wdt, (channels, 1, k_h, k_w)) # shape: [channels, 1, k, k]
W_onnx = _infer_sparse_weight_tensor(
W, k_h, k_w, channels
) # shape: [k*k*channels, channels]
# Generate inputs in expected format for ONNX and HLS node
x = gen_finn_dt_tensor(idt, (1, dim_h, dim_w, k_h * k_w * channels))
x_vvau = x.reshape(1, dim_h, dim_w, k_h * k_w, channels // pe, pe)
x_vvau = x_vvau.transpose(0, 1, 2, 4, 3, 5)
x_vvau = x_vvau.reshape(1, dim_h, dim_w, channels * k_h * k_w)
if act is None:
T = None
tdt = None
if wdt == DataType["BIPOLAR"] and idt == DataType["BIPOLAR"]:
odt = DataType["UINT32"]
else:
odt = DataType["INT32"]
else:
odt = act
(min_v, max_v) = _calculate_dot_prod_range(idt, wdt, k_h * k_w)
n_steps = act.get_num_possible_values() - 1
T = np.random.randint(min_v, max_v - 1, (channels, n_steps)).astype(np.float32)
T = np.sort(T, axis=1)
if wdt == DataType["BIPOLAR"] and idt == DataType["BIPOLAR"]:
tdt = DataType["UINT32"]
# bias thresholds to be positive
T = np.ceil((T + (k_h * k_w)) / 2)
assert (T >= 0).all()
else:
tdt = DataType["INT32"]
model = _make_single_vvau_modelwrapper(
W, pe, simd, k_h, k_w, channels, dim_h, dim_w, wdt, idt, odt, T, tdt, mem_mode
)
if exec_mode == "cppsim":
model = model.transform(SetExecMode("cppsim"))
model = model.transform(PrepareCppSim())
model = model.transform(CompileCppSim())
elif exec_mode == "rtlsim":
model = model.transform(SetExecMode("rtlsim"))
model = model.transform(GiveUniqueNodeNames())
model = model.transform(PrepareIP("xc7z020clg400-1", 5))
model = model.transform(HLSSynthIP())
model = model.transform(PrepareRTLSim())
else:
raise Exception("Unknown exec_mode in test_fpgadataflow_vvau")
input_dict = prepare_inputs(x_vvau)
# Calculate output
if wdt == DataType["BIPOLAR"] and idt == DataType["BIPOLAR"]:
# Simulate XNOR-popcount matrix multiplication, see
# qonnx.custom_op.general.xnorpopcount (not usable due to sparse W)
y_expected = np.matmul(x, W_onnx)
y_expected = (y_expected + (k_h * k_w)) / 2
else:
y_expected = np.matmul(x, W_onnx) # Y is in [N, H, W, C] format
if T is not None:
# Reshape Y, as multithreshold expects Y to be in [N, C, H, W] format
y_expected = np.transpose(y_expected, (0, 3, 1, 2))
y_expected = multithreshold(y_expected, T)
y_expected = np.transpose(y_expected, (0, 2, 3, 1))
if act == DataType["BIPOLAR"]:
# binary to bipolar
y_expected = 2 * y_expected - 1
else:
# signed offset
y_expected += act.min()
y_produced = oxe.execute_onnx(model, input_dict, return_full_exec_context=False)[
"outp"
]
assert (y_produced == y_expected).all(), "incorrect result"
if exec_mode == "rtlsim":
node = model.get_nodes_by_op_type("VectorVectorActivation")[0]
inst = getCustomOp(node)
cycles_rtlsim = inst.get_nodeattr("cycles_rtlsim")
exp_cycles_dict = model.analysis(exp_cycles_per_layer)
exp_cycles = exp_cycles_dict[node.name]
assert np.isclose(exp_cycles, cycles_rtlsim, atol=10)
assert exp_cycles != 0