# layer 1: conv2d, relu, reshape
w1 = ng.variable(weight_dtype, shape=(64, 3, 3, a0.shape[-1]), name='w1')
b1 = ng.variable(bias_dtype, shape=(w1.shape[0], ), name='b1')
s1 = ng.variable(scale_dtype, shape=(w1.shape[0], ), name='s1')
a1 = ng.conv2d(a0p,
w1,
strides=(1, 1, 1, 1),
bias=b1,
scale=s1,
act_func=ng.relu,
dtype=act_dtype,
sum_dtype=ng.int32)
a1r = ng.reshape(a1, [batchsize, -1])
# layer 2: full-connection, relu
w2 = ng.variable(weight_dtype, shape=(256, a1r.shape[-1]), name='w2')
b2 = ng.variable(bias_dtype, shape=(w2.shape[0], ), name='b2')
s2 = ng.variable(scale_dtype, shape=(w2.shape[0], ), name='s2')
a2 = ng.matmul(a1r,
w2,
bias=b2,
scale=s2,
transposed_b=True,
act_func=ng.relu,
dtype=act_dtype,
sum_dtype=ng.int32)
def run(
act_dtype=ng.int8,
weight_dtype=ng.int8,
bias_dtype=ng.int32,
scale_dtype=ng.int8,
par_ich=2,
par_och=2,
chunk_size=64,
axi_datawidth=32,
silent=False,
weight_filename='cnn.npy',
verilog_filename=None,
sim_filename=None,
# simtype='iverilog',
simtype='verilator',
# simtype=None, # no RTL simulation
):
# --------------------
# (1) Represent a DNN model as a dataflow by NNgen operators
# --------------------
# input
input_layer = ng.placeholder(
dtype=act_dtype,
shape=(1, 32, 32, 3), # N, H, W, C
name='input_layer')
# layer 0: conv2d (with bias and scale (= batchnorm)), relu, max_pool
w0 = ng.variable(
dtype=weight_dtype,
shape=(64, 3, 3, 3), # Och, Ky, Kx, Ich
name='w0')
b0 = ng.variable(dtype=bias_dtype, shape=(w0.shape[0], ), name='b0')
s0 = ng.variable(dtype=scale_dtype, shape=(w0.shape[0], ), name='s0')
a0 = ng.conv2d(input_layer,
w0,
strides=(1, 1, 1, 1),
bias=b0,
scale=s0,
act_func=ng.relu,
dtype=act_dtype,
sum_dtype=ng.int32)
a0p = ng.max_pool_serial(a0, ksize=(1, 2, 2, 1), strides=(1, 2, 2, 1))
# layer 1: conv2d, relu, reshape
w1 = ng.variable(weight_dtype, shape=(64, 3, 3, a0.shape[-1]), name='w1')
b1 = ng.variable(bias_dtype, shape=(w1.shape[0], ), name='b1')
s1 = ng.variable(scale_dtype, shape=(w1.shape[0], ), name='s1')
a1 = ng.conv2d(a0p,
w1,
strides=(1, 1, 1, 1),
bias=b1,
scale=s1,
act_func=ng.relu,
dtype=act_dtype,
sum_dtype=ng.int32)
a1r = ng.reshape(a1, [1, -1])
# layer 2: full-connection, relu
w2 = ng.variable(weight_dtype, shape=(256, a1r.shape[-1]), name='w2')
b2 = ng.variable(bias_dtype, shape=(w2.shape[0], ), name='b2')
s2 = ng.variable(scale_dtype, shape=(w2.shape[0], ), name='s2')
a2 = ng.matmul(a1r,
w2,
bias=b2,
scale=s2,
transposed_b=True,
act_func=ng.relu,
dtype=act_dtype,
sum_dtype=ng.int32)
# layer 3: full-connection, relu
w3 = ng.variable(weight_dtype, shape=(10, a2.shape[-1]), name='w3')
b3 = ng.variable(bias_dtype, shape=(w3.shape[0], ), name='b3')
s3 = ng.variable(scale_dtype, shape=(w3.shape[0], ), name='s3')
# output
output_layer = ng.matmul(a2,
w3,
bias=b3,
scale=s3,
transposed_b=True,
name='output_layer',
dtype=act_dtype,
sum_dtype=ng.int32)
# --------------------
# (2) Assign weights to the NNgen operators
# --------------------
# In this example, random floating-point values are assigned.
# In a real case, you should assign actual weight values
# obtianed by a training on DNN framework.
# If you don't you NNgen's quantizer, you can assign integer weights to each tensor.
w0_value = np.random.normal(size=w0.length).reshape(w0.shape)
w0_value = np.clip(w0_value, -3.0, 3.0)
w0.set_value(w0_value)
b0_value = np.random.normal(size=b0.length).reshape(b0.shape)
b0_value = np.clip(b0_value, -3.0, 3.0)
b0.set_value(b0_value)
s0_value = np.ones(s0.shape)
s0.set_value(s0_value)
w1_value = np.random.normal(size=w1.length).reshape(w1.shape)
w1_value = np.clip(w1_value, -3.0, 3.0)
w1.set_value(w1_value)
b1_value = np.random.normal(size=b1.length).reshape(b1.shape)
b1_value = np.clip(b1_value, -3.0, 3.0)
b1.set_value(b1_value)
s1_value = np.ones(s1.shape)
s1.set_value(s1_value)
w2_value = np.random.normal(size=w2.length).reshape(w2.shape)
w2_value = np.clip(w2_value, -3.0, 3.0)
w2.set_value(w2_value)
b2_value = np.random.normal(size=b2.length).reshape(b2.shape)
b2_value = np.clip(b2_value, -3.0, 3.0)
b2.set_value(b2_value)
s2_value = np.ones(s2.shape)
s2.set_value(s2_value)
w3_value = np.random.normal(size=w3.length).reshape(w3.shape)
w3_value = np.clip(w3_value, -3.0, 3.0)
w3.set_value(w3_value)
b3_value = np.random.normal(size=b3.length).reshape(b3.shape)
b3_value = np.clip(b3_value, -3.0, 3.0)
b3.set_value(b3_value)
s3_value = np.ones(s3.shape)
s3.set_value(s3_value)
# Quantizing the floating-point weights by the NNgen quantizer.
# Alternatively, you can assign integer weights by yourself to each tensor.
imagenet_mean = np.array([0.485, 0.456, 0.406]).astype(np.float32)
imagenet_std = np.array([0.229, 0.224, 0.225]).astype(np.float32)
if act_dtype.width > 8:
act_scale_factor = 128
else:
act_scale_factor = int(round(2**(act_dtype.width - 1) * 0.5))
input_scale_factors = {'input_layer': act_scale_factor}
input_means = {'input_layer': imagenet_mean * act_scale_factor}
input_stds = {'input_layer': imagenet_std * act_scale_factor}
ng.quantize([output_layer], input_scale_factors, input_means, input_stds)
# --------------------
# (3) Assign hardware attributes
# --------------------
# conv2d, matmul
# par_ich: parallelism in input-channel
# par_och: parallelism in output-channel
# par_col: parallelism in pixel column
# par_row: parallelism in pixel row
a0.attribute(par_ich=par_ich, par_och=par_och)
a1.attribute(par_ich=par_ich, par_och=par_och)
a2.attribute(par_ich=par_ich, par_och=par_och)
output_layer.attribute(par_ich=par_ich, par_och=par_och)
# cshamt_out: right shift amount after applying bias/scale
# If you assign integer weights by yourself to each tensor,
# cshamt (constant shift amount) must be assigned to each operator.
# a0.attribute(cshamt_out=weight_dtype.width + 1)
# a1.attribute(cshamt_out=weight_dtype.width + 1)
# a2.attribute(cshamt_out=weight_dtype.width + 1)
# output_layer.attribute(cshamt_out=weight_dtype.width + 1)
# max_pool
# par: parallelism in in/out channel
par = par_och
a0p.attribute(par=par)
# --------------------
# (4) Verify the DNN model behavior by executing the NNgen dataflow as a software
# --------------------
# In this example, random integer values are assigned.
# In real case, you should assign actual integer activation values, such as an image.
input_layer_value = np.random.normal(size=input_layer.length).reshape(
input_layer.shape)
input_layer_value = input_layer_value * imagenet_std + imagenet_mean
input_layer_value = np.clip(input_layer_value, -5.0, 5.0)
input_layer_value = input_layer_value * act_scale_factor
input_layer_value = np.clip(input_layer_value,
-1 * 2**(act_dtype.width - 1) - 1,
2**(act_dtype.width - 1))
input_layer_value = np.round(input_layer_value).astype(np.int64)
eval_outs = ng.eval([output_layer], input_layer=input_layer_value)
output_layer_value = eval_outs[0]
# print(output_layer_value)
# breakpoint()
# --------------------
# (5) Convert the NNgen dataflow to a hardware description (Verilog HDL and IP-XACT)
# --------------------
# to Veriloggen object
# targ = ng.to_veriloggen([output_layer], 'cnn', silent=silent,
# config={'maxi_datawidth': axi_datawidth})
# to IP-XACT (the method returns Veriloggen object, as well as to_veriloggen)
targ = ng.to_ipxact([output_layer],
'cnn',
silent=silent,
config={'maxi_datawidth': axi_datawidth})
# to Verilog HDL RTL (the method returns a source code text)
# rtl = ng.to_verilog([output_layer], 'cnn', silent=silent,
# config={'maxi_datawidth': axi_datawidth})
# --------------------
# (6) Save the quantized weights
# --------------------
# convert weight values to a memory image:
# on a real FPGA platform, this image will be used as a part of the model definition.
param_filename = 'hello_nngen.npy'
chunk_size = 64
param_data = ng.export_ndarray([output_layer], chunk_size)
np.save(weight_filename, param_data)
# --------------------
# (7) Simulate the generated hardware by Veriloggen and Verilog simulator
# --------------------
if simtype is None:
sys.exit()
param_bytes = len(param_data)
variable_addr = int(
math.ceil((input_layer.addr + input_layer.memory_size) /
chunk_size)) * chunk_size
check_addr = int(math.ceil(
(variable_addr + param_bytes) / chunk_size)) * chunk_size
tmp_addr = int(
math.ceil(
(check_addr + output_layer.memory_size) / chunk_size)) * chunk_size
memimg_datawidth = 32
mem = np.zeros([1024 * 1024 * 256 // (memimg_datawidth // 8)],
dtype=np.int64)
mem = mem + [100]
# placeholder
axi.set_memory(
mem, input_layer_value, memimg_datawidth, act_dtype.width,
input_layer.addr,
max(int(math.ceil(axi_datawidth / act_dtype.width)), par_ich))
# parameters (variable and constant)
axi.set_memory(mem, param_data, memimg_datawidth, 8, variable_addr)
# verification data
axi.set_memory(
mem, output_layer_value, memimg_datawidth, act_dtype.width, check_addr,
max(int(math.ceil(axi_datawidth / act_dtype.width)), par_och))
# test controller
m = Module('test')
params = m.copy_params(targ)
ports = m.copy_sim_ports(targ)
clk = ports['CLK']
resetn = ports['RESETN']
rst = m.Wire('RST')
rst.assign(Not(resetn))
# AXI memory model
if sim_filename is None:
sim_filename = os.path.splitext(os.path.basename(__file__))[0] + '.out'
memimg_name = 'memimg_' + sim_filename
memory = axi.AxiMemoryModel(m,
'memory',
clk,
rst,
datawidth=axi_datawidth,
memimg=mem,
memimg_name=memimg_name,
memimg_datawidth=memimg_datawidth)
memory.connect(ports, 'maxi')
# AXI-Slave controller
_saxi = vthread.AXIMLite(m, '_saxi', clk, rst, noio=True)
_saxi.connect(ports, 'saxi')
# timer
time_counter = m.Reg('time_counter', 32, initval=0)
seq = Seq(m, 'seq', clk, rst)
seq(time_counter.inc())
def ctrl():
for i in range(100):
pass
ng.sim.set_global_addrs(_saxi, tmp_addr)
start_time = time_counter.value
ng.sim.start(_saxi)
print('# start')
ng.sim.wait(_saxi)
end_time = time_counter.value
print('# end')
print('# execution cycles: %d' % (end_time - start_time))
# verify
ok = True
for bat in range(output_layer.shape[0]):
for x in range(output_layer.shape[1]):
orig = memory.read_word(
bat * output_layer.aligned_shape[1] + x, output_layer.addr,
act_dtype.width)
check = memory.read_word(
bat * output_layer.aligned_shape[1] + x, check_addr,
act_dtype.width)
if vthread.verilog.NotEql(orig, check):
print('NG (', bat, x, ') orig: ', orig, ' check: ', check)
ok = False
else:
print('OK (', bat, x, ') orig: ', orig, ' check: ', check)
if ok:
print('# verify: PASSED')
else:
print('# verify: FAILED')
vthread.finish()
th = vthread.Thread(m, 'th_ctrl', clk, rst, ctrl)
fsm = th.start()
uut = m.Instance(targ,
'uut',
params=m.connect_params(targ),
ports=m.connect_ports(targ))
# simulation.setup_waveform(m, uut)
simulation.setup_clock(m, clk, hperiod=5)
init = simulation.setup_reset(m,
resetn,
m.make_reset(),
period=100,
polarity='low')
init.add(
Delay(10000000),
Systask('finish'),
)
# output source code
if verilog_filename is not None:
m.to_verilog(verilog_filename)
# run simulation
sim = simulation.Simulator(m, sim=simtype)
rslt = sim.run(outputfile=sim_filename)
lines = rslt.splitlines()
if simtype == 'verilator' and lines[-1].startswith('-'):
rslt = '\n'.join(lines[:-1])
return rslt
def run(a_shape=(15, 15),
b_shape=(75, 3),
a_dtype=ng.int32,
b_dtype=ng.int32,
axi_datawidth=32,
silent=False,
filename=None,
simtype='iverilog',
outputfile=None):
a = ng.placeholder(a_dtype, shape=a_shape, name='a')
x = ng.reshape(a, b_shape, b_dtype)
z = ng.add(x, x)
b = ng.sub(z, x)
b = ng.reshape(b, b_shape, b_dtype)
targ = ng.to_veriloggen([b],
'matrix_reshape',
silent=silent,
config={'maxi_datawidth': axi_datawidth})
# verification data
va = np.arange(a.length, dtype=np.int64).reshape(a.shape) % [16]
vb = ng.verify.reshape(va, b_shape, dtype=b_dtype, tensor_dtype=a_dtype)
# to memory image
size_max = int(math.ceil(max(a.memory_size, b.memory_size) / 4096)) * 4096
check_addr = max(a.addr, b.addr) + size_max
size_check = size_max
tmp_addr = check_addr + size_check
memimg_datawidth = 32
mem = np.zeros([1024 * 1024 * 8 // memimg_datawidth], dtype=np.int64)
mem = mem + [100]
axi.set_memory(mem, va, memimg_datawidth, a_dtype.width, a.addr,
max(int(math.ceil(axi_datawidth / a_dtype.width)), 1))
axi.set_memory(mem, vb, memimg_datawidth, b_dtype.width, check_addr,
max(int(math.ceil(axi_datawidth / b_dtype.width)), 1))
# test controller
m = Module('test')
params = m.copy_params(targ)
ports = m.copy_sim_ports(targ)
clk = ports['CLK']
resetn = ports['RESETN']
rst = m.Wire('RST')
rst.assign(Not(resetn))
# AXI memory model
if outputfile is None:
outputfile = os.path.splitext(os.path.basename(__file__))[0] + '.out'
memimg_name = 'memimg_' + outputfile
memory = axi.AxiMemoryModel(m,
'memory',
clk,
rst,
datawidth=axi_datawidth,
memimg_datawidth=memimg_datawidth,
memimg=mem,
memimg_name=memimg_name)
memory.connect(ports, 'maxi')
# AXI-Slave controller
_saxi = vthread.AXIMLite(m, '_saxi', clk, rst, noio=True)
_saxi.connect(ports, 'saxi')
# timer
time_counter = m.Reg('time_counter', 32, initval=0)
seq = Seq(m, 'seq', clk, rst)
seq(time_counter.inc())
num_rep = functools.reduce(lambda x, y: x * y, b.shape[:-1], 1)
def ctrl():
for i in range(100):
pass
ng.sim.set_global_addrs(_saxi, tmp_addr)
start_time = time_counter.value
ng.sim.start(_saxi)
print('# start')
ng.sim.wait(_saxi)
end_time = time_counter.value
print('# end')
print('# execution cycles: %d' % (end_time - start_time))
# verify
ok = True
for i in range(num_rep):
for j in range(b.shape[-1]):
orig = memory.read_word(i * b.aligned_shape[-1] + j, b.addr,
b_dtype.width)
check = memory.read_word(i * b.aligned_shape[-1] + j,
check_addr, b_dtype.width)
if vthread.verilog.NotEql(orig, check):
print('NG', i, j, orig, check)
ok = False
# else:
# print('OK', i, j, orig, check)
if ok:
print('# verify: PASSED')
else:
print('# verify: FAILED')
vthread.finish()
th = vthread.Thread(m, 'th_ctrl', clk, rst, ctrl)
fsm = th.start()
uut = m.Instance(targ,
'uut',
params=m.connect_params(targ),
ports=m.connect_ports(targ))
# simulation.setup_waveform(m, uut)
simulation.setup_clock(m, clk, hperiod=5)
init = simulation.setup_reset(m,
resetn,
m.make_reset(),
period=100,
polarity='low')
init.add(
Delay(1000000),
Systask('finish'),
)
# output source code
if filename is not None:
m.to_verilog(filename)
# run simulation
sim = simulation.Simulator(m, sim=simtype)
rslt = sim.run(outputfile=outputfile)
lines = rslt.splitlines()
if simtype == 'verilator' and lines[-1].startswith('-'):
rslt = '\n'.join(lines[:-1])
return rslt
def mkTest(ich=3, och=10, ch=64, ksize=3, stride=1, col=28, row=28):
# create target hardware
# layer 0: conv2d, max_pool_serial, relu
input_layer = ng.placeholder(ng.int32,
shape=(1, row, col, ich),
name='input_layer')
w0 = ng.variable(ng.int32, shape=(ch, ksize, ksize, ich), name='w0')
a0 = ng.conv2d(input_layer, w0, strides=(1, stride, stride, 1))
a0 = ng.max_pool_serial(a0, ksize=(1, 2, 2, 1), strides=(1, 2, 2, 1))
a0 = ng.relu(a0)
# layer 1: conv2d, relu, reshape
w1 = ng.variable(ng.int32,
shape=(ch, ksize, ksize, a0.shape[-1]),
name='w1')
a1 = ng.conv2d(a0, w1, strides=(1, stride, stride, 1))
a1 = ng.relu(a1)
a1 = ng.reshape(a1, [-1])
# layer 2: full-connection
w2 = ng.variable(ng.int32, shape=(16, a1.shape[-1]), name='w2')
a2 = ng.matmul(a1, w2, transposed_b=True)
a2 = ng.relu(a2)
# layer 3: full-connection
w3 = ng.variable(ng.int32, shape=(och, a2.shape[-1]), name='w3')
output_layer = ng.matmul(a2, w3, transposed_b=True, name='output_layer')
targ = ng.to_veriloggen([output_layer], 'cnn')
#targ = ng.to_ipxact([output_layer], 'cnn')
# test controller
m = Module('test')
params = m.copy_params(targ)
ports = m.copy_sim_ports(targ)
clk = ports['CLK']
resetn = ports['RESETN']
rst = m.Wire('RST')
rst.assign(Not(resetn))
# AXI memory model
memory = axi.AxiMemoryModel(m, 'memory', clk, rst, mem_addrwidth=23)
memory.connect(ports, 'maxi')
# AXI-Slave controller
_saxi = vthread.AXIMLite(m, '_saxi', clk, rst, noio=True)
_saxi.connect(ports, 'saxi')
# timer
time_counter = m.Reg('time_counter', 32, initval=0)
seq = Seq(m, 'seq', clk, rst)
seq(time_counter.inc())
def ctrl():
for i in range(100):
pass
start_time = time_counter.value
ng.sim.start(_saxi)
print('# start')
ng.sim.wait(_saxi)
end_time = time_counter.value
print('# end')
print('# execution cycles: %d' % (end_time - start_time))
vthread.finish()
th = vthread.Thread(m, 'th_ctrl', clk, rst, ctrl)
fsm = th.start()
uut = m.Instance(targ,
'uut',
params=m.connect_params(targ),
ports=m.connect_ports(targ))
# simulation.setup_waveform(m, uut)
simulation.setup_clock(m, clk, hperiod=5)
init = simulation.setup_reset(m,
resetn,
m.make_reset(),
period=100,
polarity='low')
init.add(
Delay(10000000),
Systask('finish'),
)
return m
name='w1')
b1 = ng.variable(bias_dtype,
shape=(w1.shape[0],),
name='b1')
s1 = ng.variable(scale_dtype,
shape=(w1.shape[0],),
name='s1')
a1 = ng.conv2d(a0p, w1,
strides=(1, 1, 1, 1),
bias=b1,
scale=s1,
act_func=ng.relu,
sum_dtype=ng.int64)
a1r = ng.reshape(a1, [1, -1])
# layer 2: full-connection, relu
w2 = ng.variable(weight_dtype,
shape=(256, a1r.shape[-1]),
name='w2')
b2 = ng.variable(bias_dtype,
shape=(w2.shape[0],),
name='b2')
s2 = ng.variable(scale_dtype,
shape=(w2.shape[0],),
name='s2')
a2 = ng.matmul(a1r, w2,
bias=b2,
scale=s2,