def test_alter_return_none():
"""Test doing nothing by returning 'None' """
def before():
x = relay.var("x", shape=(1, 64, 56, 56))
y = relay.nn.global_max_pool2d(x)
y = relay.Function([x], y)
return y
called = [False]
def alter_conv2d(attrs, inputs, tinfos, out_type):
called[0] = True
return None
with TempOpAttr("nn.global_max_pool2d", "FTVMAlterOpLayout", alter_conv2d):
a = before()
a = run_opt_pass(a, transform.AlterOpLayout())
b = run_opt_pass(before(), transform.InferType())
assert tvm.ir.structural_equal(a, b), "Actual = \n" + str(a)
assert called[0]
def test_alter_layout_nhwc_arm():
""" Check that AlterOplayout does not alter NHWC data layout. """
def alter_conv2d(attrs, inputs, tinfos, out_type):
from tvm import topi
with tvm.target.create("llvm -device=arm_cpu"):
return topi.nn.conv2d_alter_layout(attrs, inputs, tinfos, out_type)
# Check NHWC conversion.
def before_nhwc():
x = relay.var("x", shape=(1, 56, 56, 64))
weight1 = relay.var('weight1', shape=(3, 3, 64, 64))
weight2 = relay.var('weight2', shape=(3, 3, 64, 64))
y = relay.nn.conv2d(x,
weight1,
channels=64,
kernel_size=(3, 3),
data_layout='NHWC',
kernel_layout='HWIO')
y = relay.nn.relu(y)
y = relay.nn.avg_pool2d(y, pool_size=(1, 1), layout='NHWC')
y = relay.nn.conv2d(y,
weight2,
channels=64,
kernel_size=(3, 3),
data_layout='NHWC',
kernel_layout='HWIO')
y = relay.nn.relu(y)
y = relay.Function(analysis.free_vars(y), y)
return y
def expected_nhwc():
return before_nhwc()
with TempOpAttr("nn.conv2d", "FTVMAlterOpLayout", alter_conv2d):
a = before_nhwc()
a = run_opt_pass(a, transform.AlterOpLayout())
b = run_opt_pass(expected_nhwc(), transform.InferType())
assert tvm.ir.structural_equal(a, b), "Actual = \n" + str(a)
def test_alter_layout_scalar():
"""Test alternating the layout of a conv2d.
The layout of broadcast operators and the weight should be changed accordingly.
"""
def before():
x = relay.var("x", shape=(1, 64, 56, 56))
weight = relay.var("weight")
y = relay.nn.conv2d(x, weight, channels=64, kernel_size=(3, 3), padding=(1, 1))
y = relay.add(y, relay.const(1, "float32"))
y = relay.Function(analysis.free_vars(y), y)
return y
def alter_conv2d(attrs, inputs, tinfos, out_type):
data, weight = inputs
new_attrs = dict(attrs)
new_attrs["data_layout"] = "NCHW16c"
return relay.nn.conv2d(data, weight, **new_attrs)
def expected():
x = relay.var("x", shape=(1, 64, 56, 56))
w = relay.var("weight")
y = relay.layout_transform(x, "NCHW", "NCHW16c")
y = relay.nn.conv2d(
y, w, channels=64, kernel_size=(3, 3), padding=(1, 1), data_layout="NCHW16c"
)
y = relay.add(y, relay.const(1.0, "float32"))
y = relay.layout_transform(y, "NCHW16c", "NCHW")
y = relay.Function(analysis.free_vars(y), y)
return y
with TempOpAttr("nn.conv2d", "FTVMAlterOpLayout", alter_conv2d):
a = before()
a = run_opt_pass(a, [transform.CanonicalizeOps(), transform.AlterOpLayout()])
b = run_opt_pass(expected(), transform.InferType())
assert tvm.ir.structural_equal(a, b), "Actual = \n" + str(a)
def test_legalize():
"""Test directly replacing an operator with a new one"""
def before():
x = relay.var("x", shape=(1, 64, 56, 56))
weight = relay.var('weight', shape=(64, 64, 3, 3))
y = relay.nn.conv2d(x,
weight,
channels=64,
kernel_size=(3, 3),
padding=(1, 1))
y = relay.nn.relu(y)
y = relay.Function([x, weight], y)
return y
def legalize_conv2d(attrs, inputs, types):
data, weight = inputs
weight = relay.multiply(weight, relay.const(2.0, "float32"))
return relay.nn.conv2d(data, weight, **attrs)
def expected():
x = relay.var("x", shape=(1, 64, 56, 56))
weight = relay.var('weight', shape=(64, 64, 3, 3))
y = relay.nn.conv2d(x,
relay.multiply(weight, relay.const(2.0,
"float32")),
channels=64,
kernel_size=(3, 3),
padding=(1, 1))
y = relay.nn.relu(y)
y = relay.Function([x, weight], y)
return y
with TempOpAttr("nn.conv2d", "FTVMLegalize", legalize_conv2d):
a = before()
a = run_opt_pass(a, transform.Legalize())
b = run_opt_pass(expected(), transform.InferType())
assert tvm.ir.structural_equal(a, b), "Actual = \n" + str(a)
def test_alter_layout_prelu():
"""Test PRelu operator"""
def before():
x = relay.var("x", shape=(1, 64, 56, 56))
weight = relay.var("weight")
alpha = relay.var("alpha", relay.IncompleteType())
y = relay.nn.conv2d(x, weight, channels=64, kernel_size=(3, 3), padding=(1, 1))
y = relay.nn.prelu(y, alpha)
y = relay.Function(analysis.free_vars(y), y)
return y
def alter_conv2d(attrs, inputs, tinfos, out_type):
data, weight = inputs
new_attrs = dict(attrs)
new_attrs["data_layout"] = "NCHW16c"
return relay.nn.conv2d(data, weight, **new_attrs)
def expected():
x = relay.var("x", shape=(1, 64, 56, 56))
w = relay.var("weight")
alpha = relay.var("alpha", relay.IncompleteType())
y = relay.layout_transform(x, "NCHW", "NCHW16c")
y = relay.nn.conv2d(
y, w, channels=64, kernel_size=(3, 3), padding=(1, 1), data_layout="NCHW16c"
)
y = relay.layout_transform(y, "NCHW16c", "NCHW")
y = relay.nn.prelu(y, alpha)
y = relay.Function(analysis.free_vars(y), y)
return y
with TempOpAttr("nn.conv2d", "FTVMAlterOpLayout", alter_conv2d):
a = before()
a = run_opt_pass(a, [transform.CanonicalizeOps(), transform.AlterOpLayout()])
b = run_opt_pass(expected(), transform.InferType())
assert tvm.ir.structural_equal(a, b)
def test_tflite_large_irregular():
with TempOpAttr("qnn.conv2d", "FTVMQnnLegalize", legalize_qnn_conv2d):
# uint8 input
data_shape = (1, 1024, 1, 1)
data_dtype = "uint8"
kernel_shape = (1001, 1024, 1, 1)
kernel_dtype = "uint8"
ref_func, qnn_func = get_funcs(
data_shape=data_shape,
data_dtype=data_dtype,
kernel_shape=kernel_shape,
kernel_dtype=kernel_dtype,
input_zero_point=127,
kernel_zero_point=127,
input_scale=1.0,
kernel_scale=1.0,
kernel_size=(1, 1),
padding=(0, 0),
strides=(1, 1),
dilation=(1, 1),
data_layout="NCHW",
kernel_layout="OIHW",
out_dtype="int32",
)
golden_data = np.full(data_shape, 127).astype("uint8")
golden_weight = np.full(kernel_shape, 127).astype("uint8")
with tvm.transform.PassContext(opt_level=2):
params = {"kernel": golden_weight}
graph, lib, params = relay.build(qnn_func, "llvm", params=params)
mod = graph_runtime.create(graph, lib, ctx=tvm.cpu(0))
mod.set_input("data", golden_data)
mod.set_input(**params)
mod.run()
qnn_output = mod.get_output(0).asnumpy()
golden_output = np.full((1, 1001, 1, 1), 0).astype("uint8")
np.testing.assert_equal(qnn_output, golden_output)
def test_broadcast_layout():
with TempOpAttr("qnn.conv2d", "FTVMQnnLegalize", legalize_qnn_conv2d):
# Test broadcast support for NHWC layout.
data_shape = (1, 229, 229, 3) # NHWC
data_dtype = "uint8"
kernel_shape = (7, 7, 3, 64) # HWIO
kernel_dtype = "int8"
_, qnn_func = get_funcs(
data_shape=data_shape,
data_dtype=data_dtype,
kernel_shape=kernel_shape,
kernel_dtype=kernel_dtype,
input_zero_point=8,
kernel_zero_point=3,
input_scale=1.0,
kernel_scale=1.0,
kernel_size=(7, 7),
padding=(1, 1),
strides=(1, 1),
dilation=(1, 1),
data_layout="NHWC",
kernel_layout="HWIO",
out_dtype="int32",
)
func = qnn_func["main"].body
bias = relay.var("bias", shape=(64,), dtype="int32")
bias2 = relay.var("bias2", shape=(1, 225, 225, 1), dtype="int32")
# Check broadcast support on both lhs and rhs
func = relay.add(func, bias2)
func = relay.add(bias2, func)
func = relay.add(bias, func)
func = relay.add(func, bias)
func = relay.Function(relay.analysis.free_vars(func), func)
mod = tvm.IRModule.from_expr(func)
with tvm.transform.PassContext(opt_level=3):
graph, lib, params = relay.build(mod, "llvm -mcpu=skylake-avx512")
def test_alter_op():
"""Test directly replacing an operator with a new one"""
def before():
x = relay.var("x", shape=(1, 64, 56, 56))
weight = relay.var('weight', shape=(64, 64, 3, 3))
y = relay.nn.conv2d(x,
weight,
channels=64,
kernel_size=(3, 3),
padding=(1, 1))
y = relay.nn.relu(y)
y = relay.Function([x, weight], y)
return y
def alter_conv2d(attrs, inputs, tinfos, out_type):
data, weight = inputs
weight = relay.multiply(weight, relay.const(2.0, "float32"))
return relay.nn.conv2d(data, weight, **attrs)
def expected():
x = relay.var("x", shape=(1, 64, 56, 56))
weight = relay.var('weight', shape=(64, 64, 3, 3))
y = relay.nn.conv2d(x,
relay.multiply(weight, relay.const(2.0,
"float32")),
channels=64,
kernel_size=(3, 3),
padding=(1, 1))
y = relay.nn.relu(y)
y = relay.Function([x, weight], y)
return y
with TempOpAttr("nn.conv2d", "FTVMAlterOpLayout", alter_conv2d):
a = before()
a = run_opt_pass(a, transform.AlterOpLayout())
b = run_opt_pass(expected(), transform.InferType())
assert analysis.alpha_equal(a, b), "Actual = \n" + str(a)
def test_alter_layout_nchw_upsamping_op():
"""Test upsamping operators """
def before():
x = relay.var("x", shape=(1, 32, 28, 28))
weight = relay.var("weight", shape=(32, 32, 3, 3))
y = relay.nn.conv2d(x, weight, channels=32, kernel_size=(3, 3), padding=(1, 1))
y = relay.nn.upsampling(y, scale_h=2, scale_w=2)
y = relay.nn.avg_pool2d(y, pool_size=(2, 2), strides=(2, 2))
y = relay.Function(analysis.free_vars(y), y)
return y
def alter_conv2d(attrs, inputs, tinfos, out_type):
data, weight = inputs
new_attrs = dict(attrs)
new_attrs["data_layout"] = "NCHW16c"
return relay.nn.conv2d(data, weight, **new_attrs)
def expected():
x = relay.var("x", shape=(1, 32, 28, 28))
weight = relay.var("weight")
x = relay.layout_transform(x, "NCHW", "NCHW16c")
y = relay.nn.conv2d(
x, weight, channels=32, kernel_size=(3, 3), padding=(1, 1), data_layout="NCHW16c"
)
y = relay.nn.upsampling(y, scale_h=2, scale_w=2, layout="NCHW16c")
y = relay.nn.avg_pool2d(y, pool_size=(2, 2), strides=(2, 2), layout="NCHW16c")
y = relay.layout_transform(y, "NCHW16c", "NCHW")
y = relay.Function(analysis.free_vars(y), y)
return y
with TempOpAttr("nn.conv2d", "FTVMAlterOpLayout", alter_conv2d):
a = before()
a = run_opt_pass(a, transform.AlterOpLayout())
b = run_opt_pass(expected(), transform.InferType())
assert tvm.ir.structural_equal(a, b), "Actual = \n" + str(a)
def test_conv2d():
N, IC, H, W = 1, 64, 56, 56
OC, IC, FH, FW = 128, 64, 3, 3
data_shape = (N, IC, H, W)
weight_shape = (OC, IC, FH, FW)
padding = (0, 0)
strides = (1, 1)
relay_mod = tvm.IRModule.from_expr(
get_conv2d(
data_shape,
weight_shape,
padding=padding,
strides=strides,
channels=OC,
kernel_size=(FH, FW),
data_layout="NCHW",
kernel_layout="OIHW",
))
data_np = np.random.randn(*data_shape).astype("float32")
weight_np = np.random.randn(*weight_shape).astype("float32")
target = "llvm"
params = {"weight": weight_np}
def schedule_fn(task, sch):
if "nn_conv2d" in task.task_name:
schedule_tir_conv2d_nchw_oihw(sch)
return True
return False
with TempOpAttr("nn.conv2d", "FTVMStrategy", _tmp_strategy):
database = apply_fixed_schedules(
relay_mod,
target,
params,
schedule_fn,
te_filter_func="meta_schedule.DefaultTaskFilterAllowExtern",
)
with ApplyHistoryBest(
database,
te_filter_func="meta_schedule.DefaultTaskFilterAllowExtern",
):
with tvm.transform.PassContext(
opt_level=3,
config={"relay.backend.use_meta_schedule": True},
):
lib = relay.build(relay_mod, target=target, params=params)
dev = tvm.device(target, 0)
runtime = tvm.contrib.graph_executor.GraphModule(lib["default"](dev))
runtime.set_input("data", data_np)
runtime.run()
out = runtime.get_output(0).numpy()
ref = get_ref(data_np, weight_np, strides, padding)
tvm.testing.assert_allclose(out, ref, atol=1e-4, rtol=1e-4)
def test_alter_layout_sum():
""" Check NCHW, NHWC sum layout conversion"""
def alter_conv2d(attrs, inputs, tinfos, out_type):
data, weight = inputs
new_attrs = dict(attrs)
new_attrs['data_layout'] = 'NCHW16c'
return relay.nn.conv2d(data, weight, **new_attrs)
# Check NCHW conversion.
def before_nchw():
x = relay.var("x", shape=(1, 64, 56, 56))
weight1 = relay.var('weight1')
y = relay.nn.conv2d(x,
weight1,
channels=32,
kernel_size=(3, 3),
padding=(1, 1))
ret = relay.sum(y, axis=1, keepdims=True)
y = relay.Function(analysis.free_vars(ret), ret)
return y
def expected_nchw():
x = relay.var("x", shape=(1, 64, 56, 56))
weight1 = relay.var('weight1')
y = relay.layout_transform(x, "NCHW", "NCHW16c")
y = relay.nn.conv2d(y,
weight1,
channels=32,
kernel_size=(3, 3),
padding=(1, 1),
data_layout="NCHW16c")
ret = relay.layout_transform(y, "NCHW16c", "NCHW")
ret = relay.sum(ret, axis=[1], keepdims=True)
y = relay.Function(analysis.free_vars(ret), ret)
return y
with TempOpAttr("nn.conv2d", "FTVMAlterOpLayout", alter_conv2d):
a = before_nchw()
a = run_opt_pass(a, transform.AlterOpLayout())
b = run_opt_pass(expected_nchw(), transform.InferType())
assert analysis.alpha_equal(a, b), "Actual = \n" + str(a)
# Check NHWC conversion.
def before_nhwc():
x = relay.var("x", shape=(1, 56, 56, 64))
weight1 = relay.var('weight1')
y = relay.nn.conv2d(x,
weight1,
channels=32,
kernel_size=(3, 3),
padding=(1, 1),
data_layout='NHWC')
ret = relay.sum(y, axis=3, keepdims=True)
y = relay.Function(analysis.free_vars(ret), ret)
return y
def expected_nhwc():
x = relay.var("x", shape=(1, 56, 56, 64))
weight1 = relay.var('weight1')
y = relay.layout_transform(x, "NHWC", "NCHW16c")
y = relay.nn.conv2d(y,
weight1,
channels=32,
kernel_size=(3, 3),
padding=(1, 1),
data_layout="NCHW16c")
ret = relay.layout_transform(y, "NCHW16c", "NCHW")
ret = relay.sum(ret, axis=[1], keepdims=True)
ret = relay.layout_transform(ret, "NCHW", "NHWC")
y = relay.Function(analysis.free_vars(ret), ret)
return y
with TempOpAttr("nn.conv2d", "FTVMAlterOpLayout", alter_conv2d):
a = before_nhwc()
a = run_opt_pass(a, transform.AlterOpLayout())
b = run_opt_pass(expected_nhwc(), transform.InferType())
assert analysis.alpha_equal(a, b), "Actual = \n" + str(a)
def test_alter_layout_dual_path():
"""
Test alternating the layout with two outputs.
One path continues to use the new layout while one path fall backs to old layout.
"""
def before():
x = relay.var("x", shape=(1, 64, 56, 56))
weight1 = relay.var('weight1')
weight2 = relay.var('weight2')
y = relay.nn.conv2d(x,
weight1,
channels=32,
kernel_size=(3, 3),
padding=(1, 1))
y = relay.nn.relu(y)
y1 = relay.nn.conv2d(y,
weight2,
channels=32,
kernel_size=(3, 3),
padding=(1, 1))
y1 = relay.nn.relu(y1)
y2 = relay.nn.batch_flatten(y)
ret = relay.Tuple([y1, y2])
y = relay.Function(analysis.free_vars(ret), ret)
return y
def alter_conv2d(attrs, inputs, tinfos, out_type):
data, weight = inputs
new_attrs = dict(attrs)
new_attrs['data_layout'] = 'NCHW16c'
return relay.nn.conv2d(data, weight, **new_attrs)
def expected():
x = relay.var("x", shape=(1, 64, 56, 56))
weight1 = relay.var('weight1')
weight2 = relay.var('weight2')
y = relay.layout_transform(x, "NCHW", "NCHW16c")
y = relay.nn.conv2d(y,
weight1,
channels=32,
kernel_size=(3, 3),
padding=(1, 1),
data_layout="NCHW16c")
y = relay.nn.relu(y)
y1 = relay.nn.conv2d(y,
weight2,
channels=32,
kernel_size=(3, 3),
padding=(1, 1),
data_layout='NCHW16c')
y1 = relay.nn.relu(y1)
y1 = relay.layout_transform(y1, "NCHW16c", "NCHW")
y2 = relay.layout_transform(y, "NCHW16c", "NCHW")
y2 = relay.nn.batch_flatten(y2)
ret = relay.Tuple([y1, y2])
y = relay.Function(analysis.free_vars(ret), ret)
return y
with TempOpAttr("nn.conv2d", "FTVMAlterOpLayout", alter_conv2d):
a = before()
a = run_opt_pass(a, transform.AlterOpLayout())
b = run_opt_pass(expected(), transform.InferType())
assert analysis.alpha_equal(a, b), "Actual = \n" + str(a)
def test_alter_layout_strided_slice():
"""Test rewriting strided_slice during alter_iop_layout"""
def before():
x = relay.var("x", shape=(1, 32, 28, 28))
weight = relay.var('weight', shape=(32, 32, 3, 3))
y = relay.nn.conv2d(x,
weight,
channels=32,
kernel_size=(3, 3),
padding=(1, 1))
y = relay.strided_slice(y,
begin=relay.const([0, 16], "int32"),
end=relay.const([1, 33], "int32"),
strides=relay.const([1, 1], "int32"))
y = relay.Function(analysis.free_vars(y), y)
return y
def alter_conv2d(attrs, inputs, tinfos, out_type):
data, weight = inputs
new_attrs = dict(attrs)
new_attrs['data_layout'] = 'NCHW4c'
return relay.nn.conv2d(data, weight, **new_attrs)
def expected():
x = relay.var("x", shape=(1, 32, 28, 28))
weight = relay.var("weight", shape=(32, 32, 3, 3))
weight = relay.layout_transform(weight, "OIHW", "OIHW4i4o")
x = relay.layout_transform(x, "NCHW", "NCHW4c")
y = relay.op.nn.contrib_conv2d_nchwc(x,
weight,
channels=32,
kernel_size=(3, 3),
padding=(1, 1),
data_layout="NCHW4c")
y = relay.strided_slice(y,
begin=relay.const([0, 4], "int32"),
end=relay.const([1, 21], "int32"),
strides=relay.const([1, 1], "int32"))
y = relay.layout_transform(y, "NCHW4c", "NCHW")
y = relay.Function(analysis.free_vars(y), y)
return y
with TempOpAttr("nn.conv2d", "FTVMAlterOpLayout", alter_conv2d):
a = before()
b = run_opt_pass(expected(), transform.InferType())
# Verify inference result
mod_before = tvm.IRModule()
mod_new = tvm.IRModule()
mod_before['main'] = a
mod_new['main'] = b
with relay.build_config(opt_level=3):
for target, ctx in ctx_list():
for kind in ["graph", "debug", "vm"]:
ex_before = relay.create_executor(kind,
mod=mod_before,
ctx=ctx,
target=target)
ex_new = relay.create_executor(kind,
mod=mod_new,
ctx=ctx,
target=target)
np_data = np.random.uniform(size=(1, 32, 28,
28)).astype("float32")
np_weight = np.random.uniform(size=(32, 32, 3,
3)).astype("float32")
result_before = ex_before.evaluate()(np_data, np_weight)
result_new = ex_new.evaluate()(np_data, np_weight)
tvm.testing.assert_allclose(result_before.asnumpy(),
result_new.asnumpy(),
rtol=1e-5,
atol=1e-5)
def test_depthwise_depth_multiplier():
with TempOpAttr("qnn.conv2d", "FTVMQnnLegalize", legalize_qnn_conv2d):
# uint8 input, NCHW and OIHW
# Depthwise multiplier = 1
data_shape = (2, 4, 16, 16)
data_dtype = 'uint8'
kernel_shape = (4, 1, 3, 3)
kernel_dtype = 'uint8'
ref_func, qnn_func = get_funcs(data_shape=data_shape,
data_dtype=data_dtype,
kernel_shape=kernel_shape,
kernel_dtype=kernel_dtype,
input_zero_point=5,
kernel_zero_point=3,
input_scale=1.0,
kernel_scale=1.0,
kernel_size=(3, 3),
padding=(0, 0),
strides=(1, 1),
dilation=(1, 1),
data_layout="NCHW",
kernel_layout="OIHW",
out_dtype="int32",
groups=4,
channels=4)
verify(ref_func, qnn_func, data_shape, data_dtype, kernel_shape,
kernel_dtype)
# Depthwise multiplier = 2
data_shape = (10, 4, 16, 16)
data_dtype = 'uint8'
kernel_shape = (4, 2, 3, 3)
kernel_dtype = 'uint8'
ref_func, qnn_func = get_funcs(data_shape=data_shape,
data_dtype=data_dtype,
kernel_shape=kernel_shape,
kernel_dtype=kernel_dtype,
input_zero_point=5,
kernel_zero_point=3,
input_scale=1.0,
kernel_scale=1.0,
kernel_size=(3, 3),
padding=(0, 0),
strides=(1, 1),
dilation=(1, 1),
data_layout="NCHW",
kernel_layout="OIHW",
out_dtype="int32",
groups=8,
channels=8)
verify(ref_func, qnn_func, data_shape, data_dtype, kernel_shape,
kernel_dtype)
# uint8 input, NHWC and HWOI
# Depthwise multiplier = 1
data_shape = (2, 16, 16, 4)
data_dtype = 'uint8'
kernel_shape = (3, 3, 4, 1)
kernel_dtype = 'uint8'
ref_func, qnn_func = get_funcs(data_shape=data_shape,
data_dtype=data_dtype,
kernel_shape=kernel_shape,
kernel_dtype=kernel_dtype,
input_zero_point=5,
kernel_zero_point=3,
input_scale=1.0,
kernel_scale=1.0,
kernel_size=(3, 3),
padding=(0, 0),
strides=(1, 1),
dilation=(1, 1),
data_layout="NHWC",
kernel_layout="HWOI",
out_dtype="int32",
groups=4,
channels=4)
verify(ref_func, qnn_func, data_shape, data_dtype, kernel_shape,
kernel_dtype)
# Depthwise multiplier = 2
data_shape = (2, 16, 16, 4)
data_dtype = 'uint8'
kernel_shape = (3, 3, 4, 2)
kernel_dtype = 'uint8'
ref_func, qnn_func = get_funcs(data_shape=data_shape,
data_dtype=data_dtype,
kernel_shape=kernel_shape,
kernel_dtype=kernel_dtype,
input_zero_point=5,
kernel_zero_point=3,
input_scale=1.0,
kernel_scale=1.0,
kernel_size=(3, 3),
padding=(0, 0),
strides=(1, 1),
dilation=(1, 1),
data_layout="NHWC",
kernel_layout="HWOI",
out_dtype="int32",
groups=8,
channels=8)
verify(ref_func, qnn_func, data_shape, data_dtype, kernel_shape,
kernel_dtype)
def test_alter_layout_pool():
""" Check NCHW, NHWC pool layout conversion"""
def alter_conv2d(attrs, inputs, tinfos, out_type):
data, weight = inputs
new_attrs = dict(attrs)
new_attrs["data_layout"] = "NCHW16c"
return relay.nn.conv2d(data, weight, **new_attrs)
# Check NCHW conversion.
def before_nchw():
x = relay.var("x", shape=(1, 64, 56, 56))
weight1 = relay.var("weight1")
y = relay.nn.conv2d(x, weight1, channels=32, kernel_size=(3, 3), padding=(1, 1))
ret = relay.nn.avg_pool2d(y, pool_size=(1, 1))
y = relay.Function(analysis.free_vars(ret), ret)
return y
def expected_nchw():
x = relay.var("x", shape=(1, 64, 56, 56))
weight1 = relay.var("weight1")
y = relay.layout_transform(x, "NCHW", "NCHW16c")
y = relay.nn.conv2d(
y, weight1, channels=32, kernel_size=(3, 3), padding=(1, 1), data_layout="NCHW16c"
)
ret = relay.nn.avg_pool2d(y, pool_size=(1, 1), layout="NCHW16c")
ret = relay.layout_transform(ret, "NCHW16c", "NCHW")
y = relay.Function(analysis.free_vars(ret), ret)
return y
with TempOpAttr("nn.conv2d", "FTVMAlterOpLayout", alter_conv2d):
a = before_nchw()
a = run_opt_pass(a, transform.AlterOpLayout())
b = run_opt_pass(expected_nchw(), transform.InferType())
assert tvm.ir.structural_equal(a, b), "Actual = \n" + str(a)
# Check NHWC conversion.
def before_nhwc():
x = relay.var("x", shape=(1, 56, 56, 64))
weight1 = relay.var("weight1")
y = relay.nn.conv2d(
x, weight1, channels=32, kernel_size=(3, 3), padding=(1, 1), data_layout="NHWC"
)
ret = relay.nn.avg_pool2d(y, pool_size=(1, 1), layout="NHWC")
y = relay.Function(analysis.free_vars(ret), ret)
return y
def expected_nhwc():
x = relay.var("x", shape=(1, 56, 56, 64))
weight1 = relay.var("weight1")
y = relay.layout_transform(x, "NHWC", "NCHW16c")
y = relay.nn.conv2d(
y, weight1, channels=32, kernel_size=(3, 3), padding=(1, 1), data_layout="NCHW16c"
)
ret = relay.nn.avg_pool2d(y, pool_size=(1, 1), layout="NCHW16c")
ret = relay.layout_transform(ret, "NCHW16c", "NHWC")
y = relay.Function(analysis.free_vars(ret), ret)
return y
with TempOpAttr("nn.conv2d", "FTVMAlterOpLayout", alter_conv2d):
a = before_nhwc()
a = run_opt_pass(a, transform.AlterOpLayout())
b = run_opt_pass(expected_nhwc(), transform.InferType())
assert tvm.ir.structural_equal(a, b), "Actual = \n" + str(a)
def test_tflite_anistropic_strides():
with TempOpAttr("qnn.conv2d", "FTVMQnnLegalize", legalize_qnn_conv2d):
# uint8 input
data_shape = (1, 1, 3, 6)
data_dtype = "uint8"
kernel_shape = (1, 1, 2, 2)
kernel_dtype = "uint8"
ref_func, qnn_func = get_funcs(
data_shape=data_shape,
data_dtype=data_dtype,
kernel_shape=kernel_shape,
kernel_dtype=kernel_dtype,
input_zero_point=127,
kernel_zero_point=127,
input_scale=1.0,
kernel_scale=1.0,
kernel_size=(2, 2),
padding=(0, 0),
strides=(1, 3),
dilation=(1, 1),
data_layout="NCHW",
kernel_layout="OIHW",
out_dtype="int32",
)
golden_data = np.array(
(
133,
131,
129,
125,
123,
121,
135,
133,
131,
123,
121,
119,
137,
135,
133,
121,
119,
117,
)
).reshape(data_shape)
golden_data = golden_data.astype("uint8")
golden_weight = np.array((129, 131, 133, 135)).reshape(kernel_shape)
golden_weight = golden_weight.astype("uint8")
with tvm.transform.PassContext(opt_level=2):
params = {"kernel": golden_weight}
graph, lib, params = relay.build(qnn_func, "llvm", params=params)
mod = graph_runtime.create(graph, lib, ctx=tvm.cpu(0))
mod.set_input("data", golden_data)
mod.set_input(**params)
mod.run()
qnn_output = mod.get_output(0).asnumpy()
golden_output = np.array((124, -92, 164, -132)).reshape(1, 1, 2, 2)
np.testing.assert_equal(qnn_output, golden_output)
def test_padding():
with TempOpAttr("qnn.conv2d", "FTVMQnnLegalize", legalize_qnn_conv2d):
# uint8 input
data_shape = (1, 4, 2, 2)
data_dtype = "uint8"
kernel_shape = (3, 4, 2, 2)
kernel_dtype = "uint8"
ref_func, qnn_func = get_funcs(
data_shape=data_shape,
data_dtype=data_dtype,
kernel_shape=kernel_shape,
kernel_dtype=kernel_dtype,
input_zero_point=8,
kernel_zero_point=5,
input_scale=1.0,
kernel_scale=1.0,
kernel_size=(2, 2),
padding=(1, 1),
strides=(1, 1),
dilation=(1, 1),
data_layout="NCHW",
kernel_layout="OIHW",
out_dtype="int32",
)
verify(ref_func, qnn_func, data_shape, data_dtype, kernel_shape, kernel_dtype)
# Try different layout
data_shape = (2, 2, 4, 4) # NHWC
data_dtype = "uint8"
kernel_shape = (2, 2, 4, 3) # HWIO
kernel_dtype = "uint8"
ref_func, qnn_func = get_funcs(
data_shape=data_shape,
data_dtype=data_dtype,
kernel_shape=kernel_shape,
kernel_dtype=kernel_dtype,
input_zero_point=8,
kernel_zero_point=3,
input_scale=1.0,
kernel_scale=1.0,
kernel_size=(2, 2),
padding=(1, 1),
strides=(1, 1),
dilation=(1, 1),
data_layout="NHWC",
kernel_layout="HWIO",
out_dtype="int32",
)
verify(ref_func, qnn_func, data_shape, data_dtype, kernel_shape, kernel_dtype)
# Try asymmetric padding
data_shape = (2, 2, 4, 4) # NHWC
data_dtype = "uint8"
kernel_shape = (2, 2, 4, 3) # HWIO
kernel_dtype = "uint8"
ref_func, qnn_func = get_funcs(
data_shape=data_shape,
data_dtype=data_dtype,
kernel_shape=kernel_shape,
kernel_dtype=kernel_dtype,
input_zero_point=8,
kernel_zero_point=3,
input_scale=1.0,
kernel_scale=1.0,
kernel_size=(2, 2),
padding=(1, 1, 2, 2),
strides=(1, 1),
dilation=(1, 1),
data_layout="NHWC",
kernel_layout="HWIO",
out_dtype="int32",
)
verify(ref_func, qnn_func, data_shape, data_dtype, kernel_shape, kernel_dtype)
def test_alter_op_layout_batch_matmul():
if not get_global_func("tvm.contrib.mlas.batch_sgemm", allow_missing=True):
print("skip because mlas is not enabled...")
return
target = "llvm -libs=mlas"
m, k, n = 32, 48, 64
B_const = np.random.uniform(size=[1, n, k]).astype("float32")
def pack_before():
A = relay.var("A", shape=(1, m, k), dtype="float32")
B = relay.const(B_const, "float32")
C = relay.nn.batch_matmul(A, B)
f = relay.Function(relay.analysis.free_vars(C), C)
return f
def pack_expected():
A = relay.var("A", shape=(1, m, k), dtype="float32")
B = relay.const(B_const, "float32")
B_packed = relay.op.mlas_packb(B, k, n)
C = relay.op.mlas_matmul(A, B_packed, True, k, n)
f = relay.Function(relay.analysis.free_vars(C), C)
return f
with tvm.target.Target(target):
with TempOpAttr(
"nn.batch_matmul", "FTVMAlterOpLayout", relay.op._mlas._alter_batch_matmul_layout
):
a = pack_before()
a = _run_opt_pass(a, relay.transform.AlterOpLayout())
b = _run_opt_pass(pack_expected(), relay.transform.InferType())
assert tvm.ir.structural_equal(a, b)
def nopack_before():
A = relay.var("A", shape=(1, m, k), dtype="float32")
B = relay.var("B", shape=(1, n, k), dtype="float32")
C = relay.nn.batch_matmul(A, B)
f = relay.Function(relay.analysis.free_vars(C), C)
return f
def nopack_expected():
A = relay.var("A", shape=(1, m, k), dtype="float32")
B = relay.var("B", shape=(1, n, k), dtype="float32")
C = relay.op.mlas_matmul(A, B, False)
f = relay.Function(relay.analysis.free_vars(C), C)
return f
with tvm.target.Target(target):
with TempOpAttr(
"nn.batch_matmul", "FTVMAlterOpLayout", relay.op._mlas._alter_batch_matmul_layout
):
a = nopack_before()
a = _run_opt_pass(a, relay.transform.AlterOpLayout())
b = _run_opt_pass(nopack_expected(), relay.transform.InferType())
assert tvm.ir.structural_equal(a, b)
def dynamic_expected():
A = relay.var("A", shape=(1, relay.Any(), k), dtype="float32")
B = relay.var("B", shape=(1, n, k), dtype="float32")
C = relay.nn.batch_matmul(A, B)
f = relay.Function(relay.analysis.free_vars(C), C)
return f
with tvm.target.Target(target):
with TempOpAttr(
"nn.batch_matmul", "FTVMAlterOpLayout", relay.op._mlas._alter_batch_matmul_layout
):
a = dynamic_expected()
a = _run_opt_pass(a, relay.transform.AlterOpLayout())
b = _run_opt_pass(dynamic_expected(), relay.transform.InferType())
assert tvm.ir.structural_equal(a, b)
def _test_legalize_conv2d(data_shape,
kernel_shape,
pad_shape,
dtype,
do_pad=True):
out_channel = kernel_shape[3]
out_shape = list(data_shape)
out_shape[3] = out_channel
db, di, do = pad_shape
def before():
x = relay.var("x", shape=data_shape, dtype=dtype)
weight = relay.var("weight", shape=kernel_shape, dtype=dtype)
y = relay.nn.conv2d(
x,
weight,
channels=out_channel,
kernel_size=(3, 3),
padding=(1, 1),
data_layout="NHWC",
kernel_layout="HWIO",
)
y = relay.Function([x, weight], y)
return y
def legalize_conv2d(attrs, inputs, types):
with tvm.target.Target("cuda"):
return topi.nn.conv2d_legalize(attrs, inputs, types)
def expected():
if not do_pad:
return before()
x = relay.var("x", shape=data_shape, dtype=dtype)
if db or di:
x_pad = relay.nn.pad(x,
pad_width=((0, db), (0, 0), (0, 0), (0,
di)))
else:
x_pad = x
weight = relay.var("weight", shape=(kernel_shape), dtype=dtype)
if di or do:
weight_pad = relay.nn.pad(weight,
pad_width=((0, 0), (0, 0), (0, di),
(0, do)))
else:
weight_pad = weight
y_pad = relay.nn.conv2d(
x_pad,
weight=weight_pad,
channels=out_channel + do,
kernel_size=(3, 3),
padding=(1, 1),
data_layout="NHWC",
kernel_layout="HWIO",
)
if db or do:
y = relay.strided_slice(y_pad,
begin=[0, 0, 0, 0],
end=out_shape)
else:
y = y_pad
y = relay.Function([x, weight], y)
return y
with TempOpAttr("nn.conv2d", "FTVMLegalize", legalize_conv2d):
a = before()
a = run_opt_pass(a, transform.Legalize())
b = run_opt_pass(expected(), transform.InferType())
assert tvm.ir.structural_equal(
a, b), "Actual = \n" + str(a) + "Expected = \n" + str(b)
def partition_for_dnnl(mod, params=None, alter_layout=True):
"""Partition the graph greedily offloading supported operators to DNNL.
Parameters
----------
mod : Module
The module to run passes on.
params : Optional[Dict[str, NDArray]]
Constant input parameters.
Returns
-------
mod : Module
Annotated and partitioned module.
"""
if params:
mod["main"] = bind_params_by_name(mod["main"], params)
with TempOpAttr("nn.conv2d", "FTVMLegalize", dnnl.legalize_group_conv):
with TempOpAttr("nn.conv2d_transpose", "FTVMLegalize",
dnnl.legalize_group_conv):
seq = tvm.transform.Sequential([
transform.CanonicalizeOps(),
transform.InferType(),
transform.SimplifyInference(),
transform.FoldConstant(),
transform.FoldScaleAxis(),
# fold consecutive add ops to simplify pattern `conv2d-bias_add-bn-relu`
transform.SimplifyExpr(),
transform.FoldConstant(),
# alter group conv /conv_transpose layout to `GOIHW` / `GIOHW`
transform.Legalize(),
transform.FoldConstant(),
])
with tvm.transform.PassContext(opt_level=3):
mod = seq(mod)
if alter_layout:
with TempOpAttr("nn.conv1d", "FTVMAlterOpLayout", dnnl.alter_conv):
with TempOpAttr("nn.conv2d", "FTVMAlterOpLayout", dnnl.alter_conv):
with TempOpAttr("nn.conv3d", "FTVMAlterOpLayout",
dnnl.alter_conv):
with TempOpAttr("nn.conv2d_transpose", "FTVMAlterOpLayout",
dnnl.alter_conv_transpose):
with TempOpAttr("nn.conv3d_transpose",
"FTVMAlterOpLayout",
dnnl.alter_conv_transpose):
alter_layout_seq = tvm.transform.Sequential([
transform.AlterOpLayout(),
transform.FoldConstant(),
])
with tvm.transform.PassContext(opt_level=3):
mod = alter_layout_seq(mod)
byoc_seq = tvm.transform.Sequential([
transform.MergeComposite(dnnl.pattern_table()),
transform.AnnotateTarget("dnnl"),
transform.MergeCompilerRegions(),
transform.PartitionGraph(),
])
with tvm.transform.PassContext(opt_level=3):
mod = byoc_seq(mod)
return mod
def test_alter_layout_nhwc_int8_aarch64():
""" Check that AlterOplayout does not alter NHWC data layout. """
from tvm import autotvm
expected_workload_shape = (20, 42, 4, 16)
# We use Int8Fallback to disable the fallback flag
# and to test the new workload produced during the pass
class Int8Fallback(autotvm.FallbackContext):
def _query_inside(self, target, workload):
key = (target, workload)
if key in self.memory:
return self.memory[key]
cfg = autotvm.task.space.FallbackConfigEntity()
cfg.is_fallback = False
cfg.cost = 0
self.memory[key] = cfg
return cfg
def update(self, target, workload, cfg):
key = (str(target), workload)
assert workload[2][1] == expected_workload_shape
assert workload[
0] == "conv2d_NHWC_quantized_without_transform.arm_cpu"
self.memory[key] = cfg
def alter_conv2d(attrs, inputs, tinfos, out_type):
import topi
with tvm.target.create(
"llvm -device=arm_cpu -mtriple=aarch64-linux-gnu"):
with Int8Fallback():
tmp = topi.nn.conv2d_alter_layout(attrs, inputs, tinfos,
out_type)
return tmp
# Check NHWC conversion.
def before_nhwc_int8():
x = relay.var("x", shape=(1, 56, 56, 73), dtype='int8')
weight = relay.var('weight1', shape=(3, 3, 73, 79), dtype='int8')
y = relay.nn.conv2d(x,
weight,
channels=79,
kernel_size=(3, 3),
data_layout='NHWC',
kernel_layout='HWIO',
out_dtype='int32')
y = relay.Function(analysis.free_vars(y), y)
return y
def expected_nhwc_int8():
x = relay.var("x", shape=(1, 56, 56, 73), dtype='int8')
weight = relay.var('weight1', shape=(3, 3, 73, 79), dtype='int8')
tile_rows = 4
tile_cols = 16
weight_transformed = relay.nn.contrib_conv2d_gemm_weight_transform(
weight, tile_rows, tile_cols)
y = relay.nn.contrib_conv2d_gemm_without_weight_transform(
x,
weight_transformed,
channels=79,
kernel_size=(3, 3),
data_layout='NHWC',
kernel_layout='HWIO',
out_dtype='int32')
y = relay.Function(analysis.free_vars(y), y)
return y
with TempOpAttr("nn.conv2d", "FTVMAlterOpLayout", alter_conv2d):
a = before_nhwc_int8()
a = run_opt_pass(a, transform.AlterOpLayout())
b = run_opt_pass(expected_nhwc_int8(), transform.InferType())
assert tvm.ir.structural_equal(a, b), "Actual = \n" + str(a)
def test_qnn_batch_matmul_with_requantized_output():
with TempOpAttr("qnn.dense", "FTVMQnnLegalize", legalize_qnn_batch_matmul):
int8_requantized_output_params = make_int_configuration(
requantize_output=True)
qnn_batch_matmul_driver(int8_requantized_output_params)
def test_alter_layout_pad():
""" Check NCHW, NHWC and corner case for pad layout conversion"""
def alter_conv2d(attrs, inputs, tinfos, out_type):
data, weight = inputs
new_attrs = dict(attrs)
new_attrs['data_layout'] = 'NCHW16c'
return relay.nn.conv2d(data, weight, **new_attrs)
# Check NCHW conversion.
def before_nchw():
x = relay.var("x", shape=(1, 64, 56, 56))
weight1 = relay.var('weight1')
y = relay.nn.conv2d(x,
weight1,
channels=32,
kernel_size=(3, 3),
padding=(1, 1))
ret = relay.nn.pad(y, pad_width=((0, 0), (0, 0), (1, 1), (1, 1)))
y = relay.Function(analysis.free_vars(ret), ret)
return y
def expected_nchw():
x = relay.var("x", shape=(1, 64, 56, 56))
weight1 = relay.var('weight1')
y = relay.layout_transform(x, "NCHW", "NCHW16c")
y = relay.nn.conv2d(y,
weight1,
channels=32,
kernel_size=(3, 3),
padding=(1, 1),
data_layout="NCHW16c")
ret = relay.nn.pad(y,
pad_width=((0, 0), (0, 0), (1, 1), (1, 1), (0, 0)))
ret = relay.layout_transform(ret, "NCHW16c", "NCHW")
y = relay.Function(analysis.free_vars(ret), ret)
return y
with TempOpAttr("nn.conv2d", "FTVMAlterOpLayout", alter_conv2d):
a = before_nchw()
a = run_opt_pass(a, transform.AlterOpLayout())
b = run_opt_pass(expected_nchw(), transform.InferType())
assert tvm.ir.structural_equal(a, b), "Actual = \n" + str(a)
# Check NHWC conversion.
def before_nhwc():
x = relay.var("x", shape=(1, 56, 56, 64))
weight1 = relay.var('weight1')
y = relay.nn.conv2d(x,
weight1,
channels=32,
kernel_size=(3, 3),
padding=(1, 1),
data_layout='NHWC')
ret = relay.nn.pad(y, pad_width=((0, 0), (1, 1), (1, 1), (0, 0)))
y = relay.Function(analysis.free_vars(ret), ret)
return y
def expected_nhwc():
x = relay.var("x", shape=(1, 56, 56, 64))
weight1 = relay.var('weight1')
y = relay.layout_transform(x, "NHWC", "NCHW16c")
y = relay.nn.conv2d(y,
weight1,
channels=32,
kernel_size=(3, 3),
padding=(1, 1),
data_layout="NCHW16c")
ret = relay.nn.pad(y,
pad_width=((0, 0), (0, 0), (1, 1), (1, 1), (0, 0)))
ret = relay.layout_transform(ret, "NCHW16c", "NHWC")
y = relay.Function(analysis.free_vars(ret), ret)
return y
with TempOpAttr("nn.conv2d", "FTVMAlterOpLayout", alter_conv2d):
a = before_nhwc()
a = run_opt_pass(a, transform.AlterOpLayout())
b = run_opt_pass(expected_nhwc(), transform.InferType())
assert tvm.ir.structural_equal(a, b), "Actual = \n" + str(a)
# Check that conversion does not happen when padding along split axis.
def before():
x = relay.var("x", shape=(1, 64, 56, 56))
weight1 = relay.var('weight1')
y = relay.nn.conv2d(x,
weight1,
channels=32,
kernel_size=(3, 3),
padding=(1, 1))
ret = relay.nn.pad(y, pad_width=((0, 0), (1, 1), (1, 1), (1, 1)))
y = relay.Function(analysis.free_vars(ret), ret)
return y
def expected():
x = relay.var("x", shape=(1, 64, 56, 56))
weight1 = relay.var('weight1')
y = relay.layout_transform(x, "NCHW", "NCHW16c")
y = relay.nn.conv2d(y,
weight1,
channels=32,
kernel_size=(3, 3),
padding=(1, 1),
data_layout="NCHW16c")
ret = relay.layout_transform(y, "NCHW16c", "NCHW")
ret = relay.nn.pad(ret, pad_width=((0, 0), (1, 1), (1, 1), (1, 1)))
y = relay.Function(analysis.free_vars(ret), ret)
return y
with TempOpAttr("nn.conv2d", "FTVMAlterOpLayout", alter_conv2d):
a = before()
a = run_opt_pass(a, transform.AlterOpLayout())
b = run_opt_pass(expected(), transform.InferType())
assert tvm.ir.structural_equal(a, b), "Actual = \n" + str(a)
def test_qnn_dense_without_bias():
with TempOpAttr("qnn.dense", "FTVMQnnLegalize", legalize_qnn_dense):
int32_output_without_bias_params = \
make_int_configuration(use_bias=False)
qnn_dense_driver(int32_output_without_bias_params)
def test_alter_layout_broadcast_scalar_op():
"""Test alternating the layout of a conv2d.
The layout of broadcast operators and the weight should be changed accordingly.
"""
def before():
x = relay.var("x", shape=(1, 500, 500, 64))
kernel = relay.var('kernel', shape=(3, 3, 64, 64), dtype='float32')
bias = relay.var("bias", shape=(64, ))
multiplier1 = relay.var('multiplier1', shape=(1, ), dtype='float32')
multiplier2 = relay.var('multiplier2', shape=(1, 1), dtype='float32')
y = relay.nn.conv2d(x,
kernel,
data_layout='NHWC',
kernel_layout="HWIO",
kernel_size=(3, 3))
y = relay.add(bias, y)
y = relay.nn.relu(y)
y = relay.multiply(multiplier1, y)
y = relay.multiply(y, multiplier2)
y = relay.Function(analysis.free_vars(y), y)
return y
def alter_conv2d(attrs, inputs, tinfos, out_type):
data, weight = inputs
new_attrs = dict(attrs)
new_attrs['data_layout'] = 'NCHW16c'
return relay.nn.conv2d(data, weight, **new_attrs)
def expected():
x = relay.var("x", shape=(1, 500, 500, 64))
kernel = relay.var('kernel', shape=(3, 3, 64, 64), dtype='float32')
bias = relay.var("bias", shape=(64, ))
multiplier1 = relay.var('multiplier1', shape=(1, ), dtype='float32')
multiplier2 = relay.var('multiplier2', shape=(1, 1), dtype='float32')
b = relay.expand_dims(bias, axis=0, num_newaxis=3)
b = relay.layout_transform(b, "NHWC", "NCHW16c")
y = relay.layout_transform(x, "NHWC", "NCHW16c")
y = relay.nn.conv2d(y,
kernel,
data_layout='NCHW16c',
kernel_layout="HWIO",
kernel_size=(3, 3))
y = relay.add(b, y)
y = relay.nn.relu(y)
y = relay.multiply(multiplier1, y)
y = relay.multiply(y, multiplier2)
y = relay.layout_transform(y, "NCHW16c", "NHWC")
y = relay.Function(analysis.free_vars(y), y)
return y
with TempOpAttr("nn.conv2d", "FTVMAlterOpLayout", alter_conv2d):
a = before()
a = run_opt_pass(
a, [transform.CanonicalizeOps(),
transform.AlterOpLayout()])
b = run_opt_pass(expected(), transform.InferType())
assert analysis.alpha_equal(a, b), "Actual = \n" + str(a)
def test_qnn_dense_with_requantized_output():
with TempOpAttr("qnn.dense", "FTVMQnnLegalize", legalize_qnn_dense):
int8_requantized_output_with_bias_params = \
make_int_configuration(use_bias=True, requantize_output=True)
qnn_dense_driver(int8_requantized_output_with_bias_params)
def test_alter_layout_concatenate():
""" NCHW, NHWC and corner case concatenate layout transform."""
def alter_conv2d(attrs, inputs, tinfos, out_type):
data, weight = inputs
new_attrs = dict(attrs)
new_attrs['data_layout'] = 'NCHW16c'
return relay.nn.conv2d(data, weight, **new_attrs)
# NCHW layout transformation.
def before_nchw():
x = relay.var("x", shape=(1, 64, 56, 56))
weight1 = relay.var('weight1')
weight2 = relay.var('weight2')
y = relay.nn.conv2d(x,
weight1,
channels=32,
kernel_size=(3, 3),
padding=(1, 1))
y1 = relay.nn.conv2d(y,
weight2,
channels=32,
kernel_size=(3, 3),
padding=(1, 1))
ret = relay.concatenate([y, y1], axis=1)
y = relay.Function(analysis.free_vars(ret), ret)
return y
def expected_nchw():
x = relay.var("x", shape=(1, 64, 56, 56))
weight1 = relay.var('weight1')
weight2 = relay.var('weight2')
y = relay.layout_transform(x, "NCHW", "NCHW16c")
y = relay.nn.conv2d(y,
weight1,
channels=32,
kernel_size=(3, 3),
padding=(1, 1),
data_layout="NCHW16c")
y1 = relay.nn.conv2d(y,
weight2,
channels=32,
kernel_size=(3, 3),
padding=(1, 1),
data_layout='NCHW16c')
ret = relay.concatenate([y, y1], axis=1)
ret = relay.layout_transform(ret, "NCHW16c", "NCHW")
y = relay.Function(analysis.free_vars(ret), ret)
return y
with TempOpAttr("nn.conv2d", "FTVMAlterOpLayout", alter_conv2d):
a = before_nchw()
a = run_opt_pass(a, transform.AlterOpLayout())
b = run_opt_pass(expected_nchw(), transform.InferType())
assert analysis.alpha_equal(a, b), "Actual = \n" + str(a)
# NHWC layout transformation.
def before_nhwc():
x = relay.var("x", shape=(1, 56, 56, 64))
weight1 = relay.var('weight1')
weight2 = relay.var('weight2')
y = relay.nn.conv2d(x,
weight1,
channels=32,
kernel_size=(3, 3),
padding=(1, 1),
data_layout='NHWC')
y1 = relay.nn.conv2d(y,
weight2,
channels=32,
kernel_size=(3, 3),
padding=(1, 1),
data_layout='NHWC')
ret = relay.concatenate([y, y1], axis=3)
y = relay.Function(analysis.free_vars(ret), ret)
return y
def expected_nhwc():
x = relay.var("x", shape=(1, 56, 56, 64))
weight1 = relay.var('weight1')
weight2 = relay.var('weight2')
y = relay.layout_transform(x, "NHWC", "NCHW16c")
y = relay.nn.conv2d(y,
weight1,
channels=32,
kernel_size=(3, 3),
padding=(1, 1),
data_layout="NCHW16c")
y1 = relay.nn.conv2d(y,
weight2,
channels=32,
kernel_size=(3, 3),
padding=(1, 1),
data_layout='NCHW16c')
ret = relay.concatenate([y, y1], axis=1)
ret = relay.layout_transform(ret, "NCHW16c", "NHWC")
y = relay.Function(analysis.free_vars(ret), ret)
return y
with TempOpAttr("nn.conv2d", "FTVMAlterOpLayout", alter_conv2d):
a = before_nhwc()
a = run_opt_pass(a, transform.AlterOpLayout())
b = run_opt_pass(expected_nhwc(), transform.InferType())
assert analysis.alpha_equal(a, b), "Actual = \n" + str(a)
def test_per_channel_weight_scale():
with TempOpAttr("qnn.dense", "FTVMQnnLegalize", legalize_qnn_dense):
config = make_int_configuration(use_bias=True,
requantize_output=True,
per_channel=True)
qnn_dense_driver(config)
def test_alter_layout():
"""Test alternating the layout of a conv2d.
The layout of broadcast operators and the weight should be changed accordingly.
"""
def before():
x = relay.var("x", shape=(1, 64, 56, 56))
bias = relay.var("bias")
weight = relay.var("weight")
y = relay.nn.conv2d(x,
weight,
channels=64,
kernel_size=(3, 3),
padding=(1, 1))
y = relay.nn.bias_add(y, bias)
# a useless tuple, which will be eliminated
y = relay.Tuple([y])[0]
y = relay.nn.relu(y)
y = relay.nn.max_pool2d(y, pool_size=(2, 2))
y = relay.cast(y, 'int32')
y = relay.nn.batch_flatten(y)
y = relay.Function(analysis.free_vars(y), y)
return y
def alter_conv2d(attrs, inputs, tinfos, out_type):
data, weight = inputs
new_attrs = dict(attrs)
new_attrs['data_layout'] = 'NCHW16c'
new_attrs['kernel_layout'] = 'OIHW16i'
return relay.nn.conv2d(data, weight, **new_attrs)
def expected():
x = relay.var("x", shape=(1, 64, 56, 56))
bias = relay.var("bias", shape=(64, ))
weight = relay.var("weight", shape=(64, 64, 3, 3))
y = relay.layout_transform(x, "NCHW", "NCHW16c")
w = relay.layout_transform(weight, "OIHW", "OIHW16i")
y = relay.nn.conv2d(y,
w,
channels=64,
kernel_size=(3, 3),
padding=(1, 1),
kernel_layout="OIHW16i",
data_layout="NCHW16c")
b = relay.expand_dims(bias, axis=1, num_newaxis=2)
b = relay.expand_dims(b, axis=0, num_newaxis=1)
b = relay.layout_transform(b, "NCHW", "NCHW16c")
y = relay.add(y, b)
y = relay.nn.relu(y)
y = relay.nn.max_pool2d(y, pool_size=(2, 2), layout="NCHW16c")
y = relay.cast(y, 'int32')
y = relay.layout_transform(y, "NCHW16c", "NCHW")
y = relay.nn.batch_flatten(y)
y = relay.Function(analysis.free_vars(y), y)
return y
with TempOpAttr("nn.conv2d", "FTVMAlterOpLayout", alter_conv2d):
a = before()
a = run_opt_pass(
a, [transform.CanonicalizeOps(),
transform.AlterOpLayout()])
b = run_opt_pass(expected(), transform.InferType())
assert analysis.alpha_equal(a, b), "Actual = \n" + str(a)
def test_alter_layout_scalar_regression():
"""regression test where scalar fails"""
def before():
x = relay.var("x", shape=(1, 56, 56, 64))
weight = relay.var("weight", shape=(3, 3, 64, 16))
bias = relay.var("bias", shape=(1, 1, 1, 16))
y = relay.nn.conv2d(
x,
weight,
channels=16,
kernel_size=(3, 3),
padding=(1, 1),
data_layout="NHWC",
kernel_layout="HWIO",
)
y = relay.add(y, bias)
mean = relay.mean(y, axis=3, exclude=True)
var = relay.variance(y, axis=3, exclude=True)
gamma = relay.var("gamma")
beta = relay.var("beta")
y = relay.nn.batch_norm(y, gamma, beta, mean, var, axis=3)
y = y[0]
y = relay.Function(analysis.free_vars(y), y)
return y
def alter_conv2d(attrs, inputs, tinfos, out_type):
data, weight = inputs
new_attrs = dict(attrs)
new_attrs["data_layout"] = "NCHW16c"
return relay.nn.conv2d(data, weight, **new_attrs)
def expected():
x = relay.var("x", shape=(1, 56, 56, 64))
weight = relay.var("weight", shape=(3, 3, 64, 16))
bias = relay.var("bias", shape=(1, 1, 1, 16))
x = relay.layout_transform(x, src_layout="NHWC", dst_layout="NCHW")
x = relay.layout_transform(x, src_layout="NCHW", dst_layout="NCHW16c")
weight = relay.layout_transform(weight, src_layout="HWIO", dst_layout="OIHW")
y = relay.nn.conv2d(
x, weight, channels=16, kernel_size=(3, 3), padding=(1, 1), data_layout="NCHW16c"
)
bias = relay.layout_transform(bias, src_layout="NHWC", dst_layout="NCHW")
bias = relay.layout_transform(bias, src_layout="NCHW", dst_layout="NCHW16c")
add = relay.add(y, bias)
y = relay.layout_transform(add, src_layout="NCHW16c", dst_layout="NCHW")
y = relay.layout_transform(y, src_layout="NCHW", dst_layout="NHWC")
mean = relay.mean(y, axis=3, exclude=True)
var = relay.variance(y, axis=3, exclude=True)
denom = relay.const(1.0) / relay.sqrt(var + relay.const(1e-05))
gamma = relay.var("gamma", shape=(16,))
denom = denom * gamma
denom_expand1 = relay.expand_dims(denom, axis=1, num_newaxis=2)
denom_expand2 = relay.expand_dims(denom_expand1, axis=0)
denom_nchwc16 = relay.layout_transform(
denom_expand2, src_layout="NCHW", dst_layout="NCHW16c"
)
out = add * denom_nchwc16
beta = relay.var("beta", shape=(16,))
numerator = (-mean) * denom + beta
numerator_expand1 = relay.expand_dims(numerator, axis=1, num_newaxis=2)
numerator_expand2 = relay.expand_dims(numerator_expand1, axis=0)
numerator_nchwc16 = relay.layout_transform(
numerator_expand2, src_layout="NCHW", dst_layout="NCHW16c"
)
out = out + numerator_nchwc16
out = relay.layout_transform(out, src_layout="NCHW16c", dst_layout="NCHW")
y = relay.layout_transform(out, src_layout="NCHW", dst_layout="NHWC")
y = relay.Function(analysis.free_vars(y), y)
return y
with TempOpAttr("nn.conv2d", "FTVMAlterOpLayout", alter_conv2d):
a = before()
desired_layouts = {"nn.conv2d": ["NCHW", "default"], "nn.batch_norm": ["NHWC", "default"]}
a = run_opt_pass(
a,
[
transform.InferType(),
relay.transform.ConvertLayout(desired_layouts),
transform.SimplifyInference(),
transform.CanonicalizeOps(),
transform.AlterOpLayout(),
],
)
b = run_opt_pass(expected(), transform.InferType())
assert tvm.ir.structural_equal(a, b), "Actual = \n" + str(a)
