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import caffe2.python.fakelowp.init_shared_libs # noqa
import numpy as np
from caffe2.python import core, workspace
from caffe2.python.onnx.onnxifi import onnxifi_caffe2_net
from hypothesis import given, strategies as st, settings
from caffe2.python.fakelowp.test_utils import print_test_debug_info
import caffe2.python.serialized_test.serialized_test_util as serial
import datetime
core.GlobalInit(["caffe2",
"--caffe2_log_level=-3",
"--glow_global_fp16=1",
"--glow_clip_quant_range_to_fp16=1",
"--glow_global_fp16_constants=1"
])
class Int8OpsTest(serial.SerializedTestCase):
def _get_scale_zp(self, tensor):
tensor_max = np.max(tensor)
tensor_min = min(0, np.min(tensor))
scale = np.float32(np.float16((tensor_max - tensor_min) / 255.0))
if scale < 1e-6:
scale = np.float32(1e-6)
zero_point = 0 - tensor_min / scale
zero_point = int(round(np.clip(zero_point, 0, 255.0)))
return (scale, zero_point)
@given(
n=st.integers(2, 1024),
rand_seed=st.integers(0, 65534),
non_zero_offset=st.booleans()
)
@settings(deadline=datetime.timedelta(seconds=50))
def test_int8_quantize(self, n, rand_seed, non_zero_offset):
print("n={}, rand_seed={}".format(n, rand_seed))
np.random.seed(rand_seed)
workspace.ResetWorkspace()
if non_zero_offset:
X_fp32 = np.random.uniform(-1, 1, size=(n, n)).astype(np.float16) \
.astype(np.float32)
else:
X_fp32 = np.random.rand(n, n).astype(np.float16).astype(np.float32)
W_fp32 = np.identity(n, dtype=np.float32)
b_fp32 = np.zeros((n,), dtype=np.float32)
X_scale, X_zero_point = self._get_scale_zp(X_fp32)
workspace.FeedBlob("X", X_fp32)
workspace.FeedBlob("W", W_fp32)
workspace.FeedBlob("b", b_fp32)
workspace.RunOperatorOnce(
core.CreateOperator(
"Int8FCPackWeight",
["W"],
["W_int8"],
engine="DNNLOWP",
save_unpacked_weights=True,
in_scale=X_scale,
)
)
ref_net = core.Net("net")
ref_net.Int8QuantizeNNPI(
["X"],
["X_int8"],
Y_scale=X_scale,
Y_zero_point=X_zero_point
)
ref_net.Int8FCFakeAcc32NNPI(
["X_int8", "W_int8", "b"],
["Y_int8"],
Y_scale=X_scale,
Y_zero_point=X_zero_point,
)
ref_net.Int8DequantizeNNPI(
["Y_int8"],
["Y"]
)
ref_net.Proto().external_output.append("Y")
# run ref_net
workspace.RunNetOnce(ref_net)
Y_fbgemm = workspace.FetchBlob("Y")
# run onnxifi net
ref_net.Proto().op[0].type = "Int8Quantize"
ref_net.Proto().op[1].type = "Int8FC"
ref_net.Proto().op[2].type = "Int8Dequantize"
net_onnxified = onnxifi_caffe2_net(
ref_net.Proto(),
{},
debug=True,
adjust_batch=False,
use_onnx=False,
weight_names=["W_int8", "b"],
)
num_onnxified_ops = sum(
1 if o.type == "Onnxifi" else 0 for o in net_onnxified.op
)
np.testing.assert_equal(num_onnxified_ops, 1)
workspace.CreateNet(net_onnxified)
workspace.RunNet(net_onnxified.name)
Y_glow = workspace.FetchBlob("Y")
if not np.allclose(Y_glow, Y_fbgemm):
diff_Y = np.abs(Y_glow - Y_fbgemm)
print_test_debug_info(
"int8_fc",
{
"seed": rand_seed,
"n": n,
"X": X_fp32,
"W": W_fp32,
"b": b_fp32,
"Y_fbgemm": Y_fbgemm,
"Y_glow": Y_glow,
"diff": diff_Y,
"maxdiff": diff_Y.max(axis=1),
},
)
assert 0
@given(
n=st.integers(1, 1024),
m=st.integers(1, 1024),
k=st.integers(1, 1024),
f=st.integers(1, 1), # TODO: figure a safe number to increase
rand_seed=st.integers(0, 65534),
quantize_bias=st.sampled_from([False]),
)
@settings(deadline=datetime.timedelta(seconds=50))
def test_int8_fc(
self, n, m, k, rand_seed, quantize_bias, f
):
print(
f"n={n}, m={m}, k={k}, rand_seed={rand_seed}, quantize_bias={quantize_bias}"
)
np.random.seed(rand_seed)
workspace.ResetWorkspace()
ff = float(f)
X_fp32 = np.random.uniform(-ff, ff, size=(m, k)).astype(np.float32)
W_fp32 = np.random.uniform(-ff, ff, size=(n, k)).astype(np.float32)
b_fp32 = np.random.uniform(-ff, ff, size=(n)).astype(np.float32)
X_scale, X_zero_point = self._get_scale_zp(X_fp32)
Y_fp32 = np.dot(X_fp32, W_fp32.T) + b_fp32
Y_scale, Y_zero_point = self._get_scale_zp(Y_fp32)
workspace.FeedBlob("X", X_fp32)
workspace.FeedBlob("W", W_fp32)
workspace.FeedBlob("b", b_fp32)
workspace.RunOperatorOnce(
core.CreateOperator(
"Int8FCPackWeight",
["W", "b"] if quantize_bias else ["W"],
["W_int8", "b_int32"] if quantize_bias else ["W_int8"],
engine="DNNLOWP",
save_unpacked_weights=True,
in_scale=X_scale,
)
)
ref_net = core.Net("net")
ref_net.Int8QuantizeNNPI(
["X"],
["X_int8"],
Y_scale=X_scale,
Y_zero_point=X_zero_point
)
ref_net.Int8FCFakeAcc32NNPI(
["X_int8", "W_int8", "b_int32" if quantize_bias else "b"],
["Y_int8"],
Y_scale=Y_scale,
Y_zero_point=Y_zero_point,
)
ref_net.Int8DequantizeNNPI(
["Y_int8"],
["Y"]
)
ref_net.Proto().external_output.append("Y")
# run ref_net
workspace.RunNetOnce(ref_net)
Y_fbgemm = workspace.FetchBlob("Y")
# run onnxifi net
ref_net.Proto().op[0].type = "Int8Quantize"
ref_net.Proto().op[1].type = "Int8FC"
ref_net.Proto().op[2].type = "Int8Dequantize"
net_onnxified = onnxifi_caffe2_net(
ref_net.Proto(),
{},
debug=True,
adjust_batch=False,
use_onnx=False,
weight_names=["W_int8", "b_int32"] if quantize_bias else ["W_int8", "b"],
)
num_onnxified_ops = sum(
1 if o.type == "Onnxifi" else 0 for o in net_onnxified.op
)
np.testing.assert_equal(num_onnxified_ops, 1)
workspace.CreateNet(net_onnxified)
workspace.RunNet(net_onnxified.name)
Y_glow = workspace.FetchBlob("Y")
if not np.allclose(Y_glow, Y_fbgemm):
diff_Y = np.abs(Y_glow - Y_fbgemm)
print_test_debug_info(
"int8_fc",
{
"seed": rand_seed,
"n": n,
"m": m,
"k": k,
"X": X_fp32,
"W": W_fp32,
"b": b_fp32,
"Y_fbgemm": Y_fbgemm,
"Y_glow": Y_glow,
"diff": diff_Y,
"maxdiff": diff_Y.max(axis=1),
},
)
assert 0
@given(
n=st.integers(1, 4),
rand_seed=st.integers(0, 65534)
)
@settings(deadline=datetime.timedelta(seconds=10))
def test_int8_small_input(self, n, rand_seed):
print("n={}, rand_seed={}".format(n, rand_seed))
np.random.seed(rand_seed)
workspace.ResetWorkspace()
X_fp32 = np.random.uniform(0.01, 0.03, size=(n, n)).astype(np.float32)
W_fp32 = np.identity(n, dtype=np.float32)
b_fp32 = np.zeros((n,), dtype=np.float32)
X_scale, X_zero_point = self._get_scale_zp(X_fp32)
workspace.FeedBlob("X", X_fp32)
workspace.FeedBlob("W", W_fp32)
workspace.FeedBlob("b", b_fp32)
workspace.RunOperatorOnce(
core.CreateOperator(
"Int8FCPackWeight",
["W"],
["W_int8"],
engine="DNNLOWP",
save_unpacked_weights=True,
in_scale=X_scale,
)
)
ref_net = core.Net("net")
ref_net.Int8QuantizeNNPI(
["X"],
["X_int8"],
Y_scale=X_scale,
Y_zero_point=X_zero_point
)
ref_net.Int8FCFakeAcc32NNPI(
["X_int8", "W_int8", "b"],
["Y_int8"],
Y_scale=X_scale,
Y_zero_point=X_zero_point,
)
ref_net.Int8DequantizeNNPI(
["Y_int8"],
["Y"]
)
ref_net.Proto().external_output.append("Y")
# run ref_net
workspace.RunNetOnce(ref_net)
Y_fbgemm = workspace.FetchBlob("Y")
# run onnxifi net
ref_net.Proto().op[0].type = "Int8Quantize"
ref_net.Proto().op[1].type = "Int8FC"
ref_net.Proto().op[2].type = "Int8Dequantize"
net_onnxified = onnxifi_caffe2_net(
ref_net.Proto(),
{},
debug=True,
adjust_batch=False,
use_onnx=False,
weight_names=["W_int8", "b"],
)
num_onnxified_ops = sum(
1 if o.type == "Onnxifi" else 0 for o in net_onnxified.op
)
np.testing.assert_equal(num_onnxified_ops, 1)
workspace.CreateNet(net_onnxified)
workspace.RunNet(net_onnxified.name)
Y_glow = workspace.FetchBlob("Y")
if not np.allclose(Y_glow, Y_fbgemm):
diff_Y = np.abs(Y_glow - Y_fbgemm)
print_test_debug_info(
"int8_fc",
{
"seed": rand_seed,
"n": n,
"X": X_fp32,
"W": W_fp32,
"b": b_fp32,
"Y_fbgemm": Y_fbgemm,
"Y_glow": Y_glow,
"diff": diff_Y,
"maxdiff": diff_Y.max(axis=1),
},
)
assert 0
|
