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|
import numpy as np
import unittest
import caffe2.python.fakelowp.init_shared_libs # noqa
from hypothesis import given, settings
from hypothesis import strategies as st
from caffe2.proto import caffe2_pb2
from caffe2.python import core
from caffe2.python import workspace
from caffe2.python.onnx.onnxifi import onnxifi_caffe2_net
from caffe2.python.fakelowp.test_utils import print_test_debug_info
import datetime
import caffe2.python.serialized_test.serialized_test_util as serial
core.GlobalInit(["caffe2", "--caffe2_log_level=-3", "--glow_global_fp16=1"])
GLOW_MATMUL_RTOL = 0
class FCTest(serial.SerializedTestCase):
@given(seed=st.integers(0, 65534))
@settings(deadline=datetime.timedelta(seconds=10))
def test_clip(self, seed):
np.random.seed(seed)
m, n, k = 8, 8, 8
dtype = np.float32
pred_net = caffe2_pb2.NetDef()
pred_net.name = "pred"
pred_net.external_input.extend(["X", "W0", "b0", "W1", "b1"])
pred_net.external_output.append("Y")
pred_net.op.add().CopyFrom(
core.CreateOperator(
"FC",
["X", "W0", "b0"],
["X1"],
)
)
pred_net.op.add().CopyFrom(
core.CreateOperator(
"FC",
["X1", "W1", "b1"],
["Y"],
)
)
workspace.GlobalInit(
['caffe2', '--caffe2_log_level=0', '--glow_global_fp16=1',
'--glow_clip_fp16', '--glow_global_fp16_constants=1'])
workspace.SwitchWorkspace("glow_test_ws", True)
workspace.ResetWorkspace()
W0 = np.full((n, k), 65536.0, dtype)
b0 = np.random.randint(low=1, high=3, size=(n)).astype(dtype)
W1 = np.random.randint(low=1, high=3, size=(n, k)).astype(dtype)
b1 = np.random.randint(low=1, high=3, size=(n)).astype(dtype)
workspace.FeedBlob("W0", W0)
workspace.FeedBlob("b0", b0)
workspace.FeedBlob("W1", W1)
workspace.FeedBlob("b1", b1)
pred_net_onnxified = onnxifi_caffe2_net(
pred_net,
{"X": (m, k)},
debug=True,
adjust_batch=False,
use_onnx=False
)
X = np.random.randint(low=1, high=3, size=(m, k)).astype(dtype)
workspace.FeedBlob("X", X)
workspace.CreateNet(pred_net_onnxified)
workspace.RunNet(pred_net_onnxified.name)
Y_glow = workspace.FetchBlob("Y")
np.testing.assert_allclose(Y_glow, np.full((m, n), 65504.0, dtype))
@given(
m=st.integers(4, 50),
k=st.integers(4, 50),
n=st.integers(4, 50),
seed=st.integers(0, 65534)
)
@settings(deadline=datetime.timedelta(seconds=10))
def test_fc_exercise(self, m, k, n, seed):
""" Test that the matmul engine is working, this doesn't test
precision
"""
np.random.seed(seed)
dtype = np.float32
pred_net = caffe2_pb2.NetDef()
pred_net.name = "pred"
pred_net.external_input.extend(["X", "W0", "b0"])
pred_net.external_output.append("Y")
pred_net.op.add().CopyFrom(
core.CreateOperator(
"FC",
["X", "W0", "b0"],
["Y"],
)
)
workspace.SwitchWorkspace("glow_test_ws", True)
workspace.ResetWorkspace()
W0 = np.random.randint(low=1, high=3, size=(n, k)).astype(dtype)
b0 = np.random.randint(low=1, high=3, size=(n)).astype(dtype)
workspace.FeedBlob("W0", W0)
workspace.FeedBlob("b0", b0)
pred_net_onnxified = onnxifi_caffe2_net(pred_net,
{"X": (m, k)},
debug=True,
adjust_batch=False,
use_onnx=False)
num_onnxified_ops = sum(
1 if o.type == "Onnxifi" else 0 for o in pred_net_onnxified.op)
np.testing.assert_equal(num_onnxified_ops, 1)
X0 = np.random.randint(low=1, high=3, size=(m, k)).astype(dtype)
workspace.FeedBlob("X", X0)
workspace.CreateNet(pred_net_onnxified)
workspace.CreateNet(pred_net)
num_iterations = 2
for _ in range(num_iterations):
X0 = np.random.randint(low=1, high=3, size=(m, k)).astype(dtype)
workspace.FeedBlob("X", X0)
# Run Glow net
workspace.RunNet(pred_net_onnxified.name)
Y_glow = workspace.FetchBlob('Y')
# Run caffe2 net
workspace.RunNet(pred_net.name)
Y_c2 = workspace.FetchBlob('Y')
if not np.allclose(Y_c2, Y_glow):
print_test_debug_info("fc", {
"seed": seed,
"m": m,
"k": k,
"n": n,
"X": X0,
"W0": W0,
"b0": b0,
"Y_glow": Y_glow,
"Y_c2": Y_c2,
"diff": np.abs((Y_c2 - Y_glow) / Y_c2)})
assert(0)
@given(seed=st.integers(0, 65534))
@settings(deadline=datetime.timedelta(seconds=10))
def test_fc_numeric_cases(self, seed):
""" Test numerics, use examples found from the unit test.
Use Fp16FCAcc16NNPI as a reference.
"""
np.random.seed(seed)
m = 1
k = 20
n = 1
dtype = np.float32
pred_net = caffe2_pb2.NetDef()
pred_net.name = "pred"
pred_net.external_input.extend(["X", "W0", "b0"])
pred_net.external_output.append("Y")
pred_net.op.add().CopyFrom(
core.CreateOperator(
"FC",
["X", "W0", "b0"],
["Y"],
)
)
pred_net_ref = caffe2_pb2.NetDef()
pred_net_ref.name = "pred"
pred_net_ref.external_input.extend(["X", "W0", "b0"])
pred_net_ref.external_output.append("Y")
pred_net_ref.op.add().CopyFrom(
core.CreateOperator(
"Fp16FCAcc32NNPI",
["X", "W0", "b0"],
["Y"],
)
)
workspace.SwitchWorkspace("glow_test_ws", True)
workspace.ResetWorkspace()
W0 = np.array([[0.04882812, 0.21520996, 0.1027832, 0.04489136,
-0.07635498, 0.14587402,
-0.06240845, 0.3918457, 0.46362305, -0.11657715,
0.29174805, 0.02890015,
0.0680542, 0.4255371, -0.42895508, -0.4128418,
-0.47973633, 0.33251953,
0.27807617, 0.3701172]], dtype=np.float32)
b0 = np.array([0.47851562], dtype=np.float32)
workspace.FeedBlob("W0", W0)
workspace.FeedBlob("b0", b0)
pred_net_onnxified = onnxifi_caffe2_net(pred_net,
{"X": (m, k)},
debug=True,
adjust_batch=False,
use_onnx=False)
num_onnxified_ops = sum(
1 if o.type == "Onnxifi" else 0 for o in pred_net_onnxified.op)
np.testing.assert_equal(num_onnxified_ops, 1)
X_inputs = [
np.array([[
-2.94921875e-01, -3.58642578e-01, -1.92871094e-01,
2.81250000e-01, -1.30126953e-01, 2.32696533e-02,
-4.55566406e-01, -2.31811523e-01, -1.95190430e-01,
-7.76977539e-02, -1.29394531e-01, 2.94677734e-01,
8.96453857e-04, 4.97314453e-01, -6.07604980e-02,
2.55371094e-01, 3.49853516e-01, -1.37695312e-01,
2.95410156e-01, -3.67187500e-01]], dtype=np.float32),
np.array([[
-0.4494629, -0.22192383, -0.1640625, 0.11480713,
-0.09851074, -0.02084351,
0.19091797, -0.17468262, -0.47485352, 0.07489014,
0.03897095, 0.00197601,
0.02835083, -0.27294922, 0.26757812, -0.20996094,
-0.31103516, -0.41601562,
0.09918213, -0.07696533]], dtype=np.float32),
np.array([[
0.01150513, -0.20507812, 0.46704102, 0.00906372,
0.19848633, 0.3720703,
0.46557617, -0.47436523, -0.35107422, -0.0362854,
-0.20812988, 0.41918945,
0.09716797, 0.19897461, 0.3876953, -0.0165863,
0.23535156, 0.29956055,
0.24389648, -0.23486328]], dtype=np.float32)
]
# keep onnxifi happy by feeding something with a shape
workspace.FeedBlob("X", X_inputs[0])
workspace.CreateNet(pred_net_onnxified)
workspace.CreateNet(pred_net_ref)
for i in range(len(X_inputs)):
workspace.FeedBlob("X", X_inputs[i])
# Run Glow net
workspace.RunNet(pred_net_onnxified.name)
Y_glow = workspace.FetchBlob('Y')
workspace.RunNet(pred_net_ref.name)
Y_c2 = workspace.FetchBlob('Y')
diff = np.abs((Y_c2 - Y_glow) / (Y_c2 + 1e-8))
rowdiff = np.max(diff, axis=1)
n_offenders = np.count_nonzero(rowdiff[rowdiff > GLOW_MATMUL_RTOL])
if n_offenders > 0:
print_test_debug_info("fc", {
"seed": seed,
"iter": i,
"m": m,
"k": k,
"n": n,
"W0": W0,
"b0": b0,
"Y_glow": Y_glow,
"Y_c2": Y_c2,
"diff": diff,
"rowdiff": rowdiff})
assert(0)
@given(
m=st.integers(1, 50),
k=st.integers(1, 1000),
n=st.integers(1, 50),
seed=st.integers(0, 65534),
use_packed=st.integers(0, 2)
)
@settings(deadline=datetime.timedelta(seconds=10))
def test_fc_num0(self, seed, m, k, n, use_packed):
""" Test numerics, fix a dimension and determine the ranges of error.
Use Fp16FCAcc16 as a reference.
"""
W = "W_packed" if use_packed else "W0"
dtype = np.float32
pred_net = caffe2_pb2.NetDef()
pred_net.name = "pred"
pred_net.external_input.extend(["X", W, "b0"])
pred_net.external_output.append("Y")
pred_net.op.add().CopyFrom(
core.CreateOperator(
"FbFCPacked" if use_packed else "FC",
["X", W, "b0"],
["Y"],
)
)
pred_net_ref = caffe2_pb2.NetDef()
pred_net_ref.name = "pred"
pred_net_ref.external_input.extend(["X", W, "b0"])
pred_net_ref.external_output.append("Y")
pred_net_ref.op.add().CopyFrom(
core.CreateOperator(
"Fp16FCAcc32NNPI",
["X", W, "b0"],
["Y"],
)
)
workspace.SwitchWorkspace("glow_test_ws", True)
workspace.ResetWorkspace()
W0 = 10 * (np.random.rand(n, k) - 0.5).astype(np.float16).astype(np.float32)
b0 = 1 * (np.random.rand(n) - 0.5).astype(np.float16).astype(np.float32)
workspace.FeedBlob("W0", W0)
workspace.FeedBlob("b0", b0)
workspace.RunOperatorOnce(
core.CreateOperator(
"FbGemmPack",
['W0'],
['W_packed'],
no_packing=True,
)
)
pred_net_onnxified = onnxifi_caffe2_net(pred_net,
{"X": (m, k)},
debug=True,
adjust_batch=False,
use_onnx=False)
num_onnxified_ops = sum(
1 if o.type == "Onnxifi" else 0 for o in pred_net_onnxified.op)
np.testing.assert_equal(num_onnxified_ops, 1)
X0 = np.random.rand(m, k).astype(dtype) - 0.5
workspace.FeedBlob("X", X0)
workspace.CreateNet(pred_net_onnxified)
workspace.CreateNet(pred_net_ref)
workspace.RunNet(pred_net_onnxified.name)
Y_glow = workspace.FetchBlob('Y')
# Run caffe2 net
workspace.RunNet(pred_net_ref.name)
Y_c2 = workspace.FetchBlob('Y')
diff = np.abs((Y_c2 - Y_glow) / (Y_c2 + 1e-8))
rowdiff = np.max(diff, axis=1)
n_offenders = np.count_nonzero(rowdiff[rowdiff > GLOW_MATMUL_RTOL])
if n_offenders > 0:
print_test_debug_info("fc", {
"seed": seed,
"use_packed": use_packed,
"m": m,
"k": k,
"n": n,
"X": X0.shape,
"W0": W0.shape,
"b0": b0.shape,
"Y_glow": Y_glow,
"Y_c2": Y_c2,
"diff": diff,
"rowdiff": rowdiff})
assert(0)
if __name__ == '__main__':
unittest.main()
|
