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()