def test_quant_stub(): normal_net = Float.QuantStub() normal_net.eval() qat_from_float = QAT.QuantStub.from_float_module(normal_net) qat_from_float.eval() disable_observer(qat_from_float) disable_fake_quant(qat_from_float) qat_net = QAT.QuantStub() qat_net.eval() disable_observer(qat_net) propagate_qconfig(qat_net, min_max_fakequant_qconfig) init_qat_net(qat_net) q_net = Q.QuantStub.from_qat_module(qat_net) q_net.eval() x = mge.tensor(np.random.normal(size=(3, 3)).astype("float32")) normal = normal_net(x) qat_without_fakequant = qat_from_float(x) fake_quant_normal = fake_quant_act(normal_net(x), act_scale) qat = qat_net(x) q = q_net(x).numpy() * act_scale np.testing.assert_allclose(qat_without_fakequant, normal) np.testing.assert_allclose(qat, fake_quant_normal) np.testing.assert_allclose(q, fake_quant_normal.numpy())
def test_linear(): normal_net = Float.Linear(3, 3, bias=True) normal_net.eval() qat_net = QAT.Linear(3, 3, bias=True) qat_net.eval() disable_observer(qat_net) propagate_qconfig(qat_net, min_max_fakequant_qconfig) init_qat_net(qat_net) x = mge.tensor(np.random.normal(size=(3, 3)).astype("float32")) x = fake_quant(x, inp_scale) x.q_dict["scale"] = inp_scale x_int8 = quant(x, inp_scale) weight = np.random.normal(size=(3, 3)).astype("float32") bias = np.random.normal(size=(3,)).astype("float32") normal_net.weight.set_value(fake_quant(weight, weight_scale)) normal_net.bias.set_value(fake_quant(bias, inp_scale * weight_scale)) qat_net.weight.set_value(weight) qat_net.bias.set_value(bias) q_net = Q.Linear.from_qat_module(qat_net) q_net.eval() normal_out = fake_quant(normal_net(x), act_scale) qat_out = qat_net(x) q_out = q_net(x_int8).numpy() * act_scale np.testing.assert_allclose(qat_out, normal_out) np.testing.assert_allclose(q_out, normal_out.numpy())
def test_enable_and_disable_observer(): net = init_qat_net() enable_observer(net) assert net.quant.act_observer.enabled == True assert net.linear.weight_observer.enabled == True assert net.linear.act_observer.enabled == True disable_observer(net) assert net.quant.act_observer.enabled == False assert net.linear.weight_observer.enabled == False assert net.linear.act_observer.enabled == False
def test_elemwise(kind): normal_net = Float.Elemwise(kind) normal_net.eval() qat_from_float = QAT.Elemwise.from_float_module(normal_net) qat_from_float.eval() disable_observer(qat_from_float) disable_fake_quant(qat_from_float) qat_net = QAT.Elemwise(kind) qat_net.eval() disable_observer(qat_net) propagate_qconfig(qat_net, min_max_fakequant_qconfig) init_qat_net(qat_net) q_net = Q.Elemwise.from_qat_module(qat_net) q_net.eval() x1_scale = np.float32(np.random.rand() + 1) x1 = mge.tensor(np.random.normal(size=(3, 3)).astype("float32")) x1 = fake_quant_act(x1, x1_scale) x1.qparams.scale = x1_scale x2_scale = np.float32(np.random.rand() + 1) x2 = mge.tensor(np.random.normal(size=(3, 3)).astype("float32")) x2 = fake_quant_act(x2, x2_scale) x2.qparams.scale = x2_scale x1_int8 = quant(x1, x1_scale) x2_int8 = quant(x2, x2_scale) # test correctness of `Float`, `QAT` and `Quantized` if kind in ("add", "mul", "fuse_add_relu"): normal = normal_net(x1, x2) qat_without_fakequant = qat_from_float(x1, x2) fake_quant_normal = fake_quant_act(normal_net(x1, x2), act_scale) qat = qat_net(x1, x2) q = q_net(x1_int8, x2_int8).numpy() * act_scale else: normal = normal_net(x1) qat_without_fakequant = qat_from_float(x1) fake_quant_normal = fake_quant_act(normal_net(x1), act_scale) qat = qat_net(x1) q = q_net(x1_int8).numpy() * act_scale np.testing.assert_allclose(qat_without_fakequant, normal) np.testing.assert_allclose(qat, fake_quant_normal) np.testing.assert_allclose(q, fake_quant_normal.numpy())
def test_conv(module): normal_net = getattr(Float, module)(3, 3, 3, 1, 1, 1, bias=True) normal_net.eval() qat_net = getattr(QAT, module)(3, 3, 3, 1, 1, 1, bias=True) qat_net.eval() disable_observer(qat_net) propagate_qconfig(qat_net, min_max_fakequant_qconfig) init_qat_net(qat_net) x = mge.tensor(np.random.normal(size=(1, 3, 3, 3)).astype("float32")) inp_scale = gen_inp_scale() x = fake_quant_act(x, inp_scale) x.qparams.update(create_qparams(QuantMode.SYMMERTIC, "qint8", inp_scale)) x_int8 = quant(x, inp_scale) weight = np.random.normal(size=(3, 3, 3, 3)).astype("float32") bias = np.random.normal(size=(1, 3, 1, 1)).astype("float32") if module in ("ConvBn2d", "ConvBnRelu2d"): normal_net.conv.weight[...] = fake_quant_weight(weight, weight_scale) normal_net.conv.bias[...] = fake_quant_bias(bias, inp_scale * weight_scale) qat_net.conv.weight[...] = Parameter(weight) qat_net.conv.bias[...] = Parameter(bias) else: normal_net.weight[...] = fake_quant_weight(weight, weight_scale) normal_net.bias[...] = fake_quant_bias(bias, inp_scale * weight_scale) qat_net.weight[...] = Parameter(weight) qat_net.bias[...] = Parameter(bias) qat_from_float = getattr(QAT, module).from_float_module(normal_net) qat_from_float.eval() disable_observer(qat_from_float) disable_fake_quant(qat_from_float) q_net = getattr(Q, module).from_qat_module(qat_net) q_net.eval() normal = normal_net(x) qat_without_fakequant = qat_from_float(x) fake_quant_normal = fake_quant_act(normal_net(x), act_scale) qat = qat_net(x) q = q_net(x_int8).numpy() * act_scale np.testing.assert_allclose(qat_without_fakequant, normal, atol=1e-5) np.testing.assert_allclose(qat, fake_quant_normal, atol=act_scale) np.testing.assert_allclose(q, fake_quant_normal.numpy(), atol=act_scale)
def test_conv(module): normal_net = getattr(Float, module)(3, 3, 3, 1, 1, 1, bias=True) normal_net.eval() qat_net = getattr(QAT, module)(3, 3, 3, 1, 1, 1, bias=True) qat_net.eval() disable_observer(qat_net) propagate_qconfig(qat_net, min_max_fakequant_qconfig) init_qat_net(qat_net) x = mge.tensor(np.random.normal(size=(1, 3, 3, 3)).astype("float32")) x = fake_quant(x, inp_scale) x.q_dict["scale"] = inp_scale x_int8 = quant(x, inp_scale) weight = np.random.normal(size=(3, 3, 3, 3)).astype("float32") bias = np.random.normal(size=(1, 3, 1, 1)).astype("float32") if module in ("ConvBn2d", "ConvBnRelu2d"): normal_net.conv.weight.set_value(fake_quant(weight, weight_scale)) normal_net.conv.bias.set_value( fake_quant(bias, inp_scale * weight_scale)) qat_net.conv.weight.set_value(weight) qat_net.conv.bias.set_value(bias) else: normal_net.weight.set_value(fake_quant(weight, weight_scale)) normal_net.bias.set_value(fake_quant(bias, inp_scale * weight_scale)) qat_net.weight.set_value(weight) qat_net.bias.set_value(bias) qat_from_float = getattr(QAT, module).from_float_module(normal_net) qat_from_float.eval() disable_observer(qat_from_float) disable_fake_quant(qat_from_float) q_net = getattr(Q, module).from_qat_module(qat_net) q_net.eval() normal = normal_net(x) qat_without_fakequant = qat_from_float(x) fake_quant_normal = fake_quant(normal_net(x), act_scale) qat = qat_net(x) q = q_net(x_int8).numpy() * act_scale np.testing.assert_allclose(qat_without_fakequant, normal, atol=1e-6) np.testing.assert_allclose(qat, fake_quant_normal) np.testing.assert_allclose(q, fake_quant_normal.numpy())
def test_linear(): normal_net = Float.Linear(3, 3, bias=True) normal_net.eval() qat_net = QAT.Linear(3, 3, bias=True) qat_net.eval() disable_observer(qat_net) propagate_qconfig(qat_net, min_max_fakequant_qconfig) init_qat_net(qat_net) x = mge.tensor(np.random.normal(size=(3, 3)).astype("float32")) inp_scale = gen_inp_scale() x = fake_quant_act(x, inp_scale) x.qparams.update(create_qparams(QuantMode.SYMMERTIC, "qint8", inp_scale)) x_int8 = quant(x, inp_scale) weight = np.random.normal(size=(3, 3)).astype("float32") bias = np.random.normal(size=(3, )).astype("float32") normal_net.weight[...] = fake_quant_weight(weight, weight_scale) normal_net.bias[...] = fake_quant_bias(bias, inp_scale * weight_scale) qat_net.weight[...] = Parameter(weight) qat_net.bias[...] = Parameter(bias) qat_from_float = QAT.Linear.from_float_module(normal_net) qat_from_float.eval() disable_fake_quant(qat_from_float) disable_observer(qat_from_float) q_net = Q.Linear.from_qat_module(qat_net) q_net.eval() normal = normal_net(x) qat_without_fakequant = qat_from_float(x) fake_quant_normal = fake_quant_act(normal_net(x), act_scale) qat = qat_net(x) q = q_net(x_int8).numpy() * act_scale np.testing.assert_allclose(qat_without_fakequant, normal) np.testing.assert_allclose(qat, fake_quant_normal.numpy()) np.testing.assert_allclose(q, fake_quant_normal.numpy())
def test_dequant_stub(): normal_net = Float.DequantStub() normal_net.eval() qat_net = QAT.DequantStub() qat_net.eval() disable_observer(qat_net) propagate_qconfig(qat_net, min_max_fakequant_qconfig) init_qat_net(qat_net) q_net = Q.DequantStub.from_qat_module(qat_net) q_net.eval() x = mge.tensor(np.random.normal(size=(3, 3)).astype("float32")) x = fake_quant(x, inp_scale) x.q_dict["scale"] = inp_scale normal_out = normal_net(x) qat_out = qat_net(x) q_out = q_net(quant(x, inp_scale)).numpy() np.testing.assert_allclose(qat_out, normal_out) np.testing.assert_allclose(q_out, normal_out.numpy())
def test_concat(): normal_net = Float.Concat() normal_net.eval() qat_net = QAT.Concat() qat_net.eval() disable_observer(qat_net) propagate_qconfig(qat_net, min_max_fakequant_qconfig) init_qat_net(qat_net) inps = [] inps_int8 = [] for i in range(3): inp_scale = gen_inp_scale() inps.append(mge.tensor( np.random.normal(size=(3, 3)).astype("float32"))) inps[i] = fake_quant_act(inps[i], inp_scale) inps[i].qparams.update( create_qparams(QuantMode.SYMMERTIC, "qint8", inp_scale)) inps_int8.append(quant(inps[i], inp_scale)) qat_from_float = QAT.Concat.from_float_module(normal_net) qat_from_float.eval() disable_fake_quant(qat_from_float) disable_observer(qat_from_float) q_net = Q.Concat.from_qat_module(qat_net) q_net.eval() normal = normal_net(inps) qat_without_fakequant = qat_from_float(inps) fake_quant_normal = fake_quant_act(normal_net(inps), act_scale) qat = qat_net(inps) q = q_net(inps_int8).numpy() * act_scale np.testing.assert_allclose(qat_without_fakequant, normal) np.testing.assert_allclose(qat, fake_quant_normal.numpy()) np.testing.assert_allclose(q, fake_quant_normal.numpy())
def test_elemwise(kind): normal_net = Float.Elemwise(kind) normal_net.eval() qat_net = QAT.Elemwise(kind) qat_net.eval() disable_observer(qat_net) propagate_qconfig(qat_net, min_max_fakequant_qconfig) init_qat_net(qat_net) q_net = Q.Elemwise.from_qat_module(qat_net) q_net.eval() x1_scale = np.float32(np.random.rand() + 1) x1 = mge.tensor(np.random.normal(size=(3, 3)).astype("float32")) x1 = fake_quant(x1, x1_scale) x1.q_dict["scale"] = x1_scale x2_scale = np.float32(np.random.rand() + 1) x2 = mge.tensor(np.random.normal(size=(3, 3)).astype("float32")) x2 = fake_quant(x2, x2_scale) x2.q_dict["scale"] = x2_scale x1_int8 = quant(x1, x1_scale) x2_int8 = quant(x2, x2_scale) if kind in ("ADD", "MUL", "FUSE_ADD_RELU"): normal_out = fake_quant(normal_net(x1, x2), act_scale) qat_out = qat_net(x1, x2) q_out = q_net(x1_int8, x2_int8).numpy() * act_scale else: normal_out = fake_quant(normal_net(x1), act_scale) qat_out = qat_net(x1) q_out = q_net(x1_int8).numpy() * act_scale np.testing.assert_allclose(qat_out, normal_out) np.testing.assert_allclose(q_out, normal_out.numpy())
def init_observer(module, data): enable_observer(module) disable_fake_quant(module) module(data) disable_observer(module) enable_fake_quant(module)