!7586 add pynative st test_case

Merge pull request !7586 from lvchangquan/transdata

tests/st/pynative/test_pynative_hook_grad.py

@@ -0,0 +1,446 @@
+# Copyright 2020 Huawei Technologies Co., Ltd
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ============================================================================
+""" test_pynative_hook_grad """
+import numpy as np
+import pytest
+import mindspore.nn as nn
+import mindspore.ops.operations as P
+from mindspore.nn import Cell
+from mindspore import context
+from mindspore.common.tensor import Tensor
+from mindspore.ops.composite import GradOperation
+from mindspore.common import ParameterTuple
+
+def setup_module():
+    context.set_context(mode=context.PYNATIVE_MODE, device_target="Ascend")
+
+class MetaFactory:
+    def __init__(self):
+        self.device_target = context.get_context('device_target')
+        self.rank_size = None
+        self.device_id = None
+        self.global_rank_id = None
+
+class HookBase(MetaFactory):
+    def __init__(self):
+        super().__init__()
+        MetaFactory.__init__(self)
+        self.grad_input_list = []
+        self.grad_output_list = []
+
+    def ms_record_hook(self, cell_id, grad_input, grad_output):
+        for grad in grad_input:
+            self.grad_input_list.append(grad)
+        for grad in grad_output:
+            self.grad_output_list.append(grad)
+
+    def ms_change_grad_double_hook(self, cell_id, grad_input, grad_output):
+        y = Tensor(np.array([2.0]).astype(np.float32))
+        mul = P.Mul()
+        grad = grad_output[0]
+        output = mul(grad, y)
+        return output
+
+class FinalNet(nn.Cell, HookBase):
+    def __init__(self):
+        super().__init__()
+        HookBase.__init__(self)
+        self.conv = nn.Conv2d(1, 3, 3)
+        self.relu = nn.ReLU()
+
+    def construct(self, x, flag):
+        if flag:
+            x = self.conv(x)
+        else:
+            x = self.relu(x)
+        return self.relu(x)
+
+class _Grad(Cell):
+    def __init__(self, grad, network, wrt_params=False, real_inputs_count=None):
+        super().__init__()
+        self.network = network
+        self.grad = grad
+        self.sens_param = self.grad.sens_param
+        self.wrt_params = wrt_params
+        self.real_inputs_count = real_inputs_count
+        if self.wrt_params:
+            self.params = ParameterTuple(self.network.trainable_params())
+
+    def construct(self, *inputs):
+        if self.wrt_params:
+            if self.real_inputs_count is None or self.sens_param is False:
+                return self.grad(self.network, self.params)(*inputs)
+            real_inputs = inputs[:self.real_inputs_count]
+            sense_param_inputs = inputs[self.real_inputs_count:]
+            return self.grad(self.network, self.params)(*real_inputs, sense_param_inputs)
+        if self.real_inputs_count is None or self.sens_param is False:
+            return self.grad(self.network)(*inputs)
+        real_inputs = inputs[:self.real_inputs_count]
+        sense_param_inputs = inputs[self.real_inputs_count:]
+        return self.grad(self.network)(*real_inputs, sense_param_inputs)
+
+class GradOfAllInputs(_Grad):
+    def __init__(self, network, sens_param=True, real_inputs_count=None):
+        super().__init__(grad=GradOperation(get_all=True, sens_param=sens_param),
+                         network=network, real_inputs_count=real_inputs_count)
+
+class MsMul4(nn.Cell):
+    def construct(self, input_mul):
+        out = input_mul * 2
+        return out
+
+class MsMul(nn.Cell):
+    def __init__(self):
+        super().__init__()
+        self.mul = P.Mul()
+
+    def construct(self, x, y):
+        x = self.mul(x, y)
+        return x
+
+class MsAdd4(nn.Cell):
+    def construct(self, input_add):
+        out = input_add + 4
+        return out
+
+class MsOneInputNet(nn.Cell, HookBase):
+    def __init__(self):
+        super().__init__()
+        HookBase.__init__(self)
+        self.add = MsAdd4()
+        self.mul = MsMul4()
+        self.relu = nn.ReLU()
+
+    def construct(self, x):
+        x = self.add(x)
+        x = self.mul(x)
+        out = self.relu(x)
+        return out
+
+class MsMultiInputNet(nn.Cell, HookBase):
+    def __init__(self):
+        super().__init__()
+        HookBase.__init__(self)
+        self.mul1 = MsMul()
+        self.mul2 = MsMul4()
+    def construct(self, x, y):
+        a = self.mul1(x, y)
+        b = self.mul2(x)
+        output = self.mul1(a, b)
+        return output
+
+class MsNetWithParameter(nn.Cell, HookBase):
+    def __init__(self):
+        super().__init__()
+        HookBase.__init__(self)
+        self.conv1 = nn.Conv2d(2, 4, kernel_size=(1, 1), has_bias=True,
+                               weight_init=Tensor(np.ones([4, 2, 1, 1]).astype(np.float32)),
+                               bias_init=Tensor(np.ones([4]).astype(np.float32)))
+        self.conv2 = nn.Conv2d(4, 8, kernel_size=(1, 1), has_bias=True,
+                               weight_init=Tensor(np.ones([8, 4, 1, 1]).astype(np.float32)),
+                               bias_init=Tensor(np.ones([8]).astype(np.float32)))
+
+    def construct(self, x):
+        x = self.conv1(x)
+        output = self.conv2(x)
+        return output
+
+class MsNetWithCellinCell(nn.Cell, HookBase):
+    def __init__(self):
+        super().__init__()
+        HookBase.__init__(self)
+        self.net1 = MsOneInputNet()
+        self.mul = MsMul4()
+
+    def construct(self, x):
+        x = self.net1(x)
+        output = self.mul(x)
+        return output
+
+class MsSingleOpNetWithBprop(nn.Cell, HookBase):
+    def __init__(self):
+        super().__init__()
+        HookBase.__init__(self)
+        self.op = nn.ReLU()
+
+    def construct(self, x):
+        return self.op(x)
+
+    def bprop(self, x, out, dout):
+        y = Tensor(np.array([5.0]).astype(np.float32))
+        mul = P.Mul()
+        return mul(x, y)
+
+class MsNetHasBpropInChild(nn.Cell, HookBase):
+    def __init__(self):
+        super().__init__()
+        HookBase.__init__(self)
+        self.add = MsAdd4()
+        self.bprop_net = MsSingleOpNetWithBprop()
+
+    def construct(self, x):
+        x = self.add(x)
+        return self.bprop_net(x)
+
+class MsMultiOpNetWithBprop(nn.Cell, HookBase):
+    def __init__(self):
+        super().__init__()
+        HookBase.__init__(self)
+        self.mul = MsMul4()
+        self.relu = nn.ReLU()
+
+    def construct(self, x):
+        x = self.mul(x)
+        return self.relu(x)
+
+    def bprop(self, x, out, dout):
+        y = Tensor(np.array([5.0]).astype(np.float32))
+        mul = P.Mul()
+        return mul(x, y)
+
+def _count_unequal_element(data_expected, data_me, rtol, atol):
+    assert data_expected.shape == data_me.shape
+    total_count = len(data_expected.flatten())
+    error = np.abs(data_expected - data_me)
+    greater = np.greater(error, atol + np.abs(data_me)*rtol)
+    loss_count = np.count_nonzero(greater)
+    assert (loss_count/total_count) < rtol,\
+        "\ndata_expected_std:{0}\ndata_me_error:{1}\nloss:{2}".\
+        format(data_expected[greater], data_me[greater], error[greater])
+
+def allclose_nparray(data_expected, data_me, rtol, atol, equal_nan=True):
+    if np.any(np.isnan(data_expected)):
+        assert np.allclose(data_expected, data_me, rtol, atol, equal_nan=equal_nan)
+    elif not np.allclose(data_expected, data_me, rtol, atol, equal_nan=equal_nan):
+        _count_unequal_element(data_expected, data_me, rtol, atol)
+    else:
+        assert True
+
+@pytest.mark.level0
+@pytest.mark.platform_arm_ascend_training
+@pytest.mark.platform_x86_ascend_training
+@pytest.mark.env_onecard
+def test_pynative_hook_if_net_register_diff_hook_at_each_hook():
+    input_np = np.ones([1, 1, 224, 224]).astype(np.float32)
+    ms_net = FinalNet()
+    ms_net.set_grad()
+    ms_net.conv.register_backward_hook(ms_net.ms_record_hook)
+    ms_net.relu.register_backward_hook(ms_net.ms_change_grad_double_hook)
+    input_ms = Tensor(input_np)
+    out_ms = ms_net(input_ms, Tensor(1))
+    grad_net = GradOfAllInputs(ms_net)
+    grad_net.set_train()
+    grad_net(input_ms, Tensor(1), out_ms)
+
+@pytest.mark.level0
+@pytest.mark.platform_arm_ascend_training
+@pytest.mark.platform_x86_ascend_training
+@pytest.mark.env_onecard
+def test_pynative_hook_one_input_network_register_hook_at_outermost_cell_not_change_grad():
+    input_np = np.ones([2, 2]).astype(np.float32)
+
+    ms_net = MsOneInputNet()
+    ms_net.set_grad()
+    ms_net.register_backward_hook(ms_net.ms_record_hook)
+    input_ms = Tensor(input_np)
+    out_ms = ms_net(input_ms)
+    grad_net = GradOfAllInputs(ms_net)
+    grad_net.set_train()
+    input_ms_grad = grad_net(input_ms, out_ms)
+
+    #input grad
+    input_torch_grad = np.array([[20, 20], [20, 20]])
+    allclose_nparray(input_torch_grad, input_ms_grad[0].asnumpy(), 0.001, 0.001)
+    #hook record grad
+    torch_net_grad_output = np.array([[10, 10], [10, 10]])
+    torch_net_grad_input = np.array([[20, 20], [20, 20]])
+    allclose_nparray(torch_net_grad_output, ms_net.grad_input_list[0].asnumpy(), 0.001, 0.001)
+    allclose_nparray(torch_net_grad_input, ms_net.grad_output_list[0].asnumpy(), 0.001, 0.001)
+
+@pytest.mark.level0
+@pytest.mark.platform_arm_ascend_training
+@pytest.mark.platform_x86_ascend_training
+@pytest.mark.env_onecard
+def test_pynative_hook_one_input_network_register_hook_to_all_cell_record_grad():
+    input_np = np.ones([2, 2]).astype(np.float32)
+
+    ms_net = MsOneInputNet()
+    ms_net.set_grad()
+    ms_net.mul.register_backward_hook(ms_net.ms_record_hook)
+    ms_net.add.register_backward_hook(ms_net.ms_record_hook)
+    ms_net.relu.register_backward_hook(ms_net.ms_record_hook)
+    input_ms = Tensor(input_np)
+    out_ms = ms_net(input_ms)
+    grad_net = GradOfAllInputs(ms_net)
+    grad_net.set_train()
+    grad_net(input_ms, out_ms)
+
+    torch_net_grad_input0 = np.array([[10, 10], [10, 10]])
+    torch_net_grad_output0 = np.array([[10, 10], [10, 10]])
+    torch_net_grad_input1 = np.array([[20, 20], [20, 20]])
+    torch_net_grad_output1 = np.array([[10, 10], [10, 10]])
+    allclose_nparray(torch_net_grad_input0, ms_net.grad_output_list[0].asnumpy(), 0.001, 0.001)
+    allclose_nparray(torch_net_grad_output0, ms_net.grad_input_list[0].asnumpy(), 0.001, 0.001)
+    allclose_nparray(torch_net_grad_input1, ms_net.grad_output_list[1].asnumpy(), 0.001, 0.001)
+    allclose_nparray(torch_net_grad_output1, ms_net.grad_input_list[1].asnumpy(), 0.001, 0.001)
+
+    torch_net_grad_input3 = np.array([[20, 20], [20, 20]])
+    torch_net_grad_output2 = np.array([[20, 20], [20, 20]])
+    allclose_nparray(torch_net_grad_input3, ms_net.grad_output_list[2].asnumpy(), 0.001, 0.001)
+    allclose_nparray(torch_net_grad_output2, ms_net.grad_input_list[2].asnumpy(), 0.001, 0.001)
+
+@pytest.mark.level0
+@pytest.mark.platform_arm_ascend_training
+@pytest.mark.platform_x86_ascend_training
+@pytest.mark.env_onecard
+def test_pynative_hook_one_input_network_register_hook_to_mul_change_input_grad():
+    input_np = np.ones([2, 2]).astype(np.float32)
+
+    ms_net = MsOneInputNet()
+    ms_net.set_grad()
+    ms_net.mul.register_backward_hook(ms_net.ms_change_grad_double_hook)
+    input_ms = Tensor(input_np)
+    out_ms = ms_net(input_ms)
+    grad_net = GradOfAllInputs(ms_net)
+    grad_net.set_train()
+    input_ms_grad = grad_net(input_ms, out_ms)
+
+    #input grad
+    input_torch_grad = np.array([[40, 40], [40, 40]])
+    allclose_nparray(input_torch_grad, input_ms_grad[0].asnumpy(), 0.001, 0.001)
+
+@pytest.mark.level0
+@pytest.mark.platform_arm_ascend_training
+@pytest.mark.platform_x86_ascend_training
+@pytest.mark.env_onecard
+def test_pynative_hook_multi_input_network_register_hook_to_mul2_change_input_grad():
+    input1_np = np.array([2.0, 3.0, 4.0]).astype(np.float32)
+    input2_np = np.array([2.0, 3.0, 4.0]).astype(np.float32)
+
+    ms_net = MsMultiInputNet()
+    ms_net.set_grad()
+    ms_net.mul2.register_backward_hook(ms_net.ms_change_grad_double_hook)
+    input1_ms = Tensor(input1_np)
+    input2_ms = Tensor(input2_np)
+    out_ms = ms_net(input1_ms, input2_ms)
+    grad_net = GradOfAllInputs(ms_net)
+    grad_net.set_train()
+    input_ms_grad = grad_net(input1_ms, input2_ms, out_ms)
+
+    #input grad
+    input1_torch_grad = np.array([384, 2916, 12288])
+    input2_torch_grad = np.array([128, 972, 4096])
+    allclose_nparray(input1_torch_grad, input_ms_grad[0].asnumpy(), 0.001, 0.001)
+    allclose_nparray(input2_torch_grad, input_ms_grad[1].asnumpy(), 0.001, 0.001)
+
+@pytest.mark.level0
+@pytest.mark.platform_arm_ascend_training
+@pytest.mark.platform_x86_ascend_training
+@pytest.mark.env_onecard
+def test_pynative_hook_network_with_cell_in_cell_register_hook_at_outermost_cell_change_grad():
+    input_np = np.ones([2, 2]).astype(np.float32)
+
+    ms_net = MsNetWithCellinCell()
+    ms_net.set_grad()
+    ms_net.register_backward_hook(ms_net.ms_change_grad_double_hook)
+    input_ms = Tensor(input_np)
+    out_ms = ms_net(input_ms)
+    grad_net = GradOfAllInputs(ms_net)
+    grad_net.set_train()
+    input_ms_grad = grad_net(input_ms, out_ms)
+
+    #input grad
+    out_torch = np.array([[20, 20], [20, 20]])
+    input_torch_grad = np.array([[160, 160], [160, 160]])
+    allclose_nparray(out_torch, out_ms.asnumpy(), 0.001, 0.001)
+    allclose_nparray(input_torch_grad, input_ms_grad[0].asnumpy(), 0.001, 0.001)
+
+@pytest.mark.level0
+@pytest.mark.platform_arm_ascend_training
+@pytest.mark.platform_x86_ascend_training
+@pytest.mark.env_onecard
+def test_pynative_hook_network_with_bprop_register_hook_at_outermost_cell_record_grad():
+    input_np = np.ones([2, 2]).astype(np.float32)
+
+    ms_net = MsSingleOpNetWithBprop()
+    ms_net.set_grad()
+    ms_net.bprop_debug = True
+    ms_net.register_backward_hook(ms_net.ms_record_hook)
+    input_ms = Tensor(input_np)
+    out_ms = ms_net(input_ms)
+    grad_net = GradOfAllInputs(ms_net)
+    grad_net.set_train()
+    input_ms_grad = grad_net(input_ms, out_ms)
+
+    if ms_net.grad_output_list or ms_net.grad_input_list:
+        assert False
+
+    #input grad
+    out_torch = np.array([[1, 1], [1, 1]])
+    input_torch_grad = np.array([[5, 5], [5, 5]])
+    allclose_nparray(out_torch, out_ms.asnumpy(), 0.001, 0.001)
+    allclose_nparray(input_torch_grad, input_ms_grad[0].asnumpy(), 0.001, 0.001)
+
+@pytest.mark.level0
+@pytest.mark.platform_arm_ascend_training
+@pytest.mark.platform_x86_ascend_training
+@pytest.mark.env_onecard
+def test_pynative_hook_network_with_bprop_in_child_register_hook_at_outermost_cell_record_grad():
+    input_np = np.ones([2, 2]).astype(np.float32)
+
+    ms_net = MsNetHasBpropInChild()
+    ms_net.set_grad()
+    ms_net.bprop_net.bprop_debug = True
+    ms_net.register_backward_hook(ms_net.ms_record_hook)
+    input_ms = Tensor(input_np)
+    out_ms = ms_net(input_ms)
+    grad_net = GradOfAllInputs(ms_net)
+    grad_net.set_train()
+    input_ms_grad = grad_net(input_ms, out_ms)
+
+    if len(ms_net.grad_output_list) != len(ms_net.grad_input_list) or not ms_net.grad_output_list:
+        assert False
+
+    #input grad
+    out_torch = np.array([[5, 5], [5, 5]])
+    input_torch_grad = np.array([[25, 25], [25, 25]])
+    allclose_nparray(out_torch, out_ms.asnumpy(), 0.001, 0.001)
+    allclose_nparray(input_torch_grad, input_ms_grad[0].asnumpy(), 0.001, 0.001)
+    #hook record grad
+    torch_net_grad_output = np.array([[5, 5], [5, 5]])
+    torch_net_grad_input = np.array([[25, 25], [25, 25]])
+    allclose_nparray(torch_net_grad_output, ms_net.grad_input_list[0].asnumpy(), 0.001, 0.001)
+    allclose_nparray(torch_net_grad_input, ms_net.grad_output_list[0].asnumpy(), 0.001, 0.001)
+
+@pytest.mark.level0
+@pytest.mark.platform_arm_ascend_training
+@pytest.mark.platform_x86_ascend_training
+@pytest.mark.env_onecard
+def test_pynative_hook_multi_op_network_with_bprop_register_hook_at_child_cell_record_grad():
+    input_np = np.ones([2, 2]).astype(np.float32)
+
+    ms_net = MsMultiOpNetWithBprop()
+    ms_net.set_grad()
+    ms_net.bprop_debug = True
+    ms_net.relu.register_backward_hook(ms_net.ms_record_hook)
+    ms_net.mul.register_backward_hook(ms_net.ms_record_hook)
+    input_ms = Tensor(input_np)
+    out_ms = ms_net(input_ms)
+    grad_net = GradOfAllInputs(ms_net)
+    grad_net.set_train()
+    grad_net(input_ms, out_ms)
+
+    if ms_net.grad_output_list or ms_net.grad_input_list:
+        assert False