!12307 Add result check for cases in test_cont_grad.py
From: @liangzelang Reviewed-by: @jjfeing,@zhoufeng54 Signed-off-by: @zhoufeng54
This commit is contained in:
Gitee
commit
176afda078
|
|
@ -46,13 +46,17 @@ def test_while_forward():
|
|||
x[idx, :, 0:2] = max_num
|
||||
idx = idx + 1
|
||||
return x
|
||||
|
||||
# graph mode
|
||||
context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
|
||||
net = MyWhileNet()
|
||||
idx = Tensor(np.array(0), dtype=ms.int32)
|
||||
end = Tensor(np.array(2), dtype=ms.int32)
|
||||
x = Tensor(np.arange(8).reshape(2, 2, 2).astype(np.float32), dtype=ms.float32)
|
||||
net(idx, end, x)
|
||||
graph_output = net(idx, end, x)
|
||||
#pynative mode
|
||||
context.set_context(mode=context.PYNATIVE_MODE, device_target="Ascend")
|
||||
pynative_output = net(idx, end, x)
|
||||
assert np.allclose(graph_output.asnumpy(), pynative_output.asnumpy(), 0.0001, 0.0001)
|
||||
|
||||
|
||||
def test_while_grad():
|
||||
|
|
@ -76,15 +80,20 @@ def test_while_grad():
|
|||
|
||||
def construct(self, *inputs):
|
||||
return grad_all(self.net)(*inputs)
|
||||
|
||||
# graph mode
|
||||
context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
|
||||
while_net = MyWhileNet()
|
||||
net = GradNet(while_net)
|
||||
idx = Tensor(np.array(0), dtype=ms.int32)
|
||||
end = Tensor(np.array(2), dtype=ms.int32)
|
||||
x = Tensor(np.random.randn(2, 2, 2).astype(np.float32), dtype=ms.float32)
|
||||
net(idx, end, x)
|
||||
|
||||
graph_output = net(idx, end, x)
|
||||
# pynative mode
|
||||
context.set_context(mode=context.PYNATIVE_MODE, device_target="Ascend")
|
||||
pynative_output = net(idx, end, x)
|
||||
assert np.allclose(graph_output[0].asnumpy(), pynative_output[0].asnumpy(), 0.0001, 0.0001)
|
||||
assert np.allclose(graph_output[1].asnumpy(), pynative_output[1].asnumpy(), 0.0001, 0.0001)
|
||||
assert np.allclose(graph_output[2].asnumpy(), pynative_output[2].asnumpy(), 0.0001, 0.0001)
|
||||
|
||||
def test_while_with_param_forward():
|
||||
class MyWhileNet(nn.Cell):
|
||||
|
|
@ -103,17 +112,21 @@ def test_while_with_param_forward():
|
|||
out = out + x + self.param
|
||||
idx = idx + 1
|
||||
return out
|
||||
|
||||
# graph mode
|
||||
context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
|
||||
net = MyWhileNet()
|
||||
idx = Tensor(np.array(0), dtype=ms.int32)
|
||||
end = Tensor(np.array(2), dtype=ms.int32)
|
||||
x = Tensor(np.arange(8).reshape(2, 2, 2).astype(np.float32), dtype=ms.float32)
|
||||
net(idx, end, x)
|
||||
graph_output = net(idx, end, x)
|
||||
# pynative mode
|
||||
context.set_context(mode=context.PYNATIVE_MODE, device_target="Ascend")
|
||||
pynative_output = net(idx, end, x)
|
||||
assert np.allclose(graph_output.asnumpy(), pynative_output.asnumpy(), 0.0001, 0.0001)
|
||||
|
||||
|
||||
def test_while_endless_case():
|
||||
"""endless case when optmization"""
|
||||
"""endless case when optimization"""
|
||||
class MyWhileNet(nn.Cell):
|
||||
def __init__(self):
|
||||
super().__init__()
|
||||
|
|
@ -128,13 +141,17 @@ def test_while_endless_case():
|
|||
out = out + part
|
||||
idx = idx + 1
|
||||
return out
|
||||
|
||||
# graph mode
|
||||
context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
|
||||
net = MyWhileNet()
|
||||
idx = Tensor(np.array(0), dtype=ms.int32)
|
||||
end = Tensor(np.array(2), dtype=ms.int32)
|
||||
x = Tensor(np.arange(8).reshape(2, 2, 2).astype(np.float32), dtype=ms.float32)
|
||||
net(idx, end, x)
|
||||
graph_output = net(idx, end, x)
|
||||
# pynative mode
|
||||
context.set_context(mode=context.PYNATIVE_MODE, device_target="Ascend")
|
||||
pynative_output = net(idx, end, x)
|
||||
assert np.allclose(graph_output.asnumpy(), pynative_output.asnumpy(), 0.0001, 0.0001)
|
||||
|
||||
|
||||
def test_while_with_param_grad():
|
||||
|
|
@ -163,15 +180,18 @@ def test_while_with_param_grad():
|
|||
|
||||
def construct(self, a, b, c):
|
||||
return grad_by_list(self.net, self.weights)(a, b, c)
|
||||
|
||||
# graph mode
|
||||
context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
|
||||
while_net = MyWhileNet()
|
||||
net = GradNet(while_net)
|
||||
idx = Tensor(np.array(0), dtype=ms.int32)
|
||||
end = Tensor(np.array(2), dtype=ms.int32)
|
||||
x = Tensor(np.arange(8).reshape(2, 2, 2).astype(np.float32), dtype=ms.float32)
|
||||
net(idx, end, x)
|
||||
|
||||
graph_output = net(idx, end, x)
|
||||
# pynative mode
|
||||
context.set_context(mode=context.PYNATIVE_MODE, device_target="Ascend")
|
||||
pynative_output = net(idx, end, x)
|
||||
assert np.allclose(graph_output[0].asnumpy(), pynative_output[0].asnumpy(), 0.0001, 0.0001)
|
||||
|
||||
def test_while_with_param_forward_with_const_branch():
|
||||
class MyWhileNet(nn.Cell):
|
||||
|
|
@ -191,14 +211,18 @@ def test_while_with_param_forward_with_const_branch():
|
|||
out = out + idx + self.param
|
||||
idx = idx + 1
|
||||
return out
|
||||
|
||||
# graph mode
|
||||
context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
|
||||
while_net = MyWhileNet()
|
||||
net = while_net
|
||||
idx = Tensor(np.array(0), dtype=ms.int32)
|
||||
end = Tensor(np.array(4), dtype=ms.int32)
|
||||
x = Tensor(np.random.randn(2, 2, 2).astype(np.float32), dtype=ms.float32)
|
||||
net(idx, end, x)
|
||||
graph_output = net(idx, end, x)
|
||||
# pynative mode
|
||||
context.set_context(mode=context.PYNATIVE_MODE, device_target="Ascend")
|
||||
pynative_output = net(idx, end, x)
|
||||
assert np.allclose(graph_output.asnumpy(), pynative_output.asnumpy(), 0.0001, 0.0001)
|
||||
|
||||
|
||||
def test_while_opt_endless():
|
||||
|
|
@ -228,15 +252,18 @@ def test_while_opt_endless():
|
|||
|
||||
def construct(self, *inputs):
|
||||
return grad_all(self.net)(*inputs)
|
||||
|
||||
# graph mode
|
||||
context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
|
||||
while_net = MyWhileNet()
|
||||
net = GradNet(while_net)
|
||||
idx = Tensor(np.array(0), dtype=ms.int32)
|
||||
end = Tensor(np.array(4), dtype=ms.int32)
|
||||
x = Tensor(np.ones([2, 2, 2]).astype(np.float32) * 3, dtype=ms.float32)
|
||||
net(idx, end, x)
|
||||
|
||||
graph_output = net(idx, end, x)
|
||||
# pynative mode
|
||||
context.set_context(mode=context.PYNATIVE_MODE, device_target="Ascend")
|
||||
pynative_output = net(idx, end, x)
|
||||
assert np.allclose(graph_output[0].asnumpy(), pynative_output[0].asnumpy(), 0.0001, 0.0001)
|
||||
|
||||
def test_no_while_call():
|
||||
class MyWhileNet(nn.Cell):
|
||||
|
|
@ -254,14 +281,18 @@ def test_no_while_call():
|
|||
else:
|
||||
out = out + idx + self.param
|
||||
return out
|
||||
|
||||
# graph mode
|
||||
context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
|
||||
while_net = MyWhileNet()
|
||||
net = while_net
|
||||
idx = Tensor(np.array(0), dtype=ms.int32)
|
||||
end = Tensor(np.array(4), dtype=ms.int32)
|
||||
x = Tensor(np.random.randn(2, 2, 2).astype(np.float32), dtype=ms.float32)
|
||||
net(idx, end, x)
|
||||
graph_output = net(idx, end, x)
|
||||
# pynative mode
|
||||
context.set_context(mode=context.PYNATIVE_MODE, device_target="Ascend")
|
||||
pynative_output = net(idx, end, x)
|
||||
assert np.allclose(graph_output.asnumpy(), pynative_output.asnumpy(), 0.0001, 0.0001)
|
||||
|
||||
|
||||
def test_while_with_param_grad_with_const_branch():
|
||||
|
|
@ -291,15 +322,18 @@ def test_while_with_param_grad_with_const_branch():
|
|||
|
||||
def construct(self, a, b, c):
|
||||
return grad_by_list(self.net, self.weights)(a, b, c)
|
||||
|
||||
# graph mode
|
||||
context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
|
||||
while_net = MyWhileNet()
|
||||
net = GradNet(while_net)
|
||||
idx = Tensor(np.array(0), dtype=ms.int32)
|
||||
end = Tensor(np.array(4), dtype=ms.int32)
|
||||
x = Tensor(np.random.randn(2, 2, 2).astype(np.float32), dtype=ms.float32)
|
||||
net(idx, end, x)
|
||||
|
||||
graph_output = net(idx, end, x)
|
||||
# pynative mode
|
||||
context.set_context(mode=context.PYNATIVE_MODE, device_target="Ascend")
|
||||
pynative_output = net(idx, end, x)
|
||||
assert np.allclose(graph_output[0].asnumpy(), pynative_output[0].asnumpy(), 0.0001, 0.0001)
|
||||
|
||||
def test_for_while_with_param_grad_with_const_branch():
|
||||
class MyWhileNet(nn.Cell):
|
||||
|
|
@ -331,15 +365,18 @@ def test_for_while_with_param_grad_with_const_branch():
|
|||
|
||||
def construct(self, a, b, c):
|
||||
return grad_by_list(self.net, self.weights)(a, b, c)
|
||||
|
||||
# graph mode
|
||||
context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
|
||||
while_net = MyWhileNet()
|
||||
net = GradNet(while_net)
|
||||
idx = Tensor(np.array(0), dtype=ms.int32)
|
||||
end = Tensor(np.array(4), dtype=ms.int32)
|
||||
x = Tensor(np.random.randn(2, 2, 2).astype(np.float32), dtype=ms.float32)
|
||||
net(idx, end, x)
|
||||
|
||||
graph_output = net(idx, end, x)
|
||||
# pynative mode
|
||||
context.set_context(mode=context.PYNATIVE_MODE, device_target="Ascend")
|
||||
pynative_output = net(idx, end, x)
|
||||
assert np.allclose(graph_output[0].asnumpy(), pynative_output[0].asnumpy(), 0.0001, 0.0001)
|
||||
|
||||
def test_for_while_with_param_grad_basic():
|
||||
class MyWhileNet(nn.Cell):
|
||||
|
|
@ -368,15 +405,18 @@ def test_for_while_with_param_grad_basic():
|
|||
|
||||
def construct(self, a, b, c):
|
||||
return grad_by_list(self.net, self.weights)(a, b, c)
|
||||
|
||||
# graph mode
|
||||
context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
|
||||
while_net = MyWhileNet()
|
||||
net = GradNet(while_net)
|
||||
idx = Tensor(np.array(0), dtype=ms.int32)
|
||||
end = Tensor(np.array(4), dtype=ms.int32)
|
||||
x = Tensor(np.random.randn(2, 2, 2).astype(np.float32), dtype=ms.float32)
|
||||
net(idx, end, x)
|
||||
|
||||
graph_output = net(idx, end, x)
|
||||
# pynative mode
|
||||
context.set_context(mode=context.PYNATIVE_MODE, device_target="Ascend")
|
||||
pynative_output = net(idx, end, x)
|
||||
assert np.allclose(graph_output[0].asnumpy(), pynative_output[0].asnumpy(), 0.0001, 0.0001)
|
||||
|
||||
def test_for_while_with_param_grad_normal():
|
||||
class MyWhileNet(nn.Cell):
|
||||
|
|
@ -405,15 +445,18 @@ def test_for_while_with_param_grad_normal():
|
|||
|
||||
def construct(self, a, b, c):
|
||||
return grad_by_list(self.net, self.weights)(a, b, c)
|
||||
|
||||
# graph mode
|
||||
context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
|
||||
while_net = MyWhileNet()
|
||||
net = GradNet(while_net)
|
||||
idx = Tensor(np.array(0), dtype=ms.int32)
|
||||
end = Tensor(np.array(4), dtype=ms.int32)
|
||||
x = Tensor(np.random.randn(2, 2, 2).astype(np.float32), dtype=ms.float32)
|
||||
net(idx, end, x)
|
||||
|
||||
graph_output = net(idx, end, x)
|
||||
# pynative mode
|
||||
context.set_context(mode=context.PYNATIVE_MODE, device_target="Ascend")
|
||||
pynative_output = net(idx, end, x)
|
||||
assert np.allclose(graph_output[0].asnumpy(), pynative_output[0].asnumpy(), 0.0001, 0.0001)
|
||||
|
||||
def test_while_with_param_basic_grad():
|
||||
class MyWhileNet(nn.Cell):
|
||||
|
|
@ -439,15 +482,18 @@ def test_while_with_param_basic_grad():
|
|||
|
||||
def construct(self, a, b, c):
|
||||
return grad_by_list(self.net, self.weights)(a, b, c)
|
||||
|
||||
# graph mode
|
||||
context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
|
||||
while_net = MyWhileNet()
|
||||
net = GradNet(while_net)
|
||||
idx = Tensor(np.array(0), dtype=ms.int32)
|
||||
end = Tensor(np.array(3), dtype=ms.int32)
|
||||
x = Tensor(np.random.randn(2, 2, 2).astype(np.float32), dtype=ms.float32)
|
||||
net(idx, end, x)
|
||||
|
||||
graph_output = net(idx, end, x)
|
||||
# pynative mode
|
||||
context.set_context(mode=context.PYNATIVE_MODE, device_target="Ascend")
|
||||
pynative_output = net(idx, end, x)
|
||||
assert np.allclose(graph_output[0].asnumpy(), pynative_output[0].asnumpy(), 0.0001, 0.0001)
|
||||
|
||||
def test_while_with_param_basic_grad_mul():
|
||||
class MyWhileNet(nn.Cell):
|
||||
|
|
@ -473,15 +519,18 @@ def test_while_with_param_basic_grad_mul():
|
|||
|
||||
def construct(self, a, b, c):
|
||||
return grad_by_list(self.net, self.weights)(a, b, c)
|
||||
|
||||
# graph mode
|
||||
context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
|
||||
while_net = MyWhileNet()
|
||||
net = GradNet(while_net)
|
||||
idx = Tensor(np.array(0), dtype=ms.int32)
|
||||
end = Tensor(np.array(3), dtype=ms.int32)
|
||||
x = Tensor(np.random.randn(2, 2, 2).astype(np.float32), dtype=ms.float32)
|
||||
net(idx, end, x)
|
||||
|
||||
graph_output = net(idx, end, x)
|
||||
# pynative mode
|
||||
context.set_context(mode=context.PYNATIVE_MODE, device_target="Ascend")
|
||||
pynative_output = net(idx, end, x)
|
||||
assert np.allclose(graph_output[0].asnumpy(), pynative_output[0].asnumpy(), 0.0001, 0.0001)
|
||||
|
||||
def test_while_with_param_basic_grad_two():
|
||||
class MyWhileNet(nn.Cell):
|
||||
|
|
@ -508,15 +557,19 @@ def test_while_with_param_basic_grad_two():
|
|||
|
||||
def construct(self, a, b, c):
|
||||
return grad_by_list(self.net, self.weights)(a, b, c)
|
||||
|
||||
# graph mode
|
||||
context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
|
||||
while_net = MyWhileNet()
|
||||
net = GradNet(while_net)
|
||||
idx = Tensor(np.array(0), dtype=ms.int32)
|
||||
end = Tensor(np.array(3), dtype=ms.int32)
|
||||
x = Tensor(np.random.randn(2, 2, 2).astype(np.float32), dtype=ms.float32)
|
||||
net(idx, end, x)
|
||||
|
||||
graph_output = net(idx, end, x)
|
||||
# pynative mode
|
||||
context.set_context(mode=context.PYNATIVE_MODE, device_target="Ascend")
|
||||
pynative_output = net(idx, end, x)
|
||||
assert np.allclose(graph_output[0].asnumpy(), pynative_output[0].asnumpy(), 0.0001, 0.0001)
|
||||
assert np.allclose(graph_output[1].asnumpy(), pynative_output[1].asnumpy(), 0.0001, 0.0001)
|
||||
|
||||
def test_while_with_param_basic_grad_three():
|
||||
class MyWhileNet(nn.Cell):
|
||||
|
|
@ -544,15 +597,20 @@ def test_while_with_param_basic_grad_three():
|
|||
|
||||
def construct(self, a, b, c):
|
||||
return grad_by_list(self.net, self.weights)(a, b, c)
|
||||
|
||||
# graph mode
|
||||
context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
|
||||
while_net = MyWhileNet()
|
||||
net = GradNet(while_net)
|
||||
idx = Tensor(np.array(0), dtype=ms.int32)
|
||||
end = Tensor(np.array(3), dtype=ms.int32)
|
||||
x = Tensor(np.random.randn(2, 2, 2).astype(np.float32), dtype=ms.float32)
|
||||
net(idx, end, x)
|
||||
|
||||
graph_output = net(idx, end, x)
|
||||
# pynative mode
|
||||
context.set_context(mode=context.PYNATIVE_MODE, device_target="Ascend")
|
||||
pynative_output = net(idx, end, x)
|
||||
assert np.allclose(graph_output[0].asnumpy(), pynative_output[0].asnumpy(), 0.0001, 0.0001)
|
||||
assert np.allclose(graph_output[1].asnumpy(), pynative_output[1].asnumpy(), 0.0001, 0.0001)
|
||||
assert np.allclose(graph_output[2].asnumpy(), pynative_output[2].asnumpy(), 0.0001, 0.0001)
|
||||
|
||||
def test_while_if_with_param_grad():
|
||||
class MyWhileNet(nn.Cell):
|
||||
|
|
@ -581,15 +639,18 @@ def test_while_if_with_param_grad():
|
|||
|
||||
def construct(self, a, b, c):
|
||||
return grad_by_list(self.net, self.weights)(a, b, c)
|
||||
|
||||
# graph mode
|
||||
context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
|
||||
while_net = MyWhileNet()
|
||||
net = GradNet(while_net)
|
||||
idx = Tensor(np.array(0), dtype=ms.int32)
|
||||
end = Tensor(np.array(3), dtype=ms.int32)
|
||||
x = Tensor(np.ones([2, 2, 2]).astype(np.float32), dtype=ms.float32)
|
||||
net(idx, end, x)
|
||||
|
||||
graph_output = net(idx, end, x)
|
||||
# pynative mode
|
||||
context.set_context(mode=context.PYNATIVE_MODE, device_target="Ascend")
|
||||
pynative_output = net(idx, end, x)
|
||||
assert np.allclose(graph_output[0].asnumpy(), pynative_output[0].asnumpy(), 0.0001, 0.0001)
|
||||
|
||||
def test_while_with_param_grad_not_enter_while():
|
||||
class MyWhileNet(nn.Cell):
|
||||
|
|
@ -614,15 +675,18 @@ def test_while_with_param_grad_not_enter_while():
|
|||
|
||||
def construct(self, a, b, c):
|
||||
return grad_by_list(self.net, self.weights)(a, b, c)
|
||||
|
||||
# graph mode
|
||||
context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
|
||||
while_net = MyWhileNet()
|
||||
net = GradNet(while_net)
|
||||
idx = Tensor(np.array(3), dtype=ms.int32)
|
||||
end = Tensor(np.array(0), dtype=ms.int32)
|
||||
x = Tensor(np.random.randn(2, 2, 2).astype(np.float32), dtype=ms.float32)
|
||||
net(idx, end, x)
|
||||
|
||||
graph_output = net(idx, end, x)
|
||||
# pynative mode
|
||||
context.set_context(mode=context.PYNATIVE_MODE, device_target="Ascend")
|
||||
pynative_output = net(idx, end, x)
|
||||
assert np.allclose(graph_output[0].asnumpy(), pynative_output[0].asnumpy(), 0.0001, 0.0001)
|
||||
|
||||
def test_with_param_if_by_if_forward():
|
||||
class MyIfByIfNet(nn.Cell):
|
||||
|
|
@ -643,14 +707,18 @@ def test_with_param_if_by_if_forward():
|
|||
else:
|
||||
out = out + x*2
|
||||
return out
|
||||
|
||||
# graph mode
|
||||
context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
|
||||
if_net = MyIfByIfNet()
|
||||
net = if_net
|
||||
idx = Tensor(np.array(0), dtype=ms.int32)
|
||||
end = Tensor(np.array(4), dtype=ms.int32)
|
||||
x = Tensor(np.ones([2, 2, 2]).astype(np.float32), dtype=ms.float32)
|
||||
net(idx, end, x)
|
||||
graph_output = net(idx, end, x)
|
||||
# pynative mode
|
||||
context.set_context(mode=context.PYNATIVE_MODE, device_target="Ascend")
|
||||
pynative_output = net(idx, end, x)
|
||||
assert np.allclose(graph_output.asnumpy(), pynative_output.asnumpy(), 0.0001, 0.0001)
|
||||
|
||||
|
||||
def test_with_param_if_by_if_grad_inputs():
|
||||
|
|
@ -676,15 +744,20 @@ def test_with_param_if_by_if_grad_inputs():
|
|||
|
||||
def construct(self, *inputs):
|
||||
return grad_all(self.net)(*inputs)
|
||||
|
||||
# graph mode
|
||||
context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
|
||||
if_net = MyIfByIfNet()
|
||||
net = GradNet(if_net)
|
||||
idx = Tensor(np.array(0), dtype=ms.int32)
|
||||
end = Tensor(np.array(0), dtype=ms.int32)
|
||||
x = Tensor(np.random.randn(2, 2, 2).astype(np.float32), dtype=ms.float32)
|
||||
net(idx, end, x)
|
||||
|
||||
graph_output = net(idx, end, x)
|
||||
# pynative mode
|
||||
context.set_context(mode=context.PYNATIVE_MODE, device_target="Ascend")
|
||||
pynative_output = net(idx, end, x)
|
||||
assert np.allclose(graph_output[0].asnumpy(), pynative_output[0].asnumpy(), 0.0001, 0.0001)
|
||||
assert np.allclose(graph_output[1].asnumpy(), pynative_output[1].asnumpy(), 0.0001, 0.0001)
|
||||
assert np.allclose(graph_output[2].asnumpy(), pynative_output[2].asnumpy(), 0.0001, 0.0001)
|
||||
|
||||
def test_with_param_if_by_if_grad_parameter():
|
||||
class MyIfByIfNet(nn.Cell):
|
||||
|
|
@ -710,15 +783,18 @@ def test_with_param_if_by_if_grad_parameter():
|
|||
|
||||
def construct(self, *inputs):
|
||||
return grad_by_list(self.net, self.weights)(*inputs)
|
||||
|
||||
# graph mode
|
||||
context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
|
||||
if_net = MyIfByIfNet()
|
||||
net = GradNet(if_net)
|
||||
idx = Tensor(np.array(0), dtype=ms.int32)
|
||||
end = Tensor(np.array(2), dtype=ms.int32)
|
||||
x = Tensor(np.random.randn(2, 2, 2).astype(np.float32), dtype=ms.float32)
|
||||
net(idx, end, x)
|
||||
|
||||
graph_output = net(idx, end, x)
|
||||
# pynative mode
|
||||
context.set_context(mode=context.PYNATIVE_MODE, device_target="Ascend")
|
||||
pynative_output = net(idx, end, x)
|
||||
assert np.allclose(graph_output[0].asnumpy(), pynative_output[0].asnumpy(), 0.0001, 0.0001)
|
||||
|
||||
def test_with_param_if_by_if_grad_param_excute_null():
|
||||
class MyIfByIfNet(nn.Cell):
|
||||
|
|
@ -742,15 +818,18 @@ def test_with_param_if_by_if_grad_param_excute_null():
|
|||
|
||||
def construct(self, *inputs):
|
||||
return grad_by_list(self.net, self.weights)(*inputs)
|
||||
|
||||
# graph mode
|
||||
context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
|
||||
if_net = MyIfByIfNet()
|
||||
net = GradNet(if_net)
|
||||
idx = Tensor(np.array(4), dtype=ms.int32)
|
||||
end = Tensor(np.array(0), dtype=ms.int32)
|
||||
x = Tensor(np.random.randn(2, 2, 2).astype(np.float32), dtype=ms.float32)
|
||||
net(idx, end, x)
|
||||
|
||||
graph_output = net(idx, end, x)
|
||||
# pynative mode
|
||||
context.set_context(mode=context.PYNATIVE_MODE, device_target="Ascend")
|
||||
pynative_output = net(idx, end, x)
|
||||
assert np.allclose(graph_output[0].asnumpy(), pynative_output[0].asnumpy(), 0.0001, 0.0001)
|
||||
|
||||
def test_if_by_if_return_inside_grad():
|
||||
class MyIfByIfNet(nn.Cell):
|
||||
|
|
@ -776,15 +855,18 @@ def test_if_by_if_return_inside_grad():
|
|||
|
||||
def construct(self, *inputs):
|
||||
return grad_by_list(self.net, self.weights)(*inputs)
|
||||
|
||||
# graph mode
|
||||
context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
|
||||
if_net = MyIfByIfNet()
|
||||
net = GradNet(if_net)
|
||||
idx = Tensor(np.array(1), dtype=ms.int32)
|
||||
end = Tensor(np.array(0), dtype=ms.int32)
|
||||
x = Tensor(np.random.randn(2, 2, 2).astype(np.float32), dtype=ms.float32)
|
||||
net(idx, end, x)
|
||||
|
||||
graph_output = net(idx, end, x)
|
||||
# pynative mode
|
||||
context.set_context(mode=context.PYNATIVE_MODE, device_target="Ascend")
|
||||
pynative_output = net(idx, end, x)
|
||||
assert np.allclose(graph_output[0].asnumpy(), pynative_output[0].asnumpy(), 0.0001, 0.0001)
|
||||
|
||||
def test_if_by_if_forward():
|
||||
class MyIfByIfNet(nn.Cell):
|
||||
|
|
@ -811,18 +893,22 @@ def test_if_by_if_forward():
|
|||
a = a * b
|
||||
out = a + b + x
|
||||
return out
|
||||
|
||||
# graph mode
|
||||
context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
|
||||
if_net = MyIfByIfNet()
|
||||
net = if_net
|
||||
idx = Tensor(np.array(2), dtype=ms.float32)
|
||||
end = Tensor(np.array(3), dtype=ms.float32)
|
||||
x = Tensor(np.array(4), dtype=ms.float32)
|
||||
net(idx, end, x)
|
||||
graph_output = net(idx, end, x)
|
||||
# pynative mode
|
||||
context.set_context(mode=context.PYNATIVE_MODE, device_target="Ascend")
|
||||
pynative_output = net(idx, end, x)
|
||||
assert np.allclose(graph_output.asnumpy(), pynative_output.asnumpy(), 0.0001, 0.0001)
|
||||
|
||||
|
||||
def test_if_by_if_forward_control_tuple_switch():
|
||||
"""tuple_get from swtich op will generate new switch inside to eliminate tuple_get"""
|
||||
"""tuple_get from switch op will generate new switch inside to eliminate tuple_get"""
|
||||
class Branch3Net(nn.Cell):
|
||||
def __init__(self):
|
||||
super().__init__()
|
||||
|
|
@ -871,14 +957,18 @@ def test_if_by_if_forward_control_tuple_switch():
|
|||
a = a * b
|
||||
out = a + b + x
|
||||
return out
|
||||
|
||||
# graph mode
|
||||
context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
|
||||
if_net = MyIfByIfNet()
|
||||
net = if_net
|
||||
idx = Tensor(np.array(2), dtype=ms.float32)
|
||||
end = Tensor(np.array(3), dtype=ms.float32)
|
||||
x = Tensor(np.array(0), dtype=ms.float32)
|
||||
net(idx, end, x)
|
||||
graph_output = net(idx, end, x)
|
||||
# pynative mode
|
||||
context.set_context(mode=context.PYNATIVE_MODE, device_target="Ascend")
|
||||
pynative_output = net(idx, end, x)
|
||||
assert np.allclose(graph_output.asnumpy(), pynative_output.asnumpy(), 0.0001, 0.0001)
|
||||
|
||||
|
||||
|
||||
|
|
@ -932,14 +1022,18 @@ def test_if_by_if_forward_control_inside_net():
|
|||
a = self.sub(a, b)
|
||||
out = self.net(a, b, x)
|
||||
return out
|
||||
|
||||
# graph mode
|
||||
context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
|
||||
if_net = MyIfByIfNet()
|
||||
net = if_net
|
||||
idx = Tensor(np.array(2), dtype=ms.float32)
|
||||
end = Tensor(np.array(3), dtype=ms.float32)
|
||||
x = Tensor(np.array(0), dtype=ms.float32)
|
||||
net(idx, end, x)
|
||||
graph_output = net(idx, end, x)
|
||||
# pynative mode
|
||||
context.set_context(mode=context.PYNATIVE_MODE, device_target="Ascend")
|
||||
pynative_output = net(idx, end, x)
|
||||
assert np.allclose(graph_output.asnumpy(), pynative_output.asnumpy(), 0.0001, 0.0001)
|
||||
|
||||
|
||||
|
||||
|
|
@ -968,14 +1062,18 @@ def test_if_by_if_forward_use_namespace():
|
|||
a = a * b
|
||||
out = a + b + x
|
||||
return out
|
||||
|
||||
# graph mode
|
||||
context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
|
||||
if_net = MyIfByIfNet()
|
||||
net = if_net
|
||||
idx = Tensor(np.array(2), dtype=ms.float32)
|
||||
end = Tensor(np.array(3), dtype=ms.float32)
|
||||
x = Tensor(np.array(0), dtype=ms.float32)
|
||||
net(idx, end, x)
|
||||
graph_output = net(idx, end, x)
|
||||
# pynative mode
|
||||
context.set_context(mode=context.PYNATIVE_MODE, device_target="Ascend")
|
||||
pynative_output = net(idx, end, x)
|
||||
assert np.allclose(graph_output.asnumpy(), pynative_output.asnumpy(), 0.0001, 0.0001)
|
||||
|
||||
|
||||
def test_if_by_if_forward_use_global_op():
|
||||
|
|
@ -1007,14 +1105,18 @@ def test_if_by_if_forward_use_global_op():
|
|||
a = a * b
|
||||
out = a + b + x
|
||||
return out
|
||||
|
||||
# graph mode
|
||||
context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
|
||||
if_net = MyIfByIfNet()
|
||||
net = if_net
|
||||
idx = Tensor(np.array(2), dtype=ms.float32)
|
||||
end = Tensor(np.array(3), dtype=ms.float32)
|
||||
x = Tensor(np.array(0), dtype=ms.float32)
|
||||
net(idx, end, x)
|
||||
graph_output = net(idx, end, x)
|
||||
# pynative mode
|
||||
context.set_context(mode=context.PYNATIVE_MODE, device_target="Ascend")
|
||||
pynative_output = net(idx, end, x)
|
||||
assert np.allclose(graph_output.asnumpy(), pynative_output.asnumpy(), 0.0001, 0.0001)
|
||||
|
||||
|
||||
def test_for_with_if_by_if_forward():
|
||||
|
|
@ -1033,14 +1135,18 @@ def test_for_with_if_by_if_forward():
|
|||
a = a * b
|
||||
out = a + b + x
|
||||
return out
|
||||
|
||||
# graph mode
|
||||
context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
|
||||
if_net = MyIfByIfNet()
|
||||
net = if_net
|
||||
idx = Tensor(np.array(2), dtype=ms.float32)
|
||||
end = Tensor(np.array(3), dtype=ms.float32)
|
||||
x = Tensor(np.array(0), dtype=ms.float32)
|
||||
net(idx, end, x)
|
||||
graph_output = net(idx, end, x)
|
||||
# pynative mode
|
||||
context.set_context(mode=context.PYNATIVE_MODE, device_target="Ascend")
|
||||
pynative_output = net(idx, end, x)
|
||||
assert np.allclose(graph_output.asnumpy(), pynative_output.asnumpy(), 0.0001, 0.0001)
|
||||
|
||||
|
||||
|
||||
|
|
@ -1062,14 +1168,18 @@ def test_for_with_if_by_if_forward_namespace():
|
|||
a = a * b
|
||||
out = a + b + x
|
||||
return out
|
||||
|
||||
# graph mode
|
||||
context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
|
||||
if_net = MyIfByIfNet()
|
||||
net = if_net
|
||||
idx = Tensor(np.array(2), dtype=ms.float32)
|
||||
end = Tensor(np.array(3), dtype=ms.float32)
|
||||
x = Tensor(np.array(0), dtype=ms.float32)
|
||||
net(idx, end, x)
|
||||
graph_output = net(idx, end, x)
|
||||
# pynative mode
|
||||
context.set_context(mode=context.PYNATIVE_MODE, device_target="Ascend")
|
||||
pynative_output = net(idx, end, x)
|
||||
assert np.allclose(graph_output.asnumpy(), pynative_output.asnumpy(), 0.0001, 0.0001)
|
||||
|
||||
|
||||
|
||||
|
|
@ -1102,14 +1212,18 @@ def test_if_by_if_forward_const_branch_inner():
|
|||
a = a * b
|
||||
out = a + b + x
|
||||
return out
|
||||
|
||||
# graph mode
|
||||
context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
|
||||
if_net = MyIfByIfNet()
|
||||
net = if_net
|
||||
idx = Tensor(np.array(2), dtype=ms.float32)
|
||||
end = Tensor(np.array(3), dtype=ms.float32)
|
||||
x = Tensor(np.array(0), dtype=ms.float32)
|
||||
net(idx, end, x)
|
||||
graph_output = net(idx, end, x)
|
||||
# pynative mode
|
||||
context.set_context(mode=context.PYNATIVE_MODE, device_target="Ascend")
|
||||
pynative_output = net(idx, end, x)
|
||||
assert np.allclose(graph_output.asnumpy(), pynative_output.asnumpy(), 0.0001, 0.0001)
|
||||
|
||||
|
||||
|
||||
|
|
@ -1143,14 +1257,18 @@ def test_if_by_if_forward_all_const_branch():
|
|||
a = a * b
|
||||
out = a + b + x
|
||||
return out
|
||||
|
||||
# graph mode
|
||||
context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
|
||||
if_net = MyIfByIfNet()
|
||||
net = if_net
|
||||
idx = Tensor(np.array(2), dtype=ms.float32)
|
||||
end = Tensor(np.array(3), dtype=ms.float32)
|
||||
x = Tensor(np.array(0), dtype=ms.float32)
|
||||
net(idx, end, x)
|
||||
graph_output = net(idx, end, x)
|
||||
# pynative mode
|
||||
context.set_context(mode=context.PYNATIVE_MODE, device_target="Ascend")
|
||||
pynative_output = net(idx, end, x)
|
||||
assert np.allclose(graph_output.asnumpy(), pynative_output.asnumpy(), 0.0001, 0.0001)
|
||||
|
||||
|
||||
@pytest.mark.level0
|
||||
|
|
|
|||
Loading…
Reference in New Issue