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!29750 add GRU support info and fix RNN ut 

Merge pull request !29750 from 吕昱峰（Nate.River）/master
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i-robot 2022-02-08 07:22:44 +00:00 committed by Gitee
parent 5e23fb3ba1 69944cbbe9
commit 6573bdd809
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GPG Key ID: 173E9B9CA92EEF8F
2 changed files with 11 additions and 8 deletions
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3
mindspore/python/mindspore/nn/layer/rnns.py
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@ -391,6 +391,9 @@ class _RNNBase(Cell):
gate_size = 4 * hidden_size
self.rnn = _DynamicLSTMAscend() if is_ascend else _DynamicLSTMCPUGPU()
elif mode == "GRU":
if is_ascend and hidden_size % 16 != 0:
raise ValueError(f"GRU on ascend do not support hidden size that is not divisible by 16, "
f"but get hidden size {hidden_size}, please reset the argument.")
gate_size = 3 * hidden_size
self.rnn = _DynamicGRUAscend() if is_ascend else _DynamicGRUCPUGPU()
elif mode == "RNN_TANH":

16
tests/st/ops/ascend/test_rnn_op.py
View File

@ -103,8 +103,8 @@ def test_sit_rnn_forward_input_3_32_32_is_32_hs_16():
fact = RNNWeightBias(num_layers, has_bias, input_size, num_directions, hidden_size, bidirectional)
w_ih_list, w_hh_list, b_ih_list, b_hh_list = fact.get_weight_bias()
h0 = Tensor(np.random.randn(num_layers * num_directions, 32, 16).astype(np.float32))
input_ms = Tensor(np.random.randn(3, 32, 32).astype(np.float32))
h0 = Tensor(np.zeros((num_layers * num_directions, 32, 16), np.float32))
input_ms = Tensor(np.ones((3, 32, 32), np.float32))
# graph mode
context.set_context(mode=context.GRAPH_MODE)
@ -126,8 +126,8 @@ def test_sit_rnn_forward_input_3_32_32_is_32_hs_16():
net_pynative.rnn.b_hh_list = b_hh_list
out_pynative, hy_pynative = net_pynative(input_ms, h0)
assert np.allclose(out.asnumpy(), out_pynative.asnumpy(), 0.0001, 0.0001)
assert np.allclose(hy.asnumpy(), hy_pynative.asnumpy(), 0.0001, 0.0001)
assert np.allclose(out.asnumpy(), out_pynative.asnumpy(), 0.001, 0.001)
assert np.allclose(hy.asnumpy(), hy_pynative.asnumpy(), 0.001, 0.001)
@pytest.mark.level1
@pytest.mark.platform_arm_ascend_training
@ -144,8 +144,8 @@ def test_sit_rnn_grad_input_3_32_32_is_32_hs_16():
fact = RNNWeightBias(num_layers, has_bias, input_size, num_directions, hidden_size, bidirectional)
w_ih_list, w_hh_list, b_ih_list, b_hh_list = fact.get_weight_bias()
h0 = Tensor(np.random.randn(num_layers * num_directions, 32, 16).astype(np.float32))
input_ms = Tensor(np.random.randn(3, 32, 32).astype(np.float32))
h0 = Tensor(np.zeros((num_layers * num_directions, 32, 16), np.float32))
input_ms = Tensor(np.ones((3, 32, 32), np.float32))
# graph mode
context.set_context(mode=context.GRAPH_MODE)
@ -177,5 +177,5 @@ def test_sit_rnn_grad_input_3_32_32_is_32_hs_16():
x_grad_pynative = out_grad_pynative[0].asnumpy()
h_grad_pynative = out_grad_pynative[1].asnumpy()
assert np.allclose(x_grad, x_grad_pynative, 0.0001, 0.0001)
assert np.allclose(h_grad, h_grad_pynative, 0.0001, 0.0001)
assert np.allclose(x_grad, x_grad_pynative, 0.001, 0.001)
assert np.allclose(h_grad, h_grad_pynative, 0.001, 0.001)