!14791 add ascend lstm test case
From: @woshixiaoli Reviewed-by: @liangchenghui,@chujinjin Signed-off-by: @liangchenghui
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# Copyright 2021 Huawei Technologies Co., Ltd
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ==============================================================================
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import math
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import pytest
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import numpy as np
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from mindspore import context
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from mindspore import nn
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from mindspore import Tensor
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from mindspore.common.initializer import initializer
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from mindspore.common.parameter import ParameterTuple
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from mindspore.common.parameter import Parameter
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from mindspore.ops import composite as c
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class GradOfAllInputsAndParams(nn.Cell):
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def __init__(self, network, sens_param):
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super().__init__()
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self.grad = c.GradOperation(get_all=True, get_by_list=True, sens_param=sens_param)
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self.network = network
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self.params = ParameterTuple(self.network.trainable_params())
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def construct(self, *inputs):
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gout = self.grad(self.network, self.params)(*inputs)
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return gout
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class LSTM(nn.Cell):
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def __init__(self, input_s, hidden_s, num_layers, has_bias, batch_first, bidirectional, dropout):
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super().__init__()
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self.lstm = nn.LSTM(input_size=input_s, hidden_size=hidden_s, num_layers=num_layers, has_bias=has_bias,
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batch_first=batch_first, bidirectional=bidirectional, dropout=dropout)
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def construct(self, inp, h0, c0):
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return self.lstm(inp, (h0, c0))
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class LSTMWeightBias():
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def __init__(self, num_layers, has_bias, input_s, num_directions, hidden_s, bidirectional):
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self.num_layers = num_layers
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self.has_bias = has_bias
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self.input_s = input_s
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self.num_directions = num_directions
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self.hidden_s = hidden_s
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self.bidirectional = bidirectional
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def get_weight_bias(self):
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stdv = 1 / math.sqrt(self.hidden_s)
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gate_size = 4 * self.hidden_s
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w_list_value = []
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b_list_value = []
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for i in range(self.num_layers):
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b0 = np.zeros(gate_size, dtype=np.float16)
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w_shape = self.input_s if i == 0 else (self.num_directions * self.hidden_s)
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w_np = np.random.uniform(-stdv, stdv, (w_shape + self.hidden_s, gate_size)).astype(np.float16)
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w_list_value.append(Parameter(initializer(Tensor(w_np), [w_shape + self.hidden_s, gate_size]),
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name="weight_fw" + str(i)))
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if self.has_bias:
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b_np = np.random.uniform(-stdv, stdv, gate_size).astype(np.float16)
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b_list_value.append(Parameter(initializer(Tensor(b_np), [gate_size]), name="bias_fw" + str(i)))
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else:
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b_list_value.append(Parameter(initializer(Tensor(b0), [gate_size]), name="bias_fw" + str(i)))
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if self.bidirectional:
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w_bw_np = np.random.uniform(-stdv, stdv, (w_shape + self.hidden_s, gate_size)).astype(np.float16)
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b_list_value.append(Parameter(initializer(Tensor(w_bw_np), [w_shape + self.hidden_s, gate_size]),
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name="weight_bw" + str(i)))
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b_bw_np = np.random.uniform(-stdv, stdv, (4 * self.hidden_s)).astype(
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np.float16) if self.has_bias else b0
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b_list_value.append(Parameter(initializer(Tensor(b_bw_np), [gate_size]), name="bias_bw" + str(i)))
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w_list_value = ParameterTuple(w_list_value)
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b_list_value = ParameterTuple(b_list_value)
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return w_list_value, b_list_value
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@pytest.mark.level0
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@pytest.mark.platform_arm_ascend_training
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@pytest.mark.platform_x86_ascend_training
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@pytest.mark.env_onecard
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def test_sit_lstm_forward_input_3_32_32_is_32_hs_16():
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input_s = 32
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hidden_s = 16
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has_bias = True
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bidirectional = False
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num_layers = 1
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num_directions = 1
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fact = LSTMWeightBias(num_layers, has_bias, input_s, num_directions, hidden_s, bidirectional)
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w_list_value, b_list_value = fact.get_weight_bias()
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h0 = Tensor(np.random.randn(num_layers * 1, 32, 16).astype(np.float32))
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c0 = Tensor(np.random.randn(num_layers * 1, 32, 16).astype(np.float32))
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input_ms = Tensor(np.random.randn(3, 32, 32).astype(np.float32))
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# graph mode
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context.set_context(mode=context.GRAPH_MODE)
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net = LSTM(input_s=input_s, hidden_s=16, num_layers=num_layers, has_bias=has_bias, batch_first=False,
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bidirectional=bidirectional, dropout=0.0)
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net.lstm.w_list = w_list_value
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net.lstm.b_list = b_list_value
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out, (hy, cy) = net(input_ms, h0, c0)
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# pynative mode
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context.set_context(mode=context.PYNATIVE_MODE)
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net_pynative = LSTM(input_s=input_s, hidden_s=16, num_layers=num_layers, has_bias=has_bias, batch_first=False,
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bidirectional=bidirectional, dropout=0.0)
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net_pynative.lstm.w_list = w_list_value
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net_pynative.lstm.b_list = b_list_value
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out_pynative, (hy_pynative, cy_pynative) = net_pynative(input_ms, h0, c0)
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assert np.allclose(out.asnumpy(), out_pynative.asnumpy(), 0.0001, 0.0001)
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assert np.allclose(hy.asnumpy(), hy_pynative.asnumpy(), 0.0001, 0.0001)
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assert np.allclose(cy.asnumpy(), cy_pynative.asnumpy(), 0.0001, 0.0001)
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@pytest.mark.level0
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@pytest.mark.platform_arm_ascend_training
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@pytest.mark.platform_x86_ascend_training
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@pytest.mark.env_onecard
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def test_sit_lstm_grad_input_3_32_32_is_32_hs_16():
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input_s = 32
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hidden_s = 16
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has_bias = True
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bidirectional = False
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num_layers = 1
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num_directions = 1
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fact = LSTMWeightBias(num_layers, has_bias, input_s, num_directions, hidden_s, bidirectional)
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w_list_value, b_list_value = fact.get_weight_bias()
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h0 = Tensor(np.random.randn(num_layers * 1, 32, 16).astype(np.float32))
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c0 = Tensor(np.random.randn(num_layers * 1, 32, 16).astype(np.float32))
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input_ms = Tensor(np.random.randn(3, 32, 32).astype(np.float32))
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# graph mode
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context.set_context(mode=context.GRAPH_MODE)
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net = LSTM(input_s=input_s, hidden_s=16, num_layers=num_layers, has_bias=has_bias, batch_first=False,
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bidirectional=bidirectional, dropout=0.0)
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net.lstm.w_list = w_list_value
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net.lstm.b_list = b_list_value
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grad_net_inp = GradOfAllInputsAndParams(net, sens_param=False)
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grad_net_inp.set_train()
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out_grad, _ = grad_net_inp(input_ms, h0, c0)
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x_grad = out_grad[0].asnumpy()
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h_grad = out_grad[1].asnumpy()
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c_grad = out_grad[2].asnumpy()
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# pynative mode
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context.set_context(mode=context.PYNATIVE_MODE)
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net_pynative = LSTM(input_s=input_s, hidden_s=16, num_layers=num_layers, has_bias=has_bias, batch_first=False,
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bidirectional=bidirectional, dropout=0.0)
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net_pynative.lstm.w_list = w_list_value
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net_pynative.lstm.b_list = b_list_value
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grad_net_inp_pynative = GradOfAllInputsAndParams(net_pynative, sens_param=False)
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grad_net_inp_pynative.set_train()
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out_grad_pynative, _ = grad_net_inp_pynative(input_ms, h0, c0)
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x_grad_pynative = out_grad_pynative[0].asnumpy()
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h_grad_pynative = out_grad_pynative[1].asnumpy()
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c_grad_pynative = out_grad_pynative[2].asnumpy()
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assert np.allclose(x_grad, x_grad_pynative, 0.0001, 0.0001)
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assert np.allclose(h_grad, h_grad_pynative, 0.0001, 0.0001)
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assert np.allclose(c_grad, c_grad_pynative, 0.0001, 0.0001)
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