1.2K Star 8.4K Fork 1.4K

GVPMindSpore / mindspore

Create your Gitee Account
Explore and code with more than 6 million developers,Free private repositories !:)
Sign up
Clone or download
lstm.py 12.62 KB
Copy Edit Web IDE Raw Blame History
# 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.
# ============================================================================
"""LSTM."""
import math
import numpy as np
from mindspore import Tensor, nn, context, Parameter, ParameterTuple
from mindspore.common.initializer import initializer
from mindspore.ops import operations as P
import mindspore.ops.functional as F
import mindspore.common.dtype as mstype
STACK_LSTM_DEVICE = ["CPU"]
# Initialize short-term memory (h) and long-term memory (c) to 0
def lstm_default_state(batch_size, hidden_size, num_layers, bidirectional):
"""init default input."""
num_directions = 2 if bidirectional else 1
h = Tensor(np.zeros((num_layers * num_directions, batch_size, hidden_size)).astype(np.float32))
c = Tensor(np.zeros((num_layers * num_directions, batch_size, hidden_size)).astype(np.float32))
return h, c
def stack_lstm_default_state(batch_size, hidden_size, num_layers, bidirectional):
"""init default input."""
num_directions = 2 if bidirectional else 1
h_list = c_list = []
for _ in range(num_layers):
h_list.append(Tensor(np.zeros((num_directions, batch_size, hidden_size)).astype(np.float32)))
c_list.append(Tensor(np.zeros((num_directions, batch_size, hidden_size)).astype(np.float32)))
h, c = tuple(h_list), tuple(c_list)
return h, c
def stack_lstm_default_state_ascend(batch_size, hidden_size, num_layers, bidirectional):
"""init default input."""
h_list = c_list = []
for _ in range(num_layers):
h_fw = Tensor(np.zeros((1, batch_size, hidden_size)).astype(np.float16))
c_fw = Tensor(np.zeros((1, batch_size, hidden_size)).astype(np.float16))
h_i = [h_fw]
c_i = [c_fw]
if bidirectional:
h_bw = Tensor(np.zeros((1, batch_size, hidden_size)).astype(np.float16))
c_bw = Tensor(np.zeros((1, batch_size, hidden_size)).astype(np.float16))
h_i.append(h_bw)
c_i.append(c_bw)
h_list.append(h_i)
c_list.append(c_i)
h, c = tuple(h_list), tuple(c_list)
return h, c
class StackLSTM(nn.Cell):
"""
Stack multi-layers LSTM together.
"""
def __init__(self,
input_size,
hidden_size,
num_layers=1,
has_bias=True,
batch_first=False,
dropout=0.0,
bidirectional=False):
super(StackLSTM, self).__init__()
self.num_layers = num_layers
self.batch_first = batch_first
self.transpose = P.Transpose()
# direction number
num_directions = 2 if bidirectional else 1
# input_size list
input_size_list = [input_size]
for i in range(num_layers - 1):
input_size_list.append(hidden_size * num_directions)
# layers
layers = []
for i in range(num_layers):
layers.append(nn.LSTMCell(input_size=input_size_list[i],
hidden_size=hidden_size,
has_bias=has_bias,
batch_first=batch_first,
bidirectional=bidirectional,
dropout=dropout))
# weights
weights = []
for i in range(num_layers):
# weight size
weight_size = (input_size_list[i] + hidden_size) * num_directions * hidden_size * 4
if has_bias:
bias_size = num_directions * hidden_size * 4
weight_size = weight_size + bias_size
# numpy weight
stdv = 1 / math.sqrt(hidden_size)
w_np = np.random.uniform(-stdv, stdv, (weight_size, 1, 1)).astype(np.float32)
# lstm weight
weights.append(Parameter(initializer(Tensor(w_np), w_np.shape), name="weight" + str(i)))
#
self.lstms = layers
self.weight = ParameterTuple(tuple(weights))
def construct(self, x, hx):
"""construct"""
if self.batch_first:
x = self.transpose(x, (1, 0, 2))
# stack lstm
h, c = hx
hn = cn = None
for i in range(self.num_layers):
x, hn, cn, _, _ = self.lstms[i](x, h[i], c[i], self.weight[i])
if self.batch_first:
x = self.transpose(x, (1, 0, 2))
return x, (hn, cn)
class LSTM_Ascend(nn.Cell):
""" LSTM in Ascend. """
def __init__(self, bidirectional=False):
super(LSTM_Ascend, self).__init__()
self.bidirectional = bidirectional
self.dynamic_rnn = P.DynamicRNN(forget_bias=0.0)
self.reverseV2 = P.ReverseV2(axis=[0])
self.concat = P.Concat(2)
def construct(self, x, h, c, w_f, b_f, w_b=None, b_b=None):
"""construct"""
x = F.cast(x, mstype.float16)
if self.bidirectional:
y1, h1, c1, _, _, _, _, _ = self.dynamic_rnn(x, w_f, b_f, None, h[0], c[0])
r_x = self.reverseV2(x)
y2, h2, c2, _, _, _, _, _ = self.dynamic_rnn(r_x, w_b, b_b, None, h[1], c[1])
y2 = self.reverseV2(y2)
output = self.concat((y1, y2))
hn = self.concat((h1, h2))
cn = self.concat((c1, c2))
return output, (hn, cn)
y1, h1, c1, _, _, _, _, _ = self.dynamic_rnn(x, w_f, b_f, None, h[0], c[0])
return y1, (h1, c1)
class StackLSTMAscend(nn.Cell):
""" Stack multi-layers LSTM together. """
def __init__(self,
input_size,
hidden_size,
num_layers=1,
has_bias=True,
batch_first=False,
dropout=0.0,
bidirectional=False):
super(StackLSTMAscend, self).__init__()
self.num_layers = num_layers
self.batch_first = batch_first
self.bidirectional = bidirectional
self.transpose = P.Transpose()
# input_size list
input_size_list = [input_size]
for i in range(num_layers - 1):
input_size_list.append(hidden_size * 2)
#weights, bias and layers init
weights_fw = []
weights_bw = []
bias_fw = []
bias_bw = []
stdv = 1 / math.sqrt(hidden_size)
for i in range(num_layers):
# forward weight init
w_np_fw = np.random.uniform(-stdv,
stdv,
(input_size_list[i] + hidden_size, hidden_size * 4)).astype(np.float32)
w_fw = Parameter(initializer(Tensor(w_np_fw), w_np_fw.shape), name="w_fw_layer" + str(i))
weights_fw.append(w_fw)
# forward bias init
if has_bias:
b_fw = np.random.uniform(-stdv, stdv, (hidden_size * 4)).astype(np.float32)
b_fw = Parameter(initializer(Tensor(b_fw), b_fw.shape), name="b_fw_layer" + str(i))
else:
b_fw = np.zeros((hidden_size * 4)).astype(np.float32)
b_fw = Parameter(initializer(Tensor(b_fw), b_fw.shape), name="b_fw_layer" + str(i))
bias_fw.append(b_fw)
if bidirectional:
# backward weight init
w_np_bw = np.random.uniform(-stdv,
stdv,
(input_size_list[i] + hidden_size, hidden_size * 4)).astype(np.float32)
w_bw = Parameter(initializer(Tensor(w_np_bw), w_np_bw.shape), name="w_bw_layer" + str(i))
weights_bw.append(w_bw)
# backward bias init
if has_bias:
b_bw = np.random.uniform(-stdv, stdv, (hidden_size * 4)).astype(np.float32)
b_bw = Parameter(initializer(Tensor(b_bw), b_bw.shape), name="b_bw_layer" + str(i))
else:
b_bw = np.zeros((hidden_size * 4)).astype(np.float32)
b_bw = Parameter(initializer(Tensor(b_bw), b_bw.shape), name="b_bw_layer" + str(i))
bias_bw.append(b_bw)
# layer init
self.lstm = LSTM_Ascend(bidirectional=bidirectional).to_float(mstype.float16)
self.weight_fw = ParameterTuple(tuple(weights_fw))
self.weight_bw = ParameterTuple(tuple(weights_bw))
self.bias_fw = ParameterTuple(tuple(bias_fw))
self.bias_bw = ParameterTuple(tuple(bias_bw))
def construct(self, x, hx):
"""construct"""
x = F.cast(x, mstype.float16)
if self.batch_first:
x = self.transpose(x, (1, 0, 2))
# stack lstm
h, c = hx
hn = cn = None
for i in range(self.num_layers):
if self.bidirectional:
x, (hn, cn) = self.lstm(x,
h[i],
c[i],
self.weight_fw[i],
self.bias_fw[i],
self.weight_bw[i],
self.bias_bw[i])
else:
x, (hn, cn) = self.lstm(x, h[i], c[i], self.weight_fw[i], self.bias_fw[i])
if self.batch_first:
x = self.transpose(x, (1, 0, 2))
x = F.cast(x, mstype.float32)
hn = F.cast(x, mstype.float32)
cn = F.cast(x, mstype.float32)
return x, (hn, cn)
class SentimentNet(nn.Cell):
"""Sentiment network structure."""
def __init__(self,
vocab_size,
embed_size,
num_hiddens,
num_layers,
bidirectional,
num_classes,
weight,
batch_size):
super(SentimentNet, self).__init__()
# Mapp words to vectors
self.embedding = nn.Embedding(vocab_size,
embed_size,
embedding_table=weight)
self.embedding.embedding_table.requires_grad = False
self.trans = P.Transpose()
self.perm = (1, 0, 2)
if context.get_context("device_target") in STACK_LSTM_DEVICE:
# stack lstm by user
self.encoder = StackLSTM(input_size=embed_size,
hidden_size=num_hiddens,
num_layers=num_layers,
has_bias=True,
bidirectional=bidirectional,
dropout=0.0)
self.h, self.c = stack_lstm_default_state(batch_size, num_hiddens, num_layers, bidirectional)
elif context.get_context("device_target") == "GPU":
# standard lstm
self.encoder = nn.LSTM(input_size=embed_size,
hidden_size=num_hiddens,
num_layers=num_layers,
has_bias=True,
bidirectional=bidirectional,
dropout=0.0)
self.h, self.c = lstm_default_state(batch_size, num_hiddens, num_layers, bidirectional)
else:
self.encoder = StackLSTMAscend(input_size=embed_size,
hidden_size=num_hiddens,
num_layers=num_layers,
has_bias=True,
bidirectional=bidirectional)
self.h, self.c = stack_lstm_default_state_ascend(batch_size, num_hiddens, num_layers, bidirectional)
self.concat = P.Concat(1)
self.squeeze = P.Squeeze(axis=0)
if bidirectional:
self.decoder = nn.Dense(num_hiddens * 4, num_classes)
else:
self.decoder = nn.Dense(num_hiddens * 2, num_classes)
def construct(self, inputs):
# input:(64,500,300)
embeddings = self.embedding(inputs)
embeddings = self.trans(embeddings, self.perm)
output, _ = self.encoder(embeddings, (self.h, self.c))
# states[i] size(64,200) -> encoding.size(64,400)
encoding = self.concat((self.squeeze(output[0:1:1]), self.squeeze(output[499:500:1])))
outputs = self.decoder(encoding)
return outputs
Python
1
https://gitee.com/mindspore/mindspore.git
git@gitee.com:mindspore/mindspore.git
mindspore
mindspore
mindspore
r1.2

Search