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Enhance unit testing.

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1. user can disable peephole connections.
2. not calculate some gradients.
mobile_baidu
dangqingqing 7 years ago
parent d851dafed5
commit d94c936bd5
7 changed files with 167 additions and 63 deletions
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9
paddle/operators/lstm_op.cc
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@ -164,16 +164,19 @@ class LSTMOpMaker : public framework::OpProtoAndCheckerMaker {
"(string, default: sigmoid)"
"The activation for input gate, forget gate and output "
"gate, `sigmoid` by default.")
.SetDefault("sigmoid");
.SetDefault("sigmoid")
.InEnum({"sigmoid", "tanh", "relu", "identity"});
AddAttr<std::string>("cell_activation",
"(string, default: tanh)"
"The activation for cell output, `tanh` by defalut.")
.SetDefault("tanh");
.SetDefault("tanh")
.InEnum({"sigmoid", "tanh", "relu", "identity"});
AddAttr<std::string>("candidate_activation",
"(string, default: tanh)"
"The activation for candidate hidden state, "
"`tanh` by default.")
.SetDefault("tanh");
.SetDefault("tanh")
.InEnum({"sigmoid", "tanh", "relu", "identity"});
AddComment(R"DOC(
Long-Short Term Memory (LSTM) Operator.

12
paddle/operators/lstm_op.h
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@ -69,7 +69,7 @@ class LSTMKernel : public framework::OpKernel<T> {
}
math::LstmMetaValue<T> lstm_value;
if (bias) {
if (bias && ctx.Attr<bool>("use_peepholes")) {
T* bias_data = const_cast<T*>(bias->data<T>());
// the code style in LstmMetaValue will be updated later.
@ -85,6 +85,7 @@ class LSTMKernel : public framework::OpKernel<T> {
Tensor ordered_c0;
if (cell_t0) {
math::CopyMatrixRowsFunctor<Place, T> row_shuffle;
ordered_c0.mutable_data<T>(cell_t0->dims(), ctx.GetPlace());
const size_t* order = batch_gate->lod()[2].data();
row_shuffle(device_ctx, *cell_t0, order, ordered_c0, true);
lstm_value.prevStateValue = ordered_c0.data<T>();
@ -124,6 +125,7 @@ class LSTMKernel : public framework::OpKernel<T> {
} else if (hidden_t0) {
math::CopyMatrixRowsFunctor<Place, T> row_shuffle;
Tensor ordered_h0;
ordered_h0.mutable_data<T>(hidden_t0->dims(), ctx.GetPlace());
const size_t* order = batch_gate->lod()[2].data();
row_shuffle(device_ctx, *hidden_t0, order, ordered_h0, true);
math::matmul<Place, T>(device_ctx, ordered_h0, false, *weight, false,
@ -199,7 +201,7 @@ class LSTMGradKernel : public framework::OpKernel<T> {
PADDLE_ENFORCE_EQ(frame_size, out_dims[1]);
math::LstmMetaValue<T> lstm_value;
if (bias) {
if (bias && ctx.Attr<bool>("use_peepholes")) {
T* bias_data = const_cast<T*>(bias->data<T>());
lstm_value.checkIg = bias_data + 4 * frame_size;
lstm_value.checkFg = lstm_value.checkIg + frame_size;
@ -211,9 +213,13 @@ class LSTMGradKernel : public framework::OpKernel<T> {
}
math::LstmMetaGrad<T> lstm_grad;
if (bias && bias_g) {
T* bias_g_data = const_cast<T*>(bias_g->mutable_data<T>(ctx.GetPlace()));
bias_g->mutable_data<T>(ctx.GetPlace());
zero(device_ctx, bias_g, static_cast<T>(0.0));
}
if (bias && bias_g && ctx.Attr<bool>("use_peepholes")) {
T* bias_g_data = bias_g->data<T>();
lstm_grad.checkIgGrad = bias_g_data + 4 * frame_size;
lstm_grad.checkFgGrad = lstm_grad.checkIgGrad + frame_size;
lstm_grad.checkOgGrad = lstm_grad.checkFgGrad + frame_size;

40
paddle/operators/math/detail/lstm_cpu_kernel.h
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@ -52,9 +52,9 @@ void naive_lstm_forward_one_sequence(Op op, LstmMetaValue<T> value,
rValueIg = valueIg[i];
rValueFg = valueFg[i];
rValueOg = valueOg[i];
rCheckI = value.checkIg[i];
rCheckF = value.checkFg[i];
rCheckO = value.checkOg[i];
rCheckI = value.checkIg ? value.checkIg[i] : 0;
rCheckF = value.checkFg ? value.checkFg[i] : 0;
rCheckO = value.checkOg ? value.checkOg[i] : 0;
if (value.prevStateValue) {
rPrevState = value.prevStateValue[i];
@ -114,9 +114,9 @@ void naive_lstm_backward_one_sequence(Op op, LstmMetaValue<T> value,
rValueIg = valueIg[i];
rValueFg = valueFg[i];
rValueOg = valueOg[i];
rCheckI = value.checkIg[i];
rCheckF = value.checkFg[i];
rCheckO = value.checkOg[i];
rCheckI = value.checkIg ? value.checkIg[i] : 0;
rCheckF = value.checkFg ? value.checkFg[i] : 0;
rCheckO = value.checkOg ? value.checkOg[i] : 0;
rState = value.stateValue[i];
rStateAtv = value.stateActiveValue[i];
rOutputGrad = grad.outputGrad[i];
@ -155,9 +155,9 @@ void avx_lstm_forward_one_sequence(Op op, LstmMetaValue<T> value, int frameSize,
__m256 rValueIg;
__m256 rValueFg;
__m256 rValueOg;
__m256 rCheckI;
__m256 rCheckF;
__m256 rCheckO;
__m256 rCheckI = _mm256_set1_ps(0.0f);
__m256 rCheckF = _mm256_set1_ps(0.0f);
__m256 rCheckO = _mm256_set1_ps(0.0f);
__m256 rState;
__m256 rPrevState = _mm256_set1_ps(0.0f);
__m256 rStateAtv;
@ -173,9 +173,11 @@ void avx_lstm_forward_one_sequence(Op op, LstmMetaValue<T> value, int frameSize,
rValueIg = valueIg[i];
rValueFg = valueFg[i];
rValueOg = valueOg[i];
rCheckI = ((__m256 *)value.checkIg)[i];
rCheckF = ((__m256 *)value.checkFg)[i];
rCheckO = ((__m256 *)value.checkOg)[i];
if (value.checkIg) {
rCheckI = ((__m256 *)value.checkIg)[i];
rCheckF = ((__m256 *)value.checkFg)[i];
rCheckO = ((__m256 *)value.checkOg)[i];
}
if (value.prevStateValue) {
rPrevState = ((__m256 *)value.prevStateValue)[i];
@ -216,9 +218,9 @@ void avx_lstm_backward_one_sequence(Op op, LstmMetaValue<T> value,
__m256 rState;
__m256 rStateAtv;
__m256 rOutputGrad;
__m256 rCheckI;
__m256 rCheckF;
__m256 rCheckO;
__m256 rCheckI = _mm256_set1_ps(0.0f);
__m256 rCheckF = _mm256_set1_ps(0.0f);
__m256 rCheckO = _mm256_set1_ps(0.0f);
__m256 rCheckIGrad;
__m256 rCheckFGrad;
__m256 rCheckOGrad;
@ -237,9 +239,11 @@ void avx_lstm_backward_one_sequence(Op op, LstmMetaValue<T> value,
rValueIg = valueIg[i];
rValueFg = valueFg[i];
rValueOg = valueOg[i];
rCheckI = ((__m256 *)value.checkIg)[i];
rCheckF = ((__m256 *)value.checkFg)[i];
rCheckO = ((__m256 *)value.checkOg)[i];
if (value.checkIg) {
rCheckI = ((__m256 *)value.checkIg)[i];
rCheckF = ((__m256 *)value.checkFg)[i];
rCheckO = ((__m256 *)value.checkOg)[i];
}
rState = ((__m256 *)value.stateValue)[i];
rStateAtv = ((__m256 *)value.stateActiveValue)[i];
rOutputGrad = ((__m256 *)grad.outputGrad)[i];

14
paddle/operators/math/detail/lstm_gpu_kernel.h
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@ -55,9 +55,10 @@ __global__ void KeLstmForward(Op op, LstmMetaValue<T> value, int frameSize,
T rValueIg;
T rValueFg;
T rValueOg;
T rCheckI = value.checkIg[frameIdx];
T rCheckF = value.checkFg[frameIdx];
T rCheckO = value.checkOg[frameIdx];
T rCheckI = value.checkIg ? value.checkIg[frameIdx] : 0;
T rCheckF = value.checkFg ? value.checkFg[frameIdx] : 0;
T rCheckO = value.checkOg ? value.checkOg[frameIdx] : 0;
rValueIn = value.gateValue[frameIdx];
rValueIg = value.gateValue[frameIdx + frameSize];
@ -121,9 +122,10 @@ __global__ void KeLstmBackward(Op op, LstmMetaValue<T> value,
T rStateGrad;
T rStateAtv;
T rOutputGrad;
T rCheckI = value.checkIg[frameIdx];
T rCheckF = value.checkFg[frameIdx];
T rCheckO = value.checkOg[frameIdx];
T rCheckI = value.checkIg ? value.checkIg[frameIdx] : 0;
T rCheckF = value.checkFg ? value.checkFg[frameIdx] : 0;
T rCheckO = value.checkOg ? value.checkOg[frameIdx] : 0;
T rCheckIGrad;
T rCheckFGrad;
T rCheckOGrad;

11
paddle/operators/math/sequence2batch.h
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@ -31,7 +31,7 @@ class CopyMatrixRowsFunctor {
// The indexed rows are based on the input index.
void operator()(const platform::DeviceContext& context,
const framework::Tensor& src, const size_t* index,
framework::Tensor* dst, bool is_src_index);
framework::Tensor& dst, bool is_src_index);
};
template <typename Place, typename T>
@ -57,7 +57,7 @@ class LoDTensor2BatchFunctor {
bool is_reverse = false) const {
if (!is_cal_batch_lod) {
auto lods = batch.lod();
PADDLE_ENFORCE_LE(lods.size(), 2UL);
PADDLE_ENFORCE_GT(lods.size(), 2UL);
PADDLE_ENFORCE_EQ(lods[1].size(),
static_cast<size_t>(lod_tensor.dims()[0]));
CopyMatrixRowsFunctor<Place, T> to_batch;
@ -68,8 +68,6 @@ class LoDTensor2BatchFunctor {
auto lods = lod_tensor.lod();
auto lod = lods[0];
PADDLE_ENFORCE_EQ(lods.size(), 1UL, "Only support one level sequence now.");
PADDLE_ENFORCE_EQ(lod_tensor.dims()[0],
static_cast<int64_t>(lod.size() - 1));
std::vector<SeqInfo> seq_info;
for (size_t seq_id = 0; seq_id < lod.size() - 1; ++seq_id) {
@ -112,7 +110,7 @@ class LoDTensor2BatchFunctor {
int num_batch = seq_info[0].length;
batch_lods[0].resize(static_cast<size_t>(num_batch + 1));
// batch_lods[1] is the raw index in the input LoDTensor
batch_lods[1].resize(static_cast<size_t>(seq_info.size()));
batch_lods[1].resize(static_cast<size_t>(lod_tensor.dims()[0]));
// batch_lods[2] is the sort order for the input LoDTensor.
batch_lods[2].resize(seq_info.size());
@ -152,8 +150,7 @@ class Batch2LoDTensorFunctor {
const framework::LoDTensor& batch,
framework::LoDTensor& lod_tensor) const {
auto in_lod = batch.lod();
PADDLE_ENFORCE_LT(in_lod.size(), 2UL,
"The LoD size of input `batch` should be 2.");
PADDLE_ENFORCE_GT(in_lod.size(), 2UL);
PADDLE_ENFORCE_EQ(in_lod[1].size(),
static_cast<size_t>(lod_tensor.dims()[0]));
CopyMatrixRowsFunctor<Place, T> to_seq;

142
python/paddle/v2/framework/tests/test_lstm_op.py
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@ -117,9 +117,9 @@ class TestLstmOp(OpTest):
self.act_cell = 'tanh'
self.act_cand = 'tanh'
self.has_initial_state = True
self.has_bias = True
self.has_initial_state = False
self.is_reverse = False
self.use_peepholes = True
def setUp(self):
self.set_argument()
@ -129,21 +129,27 @@ class TestLstmOp(OpTest):
N = len(self.lod[0]) - 1
x = np.random.normal(size=(T, 4 * self.D)).astype('float64')
h0 = np.zeros((N, self.D)).astype('float64')
c0 = np.zeros((N, self.D)).astype('float64')
if self.has_initial_state:
h0 = np.random.normal(size=(N, self.D)).astype('float64')
c0 = np.random.normal(size=(N, self.D)).astype('float64')
else:
h0 = np.zeros((N, self.D)).astype('float64')
c0 = np.zeros((N, self.D)).astype('float64')
w = np.random.normal(size=(self.D, 4 * self.D)).astype('float64')
b = np.random.normal(size=(1, 7 * self.D)).astype('float64')
if self.use_peepholes:
b = np.random.normal(size=(1, 7 * self.D)).astype('float64')
else:
b = np.random.normal(size=(1, 4 * self.D)).astype('float64')
w_b = b[:, 0:4 * self.D] if self.has_bias else None
w_c = b[:, 4 * self.D:] if self.has_bias else None
w_b = b[:, 0:4 * self.D]
w_c = b[:, 4 * self.D:] if self.use_peepholes else None
h, c = lstm(x, self.lod, h0, c0, w, w_b, w_c, self.is_reverse,
ACTVATION[self.act_gate], ACTVATION[self.act_cell],
ACTVATION[self.act_cand])
self.inputs = {'Input': (x, self.lod), 'Weight': w}
if self.has_bias:
self.inputs['Bias'] = b
self.inputs['Bias'] = b
if self.has_initial_state:
self.inputs['H0'] = h0
@ -154,18 +160,17 @@ class TestLstmOp(OpTest):
'Cell': (c, self.lod),
}
self.attrs = {
'use_peepholes': True,
'use_peepholes': self.use_peepholes,
'is_reverse': self.is_reverse,
'gate_activation': self.act_gate,
'cell_activation': self.act_cell,
'candidate_activation': self.act_cand
}
def not_test_check_output(self):
def test_check_output(self):
self.check_output(atol=1e-8)
#TODO(qingqing) add more unit testing case
def not_test_check_grad(self):
def test_check_grad(self):
# TODO(qingqing) remove folowing lines after the check_grad is refined.
N = len(self.lod[0]) - 1
self.outputs['BatchGate'] = np.zeros((N, 4 * self.D)).astype('float64')
@ -174,8 +179,38 @@ class TestLstmOp(OpTest):
self.check_grad(
['Input', 'Weight', 'Bias'], ['Hidden'], max_relative_error=5e-4)
def test_check_grad_ingore_bias(self):
N = len(self.lod[0]) - 1
self.outputs['BatchGate'] = np.zeros((N, 4 * self.D)).astype('float64')
self.outputs['BatchCellPreAct'] = np.zeros(
(N, self.D)).astype('float64')
self.check_grad(
['Input', 'Weight'], ['Hidden'],
max_relative_error=5e-4,
no_grad_set=set('Bias'))
def test_check_grad_ingore_weight(self):
N = len(self.lod[0]) - 1
self.outputs['BatchGate'] = np.zeros((N, 4 * self.D)).astype('float64')
self.outputs['BatchCellPreAct'] = np.zeros(
(N, self.D)).astype('float64')
self.check_grad(
['Input', 'Bias'], ['Hidden'],
max_relative_error=5e-4,
no_grad_set=set('Weight'))
def test_check_grad_ingore_input(self):
N = len(self.lod[0]) - 1
self.outputs['BatchGate'] = np.zeros((N, 4 * self.D)).astype('float64')
self.outputs['BatchCellPreAct'] = np.zeros(
(N, self.D)).astype('float64')
self.check_grad(
['Weight', 'Bias'], ['Hidden'],
max_relative_error=5e-4,
no_grad_set=set('Input'))
class TestLstmOpHasNoInitial(TestLstmOp):
class TestLstmOpHasInitial(TestLstmOp):
def set_argument(self):
self.lod = [[0, 2, 5, 7]]
self.D = 16
@ -184,12 +219,52 @@ class TestLstmOpHasNoInitial(TestLstmOp):
self.act_cell = 'tanh'
self.act_cand = 'tanh'
self.has_initial_state = False
self.has_initial_state = True
self.is_reverse = True
self.has_bias = True
self.use_peepholes = True
def test_check_grad(self):
# TODO(qingqing) remove folowing lines after the check_grad is refined.
N = len(self.lod[0]) - 1
self.outputs['BatchGate'] = np.zeros((N, 4 * self.D)).astype('float64')
self.outputs['BatchCellPreAct'] = np.zeros(
(N, self.D)).astype('float64')
self.check_grad(
['Input', 'Weight', 'Bias', 'H0', 'C0'], ['Hidden'],
max_relative_error=5e-4)
class TestLstmOpHasNoBias(TestLstmOp):
# In order to speed up, skip following testing
def test_check_grad_ingore_bias(self):
return
def test_check_grad_ingore_weight(self):
return
def test_check_grad_ingore_input(self):
return
def test_check_grad_ingore_h0(self):
N = len(self.lod[0]) - 1
self.outputs['BatchGate'] = np.zeros((N, 4 * self.D)).astype('float64')
self.outputs['BatchCellPreAct'] = np.zeros(
(N, self.D)).astype('float64')
self.check_grad(
['Input', 'Weight', 'Bias', 'C0'], ['Hidden'],
max_relative_error=5e-4,
no_grad_set=set('H0'))
def test_check_grad_ingore_c0(self):
N = len(self.lod[0]) - 1
self.outputs['BatchGate'] = np.zeros((N, 4 * self.D)).astype('float64')
self.outputs['BatchCellPreAct'] = np.zeros(
(N, self.D)).astype('float64')
self.check_grad(
['Input', 'Weight', 'Bias', 'H0'], ['Hidden'],
max_relative_error=5e-4,
no_grad_set=set('C0'))
class TestLstmOpRerverse(TestLstmOp):
def set_argument(self):
self.lod = [[0, 2, 5, 7]]
self.D = 16
@ -198,15 +273,22 @@ class TestLstmOpHasNoBias(TestLstmOp):
self.act_cell = 'tanh'
self.act_cand = 'tanh'
self.has_initial_state = True
self.is_reverse = False
self.has_bias = False
self.has_initial_state = False
self.is_reverse = True
self.use_peepholes = True
def test_check_output(self):
self.check_output(atol=1e-8)
# In order to speed up, skip following testing
def test_check_grad_ingore_bias(self):
return
def test_check_grad_ingore_weight(self):
return
class TestLstmOpRerverse(TestLstmOp):
def test_check_grad_ingore_input(self):
return
class TestLstmOpNotUsePeepholes(TestLstmOp):
def set_argument(self):
self.lod = [[0, 2, 5, 7]]
self.D = 16
@ -215,9 +297,19 @@ class TestLstmOpRerverse(TestLstmOp):
self.act_cell = 'tanh'
self.act_cand = 'tanh'
self.has_initial_state = True
self.has_initial_state = False
self.is_reverse = True
self.has_bias = True
self.use_peepholes = False
# In order to speed up, skip following testing
def test_check_grad_ingore_bias(self):
return
def test_check_grad_ingore_weight(self):
return
def test_check_grad_ingore_input(self):
return
if __name__ == '__main__':

2
python/paddle/v2/optimizer.py
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@ -102,7 +102,7 @@ class Momentum(Optimizer):
.. math::
v_{t} &= k * v_{t-1} - \\gamma_t / (g_{t} + \\lambda w_{t-1}) \\\\
v_{t} &= k * v_{t-1} - \\gamma_t (g_{t} + \\lambda w_{t-1}) \\\\
w_{t} &= w_{t-1} + v_{t} \\\\
where, :math:`k` is momentum, :math:`\\lambda` is decay rate,

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