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local_add_cudnn_lstm
JiabinYang 6 years ago
parent c8801e100f
commit 32e05b01f2
4 changed files with 40 additions and 31 deletions
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9
paddle/fluid/operators/hierarchical_sigmoid_op.h
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@ -86,6 +86,7 @@ class HierarchicalSigmoidOpKernel : public framework::OpKernel<T> {
trans(ctx.template device_context<DeviceContext>(), pre_out_data,
pre_out_data + pre_out->numel(), pre_out_data,
ClipFunctor<T>(static_cast<T>(-40.0), static_cast<T>(40.0)));
pre_out_mat = -1 * pre_out_mat;
bit_code->Sum(*pre_out, out, static_cast<T>(-1));
// use softrelu to calculate cross entropy
pre_out_mat.device(place) = (static_cast<T>(1.0) + pre_out_mat.exp()).log();
@ -146,6 +147,7 @@ class HierarchicalSigmoidGradOpKernel : public framework::OpKernel<T> {
auto pre_out_mat = EigenMatrix<T>::From(*pre_out);
auto pre_out_grad_mat = EigenMatrix<T>::From(pre_out_grad);
auto out_grad_mat = EigenMatrix<T>::From(*out_grad);
Eigen::array<int, 2> bcast({{1, static_cast<int>(pre_out_grad.dims()[1])}});
// softrelu derivative
@ -160,9 +162,16 @@ class HierarchicalSigmoidGradOpKernel : public framework::OpKernel<T> {
bias_grad->mutable_data<T>(ctx.GetPlace());
zero(dev_ctx, bias_grad, static_cast<T>(0.0));
bit_code->AddGrad(pre_out_grad, bias_grad);
auto bias_grad_mat = EigenMatrix<T>::From(*bias_grad);
bias_grad_mat = -1 * bias_grad_mat;
}
bit_code->MulGradWeight(pre_out_grad, w_grad, *in);
bit_code->MulGradError(pre_out_grad, *w, in_grad);
auto w_grad_mat = EigenMatrix<T>::From(*w_grad);
auto in_grad_mat = EigenMatrix<T>::From(*in_grad);
w_grad_mat = -1 * w_grad_mat;
in_grad_mat = -1 * in_grad_mat;
}
};

2
paddle/fluid/operators/math/matrix_bit_code.h
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@ -157,7 +157,7 @@ class CustomCode : public Code {
int get_length() const {
int length = 0;
for (int i = 0; i < ptable_->dims()[1]; i++) {
for (int i = 0; i < static_cast<int>(ptable_->dims()[1]); i++) {
if (ptable_->data<R>()[index_ * static_cast<int>(ptable_->dims()[1]) +
i] != -1) {
length++;

7
python/paddle/fluid/tests/unittests/op_test.py
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@ -138,11 +138,8 @@ class OpTest(unittest.TestCase):
cls.dtype = "float32"
cls.outputs = {}
# np.random.seed(123)
# random.seed(124)
np.random.seed(190)
random.seed(200)
np.random.seed(123)
random.seed(124)
@classmethod
def tearDownClass(cls):

53
python/paddle/fluid/tests/unittests/test_hsigmoid_op.py
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@ -17,6 +17,9 @@ from __future__ import print_function
import unittest
import numpy as np
import math
# import paddle.fluid as fluid
# import paddle.fluid.core as core
# from op_builder import OpBuilder
from op_test import OpTest
np.random.seed(100)
@ -51,7 +54,7 @@ class CodeTableWithCustomTree(object):
def get_length(self):
length = 0
for ele in self.ptable_[self.index_]:
for ele in self.ptable_[self.index_]: # find the first -1 to stop trace
if ele >= 0:
length = length + 1
@ -71,12 +74,10 @@ def hsigmoid(x, w, label, bias, num_classes):
pre_sum = np.zeros((batch_size, 1))
out = np.zeros((batch_size, 1)).astype("float32")
for i in range(batch_size):
#print("\n leaf {leaf}: \n".format(leaf = label[i]))
code_table = CodeTable(num_classes, label[i])
length = code_table.get_length()
for j in range(length):
idx = code_table.cal_index(j)
#print("index {index} ".format(index = j))
pre_output[i][j] += bias[0][idx]
for i in range(batch_size):
code_table = CodeTable(num_classes, label[i])
@ -87,13 +88,12 @@ def hsigmoid(x, w, label, bias, num_classes):
# clip[-40.0, 40.0]
pre_output = np.clip(pre_output, -40.0, 40.0)
# out(i, 0) = \sum_j bit(i, j) * preout(i, j)
pre_output = -1 * pre_output
for i in range(batch_size):
#print("\n leaf {leaf}: \n".format(leaf = label[i]))
code_table = CodeTable(num_classes, label[i])
length = code_table.get_length()
sum = 0.0
for j in range(length):
#print("bit {bit} ".format(bit = code_table.cal_bit(j)))
if code_table.cal_bit(j):
sum += pre_output[i][j]
out[i] = -1.0 * sum
@ -108,6 +108,7 @@ def hsigmoidWithCustomTree(x, w, ptable, pcode, label, bias, num_classes):
batch_size = x.shape[0]
code_length = len(ptable[0])
code_table = [0 for _ in range(code_length)]
# init pre_out with shape [N, code_length]
pre_output = np.zeros((batch_size, code_length))
pre_sum = np.zeros((batch_size, 1))
out = np.zeros((batch_size, 1)).astype("float32")
@ -125,6 +126,7 @@ def hsigmoidWithCustomTree(x, w, ptable, pcode, label, bias, num_classes):
pre_output[i][j] += np.dot(w[idx], x[i])
# clip[-40.0, 40.0]
pre_output = np.clip(pre_output, -40.0, 40.0)
pre_output = -1 * pre_output
# out(i, 0) = \sum_j bit(i, j) * preout(i, j)
for i in range(batch_size):
code_table = CodeTableWithCustomTree(ptable, pcode, i)
@ -141,26 +143,27 @@ def hsigmoidWithCustomTree(x, w, ptable, pcode, label, bias, num_classes):
return pre_output, out
# class TestHSigmoidOp(OpTest):
# def setUp(self):
# self.op_type = "hierarchical_sigmoid"
# num_classes = 6
# feature_size = 8
# batch_size = 7
# x = np.random.random((batch_size, feature_size)).astype("float32")
# w = np.random.random((num_classes - 1, feature_size)).astype("float32")
# label = np.random.randint(0, num_classes, (batch_size, 1))
# bias = np.random.random((1, num_classes - 1)).astype("float32")
# self.attrs = {'num_classes': num_classes}
# self.inputs = {'X': x, 'W': w, 'Label': label, 'Bias': bias}
# pre_output, out = hsigmoid(x, w, label, bias, num_classes)
# self.outputs = {'PreOut': pre_output, 'Out': out}
class TestHSigmoidOp(OpTest):
def setUp(self):
self.op_type = "hierarchical_sigmoid"
num_classes = 6
feature_size = 8
batch_size = 4
x = np.random.random((batch_size, feature_size)).astype("float32") * 2
w = np.random.random(
(num_classes - 1, feature_size)).astype("float32") * 2
label = np.random.randint(0, num_classes, (batch_size, 1))
bias = np.random.random((1, num_classes - 1)).astype("float32")
self.attrs = {'num_classes': num_classes}
self.inputs = {'X': x, 'W': w, 'Label': label, 'Bias': bias}
pre_output, out = hsigmoid(x, w, label, bias, num_classes)
self.outputs = {'PreOut': pre_output, 'Out': out}
# def test_check_output(self):
# self.check_output()
def test_check_output(self):
self.check_output()
# def test_check_grad(self):
# self.check_grad(['Bias', 'X', 'W'], ['Out'], no_grad_set=set('Label'))
def test_check_grad(self):
self.check_grad(['Bias', 'X', 'W'], ['Out'], no_grad_set=set('Label'))
class TestHSigmoidOpWithCostumTree(OpTest):
@ -169,9 +172,9 @@ class TestHSigmoidOpWithCostumTree(OpTest):
num_classes = 6 #using 1,2,3,4,5,6 to build a huffman tree and select 1,2,5,6 as sample
feature_size = 8
batch_size = 4
x = np.random.random((batch_size, feature_size)).astype("float32") * 10
x = np.random.random((batch_size, feature_size)).astype("float32") * 2
w = np.random.random(
(num_classes - 1, feature_size)).astype("float32") * 10
(num_classes - 1, feature_size)).astype("float32") * 2
label = np.array([0, 1, 4, 5])
ptable = np.array(
[(0, 2, -1, -1, -1), (0, 1, 3, -1, -1), (0, 1, 4, -1, -1),

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