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src/nnet3/nnet-derivative-test.cc
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// nnet3/nnet-derivative-test.cc
// Copyright 2015 Johns Hopkins University (author: Daniel Povey)
// See ../../COPYING for clarification regarding multiple authors
//
// 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
//
// THIS CODE IS PROVIDED *AS IS* BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
// KIND, EITHER EXPRESS OR IMPLIED, INCLUDING WITHOUT LIMITATION ANY IMPLIED
// WARRANTIES OR CONDITIONS OF TITLE, FITNESS FOR A PARTICULAR PURPOSE,
// MERCHANTABLITY OR NON-INFRINGEMENT.
// See the Apache 2 License for the specific language governing permissions and
// limitations under the License.
#include "nnet3/nnet-nnet.h"
#include "nnet3/nnet-compile.h"
#include "nnet3/nnet-analyze.h"
#include "nnet3/nnet-test-utils.h"
#include "nnet3/nnet-optimize.h"
#include "nnet3/nnet-compute.h"
namespace kaldi {
namespace nnet3 {
void ComputeMinAndMaxTimes(const std::vector<Index> &indexes,
int32 *min_t,
int32 *max_t) {
KALDI_ASSERT(!indexes.empty());
*min_t = indexes[0].t;
*max_t = *min_t;
for (int32 n = 1; n < static_cast<int32>(indexes.size()); n++) {
*min_t = std::min(*min_t, indexes[n].t);
*max_t = std::max(*max_t, indexes[n].t);
}
}
// This function is called if you want to set min_deriv_time and max_deriv_time.
// It works out some meaningful values to set, based on the config.
void SetDerivTimesOptions(const ComputationRequest &request,
NnetOptimizeOptions *opt_config) {
int32 min_t, max_t;
KALDI_ASSERT(request.inputs[0].name == "input");
const std::vector<Index> &input_indexes = request.inputs[0].indexes;
ComputeMinAndMaxTimes(input_indexes, &min_t, &max_t);
int32 orig_min_t = min_t, orig_max_t = max_t;
int t_length = max_t + 1 - min_t;
KALDI_ASSERT(t_length > 0);
if (t_length == 1)
return;
if (RandInt(0, 2) == 0) {
// remove as much as 4 frames from the left (but don't remove everything).
min_t += std::min(4, RandInt(0, t_length - 1));
opt_config->min_deriv_time = min_t;
t_length = max_t + 1 - min_t;
KALDI_ASSERT(t_length > 0);
}
if (RandInt(0, 2) == 0) {
max_t -= std::min(4, RandInt(0, t_length - 1));
opt_config->max_deriv_time = max_t;
t_length = max_t + 1 - min_t;
KALDI_ASSERT(t_length > 0);
}
if (RandInt(0, 4) == 0) {
// ensure that all derivs will be pruned away;
// this tests more code.
min_t = orig_min_t - 10;
max_t = min_t + 1;
}
int32 output_min_t, output_max_t;
KALDI_ASSERT(request.outputs[0].name == "output");
ComputeMinAndMaxTimes(request.outputs[0].indexes,
&output_min_t, &output_max_t);
KALDI_LOG << "ComputationRequest has output (min,max) = (" << output_min_t
<< ',' << output_max_t << "), input (min,max) = (" << orig_min_t
<< ',' << orig_max_t << "), limiting deriv times to ("
<< opt_config->min_deriv_time << ','
<< opt_config->max_deriv_time << ')';
}
// This test makes sure that the model-derivatives are correct.
void UnitTestNnetModelDerivatives() {
int32 N = 20;
for (int32 n = 0; n < N; n++) {
struct NnetGenerationOptions gen_config;
//gen_config.allow_nonlinearity = false;
//gen_config.allow_recursion = false;
//gen_config.allow_final_nonlinearity = true;
bool limit_deriv_times = (RandInt(0, 2) == 0);
std::vector<std::string> configs;
GenerateConfigSequence(gen_config, &configs);
Nnet nnet;
for (size_t j = 0; j < configs.size(); j++) {
KALDI_LOG << "Input config[" << j << "] is: " << configs[j];
std::istringstream is(configs[j]);
nnet.ReadConfig(is);
}
ComputationRequest request;
std::vector<Matrix<BaseFloat> > inputs;
ComputeExampleComputationRequestSimple(nnet, &request, &inputs);
// make sure that a model-derivative is requested, and an output-derivative
// is supplied.
request.need_model_derivative = true;
request.outputs[0].has_deriv = true;
// whether input-derivatives are required or not does not matter,
// so leave it as it is in that regard.
NnetOptimizeOptions optimize_opts;
CachingOptimizingCompilerOptions compiler_opts;
if (limit_deriv_times) {
SetDerivTimesOptions(request, &optimize_opts);
}
CachingOptimizingCompiler compiler(nnet, optimize_opts,
compiler_opts);
const NnetComputation &computation = *(compiler.Compile(request));
{
std::ostringstream os;
computation.Print(os, nnet);
KALDI_LOG << "Optimized computation is: " << os.str();
}
Nnet nnet_deriv(nnet);
ScaleNnet(0.0, &nnet_deriv);
SetNnetAsGradient(&nnet_deriv); // forces "simple" update and unit
// learning rate.
int32 num_directions = 4; // must be >= 1. Best if it's >1, will reduce
// the probability of random failures.
// the order of these vectors is:
// [ un-perturbed, perturbed-1, perturbed-2, perturbed-3 ].
std::vector<BaseFloat> measured_objf(num_directions + 1, 0.0),
predicted_objf_change(num_directions + 1, 0.0);
BaseFloat delta = 5.0e-04;
// output_deriv is the derivative of the objective function w.r.t. the
// (single) output. We make the objf a linear function of the output and
// just set the output_deriv to be a random matrix, which defines the
// objective function.
CuMatrix<BaseFloat> output_deriv;
output_deriv.Resize(request.outputs[0].indexes.size(),
nnet.OutputDim("output"));
output_deriv.SetRandn();
NnetComputeOptions compute_opts;
if (RandInt(0, 1) == 0)
compute_opts.debug = true;
// pass 0 is the forward pass with the un-perturbed model.
// Other passes are with various differently-perturbed versions of
// the model.
for (int32 pass = 0; pass <= num_directions; pass++) {
Nnet nnet_copy(nnet);
if (pass > 0)
PerturbParams(delta, &nnet_copy);
NnetComputer computer(compute_opts,
computation,
nnet_copy,
(pass == 0 ? &nnet_deriv : &nnet_copy));
// provide the input to the computation.
for (size_t i = 0; i < request.inputs.size(); i++) {
CuMatrix<BaseFloat> temp(inputs[i]);
computer.AcceptInput(request.inputs[i].name, &temp);
}
KALDI_LOG << "Running forward computation";
computer.Run();
const CuMatrixBase<BaseFloat> &output(computer.GetOutput("output"));
KALDI_LOG << "Output sum for pass " << pass << " is " << output.Sum();
BaseFloat objf = TraceMatMat(output, output_deriv, kTrans);
measured_objf[pass] = objf;
if (pass == 0) {
// we need to do the backward computation (to get the model derivative)
CuMatrix<BaseFloat> temp(output_deriv);
computer.AcceptInput("output", &temp);
KALDI_LOG << "Running backward computation";
computer.Run();
} else {
// work out the predicted objf-change as dot-product of deriv and
// parameter-change. The expression below can be interpreted as
// DotProduct(nnet_copy - nnet, nnet_deriv).
predicted_objf_change[pass] = DotProduct(nnet_copy, nnet_deriv) -
DotProduct(nnet, nnet_deriv);
}
}
Vector<BaseFloat> predicted_objf_change_vec(num_directions),
measured_objf_change_vec(num_directions);
for (int32 d = 0; d < num_directions; d++) {
BaseFloat predicted_change = predicted_objf_change[d+1],
measured_change = measured_objf[d+1] - measured_objf[0];
predicted_objf_change_vec(d) = predicted_change;
measured_objf_change_vec(d) = measured_change;
}
KALDI_LOG << "Vector of predicted objf-change is: "
<< predicted_objf_change_vec;
KALDI_LOG << "Vector of measured objf-change is: "
<< measured_objf_change_vec;
BaseFloat delta_thresh_warn = 0.05, delta_thresh_fail = 0.25;
if (limit_deriv_times) {
KALDI_LOG << "Not checking that predicted/measured changes matched "
<< "because we limited times of derivatives.";
} else {
if (!ApproxEqual(predicted_objf_change_vec,
measured_objf_change_vec, delta_thresh_fail)) {
if (NnetIsRecurrent(nnet)) {
KALDI_WARN << "Predicted and measured objf-changes differ too much. "
<< "(would normally be beyond error threshold, but this "
<< "nnet is recurrent, so letting it pass.";
} else {
KALDI_ERR << "Predicted and measured objf-changes differ too much.";
}
}
if (!ApproxEqual(predicted_objf_change_vec,
measured_objf_change_vec, delta_thresh_warn)) {
KALDI_WARN << "Predicted and measured objf-changes differ quite a lot.";
}
}
}
}
// This test makes sure that the input-derivatives are correct.
void UnitTestNnetInputDerivatives() {
int32 N = 20;
for (int32 n = 0; n < N; n++) {
struct NnetGenerationOptions gen_config;
//gen_config.allow_nonlinearity = false;
//gen_config.allow_recursion = false;
//gen_config.allow_final_nonlinearity = true;
bool allow_optimization = true;
std::vector<std::string> configs;
GenerateConfigSequence(gen_config, &configs);
Nnet nnet;
for (size_t j = 0; j < configs.size(); j++) {
KALDI_LOG << "Input config[" << j << "] is: " << configs[j];
std::istringstream is(configs[j]);
nnet.ReadConfig(is);
}
ComputationRequest request;
std::vector<Matrix<BaseFloat> > inputs;
ComputeExampleComputationRequestSimple(nnet, &request, &inputs);
// make sure that all inputs and outputs have derivatives requested/provided,
// and that the model-update (need_model_derivative) is not requested.
request.need_model_derivative = false;
for (int32 i = 0; i < request.inputs.size(); i++)
request.inputs[i].has_deriv = true;
request.outputs[0].has_deriv = true;
NnetComputation computation;
Compiler compiler(request, nnet);
CompilerOptions opts;
compiler.CreateComputation(opts, &computation);
{
std::ostringstream os;
computation.Print(os, nnet);
KALDI_LOG << "Generated computation is: " << os.str();
}
CheckComputationOptions check_config;
// we can do the rewrite check since it's before optimization.
check_config.check_rewrite = true;
ComputationChecker checker(check_config, nnet, computation);
checker.Check();
if (RandInt(0, 3) != 0 && allow_optimization) {
NnetOptimizeOptions opt_config;
// opt_config.initialize_undefined = false; // temp
Optimize(opt_config, nnet,
MaxOutputTimeInRequest(request),
&computation);
std::ostringstream os;
computation.Print(os, nnet);
KALDI_LOG << "Optimized computation is: " << os.str();
}
NnetComputeOptions compute_opts;
if (RandInt(0, 1) == 0)
compute_opts.debug = true;
computation.ComputeCudaIndexes();
int32 num_directions = 3; // must be >= 1. Best if it's >1, will reduce
// the probability of random failures.
// the order of these vectors is:
// [ un-perturbed, perturbed-1, perturbed-2, perturbed-3, un-perturbed ].
// we compute un-perturbed twice to double-check the model did not change.
std::vector<BaseFloat> measured_objf(num_directions + 2, 0.0),
predicted_objf_change(num_directions + 2, 0.0);
BaseFloat delta = 1.0e-03;
// output_deriv is the derivative of the objective function w.r.t. the
// (single) output. We make the objf a linear function of the output and
// just set the output_deriv to be a random matrix, which defines the
// objective function.
CuMatrix<BaseFloat> output_deriv;
output_deriv.Resize(request.outputs[0].indexes.size(),
nnet.OutputDim("output"));
output_deriv.SetRandn();
std::vector<CuMatrix<BaseFloat> > delta_inputs(inputs.size());
std::vector<CuMatrix<BaseFloat> > input_derivs(inputs.size());
// pass 0 is the forward pass with the un-perturbed features; so is
// pass num_directions + 1.
// Other passes are with various differently-perturbed versions of
// the features.
for (int32 pass = 0; pass <= num_directions + 1; pass++) {
// the only reason we might need to provide the &nnet parameter is if the
// StoreStats() operation had been requested. We made sure no model update
// is being performed.
NnetComputer computer(compute_opts,
computation,
nnet,
&nnet);
// provide the input to the computations.
for (size_t i = 0; i < request.inputs.size(); i++) {
CuMatrix<BaseFloat> temp(inputs[i]);
if (pass > 0 && pass <= num_directions) { // Perturb the input randomly.
delta_inputs[i].Resize(inputs[i].NumRows(), inputs[i].NumCols());
delta_inputs[i].SetRandn();
delta_inputs[i].Scale(delta);
// if there are >1 inputs, sometimes set the delta for input 0 to
// zero. might sometimes give more accurate test of error in iVector
// derivative computation.
if (i == 0 && request.inputs.size() > 1 && RandInt(0, 1) == 0)
delta_inputs[i].SetZero();
temp.AddMat(1.0, delta_inputs[i]);
predicted_objf_change[pass] += TraceMatMat(input_derivs[i],
delta_inputs[i], kTrans);
}
computer.AcceptInput(request.inputs[i].name, &temp);
}
KALDI_LOG << "Running forward computation";
computer.Run();
const CuMatrixBase<BaseFloat> &output(computer.GetOutput("output"));
KALDI_LOG << "Output sum for pass " << pass << " is " << output.Sum();
BaseFloat objf = TraceMatMat(output, output_deriv, kTrans);
measured_objf[pass] = objf;
if (pass == 0) {
// We need to compute the input derivatives.
CuMatrix<BaseFloat> temp(output_deriv);
computer.AcceptInput("output", &temp);
KALDI_LOG << "Running backward computation";
computer.Run();
for (size_t i = 0; i < request.inputs.size(); i++) {
input_derivs[i] = computer.GetOutput(request.inputs[i].name);
KALDI_LOG << "Input-deriv norm for '" << request.inputs[i].name
<< "' is " << input_derivs[i].FrobeniusNorm();
}
}
}
KALDI_ASSERT(ApproxEqual(measured_objf[0],
measured_objf[num_directions + 1]));
Vector<BaseFloat> predicted_objf_change_vec(num_directions),
measured_objf_change_vec(num_directions);
for (int32 d = 0; d < num_directions; d++) {
BaseFloat predicted_change = predicted_objf_change[d+1],
measured_change = measured_objf[d+1] - measured_objf[0];
predicted_objf_change_vec(d) = predicted_change;
measured_objf_change_vec(d) = measured_change;
}
KALDI_LOG << "Vector of predicted objf-change is: "
<< predicted_objf_change_vec;
KALDI_LOG << "Vector of measured objf-change is: "
<< measured_objf_change_vec;
BaseFloat delta_thresh_warn = 0.05, delta_thresh_fail = 0.25;
if (!ApproxEqual(predicted_objf_change_vec,
measured_objf_change_vec, delta_thresh_fail)) {
if (NnetIsRecurrent(nnet)) {
KALDI_WARN << "Predicted and measured objf-changes differ too much. "
<< "(would normally be beyond error threshold, but this "
<< "nnet is recurrent, so letting it pass.";
} else {
KALDI_ERR << "Predicted and measured objf-changes differ too much.";
}
} else if (!ApproxEqual(predicted_objf_change_vec,
measured_objf_change_vec, delta_thresh_warn)) {
KALDI_WARN << "Predicted and measured objf-changes differ quite a lot";
}
}
}
} // namespace nnet3
} // namespace kaldi
int main() {
using namespace kaldi;
using namespace kaldi::nnet3;
SetVerboseLevel(3);
#if HAVE_CUDA == 1
kaldi::int32 loop = 0;
for (loop = 0; loop < 2; loop++) {
CuDevice::Instantiate().SetDebugStrideMode(true);
if (loop == 0)
CuDevice::Instantiate().SelectGpuId("no");
else
CuDevice::Instantiate().SelectGpuId("yes");
#endif
UnitTestNnetModelDerivatives();
UnitTestNnetInputDerivatives();
#if HAVE_CUDA == 1
} // No for loop if 'HAVE_CUDA != 1',
CuDevice::Instantiate().PrintProfile();
#endif
KALDI_LOG << "Nnet derivative tests succeeded.";
return 0;
}
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