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lstm_common.hpp
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lstm_common.hpp
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/*******************************************************************************
*
* MIT License
*
* Copyright (c) 2023 Advanced Micro Devices, Inc.
*
* Permission is hereby granted, free of charge, to any person obtaining a copy
* of this software and associated documentation files (the "Software"), to deal
* in the Software without restriction, including without limitation the rights
* to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
* copies of the Software, and to permit persons to whom the Software is
* furnished to do so, subject to the following conditions:
*
* The above copyright notice and this permission notice shall be included in all
* copies or substantial portions of the Software.
*
* THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
* IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
* FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
* AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
* LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
* OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
* SOFTWARE.
*
*******************************************************************************/
#ifndef GUARD_MIOPEN_TEST_LSTM_COMMON_HPP
#define GUARD_MIOPEN_TEST_LSTM_COMMON_HPP
#include "workspace.hpp"
#include "driver.hpp"
#include "dropout_util.hpp"
#include "get_handle.hpp"
#include "tensor_holder.hpp"
#include "test.hpp"
#include "verify.hpp"
#include "rnn_util.hpp"
#include "random.hpp"
#include "cpu_rnn.hpp"
#include <array>
#include <cmath>
#include <ctime>
#include <iomanip>
#include <iostream>
#include <iterator>
#include <limits>
#include <memory>
#include <miopen/rnn.hpp>
#include <miopen/miopen.h>
#include <miopen/tensor.hpp>
#include <utility>
#include <cfloat>
#define MIO_LSTM_TEST_DEBUG 0
#define MIO_RNN_TIME_EVERYTHING 0
#define WORKAROUND_ISSUE_692 1
//****************************************************
// FORWARD BASE
//****************************************************
template <class T>
struct verify_forward_lstm
{
std::vector<T> input{};
std::vector<T> initHidden{};
std::vector<T> initCell{};
std::vector<T> weights{};
std::vector<int> batch_seq{};
int hiddenSize{};
int seqLength{};
int nLayers{};
int biasMode{};
int dirMode{};
int inputMode{};
int batch_n{};
int inputVecLen{};
miopenRNNDescriptor_t rnnDesc{};
size_t realHiddenSize{};
bool nohx{};
bool nocx{};
bool nohy{};
bool nocy{};
bool use_seqPadding{};
};
//****************************************************
// BACKWARDS DATA
//****************************************************
template <class T>
struct verify_backward_data_lstm
{
std::vector<T> yin; // Y
std::vector<T> dy; // dY
std::vector<T> dhy; // dHY
std::vector<T> dcy; // dHY
std::vector<T> initHidden; // HX
std::vector<T> initCell; // CX
std::vector<T> weights;
std::vector<T>& RSVgpu;
std::vector<T>& RSVcpu;
std::vector<int> batch_seq;
int hiddenSize;
int seqLength;
int nLayers;
int biasMode;
int dirMode;
int inputMode;
int batch_n;
int inputVecLen;
miopenRNNDescriptor_t rnnDesc;
size_t realHiddenSize;
bool nohx;
bool nocx;
bool nodhy;
bool nodcy;
bool nodhx;
bool nodcx;
bool use_dropout;
bool use_seqPadding;
verify_backward_data_lstm(miopenRNNDescriptor_t pRD,
const std::vector<T>& py,
const std::vector<T>& pdy,
const std::vector<T>& pdhy,
const std::vector<T>& phx,
const std::vector<T>& pdcy,
const std::vector<T>& pcx,
const std::vector<T>& pW,
std::vector<T>& pRSVgpu,
std::vector<T>& pRSVcpu,
const std::vector<int>& pBS,
const int pHS,
const int pBN,
const int pS,
const int pNL,
const int pBM,
const int pDM,
const int pIM,
const int pVL,
const size_t pHXZ,
const bool pnohx = false,
const bool pnocx = false,
const bool pnodhy = false,
const bool pnodcy = false,
const bool pnodhx = false,
const bool pnodcx = false,
const bool puse_dropout = false,
const bool puse_seqPadding = false)
: yin(py),
dy(pdy),
dhy(pdhy),
dcy(pdcy),
initHidden(phx),
initCell(pcx),
weights(pW),
RSVgpu(pRSVgpu),
RSVcpu(pRSVcpu),
batch_seq(pBS),
hiddenSize(pHS),
seqLength(pS),
nLayers(pNL),
biasMode(pBM),
dirMode(pDM),
inputMode(pIM),
batch_n(pBN),
inputVecLen(pVL),
rnnDesc(pRD),
realHiddenSize(pHXZ),
nohx(pnohx),
nocx(pnocx),
nodhy(pnodhy),
nodcy(pnodcy),
nodhx(pnodhx),
nodcx(pnodcx),
use_dropout(puse_dropout),
use_seqPadding(puse_seqPadding)
{
if(!nohx)
initHidden = phx; // this may be intentionally a nullptr
else
initHidden.resize(realHiddenSize);
if(!nocx)
initCell = pcx; // this may be intentionally a nullptr
else
initCell.resize(realHiddenSize);
if(!nodhy)
dhy = pdhy; // this may be intentionally a nullptr
else
dhy.resize(realHiddenSize);
if(!nodcy)
dcy = pdcy; // this may be intentionally a nullptr
else
dcy.resize(realHiddenSize);
}
std::tuple<std::vector<T>, std::vector<T>, std::vector<T>, std::vector<T>> cpu() const;
std::tuple<std::vector<T>, std::vector<T>, std::vector<T>, std::vector<T>> gpu() const;
void fail(int badtensor) const
{
std::cout << "./bin/MIOpenDriver rnn -n ";
for(int i = 0; i < seqLength; i++)
{
if(i < seqLength - 1)
{
std::cout << batch_seq.at(i) << ",";
}
else
{
std::cout << batch_seq.at(i);
}
}
std::cout << " -m lstm "
<< " -k " << seqLength << " -H " << hiddenSize << " -W " << inputVecLen << " -l "
<< nLayers << " -F 0 "
<< " -r " << dirMode << " -b " << biasMode << " -p " << inputMode << " -q "
<< use_seqPadding << std::endl;
std::cout << "inputMode: " << inputMode << " biasMode: " << biasMode
<< " dirMode: " << dirMode << std::endl;
std::cout << "hz: " << hiddenSize << " batch_n: " << batch_n << " seqLength: " << seqLength
<< " inputLen: " << inputVecLen << " numLayers: " << nLayers
<< " useDropout: " << int(use_dropout) << std::endl;
std::cout << "Backward Data LSTM: " << std::endl;
switch(badtensor)
{
case(0): std::cout << "Output dx tensor report." << std::endl; break;
case(1): std::cout << "Hidden state dhx tensor report." << std::endl; break;
case(2): std::cout << "Hidden cell dcx tensor report." << std::endl; break;
case(3): std::cout << "Workspace space tensor report." << std::endl; break;
default: break;
}
}
};
//~~~~~~~~~~~~ END BACKWARD DATA ~~~~~~~~~~~~~~~~~~~~~~~~
//****************************************************
// BACKWARDS WEIGHTS
//****************************************************
template <class T>
struct verify_backward_weights_lstm
{
std::vector<T> input; // Y
std::vector<T> dy; // dY
std::vector<T> initHidden; // HX
std::vector<T> reserveSpace_gpu;
std::vector<T> reserveSpace_cpu;
std::vector<T> workSpace;
std::vector<int> batch_seq;
int weightSize;
int hiddenSize;
int seqLength;
int nLayers;
int biasMode;
int dirMode;
int inputMode;
int batch_n;
int inputVecLen;
miopenRNNDescriptor_t rnnDesc;
size_t realHiddenSize;
bool nohx;
bool use_dropout;
bool use_seqPadding;
verify_backward_weights_lstm(miopenRNNDescriptor_t pRD,
const std::vector<T>& px,
const std::vector<T>& pdy,
const std::vector<T>& phx,
const std::vector<T>& pRSVgpu,
const std::vector<T>& pRSVcpu,
const std::vector<T>& pWS,
const std::vector<int>& pBS,
const int pHS,
const int pW,
const int pBN,
const int pS,
const int pNL,
const int pBM,
const int pDM,
const int pIM,
const int pVL,
const size_t pHXZ,
const bool pnohx = false,
const bool puse_dropout = false,
const bool puse_seqPadding = false)
: input(px),
dy(pdy),
initHidden(phx),
reserveSpace_gpu(pRSVgpu),
reserveSpace_cpu(pRSVcpu),
workSpace(pWS),
batch_seq(pBS),
weightSize(pW),
hiddenSize(pHS),
seqLength(pS),
nLayers(pNL),
biasMode(pBM),
dirMode(pDM),
inputMode(pIM),
batch_n(pBN),
inputVecLen(pVL),
rnnDesc(pRD),
realHiddenSize(pHXZ),
nohx(pnohx),
use_dropout(puse_dropout),
use_seqPadding(puse_seqPadding)
{
if(!nohx)
initHidden = phx; // this may be intentionally a nullptr
else
initHidden.resize(realHiddenSize);
}
std::vector<T> cpu() const;
std::vector<T> gpu() const;
void fail(int) const
{
std::cout << "./bin/MIOpenDriver rnn -n ";
for(int i = 0; i < seqLength; i++)
{
if(i < seqLength - 1)
{
std::cout << batch_seq.at(i) << ",";
}
else
{
std::cout << batch_seq.at(i);
}
}
std::cout << " -m lstm "
<< " -k " << seqLength << " -H " << hiddenSize << " -W " << inputVecLen << " -l "
<< nLayers << " -F 0 "
<< " -r " << dirMode << " -b " << biasMode << " -p " << inputMode << " -q "
<< use_seqPadding << std::endl;
std::cout << "inputMode: " << inputMode << " biasMode: " << biasMode
<< " dirMode: " << dirMode << std::endl;
std::cout << "hz: " << hiddenSize << " batch_n: " << batch_n << " seqLength: " << seqLength
<< " inputLen: " << inputVecLen << " numLayers: " << nLayers
<< " useDropout: " << int(use_dropout) << std::endl;
std::cout << "Backward Weights LSTM: " << std::endl;
}
};
//~~~~~~~~~~~~ END BACKWARD WEIGHTS ~~~~~~~~~~~~~~~~~~~~~~~~
//****************************************************
// FORWARD INFERENCE
//****************************************************
template <class T>
struct verify_forward_infer_lstm : verify_forward_lstm<T>
{
using verify_forward_lstm<T>::input;
using verify_forward_lstm<T>::initHidden;
using verify_forward_lstm<T>::initCell;
using verify_forward_lstm<T>::weights;
using verify_forward_lstm<T>::batch_seq;
using verify_forward_lstm<T>::hiddenSize;
using verify_forward_lstm<T>::seqLength;
using verify_forward_lstm<T>::nLayers;
using verify_forward_lstm<T>::biasMode;
using verify_forward_lstm<T>::dirMode;
using verify_forward_lstm<T>::inputMode;
using verify_forward_lstm<T>::batch_n;
using verify_forward_lstm<T>::inputVecLen;
using verify_forward_lstm<T>::rnnDesc;
using verify_forward_lstm<T>::realHiddenSize;
using verify_forward_lstm<T>::nohx;
using verify_forward_lstm<T>::nocx;
using verify_forward_lstm<T>::nohy;
using verify_forward_lstm<T>::nocy;
using verify_forward_lstm<T>::use_seqPadding;
verify_forward_infer_lstm(miopenRNNDescriptor_t pRD,
const std::vector<T>& px,
const std::vector<T>& phx,
const std::vector<T>& pcx,
const std::vector<T>& pW,
const std::vector<int>& pBS,
const int pHS,
const int pBN,
const int pS,
const int pNL,
const int pBM,
const int pDM,
const int pIM,
const int pVL,
const size_t pHXZ,
const bool pnohx = false,
const bool pnocx = false,
const bool pnohy = false,
const bool pnocy = false,
const bool puse_seqPadding = false)
{
input = px;
initHidden = phx;
initCell = pcx;
weights = pW;
batch_seq = pBS;
hiddenSize = pHS;
seqLength = pS;
nLayers = pNL;
biasMode = pBM;
dirMode = pDM;
inputMode = pIM;
batch_n = pBN;
inputVecLen = pVL;
rnnDesc = pRD;
realHiddenSize = pHXZ;
nohx = pnohx;
nocx = pnocx;
nohy = pnohy;
nocy = pnocy;
use_seqPadding = puse_seqPadding;
if(!nohx)
initHidden = phx; // this may be intentionally a nullptr
else
initHidden.resize(realHiddenSize);
if(!nocx)
initCell = pcx; // this may be intentionally a nullptr
else
initCell.resize(realHiddenSize);
}
std::vector<T> cpu() const
{
#if(MIO_RNN_TIME_EVERYTHING == 1)
auto t_start = std::chrono::high_resolution_clock::now();
#endif
auto&& handle = get_handle();
int bi = dirMode != 0 ? 2 : 1;
int hy_h = hiddenSize;
int bi_stride = bi * hy_h;
size_t out_sz = 0;
size_t reserveSpaceSize;
std::vector<miopen::TensorDescriptor> inputCPPDescs;
std::vector<miopenTensorDescriptor_t> inputDescs;
createTensorDescArray(
inputCPPDescs, inputDescs, batch_seq, inputVecLen, miopen::deref(rnnDesc).dataType);
std::vector<miopen::TensorDescriptor> outputCPPDescs;
std::vector<miopenTensorDescriptor_t> outputDescs;
createTensorDescArray(outputCPPDescs,
outputDescs,
batch_seq,
hiddenSize * ((dirMode != 0) ? 2 : 1),
miopen::deref(rnnDesc).dataType);
miopenGetRNNInputTensorSize(&handle, rnnDesc, seqLength, outputDescs.data(), &out_sz);
miopenGetRNNTrainingReserveSize(
&handle, rnnDesc, seqLength, inputDescs.data(), &reserveSpaceSize);
if(miopen::deref(rnnDesc).algoMode == miopenRNNdefault)
{
reserveSpaceSize /= sizeof(T);
reserveSpaceSize -=
nLayers * std::accumulate(batch_seq.begin(), batch_seq.begin() + seqLength, 0ULL) *
hiddenSize * bi;
reserveSpaceSize *= 2;
reserveSpaceSize *= sizeof(T);
}
std::vector<T> reserveSpace(reserveSpaceSize / sizeof(T));
std::vector<T> output(out_sz / sizeof(T));
std::vector<T> hiddenState(initHidden.size());
std::vector<T> cellState(initCell.size());
LSTMFwdCPUVerify(handle,
false,
miopen::deref(miopen::deref(rnnDesc).dropoutDesc),
input,
weights, // [ input_state_weight_trans
// hidden_state_weight0_trans input1_trans
// hidden1_trans ... output_weight;
// bidirectional reversed weights ]
hiddenState, // current/final hidden state
initHidden, // initial hidden state
cellState, // current/final cell state
initCell, // initial cell state
output,
batch_seq, // input batch size
inputVecLen, // input data length
seqLength, // Number of iterations to unroll over
dirMode, // whether using bidirectional net
biasMode, // whether using bias
bi * nLayers, // 1 by numlayer (number of stacks of hidden layers)
// for unidirection, 2 by numlayer for bidirection
batch_seq.at(0), // equal to input batch size in_n[0]
hiddenSize, // hidden state number
bi_stride, // 1 by hy_h related function for unidirection, 2 by hy_h
// related function for bidirection
inputMode,
reserveSpace,
nohx,
nocx);
#if(MIO_LSTM_TEST_DEBUG == 2)
for(int i = 0; i < output.size(); i++)
{
printf("CPU outdata[%d]: %f\n", i, output[i]);
}
#endif
#if(MIO_RNN_TIME_EVERYTHING == 1)
auto t_end = std::chrono::high_resolution_clock::now();
std::cout << "Wall clock: CPU forward inference LSTM pass time: "
<< std::chrono::duration<double>(t_end - t_start).count() << " seconds."
<< std::endl;
#endif
#if(MIO_LSTM_TEST_DEBUG > 0)
std::cout << "Done with LSTM forward inference CPU" << std::endl;
std::cout << "---------------------------------\n" << std::endl;
#endif
return output;
}
std::vector<T> gpu() const
{
#if(MIO_RNN_TIME_EVERYTHING == 1)
auto t_start = std::chrono::high_resolution_clock::now();
#endif
auto&& handle = get_handle();
size_t out_sz = 0;
std::vector<miopen::TensorDescriptor> inputCPPDescs;
std::vector<miopenTensorDescriptor_t> inputDescs;
createTensorDescArray(
inputCPPDescs, inputDescs, batch_seq, inputVecLen, miopen::deref(rnnDesc).dataType);
std::vector<miopen::TensorDescriptor> outputCPPDescs;
std::vector<miopenTensorDescriptor_t> outputDescs;
createTensorDescArray(outputCPPDescs,
outputDescs,
batch_seq,
hiddenSize * ((dirMode != 0) ? 2 : 1),
miopen::deref(rnnDesc).dataType);
size_t workspace_size = 0;
miopenGetRNNWorkspaceSize(&handle, rnnDesc, seqLength, inputDescs.data(), &workspace_size);
Workspace wspace{workspace_size};
auto input_dev = handle.Write(input);
miopenGetRNNInputTensorSize(&handle, rnnDesc, seqLength, outputDescs.data(), &out_sz);
std::vector<T> output(out_sz / sizeof(T));
auto output_dev = handle.Write(output);
auto weights_dev = handle.Write(weights);
auto hy = initHidden;
std::fill(hy.begin(), hy.end(), 0.);
auto cy = initCell;
std::fill(cy.begin(), cy.end(), 0.);
std::vector<int> hlens(3, 0);
hlens[0] = nLayers * (dirMode != 0 ? 2 : 1);
hlens[1] = batch_seq[0];
hlens[2] = hiddenSize;
miopen::TensorDescriptor hiddenDesc(miopen::deref(rnnDesc).dataType, hlens);
std::vector<int> wlen(1, 0);
wlen[0] = weights.size();
miopen::TensorDescriptor weightDesc(miopen::deref(rnnDesc).dataType, wlen);
/// \todo: fix the handle.Write() calls below because they generate
/// temporary objects that may get destroyed before the
/// miopenRNNForwardInference call happens
miopenRNNForwardInference(&handle,
rnnDesc,
seqLength,
inputDescs.data(),
input_dev.get(),
&hiddenDesc,
((nohx) ? nullptr : handle.Write(initHidden).get()),
&hiddenDesc,
((nocx) ? nullptr : handle.Write(initCell).get()),
&weightDesc,
weights_dev.get(),
outputDescs.data(),
output_dev.get(),
&hiddenDesc,
((nohy) ? nullptr : handle.Write(hy).get()),
&hiddenDesc,
((nocy) ? nullptr : handle.Write(cy).get()),
wspace.ptr(),
wspace.size());
#if(MIO_LSTM_TEST_DEBUG == 2)
auto outdata = handle.Read<T>(output_dev, output.size());
for(int i = 0; i < outdata.size(); i++)
{
printf("GPU outdata[%d]: %f\n", i, outdata[i]);
}
#endif
#if(MIO_RNN_TIME_EVERYTHING == 1)
auto t_end = std::chrono::high_resolution_clock::now();
std::cout << "Wall clock: GPU forward_infer LSTM pass time: "
<< std::chrono::duration<double>(t_end - t_start).count() << " seconds."
<< std::endl;
#endif
#if(MIO_LSTM_TEST_DEBUG > 0)
std::cout << "Done with LSTM forward inference GPU" << std::endl;
#endif
return (handle.Read<T>(output_dev, output.size()));
}
void fail(int) const
{
std::cout << "./bin/MIOpenDriver rnn -n ";
for(int i = 0; i < seqLength; i++)
{
if(i < seqLength - 1)
{
std::cout << batch_seq.at(i) << ",";
}
else
{
std::cout << batch_seq.at(i);
}
}
std::cout << " -m lstm -k " << seqLength << " -H " << hiddenSize << " -W " << inputVecLen
<< " -l " << nLayers << " -F 0 -r " << dirMode << " -b " << biasMode << " -p "
<< inputMode << " -q " << use_seqPadding << std::endl;
std::cout << "inputMode: " << inputMode << " biasMode: " << biasMode
<< " dirMode: " << dirMode << std::endl;
std::cout << "hz: " << hiddenSize << " batch_n: " << batch_n << " seqLength: " << seqLength
<< " inputLen: " << inputVecLen << " numLayers: " << nLayers << std::endl;
std::cout << "Forward Inference LSTM: " << std::endl;
std::cout << "Output tensor report." << std::endl;
}
};
//~~~~~~~~~~~~ END FWD INFERENCE ~~~~~~~~~~~~~~~~~~~~~~~~
//****************************************************
// FORWARD TRAIN
//****************************************************
template <class T>
struct verify_forward_train_lstm : verify_forward_lstm<T>
{
using verify_forward_lstm<T>::input;
using verify_forward_lstm<T>::initHidden;
using verify_forward_lstm<T>::initCell;
using verify_forward_lstm<T>::weights;
using verify_forward_lstm<T>::batch_seq;
using verify_forward_lstm<T>::hiddenSize;
using verify_forward_lstm<T>::seqLength;
using verify_forward_lstm<T>::nLayers;
using verify_forward_lstm<T>::biasMode;
using verify_forward_lstm<T>::dirMode;
using verify_forward_lstm<T>::inputMode;
using verify_forward_lstm<T>::batch_n;
using verify_forward_lstm<T>::inputVecLen;
using verify_forward_lstm<T>::rnnDesc;
using verify_forward_lstm<T>::realHiddenSize;
using verify_forward_lstm<T>::nohx;
using verify_forward_lstm<T>::nocx;
using verify_forward_lstm<T>::nohy;
using verify_forward_lstm<T>::nocy;
using verify_forward_lstm<T>::use_seqPadding;
std::vector<T>& RSVgpu;
std::vector<T>& RSVcpu;
bool use_dropout;
verify_forward_train_lstm(miopenRNNDescriptor_t pRD,
const std::vector<T>& px,
const std::vector<T>& phx,
const std::vector<T>& pcx,
const std::vector<T>& pW,
const std::vector<int>& pBS,
std::vector<T>& pRSVgpu,
std::vector<T>& pRSVcpu,
const int pHS,
const int pBN,
const int pS,
const int pNL,
const int pBM,
const int pDM,
const int pIM,
const int pVL,
const size_t pHXZ,
const bool pnohx = false,
const bool pnocx = false,
const bool pnohy = false,
const bool pnocy = false,
const bool puse_dropout = false,
const bool puse_seqPadding = false)
: RSVgpu(pRSVgpu), RSVcpu(pRSVcpu)
{
input = px;
initHidden = phx;
initCell = pcx;
weights = pW;
batch_seq = pBS;
hiddenSize = pHS;
seqLength = pS;
nLayers = pNL;
biasMode = pBM;
dirMode = pDM;
inputMode = pIM;
batch_n = pBN;
inputVecLen = pVL;
rnnDesc = pRD;
realHiddenSize = pHXZ;
nohx = pnohx;
nocx = pnocx;
nohy = pnohy;
nocy = pnocy;
use_dropout = puse_dropout;
use_seqPadding = puse_seqPadding;
if(!nohx)
initHidden = phx; // this may be intentionally a nullptr
else
initHidden.resize(realHiddenSize);
if(!nocx)
initCell = pcx; // this may
else
initCell.resize(realHiddenSize);
}
std::tuple<std::vector<T>, std::vector<T>, std::vector<T>> cpu() const
{
#if(MIO_RNN_TIME_EVERYTHING == 1)
auto t_start = std::chrono::high_resolution_clock::now();
#endif
auto&& handle = get_handle();
int bi = dirMode != 0 ? 2 : 1;
int hy_h = hiddenSize;
int bi_stride = bi * hy_h;
int out_h = hiddenSize * ((dirMode != 0) ? 2 : 1);
size_t out_sz = getSuperTensorSize(batch_seq,
seqLength,
inputVecLen,
hiddenSize,
batch_seq[0],
dirMode != 0,
false,
use_seqPadding);
std::vector<miopen::TensorDescriptor> inputCPPDescs;
std::vector<miopenTensorDescriptor_t> inputDescs;
createTensorDescArray(
inputCPPDescs, inputDescs, batch_seq, inputVecLen, miopen::deref(rnnDesc).dataType);
std::vector<miopen::TensorDescriptor> outputCPPDescs;
std::vector<miopenTensorDescriptor_t> outputDescs;
createTensorDescArray(
outputCPPDescs, outputDescs, batch_seq, out_h, miopen::deref(rnnDesc).dataType);
size_t inputBatchLenSum =
std::accumulate(batch_seq.begin(), batch_seq.begin() + seqLength, 0ULL);
size_t reserveSpaceSize;
reserveSpaceSize = 2 * 6 * miopen::deref(rnnDesc).nLayers * inputBatchLenSum * out_h;
if(use_dropout)
{
reserveSpaceSize += (miopen::deref(rnnDesc).nLayers - 1) * inputBatchLenSum * out_h;
reserveSpaceSize *= sizeof(T);
reserveSpaceSize += (miopen::deref(rnnDesc).nLayers - 1) * inputBatchLenSum * out_h;
reserveSpaceSize = (reserveSpaceSize + sizeof(T) - 1) / sizeof(T);
}
std::vector<T> reserveSpace(reserveSpaceSize);
std::vector<T> output(out_sz);
std::vector<T> hiddenState(initHidden.size());
std::vector<T> cellState(initCell.size());
std::vector<T> converted_input;
std::vector<T> converted_output;
const std::vector<T>* packed_input;
std::vector<T>* packed_output;
if(use_seqPadding)
{
size_t packedXInSize, packedYOutSize;
std::tie(packedXInSize, packedYOutSize) =
GetTempPackedBuffersSize(batch_seq, inputVecLen, out_h);
converted_input.resize(packedXInSize);
converted_output.resize(packedYOutSize);
ChangeDataPadding(input, converted_input, batch_seq, batch_seq[0], inputVecLen, false);
packed_input = &converted_input;
packed_output = &converted_output;
}
else
{
packed_input = &input;
packed_output = &output;
}
LSTMFwdCPUVerify(handle,
use_dropout,
miopen::deref(miopen::deref(rnnDesc).dropoutDesc),
*packed_input,
weights, // [ input_state_weight_trans
// hidden_state_weight0_trans input1_trans
// hidden1_trans ... output_weight;
// bidirectional reversed weights ]
hiddenState, // current/final hidden state
initHidden, // initial hidden state
cellState, // current/final cell state
initCell, // initial cell state
*packed_output,
batch_seq, // input batch size
inputVecLen, // input data length
seqLength, // Number of iterations to unroll over
dirMode, // whether using bidirectional net
biasMode, // whether using bias
bi * nLayers, // 1 by numlayer (number of stacks of hidden layers)
// for unidirection, 2 by numlayer for bidirection
batch_seq.at(0), // equal to input batch size in_n[0]
hiddenSize, // hidden state number
bi_stride, // 1 by hy_h related function for unidirection, 2 by hy_h
// related function for bidirection
inputMode,
reserveSpace,
nohx,
nocx);
#if(MIO_LSTM_TEST_DEBUG == 2)
for(int i = 0; i < output.size(); i++)
{
std::cout << "CPU outdata[" << i << "]: " << output[i] << std::endl;
}
#endif
#if(MIO_RNN_TIME_EVERYTHING == 1)
auto t_end = std::chrono::high_resolution_clock::now();
std::cout << "Wall clock: CPU forward train LSTM pass time: "
<< std::chrono::duration<double>(t_end - t_start).count() << " seconds."
<< std::endl;
#endif
if(use_seqPadding)
{
ChangeDataPadding(*packed_output, output, batch_seq, batch_seq[0], out_h, true);
}
if(reserveSpace.size() != RSVcpu.size())
{
std::abort();
}
std::copy(reserveSpace.begin(), reserveSpace.end(), RSVcpu.begin());
auto retSet = std::make_tuple(
output, (nohy ? initHidden : hiddenState), (nocy ? initCell : cellState));
#if(MIO_LSTM_TEST_DEBUG > 0)
std::cout << "Done with LSTM forward train CPU" << std::endl;
std::cout << "---------------------------------\n" << std::endl;
#endif
return retSet;
}
std::tuple<std::vector<T>, std::vector<T>, std::vector<T>> gpu() const
{
#if(MIO_RNN_TIME_EVERYTHING == 1)
auto t_start = std::chrono::high_resolution_clock::now();
#endif
auto&& handle = get_handle();
std::vector<miopen::TensorDescriptor> inputCPPDescs;
std::vector<miopenTensorDescriptor_t> inputDescs;
createTensorDescArray(
inputCPPDescs, inputDescs, batch_seq, inputVecLen, miopen::deref(rnnDesc).dataType);
auto input_dev = handle.Write(input);
std::vector<miopen::TensorDescriptor> outputCPPDescs;
std::vector<miopenTensorDescriptor_t> outputDescs;
createTensorDescArray(outputCPPDescs,
outputDescs,
batch_seq,
hiddenSize * ((dirMode != 0) ? 2 : 1),
miopen::deref(rnnDesc).dataType);
std::vector<T> output(getSuperTensorSize(batch_seq,
seqLength,
inputVecLen,
hiddenSize,
batch_seq[0],
dirMode != 0,
false,
use_seqPadding));
std::fill(output.begin(), output.end(), static_cast<T>(0));
auto output_dev = handle.Write(output);
size_t workspace_size = 0;
miopenGetRNNWorkspaceSize(&handle, rnnDesc, seqLength, inputDescs.data(), &workspace_size);
Workspace wspace{workspace_size};
size_t reserveSpaceSize = 0;
miopenGetRNNTrainingReserveSize(
&handle, rnnDesc, seqLength, inputDescs.data(), &reserveSpaceSize);
reserveSpaceSize = (reserveSpaceSize + (sizeof(T) - 1)) & ~(sizeof(T) - 1);
Workspace rspace{reserveSpaceSize};
auto weights_dev = handle.Write(weights);
auto hy = initHidden;
std::fill(hy.begin(), hy.end(), 0.);
auto hy_dev = handle.Write(hy);
auto cy = initCell;
std::fill(cy.begin(), cy.end(), 0.);
auto cy_dev = handle.Write(cy);
std::vector<int> hlens(3, 0);
hlens[0] = nLayers * (dirMode != 0 ? 2 : 1);
hlens[1] = batch_seq[0];
hlens[2] = hiddenSize;
miopen::TensorDescriptor hiddenDesc(miopen::deref(rnnDesc).dataType, hlens);
std::vector<int> wlen(1, 0);
wlen[0] = weights.size();
miopen::TensorDescriptor weightDesc(miopen::deref(rnnDesc).dataType, wlen);
miopenRNNForwardTraining(&handle,
rnnDesc,
seqLength,
inputDescs.data(),
input_dev.get(),
&hiddenDesc,
((nohx) ? nullptr : handle.Write(initHidden).get()),
&hiddenDesc,
((nocx) ? nullptr : handle.Write(initCell).get()),
&weightDesc,
weights_dev.get(),
outputDescs.data(),
output_dev.get(),
&hiddenDesc,
((nohy) ? nullptr : hy_dev.get()),
&hiddenDesc,
((nocy) ? nullptr : cy_dev.get()),
wspace.ptr(),
wspace.size(),
rspace.ptr(),
rspace.size());
#if(MIO_LSTM_TEST_DEBUG == 2)
auto outdata = handle.Read<T>(output_dev, output.size());
for(int i = 0; i < outdata.size(); i++)
{
printf("GPU outdata[%d]: %f\n", i, outdata[i]);
}
#endif
rspace.ReadTo(RSVgpu);
std::vector<T> output_gpu = handle.Read<T>(output_dev, output.size());
auto retSet = std::make_tuple(output_gpu,
(nohy ? initHidden : handle.Read<T>(hy_dev, hy.size())),
(nocy ? initCell : handle.Read<T>(cy_dev, cy.size())));
#if(MIO_RNN_TIME_EVERYTHING == 1)
auto t_end = std::chrono::high_resolution_clock::now();
std::cout << "Wall clock: GPU forward_train LSTM pass time: "
<< std::chrono::duration<double>(t_end - t_start).count() << " seconds."
<< std::endl;
#endif
#if(MIO_LSTM_TEST_DEBUG > 0)
std::cout << "Done with RNN forward train GPU" << std::endl;
#endif
return retSet;
}
void fail(int badtensor) const
{