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Merge pull request #3907 from naxingyu/sync-pybind11-with-master
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Sync pybind11 with master
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@danpovey
danpovey authored Feb 4, 2020
2 parents a1091f4 + d0f7b5e commit ee517cd
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Showing 10 changed files with 167 additions and 79 deletions.
4 changes: 2 additions & 2 deletions src/cudafeat/feature-online-batched-cmvn-cuda-kernels.cu
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Original file line number Diff line number Diff line change
Expand Up @@ -18,13 +18,13 @@
#include <cub/cub.cuh>
#include "cudafeat/feature-online-batched-cmvn-cuda-kernels.h"

__device__ inline float2 operator-(const float2 &a, const float2 &b) {
__host__ __device__ inline float2 operator-(const float2 &a, const float2 &b) {
float2 retval;
retval.x = a.x - b.x;
retval.y = a.y - b.y;
return retval;
}
__device__ inline float2 operator+(const float2 &a, const float2 &b) {
__host__ __device__ inline float2 operator+(const float2 &a, const float2 &b) {
float2 retval;
retval.x = a.x + b.x;
retval.y = a.y + b.y;
Expand Down
164 changes: 109 additions & 55 deletions src/cudafeat/feature-online-batched-ivector-cuda-kernels.cu
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Original file line number Diff line number Diff line change
Expand Up @@ -21,18 +21,17 @@
#include "cudamatrix/cu-common.h"
namespace kaldi {

// computes feats^2. This works in place and out of place.
// computes pointwise square of each matrix
__global__ void square_batched_matrix_kernel(
int32_t chunk_frames, int32_t num_cols, const float *feats, int32_t ldf,
int32_t stridef, float *feats_sq, int32_t lds, int32_t strides,
const LaneDesc *lanes, int32_t num_lanes) {
int32_t lane = blockIdx.z;
int32_t num_chunk_frames = lanes[lane].num_chunk_frames;

feats = feats + lane * stridef;
feats_sq = feats_sq + lane * strides;

for (int i = blockIdx.y * blockDim.y + threadIdx.y; i < num_chunk_frames;
for (int i = blockIdx.y * blockDim.y + threadIdx.y; i < chunk_frames;
i += blockDim.y * gridDim.y) {
for (int j = blockIdx.x * blockDim.x + threadIdx.x; j < num_cols;
j += blockDim.x * gridDim.x) {
Expand All @@ -56,6 +55,55 @@ void square_batched_matrix(int32_t chunk_frames, int32_t num_cols,
CU_SAFE_CALL(cudaGetLastError());
}

// after computing posteriors some rows are invalid because they were created
// with rows with undefined data. This kernel zeros those rows out so that
// they will not contribue to stats.
__global__ void zero_invalid_posteriors_kernel(
int32_t chunk_size, int32_t num_gauss, float *posteriors, int32_t ldp,
int32_t stridep, int32_t right, const LaneDesc *lanes, int32_t num_lanes) {
int32_t lane = blockIdx.z;

LaneDesc desc = lanes[lane];
int32_t num_chunk_frames = desc.num_chunk_frames;
int32_t current_frame = desc.current_frame;
bool last = desc.last;

// last valid frame for reading
int32_t num_computed_rows = current_frame + num_chunk_frames;

// if not the last frame remove right context
if (!last) {
num_computed_rows -= right;
}

// offset by lane
posteriors = posteriors + lane * stridep;

for (int r = blockIdx.y * blockDim.y + threadIdx.y; r < chunk_size;
r += blockDim.y * gridDim.y) {
int global_row = current_frame + r - right;
if (global_row < 0 || global_row >= num_computed_rows) {
// zero this row out
for (int c = blockIdx.x * blockDim.x + threadIdx.x; c < num_gauss;
c += blockDim.x * gridDim.x) {
posteriors[r * ldp + c] = 0.0f;
}
}
}
}

void zero_invalid_posteriors(int32_t num_chunk_frames, int32_t num_gauss,
float *posteriors, int32_t ldp, int32_t stridep,
int32_t right, const LaneDesc *lanes,
int32_t num_lanes) {
dim3 threads(32, 32);
dim3 blocks((num_gauss + 31) / 32, (num_chunk_frames + 31) / 32, num_lanes);

zero_invalid_posteriors_kernel<<<blocks, threads>>>(
num_chunk_frames, num_gauss, posteriors, ldp, stridep, right, lanes,
num_lanes);
}

// Meant to be called with blockDim= 32x32
// takes features in feat and writes them into sfeats while applying
// the splicing algorithm for the left and right context.
Expand All @@ -67,39 +115,48 @@ __global__ void splice_features_batched_kernel(
float *__restrict__ feats_out, int32_t ldo, int32_t strideo,
const LaneDesc *lanes, int32_t num_lanes) {
int32_t lane = blockIdx.y;
int32_t frame = blockIdx.x;
// output frame index
int32_t oframe = blockIdx.x;
int32_t tid = threadIdx.x;

LaneDesc desc = lanes[lane];
int32_t num_chunk_frames = desc.num_chunk_frames;
int32_t channel = desc.channel;
int32_t start_frame = desc.current_frame;
int32_t current_frame = desc.current_frame;
bool last = desc.last;

bool valid_frame = true;
// check that we have valid input
if (frame >= num_chunk_frames) {
valid_frame = false;
}
// offset by lane
feats_in = feats_in + lane * stridei;
feats_out = feats_out + lane * strideo;

// for first chunk we process less frames
if (start_frame == 0 && frame >= num_chunk_frames - right) {
valid_frame = false;
}
// offset by channel
feats_stash = feats_stash + channel * stridest;

// the stash size
// offset feature output to process oframe
feats_out = feats_out + ldo * oframe;

// the size of the stash
int32_t ssize = left + right;
// the size of the window
int32_t size = ssize + 1;

// offset by lane
feats_in = feats_in + lane * stridei;
feats_out = feats_out + lane * strideo;
feats_stash = feats_stash + channel * stridest;
// number of valid frame for reading
int32_t num_valid_frames = current_frame + num_chunk_frames;

// offset feature output to process frame
feats_out = feats_out + ldo * frame;
// number of valid frames for writing
int32_t num_computed_frames = num_valid_frames;

if (!valid_frame) {
// this frames output is not valid, zero it here
// if not the last frame remove right context
if (!last) {
num_computed_frames -= right;
}

// subtract right context from logical frame to delay output
int32_t local_frame = oframe - right;
int32_t global_frame = current_frame + local_frame;

// these frames are set to zeros
if (global_frame < 0 || global_frame >= num_computed_frames) {
for (int i = 0; i < size; i++) {
for (int c = tid; c < feat_dim; c += blockDim.x) {
feats_out[i * feat_dim + c] = 0.0f;
Expand All @@ -108,44 +165,40 @@ __global__ void splice_features_batched_kernel(
return;
}

// for each splice of input
for (int i = 0; i < size; i++) {
const float *feats_src = feats_in;
int32_t ld = ldi;

// shift input row by left context
int r = frame + i - left;
for (int i = -left; i <= right; i++) {
int32_t g_in = global_frame + i; // global frame index
int32_t l_in = local_frame + i; // local frame index

// clamp input row if necessary
if (start_frame + r < 0) {
r = 0;
}
// if global row is below zero clamp local to zero
if (g_in < 0) l_in = 0;

// if we have a right context shift input row by that too
if (start_frame > 0) {
r = r - right;
// if global row is larger than the number of valid frames
if (g_in >= num_valid_frames) {
// should only happen on last chunk
assert(last);
// clamp input
l_in = num_chunk_frames - 1;
}

if (r > num_chunk_frames - 1) {
// This should only happen on the last chunk
assert(desc.last == true);
r = num_chunk_frames - 1;
}
// set default input location
const float *feats = feats_in;
int32_t ld = ldi;

if (r < 0) {
// feats are located in stash from previous chunk
feats_src = feats_stash;
// if l < 0 then feats come from the stash
if (l_in < 0) {
// input is from stash
feats = feats_stash;
ld = ldst;
r = r + ssize;
l_in += ssize; // offset by stash size
}

// for each column of input in parallel
for (int c = tid; c < feat_dim; c += blockDim.x) {
// read feature from input row offset by column
float val = feats_src[r * ld + c];
float val = feats[l_in * ld + c];

// write feature to output offset by splice index and column
feats_out[i * feat_dim + c] = val;
feats_out[(i + left) * feat_dim + c] = val;
}
}
}
Expand All @@ -159,6 +212,7 @@ void splice_features_batched(int32_t num_chunk_frames, int32_t feat_dim,
const LaneDesc *lanes, int32_t num_lanes) {
int threads = (feat_dim + 31) / 32 * 32; // round up to the nearest warp size
if (threads > 1024) threads = 1024; // Max block size is 1024 threads

dim3 blocks(num_chunk_frames, num_lanes);

splice_features_batched_kernel<<<blocks, threads>>>(
Expand Down Expand Up @@ -302,12 +356,15 @@ __global__ void batched_update_linear_and_quadratic_terms_kernel(
linear = linear + lane * stridel;
quadratic = quadratic + lane * strideq;

// This is always zero. not 100% certain as why we don't need
// to account for earlier chunk. maybe Dan knows.
// This is always zero because linear and quadratic terms are not
// being carried forward. Thus we don't need to remove old prior
// scale. Keeping the code below so that it logically matches
// the CPU code in case someone is looking at this in the future.
float old_num_frames = 0;
// float old_num_frames = desc.current_frame;
float new_num_frames = desc.current_frame + desc.num_chunk_frames;

// in CPU code the frame counts are scaled by posterior scale
new_num_frames *= posterior_scale;
old_num_frames *= posterior_scale;

Expand Down Expand Up @@ -458,9 +515,6 @@ __global__ void batched_sum_posteriors_kernel(
int32_t stridep, float *gamma, int32_t strideg, float post_scale,
const LaneDesc *lanes, int32_t num_lanes) {
int32_t lane = blockIdx.y;
LaneDesc desc = lanes[lane];

int32_t num_rows = desc.num_chunk_frames;

// offset input and output by lane
posteriors = posteriors + lane * stridep;
Expand All @@ -471,7 +525,7 @@ __global__ void batched_sum_posteriors_kernel(
col += blockDim.x * gridDim.x) {
// compute sum across rows for this column
float sum = 0.0f;
for (int row = 0; row < num_rows; row++) {
for (int row = 0; row < chunk_size; row++) {
sum += posteriors[row * ldp + col];
}

Expand Down Expand Up @@ -509,7 +563,7 @@ __global__ void initialize_channels_kernel(int32_t num_gauss, int32_t feat_dim,

// initialize stashes to zero
for (int i = threadIdx.y * blockDim.x + threadIdx.x; i < num_gauss;
i += blockDim.x * gridDim.x) {
i += blockDim.y * blockDim.x) {
gamma[i] = 0.0f;
}

Expand Down
5 changes: 5 additions & 0 deletions src/cudafeat/feature-online-batched-ivector-cuda-kernels.h
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Original file line number Diff line number Diff line change
Expand Up @@ -29,6 +29,11 @@ namespace kaldi {
// Thus to compute the matrix pointer of a matrix you use this formula:
// matrix_pointer = base_pointer + batch_number * stride

void zero_invalid_posteriors(int32_t num_chunk_frames, int32_t num_gauss,
float *posteriors, int32_t ldp, int32_t stridep,
int32_t right, const LaneDesc *lanes,
int32_t num_lanes);

void splice_features_batched(int32_t num_chunk_frames, int32_t feat_dim,
int32_t left, int32_t right, const float *feats,
int32_t ldf, int32_t stridef,
Expand Down
33 changes: 18 additions & 15 deletions src/cudafeat/feature-online-batched-ivector-cuda.cc
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Original file line number Diff line number Diff line change
Expand Up @@ -26,6 +26,11 @@ BatchedIvectorExtractorCuda::BatchedIvectorExtractorCuda(
chunk_size_(chunk_size),
max_lanes_(num_lanes),
num_channels_(num_channels) {
#if CUDA_VERSION < 9010
// some components require newer cuda versions. If you see this error
// upgrade to a more recent CUDA version.
KALDI_ERR << "BatchedIvectorExtractorCuda requires CUDA 9.1 or newer.";
#endif
info_.Init(config);
Read(config);

Expand Down Expand Up @@ -255,6 +260,8 @@ void BatchedIvectorExtractorCuda::LDATransform(const CuMatrix<BaseFloat> &feats,
void BatchedIvectorExtractorCuda::ComputePosteriors(CuMatrix<BaseFloat> &feats,
const LaneDesc *lanes,
int32_t num_lanes) {
int right = info_.splice_opts.right_context;

// inititalize posteriors
posteriors_.CopyRowsFromVec(ubm_gconsts_);

Expand All @@ -271,6 +278,13 @@ void BatchedIvectorExtractorCuda::ComputePosteriors(CuMatrix<BaseFloat> &feats,
posteriors_.AddMatMat(-0.5, feats, kNoTrans, ubm_inv_vars_, kTrans, 1.0);

posteriors_.ApplySoftMaxPerRow();

// At this point some rows of posteriors are invalid because they
// didn't have valid input rows. Zero those out now so that
// they don't impact stats
zero_invalid_posteriors(
chunk_size_, num_gauss_, posteriors_.Data(), posteriors_.Stride(),
posteriors_.Stride() * chunk_size_, right, lanes, num_lanes);
}

void BatchedIvectorExtractorCuda::ComputeIvectorStats(
Expand All @@ -281,6 +295,7 @@ void BatchedIvectorExtractorCuda::ComputeIvectorStats(
posteriors_.Stride() * chunk_size_, gamma_.Data(),
num_gauss_, info_.posterior_scale, lanes, num_lanes);

#if CUDA_VERSION >= 9010
int32_t m = feat_dim_;
int32_t n = num_gauss_;
int32_t k = chunk_size_;
Expand All @@ -296,11 +311,12 @@ void BatchedIvectorExtractorCuda::ComputeIvectorStats(
int32_t ldc = X_.Stride();
int32_t strideC = ldc * num_gauss_;

// multiplying X = stash * feats
// multiplying X = post * feats
CUBLAS_SAFE_CALL(cublasGemmStridedBatchedEx(
GetCublasHandle(), CUBLAS_OP_N, CUBLAS_OP_T, m, n, k, &alpha, A,
CUDA_R_32F, lda, strideA, B, CUDA_R_32F, ldb, strideB, &beta, C,
CUDA_R_32F, ldc, strideC, num_lanes, CUDA_R_32F, CUBLAS_GEMM_DEFAULT))
#endif

apply_and_update_stash(
num_gauss_, feat_dim_, gamma_.Data(), gamma_stash_.Data(), num_gauss_,
Expand All @@ -311,9 +327,6 @@ void BatchedIvectorExtractorCuda::ComputeIvectorStats(
void BatchedIvectorExtractorCuda::ComputeIvectorsFromStats(
CuVectorBase<BaseFloat> *ivectors, const LaneDesc *lanes,
int32_t num_lanes) {
static int current_frame = 0;
current_frame++;

// Computing Linear Term
{
// need to set this term to zero because batched_compute_linear_term
Expand Down Expand Up @@ -348,9 +361,7 @@ void BatchedIvectorExtractorCuda::ComputeIvectorsFromStats(
ivector_dim_, lanes, num_lanes);

#if CUDA_VERSION >= 9010

int nrhs = 1;

// perform factorization in batched
CUSOLVER_SAFE_CALL(cusolverDnSpotrfBatched(
GetCusolverDnHandle(), CUBLAS_FILL_MODE_LOWER, ivector_dim_, quad_array_,
Expand All @@ -361,18 +372,10 @@ void BatchedIvectorExtractorCuda::ComputeIvectorsFromStats(
GetCusolverDnHandle(), CUBLAS_FILL_MODE_LOWER, ivector_dim_, nrhs,
quad_array_, ivector_dim_, ivec_array_, ivector_dim_, d_infoArray_,
num_lanes));
#endif

// cusolver solves in place. Ivectors are now in linear_

#else
// We could make a fallback if necessary. This would likely just loop
// over each matrix and call Invert not batched. This would be very slow and
// throwing an error is probably better force people to use a more recent
// version of CUDA.
KALDI_ERR << "Online Ivectors in CUDA is not supported by your CUDA version. "
<< "Upgrade to CUDA 9.1 or later";
#endif

// Create a submatrix which points to the first element of each ivector
CuSubMatrix<BaseFloat> ivector0(linear_.Data(), num_lanes, 1, ivector_dim_);
// remove prior
Expand Down
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