Merge pull request #3907 from naxingyu/sync-pybind11-with-master

Sync pybind11 with master

Author: danpovey

src/cudafeat/feature-online-batched-cmvn-cuda-kernels.cu

@@ -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;

src/cudafeat/feature-online-batched-ivector-cuda-kernels.cu

@@ -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) {
@@ -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.
@@ -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;
@@ -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;
     }
   }
 }
@@ -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>>>(
@@ -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;
 
@@ -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;
@@ -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];
     }
 
@@ -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;
     }
 

src/cudafeat/feature-online-batched-ivector-cuda-kernels.h

@@ -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,

src/cudafeat/feature-online-batched-ivector-cuda.cc

@@ -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);
 
@@ -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_);
 
@@ -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(
@@ -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_;
@@ -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_,
@@ -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
@@ -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_,
@@ -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