Work on backward kernel

torch_learned_nonlin/learned_nonlin_cuda_kernel.cu

@@ -180,7 +180,6 @@ void learned_nonlin_kernel(
       output[b][c][t] = (x - n) * params_buf[n] + y_vals[n];
     }
   }
-
 }
 
 
@@ -297,7 +296,9 @@ void learned_nonlin_backward_kernel(
                                               // spaces between here and
                                               // `params_buf` for storing scale
                                               // and inv_scale and l == params[c][0].
-      *params_buf = (scalar_t*) y_vals + 3 + N;  // [N].  Caution: contains params[c][1] through params[c][N].
+      *params_buf = (scalar_t*) y_vals + 3 + N;  // [N].  Contains parameters (not times scale!)
+                                                 // Caution: contains params[c][1] through params[c][N],
+                                                 // i.e. numbering is off by 1 versus params.
                                                  //  params_buf[-1] contains params[c][0] == log of scale;
                                                  // params_buf[-2] and params_buf[-3] contain scale and inv_scale.
 
@@ -312,58 +313,56 @@ void learned_nonlin_backward_kernel(
                                   // determines which piece of the piecewise
                                   // linear function we are in.
 
-  // this_params_grad and this_y_grad pertain to the 'n' value (i.e. the n'th
-  // linear interval) corresponding to n == threadIdx.x % N.  For example, if
-  // threadIdx.x == 0, this thread's gradient corresponds to the left-most
-  // linear interval.
-  scalar_t this_params_grad = 0.0,
-      this_y_vals_grad = 0.0;
 
   // Load parameters
   if (threadIdx.x <= N)
     params_buf[threadIdx.x - 1] = params[c][threadIdx.x];
-
   __syncthreads();
-  // The easiest way to understand this code is to compare it with the CPU code
-  // in learned_nonlin_cpu.cpp.
 
-  // This next block computes `y_vals`.
-  if ((((int)threadIdx.x & ~(int)32)) == 0) {
-    // threadIdx.x == 0 or 32.  These are in separate warps so we can
-    // allow them to do separate jobs.  This code takes linear time in K which
-    // is not at all ideal and could be improved if K is largish, but it shouldn't
-    // dominate the total time taken if we are processing a lot of data;
-    // and anyway, we doubt that K will be need to be more than 4 or 8 or so,
-    // so the potential savings are quite small.
+  if (threadIdx.x == 0) {
     scalar_t scale = exp(params_buf[-1]),
         inv_scale = 1.0 / scale;
-    params_buf[-2] = scale;  // both threads write these but it's OK, it's the
-                             // same value.
+    params_buf[-2] = scale;
     params_buf[-3] = inv_scale;
-    int sign,
-        Koffset;  // Koffset == K for threads handling sum_positive and K - 1
-                  // for threads handling sum_negative, see
-                  // learned_nonlin_cpu.cpp for reference code.  This would be K
-                  // + 1 and K respectively, except our params_buf has its index
-                  // shifted by one versus params.
-    if (threadIdx.x == 0) {  // sum_positive
-      sign = 1;
-      Koffset = K;
-    } else {  // threadIdx.x == 32.  sum_negative.
-      scale *= -1;  // this is a local variable..
-      sign = -1;
-      Koffset = K - 1;
   }
-    scalar_t sum = 0.0;
+  __syncthreads();
+
+  scalar_t scale = params_buf[-2];
+
+  // The easiest way to understand this code is to compare it with the CPU code
+  // in learned_nonlin_cpu.cpp.
+
+  if (threadIdx.x == 0) {
+    scalar_t sum_positive = 0.0;
+    for (int i = 0; i < K; i++) {
+      y_vals[K + i] = sum_positive;
+      // versus the CPU code, the params_buf is indexed off by 1; and it already
+      // contains the factor "scale".
+      sum_positive += params_buf[K + i] * scale;
+    }
+
+  } else if (threadIdx.x == 64) {
+    scalar_t sum_negative = 0.0;
     for (int i = 0; i < K; i++) {
-      int isign = i * sign;
-      y_vals[K + isign] = sum * scale;
-      sum += params_buf[Koffset + isign];
+      y_vals[K - i] = sum_negative;
+      // versus the CPU code, the params_buf is indexed off by 1; and it already
+      // contains the factor "scale".
+      sum_negative -= params_buf[K - 1 - i] * scale;
     }
-    if (threadIdx.x != 0)  // sum_negative
-      y_vals[0] = sum * scale;
+    y_vals[0] = sum_negative;
   }
   __syncthreads();
+
+
+  // this_params_grad and this_y_grad pertain to the 'n' value (i.e. the n'th
+  // linear interval) corresponding to n == threadIdx.x % N.  For example, if
+  // threadIdx.x == 0, this thread's gradient corresponds to the left-most
+  // linear interval.
+  // "this_params_grad" actually contains the derivative w.r.t. scaled params, i.e.
+  // params[n] * scale.
+  scalar_t this_scaled_param_grad = 0.0,
+      this_y_vals_grad = 0.0;
+
   scalar_t inv_scale = params_buf[-3];
 
   int T_inc = THREADS_PER_BLOCK / images_per_thread_block,
@@ -408,8 +407,11 @@ void learned_nonlin_backward_kernel(
       // The forward code did:
       // output[b][c][t] = (x - n) * params_buf[n] + y_vals[n];
 
-      if (t < T)
+      if (t < T) {
+        // In a sense this expression should contain "* inv_scale * scale"...
+        // of course, their product equals 1.
         input_grad[b][c][t] = this_output_grad * params_buf[n];
+      }
 
       int this_block_start = threadIdx.x & ~(N-1),  // ==  N * (threadIdx.x / N),
           this_n = threadIdx.x & (N-1); // == threadIdx.x % N.
@@ -471,9 +473,9 @@ void learned_nonlin_backward_kernel(
             src_thread = this_block_start + src_idx;
         scalar_t output_grad = output_grad_buf[src_thread],
             x_residual = x_residual_buf[src_thread];
-        // Backprop for: output = x_residual * params_buf[n] + y_vals[n].
+        // Backprop for: output = x_residual * (params_buf[n] * scale) + y_vals[n].
         // Here, n == this_n; this is how we selected these `src_idx` values.
-        this_params_grad += output_grad * x_residual;
+        this_scaled_param_grad += output_grad * x_residual;
         this_y_vals_grad += output_grad;
       }
     }
@@ -482,14 +484,14 @@ void learned_nonlin_backward_kernel(
   __syncthreads();  // sync threads because we are about to re-use
                     // output_grad_buf for reduction.
 
-  this_params_grad = strided_reduce_sum(N, output_grad_buf, this_params_grad);
+  this_scaled_param_grad = strided_reduce_sum(N, output_grad_buf, this_scaled_param_grad);
   this_y_vals_grad = strided_reduce_sum(N, output_grad_buf, this_y_vals_grad);
 
   __syncthreads();  // sync threads because we are about to re-use
                     // output_grad_buf.
 
   // Re-use some buffers..
-  scalar_t *params_grad_buf = x_residual_buf,  // [N]
+  scalar_t *scaled_params_grad_buf = x_residual_buf,  // [N]  ... a
       *y_vals_grad_buf = output_grad_buf;   // [N]
 
   if (threadIdx.x < N) {
@@ -497,7 +499,7 @@ void learned_nonlin_backward_kernel(
     // the position in 'params'.  To keep the backprop code similar to the CPU
     // backprop code we restore that offset here, i.e. use the same layout
     // as the params.
-    params_grad_buf[threadIdx.x + 1] = this_params_grad;
+    scaled_params_grad_buf[threadIdx.x] = this_scaled_param_grad;
     y_vals_grad_buf[threadIdx.x] = this_y_vals_grad;
   }
 
@@ -514,31 +516,47 @@ void learned_nonlin_backward_kernel(
     if (threadIdx.x == 0) {
       scalar_t sum_positive_grad = 0.0;
       for (int i = K - 1; i >= 0; i--) {
-        // This is like the CPU code but with an offset of 1 for 'params_buf'
-        // versus 'params_a'.
-        params_grad_buf[1 + K + i] += sum_positive_grad * scale;
-        scale_grad += sum_positive_grad * params_buf[K + i];
+        // This is like the CPU code but with an offset of -1 for indexes into 'params_buf';
+        // also there is no scale because we are dealing with pre-scaled parameters.
+        scaled_params_grad_buf[K + i] += sum_positive_grad;
         sum_positive_grad += y_vals_grad_buf[K + i];
       }
-      params_grad_buf[0] += scale * scale_grad;
     } else if (threadIdx.x == 64) {
       scalar_t sum_negative_grad = y_vals_grad_buf[0];
       for (int i = K - 1; i >= 0; i--) {
-        // This is like the CPU code but with an offset of 1 for 'params_buf'
-        // versus 'params_a'.
-        params_grad_buf[K - i] -= sum_negative_grad * scale;
-        scale_grad -= sum_negative_grad * params_buf[K - 1 - i];
+        // This is like the CPU code but with an offset of 1 for 'params_buf';
+        // also there is no scale because we are dealing with pre-scaled parameters.
+        scaled_params_grad_buf[K - 1 - i] -= sum_negative_grad;
         sum_negative_grad += y_vals_grad_buf[K - i];
       }
     }
     __syncthreads();
-    if (threadIdx.x == 64)
-      params_grad_buf[0] += scale * scale_grad;
+
+    if (threadIdx.x < N) {
+      // this_scaled_param_grad is the gradient w.r.t. params_buf[n] * scale
+      // which is equal to  params[c][n + 1] * scale.
+      int n = threadIdx.x;
+      scalar_t this_scaled_param_grad = scaled_params_grad_buf[n],
+          this_scale_grad = this_scaled_param_grad * params_buf[n],
+          scale = params_buf[-2],
+          this_param_grad = scale * this_scaled_param_grad;
+
+      // re-use x_residual_buf as 'param_grad_buf'.
+      x_residual_buf[n + 1] = this_param_grad;
+
+      scalar_t scale_grad = tiled_warp_reduce_sum(N, y_vals_grad_buf, this_scale_grad);
+
+      if (threadIdx.x == 0)
+        x_residual_buf[0] = scale_grad * scale;  // deriv w.r.t. l.
+
+
+    }
     __syncthreads();
   }
 
   if (threadIdx.x <= N) {
-    params_grad[blockIdx.y][c][threadIdx.x] = params_grad_buf[threadIdx.x];
+    // note, we are re-using x_residual_buf for the params_grad.
+    params_grad[blockIdx.y][c][threadIdx.x] = x_residual_buf[threadIdx.x];
   }
 }