Merge branch 'master' of https://github.com/NVIDIA/apex

csrc/multi_tensor_lamb_stage_1.cu

@@ -62,19 +62,47 @@ struct LAMBStage1Functor
             i_start < n && i_start < chunk_size;
             i_start += blockDim.x*ILP)
     {
+      GRAD_T r_g[ILP];
+      T r_p[ILP];
+      T r_m[ILP];
+      T r_v[ILP];
 #pragma unroll
       for(int ii = 0; ii < ILP; ii++)
       {
         int i = i_start + threadIdx.x + ii*blockDim.x;
         if(i < n && i < chunk_size)
         {
-          T scaled_grad = g[i] / clipped_global_grad_norm;
-          m[i] = m[i] * beta1 + (1-beta1) * scaled_grad;
-          v[i] = v[i] * beta2 + (1-beta2) * scaled_grad * scaled_grad;
-          T next_m_unbiased = m[i] / beta1_correction;
-          T next_v_unbiased = v[i] / beta2_correction;
-          T denom = std::sqrt(next_v_unbiased) + epsilon;
-          update[i] = (next_m_unbiased/denom) + (decay*p[i]);
+          r_g[ii] = g[i];
+          r_p[ii] = p[i];
+          r_m[ii] = m[i];
+          r_v[ii] = v[i];
+        } else {
+          r_g[ii] = GRAD_T(0);
+          r_p[ii] = T(0);
+          r_m[ii] = T(0);
+          r_v[ii] = T(0);
+        }
+      }
+#pragma unroll
+      for(int ii = 0; ii < ILP; ii++)
+      {
+        T scaled_grad = r_g[ii] / clipped_global_grad_norm;
+        r_m[ii] = r_m[ii] * beta1 + (1-beta1) * scaled_grad;
+        r_v[ii] = r_v[ii] * beta2 + (1-beta2) * scaled_grad * scaled_grad;
+        T next_m_unbiased = r_m[ii] / beta1_correction;
+        T next_v_unbiased = r_v[ii] / beta2_correction;
+        T denom = std::sqrt(next_v_unbiased) + epsilon;
+        r_p[ii] = (next_m_unbiased/denom) + (decay*r_p[ii]);
+      }
+#pragma unroll
+      for(int ii = 0; ii < ILP; ii++)
+      {
+        int i = i_start + threadIdx.x + ii*blockDim.x;
+        if(i < n && i < chunk_size)
+        {
+          update[i] = r_p[ii];
+          m[i] = r_m[ii];
+          v[i] = r_v[ii];
         }
       }
     }

csrc/multi_tensor_lamb_stage_2.cu

@@ -51,14 +51,30 @@ struct LAMBStage2Functor
             i_start < n && i_start < chunk_size;
             i_start += blockDim.x*ILP)
     {
-      // see note in multi_tensor_scale_kernel.cu
+      T r_p[ILP];
+      T r_update[ILP];
 #pragma unroll
       for(int ii = 0; ii < ILP; ii++)
       {
         int i = i_start + threadIdx.x + ii*blockDim.x;
         if(i < n && i < chunk_size)
         {
-          p[i] = p[i] - (ratio*update[i]);
+          r_p[ii] = p[i];
+          r_update[ii] = update[i];
+        }
+      }
+#pragma unroll
+      for(int ii = 0; ii < ILP; ii++)
+      {
+        r_p[ii] = r_p[ii] - (ratio*r_update[ii]);
+      }
+#pragma unroll
+      for(int ii = 0; ii < ILP; ii++)
+      {
+        int i = i_start + threadIdx.x + ii*blockDim.x;
+        if(i < n && i < chunk_size)
+        {
+          p[i] = r_p[ii];
         }
       }
     }