Enable Distributed FusedLAMB (#57)

apex/contrib/csrc/optimizers/multi_tensor_distopt_lamb.cpp

@@ -8,11 +8,13 @@ void multi_tensor_lamb_compute_update_term_cuda(
   at::Tensor per_tensor_beta2,
   at::Tensor per_tensor_beta3,
   at::Tensor per_tensor_bias_correction,
-  const int step,
+  at::Tensor step,
   at::Tensor per_tensor_epsilon,
   const int mode,
   at::Tensor per_tensor_decay,
-  const float grad_scale);
+  at::Tensor global_scale,
+  at::Tensor global_grad_norm,
+  const float max_grad_norm);
 
 void multi_tensor_lamb_update_weights_cuda(
   int chunk_size,
@@ -20,8 +22,10 @@ void multi_tensor_lamb_update_weights_cuda(
   std::vector<std::vector<at::Tensor>> tensor_lists,
   at::Tensor per_tensor_param_norm,
   at::Tensor per_tensor_update_norm,
-  const float learning_rate,
+  at::Tensor update_norm_offset,
+  at::Tensor learning_rate,
   at::Tensor per_tensor_decay,
+  at::Tensor global_grad_norm,
   bool use_nvlamb);
 
 PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {

apex/contrib/csrc/optimizers/multi_tensor_distopt_lamb_kernel.cu

@@ -116,28 +116,36 @@ struct DistOptLAMBStage1Functor
     const MATH_T* per_tensor_beta2,
     const MATH_T* per_tensor_beta3,
     const int* per_tensor_bias_correction,
-    const int step,
+    const int* step,
     const MATH_T* per_tensor_epsilon,
     adamMode_t mode,
     const MATH_T* per_tensor_decay,
-    const float grad_scale)
+    const MATH_T* global_scale,
+    const MATH_T* global_grad_norm,
+    const float max_grad_norm)
   {
     // I'd like this kernel to propagate infs/nans.
-    // if(*noop_gmem == 1)
-    //   return;
+    if (*noop_gmem == 1)
+        return;
 
     int tensor_loc = tl.block_to_tensor[blockIdx.x];
     int tensor_num = tl.start_tensor_this_launch + tensor_loc;
     int chunk_idx = tl.block_to_chunk[blockIdx.x];
     int n = tl.sizes[tensor_loc];
 
+    float combined_scale = *global_scale;
+    if (max_grad_norm > 0) {
+        combined_scale = max_grad_norm / (*global_grad_norm / *global_scale + 1e-6);
+	combined_scale = *global_scale / std::min((float) 1.0, combined_scale);
+    }
+
     MATH_T beta1 = per_tensor_beta1[tensor_num];
     MATH_T beta2 = per_tensor_beta2[tensor_num];
     MATH_T beta3 = 1 - beta1;
     MATH_T beta1_correction, beta2_correction;
     if (per_tensor_bias_correction[tensor_num] == 1) {
-        beta1_correction = 1 - pow(beta1, step);
-        beta2_correction = 1 - pow(beta2, step);
+        beta1_correction = 1 - pow(beta1, *step);
+        beta2_correction = 1 - pow(beta2, *step);
     } else {
         beta1_correction = (MATH_T) 1.0;
         beta2_correction = (MATH_T) 1.0;
@@ -204,7 +212,7 @@ struct DistOptLAMBStage1Functor
         for(int ii = 0; ii < ILP; ii++)
         {
           if (mode == MOMENT_MODE_0) {
-            MATH_T scaled_grad = r_g[ii] / grad_scale;
+            MATH_T scaled_grad = r_g[ii] / combined_scale;
             // L2 on scaled grad
             scaled_grad = scaled_grad + decay*r_p[ii];
             r_m[ii] = r_m[ii] * beta1 + beta3 * scaled_grad;
@@ -215,7 +223,7 @@ struct DistOptLAMBStage1Functor
             r_p[ii] = next_m_unbiased / denom;
           }
           else {
-            MATH_T scaled_grad = r_g[ii] / grad_scale;
+            MATH_T scaled_grad = r_g[ii] / combined_scale;
             r_m[ii] = r_m[ii] * beta1 + beta3 * scaled_grad;
             r_v[ii] = r_v[ii] * beta2 + (1-beta2) * scaled_grad * scaled_grad;
             MATH_T next_m_unbiased = r_m[ii] / beta1_correction;
@@ -274,7 +282,7 @@ struct DistOptLAMBStage1Functor
         for(int ii = 0; ii < ILP; ii++)
         {
           if (mode == MOMENT_MODE_0) {
-            MATH_T scaled_grad = r_g[ii] / grad_scale;
+            MATH_T scaled_grad = r_g[ii] / combined_scale;
             // L2 on scaled grad
             scaled_grad = scaled_grad + decay*r_p[ii];
             r_m[ii] = r_m[ii] * beta1 + beta3 * scaled_grad;
@@ -285,7 +293,7 @@ struct DistOptLAMBStage1Functor
             r_p[ii] = next_m_unbiased / denom;
           }
           else {
-            MATH_T scaled_grad = r_g[ii] / grad_scale;
+            MATH_T scaled_grad = r_g[ii] / combined_scale;
             r_m[ii] = r_m[ii] * beta1 + beta3 * scaled_grad;
             r_v[ii] = r_v[ii] * beta2 + (1-beta2) * scaled_grad * scaled_grad;
             MATH_T next_m_unbiased = r_m[ii] / beta1_correction;
@@ -321,13 +329,15 @@ struct DistOptLAMBStage2Functor
     TensorListMetadata<3>& tl,
     const MATH_T* per_tensor_param_norm,
     const MATH_T* per_tensor_update_norm,
-    const MATH_T learning_rate,
+    const long* update_norm_offset,
+    const MATH_T* learning_rate,
     const MATH_T* per_tensor_decay,
+    const MATH_T* global_grad_norm,
     bool use_nvlamb)
   {
     // I'd like this kernel to propagate infs/nans.
-    // if(*noop_gmem == 1)
-    //   return;
+    if (*noop_gmem == 1)
+        return;
 
     int tensor_loc = tl.block_to_tensor[blockIdx.x];
     int tensor_num = tl.start_tensor_this_launch + tensor_loc;
@@ -336,14 +346,14 @@ struct DistOptLAMBStage2Functor
 
     MATH_T decay = per_tensor_decay[tensor_num];
 
-    MATH_T ratio = learning_rate;
+    MATH_T ratio = *learning_rate;
     // nvlamb: apply adaptive learning rate to all parameters
     // otherwise, only apply to those with non-zero weight decay
     if (use_nvlamb || (decay != (MATH_T) 0.0))
     {
       MATH_T param_norm = per_tensor_param_norm[tensor_num];
-      MATH_T update_norm = per_tensor_update_norm[tensor_num];
-      ratio = (update_norm != 0.0 && param_norm != 0.0) ? learning_rate * (param_norm / update_norm) : learning_rate;
+      MATH_T update_norm = per_tensor_update_norm[update_norm_offset[tensor_num]];
+      ratio = (update_norm != 0.0 && param_norm != 0.0) ? (*learning_rate) * (param_norm / update_norm) : (*learning_rate);
     }
 
     MATH_T* update = (MATH_T*)tl.addresses[0][tensor_loc];
@@ -374,7 +384,7 @@ struct DistOptLAMBStage2Functor
 #pragma unroll
         for(int ii = 0; ii < ILP; ii++)
         {
-          r_p[ii] = static_cast<MATH_T>(r_p[ii]) - (ratio * r_update[ii]);
+	  r_p[ii] = static_cast<MATH_T>(r_p[ii]) - (ratio * r_update[ii]);
           convert(r_p[ii], r_p_copy[ii]);
         }
         load_store(p, r_p, i_start, 0);
@@ -427,11 +437,13 @@ void multi_tensor_lamb_compute_update_term_cuda(
   at::Tensor per_tensor_beta2,
   at::Tensor per_tensor_beta3,
   at::Tensor per_tensor_bias_correction,
-  const int step,
+  at::Tensor step,
   at::Tensor per_tensor_epsilon,
   const int mode,
   at::Tensor per_tensor_decay,
-  const float grad_scale)
+  at::Tensor global_scale,
+  at::Tensor global_grad_norm,
+  const float max_grad_norm)
 {
   using namespace at;
 
@@ -448,11 +460,13 @@ void multi_tensor_lamb_compute_update_term_cuda(
           per_tensor_beta2.DATA_PTR<scalar_t_2>(),
           per_tensor_beta3.DATA_PTR<scalar_t_2>(),
           per_tensor_bias_correction.DATA_PTR<int>(),
-          step,
+          step.DATA_PTR<int>(),
           per_tensor_epsilon.DATA_PTR<scalar_t_2>(),
           (adamMode_t) mode,
           per_tensor_decay.DATA_PTR<scalar_t_2>(),
-          grad_scale); )))
+          global_scale.DATA_PTR<scalar_t_2>(),
+	  global_grad_norm.DATA_PTR<scalar_t_2>(),
+	  max_grad_norm); )))
 
   AT_CUDA_CHECK(cudaGetLastError());
 }
@@ -463,8 +477,10 @@ void multi_tensor_lamb_update_weights_cuda(
   std::vector<std::vector<at::Tensor>> tensor_lists,
   at::Tensor per_tensor_param_norm,
   at::Tensor per_tensor_update_norm,
-  const float learning_rate,
+  at::Tensor update_norm_offset,
+  at::Tensor learning_rate,
   at::Tensor per_tensor_decay,
+  at::Tensor global_grad_norm,
   bool use_nvlamb)
 {
   using namespace at;
@@ -480,8 +496,10 @@ void multi_tensor_lamb_update_weights_cuda(
           DistOptLAMBStage2Functor<scalar_t_0, scalar_t_1, scalar_t_2>(),
           per_tensor_param_norm.DATA_PTR<scalar_t_2>(),
           per_tensor_update_norm.DATA_PTR<scalar_t_2>(),
-          (scalar_t_2) learning_rate,
+          update_norm_offset.DATA_PTR<long>(),
+	  learning_rate.DATA_PTR<scalar_t_2>(),
           per_tensor_decay.DATA_PTR<scalar_t_2>(),
+	  global_grad_norm.DATA_PTR<scalar_t_2>(),
           use_nvlamb); )))
 
   AT_CUDA_CHECK(cudaGetLastError());