diff --git a/csrc/adam/multi_tensor_adam.cu b/csrc/adam/multi_tensor_adam.cu
index d6b9b2f70710..a1fc7d15aec9 100644
--- a/csrc/adam/multi_tensor_adam.cu
+++ b/csrc/adam/multi_tensor_adam.cu
@@ -30,7 +30,7 @@ typedef enum : int {
 
 using MATH_T = float;
 
-template <typename T>
+template <typename T, typename index_t>
 struct AdamFunctor {
     __device__ __forceinline__ void operator()(int chunk_size,
                                                volatile int* noop_gmem,
@@ -48,13 +48,13 @@ struct AdamFunctor {
         // if(*noop_gmem == 1)
         //   return;
 
-        int tensor_loc = tl.block_to_tensor[blockIdx.x];
+        index_t tensor_loc = tl.block_to_tensor[blockIdx.x];
 
         // potentially use to pass in list of scalar
         // 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];
+        index_t chunk_idx = tl.block_to_chunk[blockIdx.x];
+        index_t n = tl.sizes[tensor_loc];
 
         T* g = (T*)tl.addresses[0][tensor_loc];
         g += chunk_idx * chunk_size;
@@ -71,7 +71,8 @@ struct AdamFunctor {
         n -= chunk_idx * chunk_size;
 
         // see note in multi_tensor_scale_kernel.cu
-        for (int i_start = 0; i_start < n && i_start < chunk_size; i_start += blockDim.x * ILP) {
+        for (index_t i_start = 0; i_start < n && i_start < chunk_size;
+             i_start += blockDim.x * ILP) {
             MATH_T r_g[ILP];
             MATH_T r_p[ILP];
             MATH_T r_m[ILP];
@@ -146,23 +147,57 @@ void multi_tensor_adam_cuda(int chunk_size,
         bias_correction2 = 1 - std::pow(beta2, step);
     }
 
+    size_t max_size = 0;
+    bool requires_64bit_indexing = false;
+    for (auto it = tensor_lists.begin(); it != tensor_lists.end(); it++) {
+        for (auto it2 = it->begin(); it2 != it->end(); it2++) {
+            if (it2->numel() > max_size) {
+                max_size = it2->numel();
+                if (max_size >= INT_MAX) {
+                    requires_64bit_indexing = true;
+                    break;
+                }
+            }
+        }
+        if (requires_64bit_indexing) { break; }
+    }
+
     // Assume single type across p,g,m1,m2 now
-    DISPATCH_DOUBLE_FLOAT_AND_HALF(tensor_lists[0][0].scalar_type(),
-                                   0,
-                                   "adam",
-                                   multi_tensor_apply<4>(BLOCK_SIZE,
-                                                         chunk_size,
-                                                         noop_flag,
-                                                         tensor_lists,
-                                                         AdamFunctor<scalar_t_0>(),
-                                                         beta1,
-                                                         beta2,
-                                                         bias_correction1,
-                                                         bias_correction2,
-                                                         epsilon,
-                                                         lr,
-                                                         (adamMode_t)mode,
-                                                         weight_decay);)
+    if (requires_64bit_indexing) {
+        DISPATCH_DOUBLE_FLOAT_AND_HALF(tensor_lists[0][0].scalar_type(),
+                                       0,
+                                       "adam",
+                                       multi_tensor_apply<4>((int64_t)BLOCK_SIZE,
+                                                             (int64_t)chunk_size,
+                                                             noop_flag,
+                                                             tensor_lists,
+                                                             AdamFunctor<scalar_t_0, int64_t>(),
+                                                             beta1,
+                                                             beta2,
+                                                             bias_correction1,
+                                                             bias_correction2,
+                                                             epsilon,
+                                                             lr,
+                                                             (adamMode_t)mode,
+                                                             weight_decay);)
+    } else {
+        DISPATCH_DOUBLE_FLOAT_AND_HALF(tensor_lists[0][0].scalar_type(),
+                                       0,
+                                       "adam",
+                                       multi_tensor_apply<4>(BLOCK_SIZE,
+                                                             chunk_size,
+                                                             noop_flag,
+                                                             tensor_lists,
+                                                             AdamFunctor<scalar_t_0, int32_t>(),
+                                                             beta1,
+                                                             beta2,
+                                                             bias_correction1,
+                                                             bias_correction2,
+                                                             epsilon,
+                                                             lr,
+                                                             (adamMode_t)mode,
+                                                             weight_decay);)
+    }
 
     AT_CUDA_CHECK(cudaGetLastError());
 }
diff --git a/csrc/adam/multi_tensor_apply.cuh b/csrc/adam/multi_tensor_apply.cuh
index 12f41cb49c6b..342376c141be 100644
--- a/csrc/adam/multi_tensor_apply.cuh
+++ b/csrc/adam/multi_tensor_apply.cuh
@@ -35,7 +35,7 @@ struct TensorListMetadata {
 };
 
 template <typename T, typename U, typename... ArgTypes>
-__global__ void multi_tensor_apply_kernel(int chunk_size,
+__global__ void multi_tensor_apply_kernel(int64_t chunk_size,
                                           volatile int* noop_flag,
                                           T tl,
                                           U callable,
@@ -46,8 +46,8 @@ __global__ void multi_tensor_apply_kernel(int chunk_size,
 }
 
 template <int depth, typename T, typename... ArgTypes>
-void multi_tensor_apply(int block_size,
-                        int chunk_size,
+void multi_tensor_apply(int64_t block_size,
+                        int64_t chunk_size,
                         const at::Tensor& noop_flag,
                         const std::vector<std::vector<at::Tensor>>& tensor_lists,
                         T callable,
@@ -91,9 +91,9 @@ void multi_tensor_apply(int block_size,
             tl.addresses[d][loc_tensor_info] = tensor_lists[d][t].data_ptr();
         loc_tensor_info++;
 
-        int chunks_this_tensor = (tensor_lists[0][t].numel() + chunk_size - 1) / chunk_size;
+        auto chunks_this_tensor = (tensor_lists[0][t].numel() + chunk_size - 1) / chunk_size;
 
-        for (int chunk = 0; chunk < chunks_this_tensor; chunk++) {
+        for (auto chunk = 0; chunk < chunks_this_tensor; chunk++) {
             // std::cout << chunks_this_tensor << std::endl;
             tl.block_to_tensor[loc_block_info] = loc_tensor_info - 1;
             tl.block_to_chunk[loc_block_info] = chunk;