diff --git a/csrc/flash_attn/fmha_api.cpp b/csrc/flash_attn/fmha_api.cpp
index 8dbfba2..be2f501 100644
--- a/csrc/flash_attn/fmha_api.cpp
+++ b/csrc/flash_attn/fmha_api.cpp
@@ -359,7 +359,7 @@ mha_bwd(const at::Tensor &dout,  // total_q x num_heads, x head_size
         const bool is_causal,
         const int num_splits,
         c10::optional<at::Generator> gen_,
-        const at::Tensor &rng_state
+        c10::optional<at::Tensor> &rng_state
 ) {
     auto dprops = at::cuda::getCurrentDeviceProperties();
     bool is_sm75 = dprops->major == 7 && dprops->minor == 5;
@@ -494,7 +494,16 @@ mha_bwd(const at::Tensor &dout,  // total_q x num_heads, x head_size
 
     // We use a custom RNG that increases the offset by batch_size * nheads * 32.
     int64_t counter_offset = params.b * params.h * 32;
-    params.rng_state = reinterpret_cast<uint64_t*>(rng_state.data_ptr());
+    if ( rng_state.has_value() ) {
+        params.rng_state = reinterpret_cast<uint64_t*>(rng_state.value().data_ptr());
+    } else if( is_dropout ) {
+        // See Note [Acquire lock when using random generators]
+        std::lock_guard<std::mutex> lock(gen->mutex_);
+        params.philox_args = gen->philox_cuda_state(counter_offset);
+        auto seeds = at::cuda::philox::unpack(params.philox_args);
+        params.rng_state[0] = std::get<0>(seeds);
+        params.rng_state[1] = std::get<1>(seeds);
+    }
 
     launch(params, stream, /*configure=*/false);
 
diff --git a/flash_attn/flash_attn_interface.py b/flash_attn/flash_attn_interface.py
index 49eab12..4d41310 100644
--- a/flash_attn/flash_attn_interface.py
+++ b/flash_attn/flash_attn_interface.py
@@ -29,8 +29,8 @@ def _flash_attn_forward(q, k, v, out, cu_seqlens_q, cu_seqlens_k, max_seqlen_q,
 
 
 def _flash_attn_backward(dout, q, k, v, out, softmax_lse, dq, dk, dv, cu_seqlens_q, cu_seqlens_k,
-                         max_seqlen_q, max_seqlen_k, dropout_p, softmax_scale, causal, rng_state,
-                         num_splits=0, generator=None):
+                         max_seqlen_q, max_seqlen_k, dropout_p, softmax_scale, causal,
+                         rng_state=None, num_splits=0, generator=None):
     """
     num_splits: whether to parallelize over the seqlen_k dimension (num_splits > 1) or
     not (num_splits = 1). num_splits=0 means it will be set by an internal heuristic.
@@ -76,7 +76,7 @@ class FlashAttnQKVPackedFunc(torch.autograd.Function):
             dout, qkv[:, 0], qkv[:, 1], qkv[:, 2], out, softmax_lse,
             dqkv[:, 0], dqkv[:, 1], dqkv[:, 2], cu_seqlens, cu_seqlens,
             ctx.max_seqlen, ctx.max_seqlen, ctx.dropout_p, ctx.softmax_scale, ctx.causal,
-            rng_state, num_splits=1 if ctx.deterministic else 0,
+            rng_state=rng_state, num_splits=1 if ctx.deterministic else 0,
         )
         return dqkv, None, None, None, None, None, None, None
 
@@ -110,7 +110,7 @@ class FlashAttnKVPackedFunc(torch.autograd.Function):
             dout, q, kv[:, 0], kv[:, 1], out, softmax_lse,
             dq, dkv[:, 0], dkv[:, 1], cu_seqlens_q, cu_seqlens_k,
             ctx.max_seqlen_q, ctx.max_seqlen_k, ctx.dropout_p, ctx.softmax_scale, ctx.causal,
-            rng_state, num_splits=1 if ctx.deterministic else 0,
+            rng_state=rng_state, num_splits=1 if ctx.deterministic else 0,
         )
         return dq, dkv, None, None, None, None, None, None, None, None, None
 
@@ -142,7 +142,7 @@ class FlashAttnFunc(torch.autograd.Function):
         _flash_attn_backward(
             dout, q, k, v, out, softmax_lse, dq, dk, dv, cu_seqlens_q, cu_seqlens_k,
             ctx.max_seqlen_q, ctx.max_seqlen_k, ctx.dropout_p, ctx.softmax_scale, ctx.causal,
-            rng_state, num_splits=1 if ctx.deterministic else 0,
+            rng_state=rng_state, num_splits=1 if ctx.deterministic else 0,
         )
         return dq, dk, dv, None, None, None, None, None, None, None, None, None