diff --git a/aten/src/ATen/native/Distributions.cpp b/aten/src/ATen/native/Distributions.cpp
index 0de9632ddb1..bd1d974e9be 100644
--- a/aten/src/ATen/native/Distributions.cpp
+++ b/aten/src/ATen/native/Distributions.cpp
@@ -23,6 +23,7 @@
 #include <ATen/ops/_sample_dirichlet_native.h>
 #include <ATen/ops/_standard_gamma_grad_native.h>
 #include <ATen/ops/_standard_gamma_native.h>
+#include <ATen/ops/_assert_async.h>
 #include <ATen/ops/argmax.h>
 #include <ATen/ops/bernoulli_native.h>
 #include <ATen/ops/binomial_native.h>
@@ -585,19 +586,15 @@ Tensor& multinomial_out(const Tensor& self,
   // https://github.com/pytorch/pytorch/issues/11931#issuecomment-625882503
   if (!with_replacement || n_sample == 1) {
     // Sanity checks on `self`.
-    auto is_valid = ((self.max() < INFINITY) & (self.min() >= 0)).item();
-    TORCH_CHECK(
-        is_valid.to<bool>(),
-        "probability tensor contains either `inf`, `nan` or element < 0");
-    bool zero_prob_condition = false;
+    auto is_valid = ((self.max() < INFINITY) & (self.min() >= 0));
+    at::_assert_async(is_valid, "probability tensor contains either `inf`, `nan` or element < 0");
+    at::Tensor zero_prob_condition;
     if (self.dim() == 1){
-      zero_prob_condition = (self.sum() == 0).item().to<bool>();
+      zero_prob_condition = (self.sum() == 0);
     } else {
-      zero_prob_condition = (self.sum(1) == 0).sum().item().to<bool>();
+      zero_prob_condition = (self.sum(1) == 0).any();
     }
-    TORCH_CHECK(
-        !zero_prob_condition,
-        "invalid multinomial distribution (sum of probabilities <= 0)");
+    at::_assert_async(~zero_prob_condition, "invalid multinomial distribution (sum of probabilities <= 0)");
 
     // The algorithm is from gumbel softmax.
     // s = argmax( logp - log(-log(eps)) ) where eps ~ U(0, 1)
diff --git a/torch/testing/_internal/common_methods_invocations.py b/torch/testing/_internal/common_methods_invocations.py
index a64cb62cede..2120f4f019e 100644
--- a/torch/testing/_internal/common_methods_invocations.py
+++ b/torch/testing/_internal/common_methods_invocations.py
@@ -2849,28 +2849,29 @@ def error_inputs_multinomial(op_info, device, **kwargs):
 
     rep_arg = (False, True) if torch.device(device).type == 'cpu' else (False,)
 
-    for rep in rep_arg:
-        kwargs = {'num_samples': 2, 'replacement': rep}
+    if torch.device(device).type == 'cpu':
+        for rep in rep_arg:
+            kwargs = {'num_samples': 2, 'replacement': rep}
 
-        for shape in inputs:
-            # error case when input tensor contains `inf`, `nan` or negative element
-            yield ErrorInput(SampleInput(torch.tensor(shape), kwargs=kwargs),
-                             error_regex=err_msg1 if rep is False else err_msg2)
+            for shape in inputs:
+                # error case when input tensor contains `inf`, `nan` or negative element
+                yield ErrorInput(SampleInput(torch.tensor(shape), kwargs=kwargs),
+                                 error_regex=err_msg1 if rep is False else err_msg2)
 
-        # error case for the invalid multinomial distribution (sum of probabilities <= 0), 1-D input
-        x = torch.zeros(3, device=device)
-        yield ErrorInput(SampleInput(x, kwargs=kwargs),
-                         error_regex=err_msg2)
+            # error case for the invalid multinomial distribution (sum of probabilities <= 0), 1-D input
+            x = torch.zeros(3, device=device)
+            yield ErrorInput(SampleInput(x, kwargs=kwargs),
+                             error_regex=err_msg2)
 
-        # error case for the invalid multinomial distribution (sum of probabilities <= 0), 2-D input
-        x = torch.zeros(3, 3, device=device)
-        yield ErrorInput(SampleInput(x, kwargs=kwargs),
-                         error_regex=err_msg2)
+            # error case for the invalid multinomial distribution (sum of probabilities <= 0), 2-D input
+            x = torch.zeros(3, 3, device=device)
+            yield ErrorInput(SampleInput(x, kwargs=kwargs),
+                             error_regex=err_msg2)
 
-        # error case for the invalid multinomial distribution
-        x[1, :] = 1
-        yield ErrorInput(SampleInput(x, kwargs=kwargs),
-                         error_regex=err_msg2)
+            # error case for the invalid multinomial distribution
+            x[1, :] = 1
+            yield ErrorInput(SampleInput(x, kwargs=kwargs),
+                             error_regex=err_msg2)
 
 def error_inputs_gradient(op_info, device, **kwargs):
     for dtype in [torch.long, torch.float32, torch.complex64]: