[Bugfix] opcheck false mutation error in rms_norm_per_block_quant (#36688) (#36779)

Signed-off-by: Krish Gupta <krishom70@gmail.com>

csrc/quantization/fused_kernels/fused_layernorm_dynamic_per_token_quant.cu

@@ -286,6 +286,15 @@ void rms_norm_per_block_quant(torch::Tensor& out, torch::Tensor const& input,
                 "Outer scale stride must be 1 when scales are not transposed");
   }
 
+  int64_t hidden_size = input.size(-1);
+  TORCH_CHECK(hidden_size > 0 && hidden_size % group_size == 0,
+              "hidden_size must be a positive multiple of group_size");
+  int64_t num_tokens = input.numel() / hidden_size;
+  int64_t num_groups = hidden_size / group_size;
+  TORCH_CHECK(scales.numel() >= num_tokens * num_groups,
+              "scales buffer too small: need ", num_tokens * num_groups,
+              " elements, got ", scales.numel());
+
   rms_norm_per_block_quant_dispatch(out, input, weight, scales, group_size,
                                     var_epsilon, scale_ub, residual,
                                     is_scale_transposed);

tests/kernels/core/test_fused_quant_layernorm.py

@@ -280,21 +280,22 @@ def test_rms_norm(
         assert torch.allclose(ref_residual, ops_residual)
 
     output = torch.empty(x.shape, dtype=quant_dtype, device=x.device)
-    scales = torch.empty(
-        (x.numel() // x.shape[-1], 1), device=x.device, dtype=torch.float32
-    )
-
     if group_size is None:
+        scales = torch.empty(
+            (x.numel() // x.shape[-1], 1), device=x.device, dtype=torch.float32
+        )
         opcheck(
             torch.ops._C.rms_norm_dynamic_per_token_quant,
             (output, x, layer.weight, scales, 1e-5, scale_ub, residual),
         )
     else:
-        # TODO(luka/eliza) opcheck is broken?
-        #  Somehow the cloned args are getting mutated in-place,
-        #  which causes the opcheck to fail.
-        # https://github.com/vllm-project/vllm/issues/36688
-        return
+        assert hidden_size % group_size[1] == 0
+        num_groups = hidden_size // group_size[1]
+        scales = torch.empty(
+            (num_groups, num_tokens),
+            device=x.device,
+            dtype=torch.float32,
+        ).transpose(0, 1)
         opcheck(
             torch.ops._C.rms_norm_per_block_quant,
             (