diff --git a/test/test_ops.py b/test/test_ops.py
index 8f8756fe675..0a412d6930b 100644
--- a/test/test_ops.py
+++ b/test/test_ops.py
@@ -474,6 +474,33 @@ def expected_fn(
     def test_boxes_shape(self):
         self._helper_boxes_shape(ops.roi_align)
 
+    @needs_mps
+    @pytest.mark.parametrize("seed", range(3))
+    def test_backward_mps_consistency(self, seed):
+        # Regression test for over-accumulation in the MPS roi_align backward
+        # kernel. The kernel used a grid-stride loop dispatched over multiple
+        # threadgroups, so each pooled-output element's gradient was scattered
+        # once per threadgroup. The error is invisible for a handful of RoIs
+        # (output fits in one threadgroup) but grows with the RoI count, so use
+        # enough RoIs for the output to span several threadgroups.
+        torch.random.manual_seed(seed)
+        pool_size = 5
+        num_rois = 100
+        x = torch.rand(1, 2 * (pool_size**2), 40, 40, dtype=torch.float32)
+        rois = torch.empty(num_rois, 5)
+        rois[:, 0] = 0
+        xy = torch.rand(num_rois, 2) * 20
+        wh = torch.rand(num_rois, 2) * 20
+        rois[:, 1:3] = xy
+        rois[:, 3:5] = xy + wh + 1  # ensure x2 > x1 and y2 > y1
+
+        def grad_on(device):
+            xd = x.to(device).detach().requires_grad_(True)
+            self.fn(xd, rois.to(device), pool_size, pool_size, spatial_scale=1, sampling_ratio=2).sum().backward()
+            return xd.grad.cpu()
+
+        torch.testing.assert_close(grad_on("mps"), grad_on("cpu"), rtol=1e-4, atol=1e-4)
+
     @pytest.mark.parametrize("aligned", (True, False))
     @pytest.mark.parametrize("device", cpu_and_cuda_and_mps())
     @pytest.mark.parametrize("x_dtype", (torch.float16, torch.float32, torch.float64))  # , ids=str)
diff --git a/torchvision/csrc/ops/mps/mps_kernels.h b/torchvision/csrc/ops/mps/mps_kernels.h
index 90c0aee9eb7..e06235fe284 100644
--- a/torchvision/csrc/ops/mps/mps_kernels.h
+++ b/torchvision/csrc/ops/mps/mps_kernels.h
@@ -486,11 +486,15 @@ kernel void roi_align_backward(
     constant int64_t & c_stride       [[buffer(13)]],
     constant int64_t & h_stride       [[buffer(14)]],
     constant int64_t & w_stride       [[buffer(15)]],
-    uint2     tgid   [[threadgroup_position_in_grid]],
-    uint2     tptg   [[threads_per_threadgroup]],
-    uint2     tid2   [[thread_position_in_threadgroup]]){
+    uint     index   [[thread_position_in_grid]]){
 
-  MPS_1D_KERNEL_LOOP(index, output_size, 1) {
+  // One thread per pooled-output element. Dispatched via dispatchThreads, so
+  // each element is processed exactly once; redundant passes would otherwise
+  // be silently summed by the atomic_add below (overlapping RoIs share pixels).
+  if (index >= static_cast<uint>(output_size)) {
+    return;
+  }
+  {
     // (n, c, ph, pw) is an element in the pooled output
     integer_t pw = index % pooled_width;
     integer_t ph = (index / pooled_width) % pooled_height;
@@ -602,9 +606,7 @@ kernel void roi_align_backward<DTYPE, INT_DTYPE>(          \
     constant int64_t & c_stride       [[buffer(13)]],      \
     constant int64_t & h_stride       [[buffer(14)]],      \
     constant int64_t & w_stride       [[buffer(15)]],      \
-    uint2     tgid   [[threadgroup_position_in_grid]],     \
-    uint2     tptg   [[threads_per_threadgroup]],          \
-    uint2     tid2   [[thread_position_in_threadgroup]]);
+    uint     index   [[thread_position_in_grid]]);
 
 template<typename T, typename integer_t>
 kernel void roi_pool(
diff --git a/torchvision/csrc/ops/mps/roi_align_kernel.mm b/torchvision/csrc/ops/mps/roi_align_kernel.mm
index 6cf07761060..918f69a5ffa 100644
--- a/torchvision/csrc/ops/mps/roi_align_kernel.mm
+++ b/torchvision/csrc/ops/mps/roi_align_kernel.mm
@@ -132,10 +132,6 @@
   dispatch_sync(mpsStream->queue(), ^() {
     @autoreleasepool {
       id<MTLComputeCommandEncoder> computeEncoder = mpsStream->commandEncoder();
-      MTLSize threadgroupsPerGrid = MTLSizeMake(
-          std::min(ceil_div(static_cast<int64_t>(grad.numel()), static_cast<int64_t>(512)), static_cast<int64_t>(4096)),
-          1,
-          1);
 
       const std::string kernel = "roi_align_backward_" + scalarToMetalTypeString(grad.scalar_type());
       id<MTLComputePipelineState> visionPSO = mps::visionPipelineState(device, kernel);
@@ -163,14 +159,13 @@
       [computeEncoder setBytes:&h_stride length:sizeof(int64_t) atIndex:14];
       [computeEncoder setBytes:&w_stride length:sizeof(int64_t) atIndex:15];
 
-      // A threadGroup is equivalent to a cuda's block.
-      NSUInteger tgSize = visionPSO.maxTotalThreadsPerThreadgroup;
-      if (tgSize > threadsPerBlock) {
-        tgSize = threadsPerBlock;
-      }
-
-      MTLSize threadGroupSize = MTLSizeMake(tgSize, 1, 1);
-      [computeEncoder dispatchThreadgroups:threadgroupsPerGrid threadsPerThreadgroup:threadGroupSize];
+      // One thread per pooled-output element. dispatchThreads guarantees each
+      // index is handled exactly once; the kernel scatters into grad_input with
+      // atomic_add for overlapping RoIs.
+      MTLSize threadsPerGrid = MTLSizeMake(output_size, 1, 1);
+      NSUInteger tgSize = std::min(static_cast<int64_t>(visionPSO.maxTotalThreadsPerThreadgroup), output_size);
+      MTLSize threadGroupSize = MTLSizeMake(std::max<NSUInteger>(tgSize, 1), 1, 1);
+      [computeEncoder dispatchThreads:threadsPerGrid threadsPerThreadgroup:threadGroupSize];
 
       getMPSProfiler().endProfileKernel(visionPSO);
     }