Add MPS kernels for nms and roi ops (pytorch#7643)

Co-authored-by: Nicolas Hug <contact@nicolas-hug.com>
Co-authored-by: Nicolas Hug <nh.nicolas.hug@gmail.com>

Author: qqaatw

.github/scripts/run-clang-format.py

@@ -48,7 +48,7 @@
     DEVNULL = open(os.devnull, "wb")
 
 
-DEFAULT_EXTENSIONS = "c,h,C,H,cpp,hpp,cc,hh,c++,h++,cxx,hxx,cu"
+DEFAULT_EXTENSIONS = "c,h,C,H,cpp,hpp,cc,hh,c++,h++,cxx,hxx,cu,mm"
 
 
 class ExitStatus:

CMakeLists.txt

@@ -4,6 +4,7 @@ set(CMAKE_CXX_STANDARD 17)
 file(STRINGS version.txt TORCHVISION_VERSION)
 
 option(WITH_CUDA "Enable CUDA support" OFF)
+option(WITH_MPS "Enable MPS support" OFF)
 option(WITH_PNG "Enable features requiring LibPNG." ON)
 option(WITH_JPEG "Enable features requiring LibJPEG." ON)
 option(USE_PYTHON "Link to Python when building" OFF)
@@ -15,6 +16,11 @@ if(WITH_CUDA)
   set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} --expt-relaxed-constexpr")
 endif()
 
+if(WITH_MPS)
+  enable_language(OBJC OBJCXX)
+  add_definitions(-DWITH_MPS)
+endif()
+
 find_package(Torch REQUIRED)
 
 if (WITH_PNG)
@@ -79,6 +85,9 @@ list(APPEND ALLOW_LISTED ${TVCPP} ${TVCPP}/io/image ${TVCPP}/io/image/cpu ${TVCP
 if(WITH_CUDA)
     list(APPEND ALLOW_LISTED ${TVCPP}/ops/cuda ${TVCPP}/ops/autocast)
 endif()
+if(WITH_MPS)
+    list(APPEND ALLOW_LISTED ${TVCPP}/ops/mps)
+endif()
 
 FOREACH(DIR ${ALLOW_LISTED})
     file(GLOB ALL_SOURCES ${ALL_SOURCES} ${DIR}/*.*)

setup.py

@@ -137,10 +137,13 @@ def get_extensions():
         + glob.glob(os.path.join(extensions_dir, "ops", "cpu", "*.cpp"))
         + glob.glob(os.path.join(extensions_dir, "ops", "quantized", "cpu", "*.cpp"))
     )
+    source_mps = glob.glob(os.path.join(extensions_dir, "ops", "mps", "*.mm"))
 
     print("Compiling extensions with following flags:")
     force_cuda = os.getenv("FORCE_CUDA", "0") == "1"
     print(f"  FORCE_CUDA: {force_cuda}")
+    force_mps = os.getenv("FORCE_MPS", "0") == "1"
+    print(f"  FORCE_MPS: {force_mps}")
     debug_mode = os.getenv("DEBUG", "0") == "1"
     print(f"  DEBUG: {debug_mode}")
     use_png = os.getenv("TORCHVISION_USE_PNG", "1") == "1"
@@ -202,6 +205,8 @@ def get_extensions():
             define_macros += [("WITH_HIP", None)]
             nvcc_flags = []
         extra_compile_args["nvcc"] = nvcc_flags
+    elif torch.backends.mps.is_available() or force_mps:
+        sources += source_mps
 
     if sys.platform == "win32":
         define_macros += [("torchvision_EXPORTS", None)]

test/common_utils.py

@@ -34,6 +34,7 @@
 IN_RE_WORKER = os.environ.get("INSIDE_RE_WORKER") is not None
 IN_FBCODE = os.environ.get("IN_FBCODE_TORCHVISION") == "1"
 CUDA_NOT_AVAILABLE_MSG = "CUDA device not available"
+MPS_NOT_AVAILABLE_MSG = "MPS device not available"
 OSS_CI_GPU_NO_CUDA_MSG = "We're in an OSS GPU machine, and this test doesn't need cuda."
 
 
@@ -130,12 +131,22 @@ def cpu_and_cuda():
     return ("cpu", pytest.param("cuda", marks=pytest.mark.needs_cuda))
 
 
+def cpu_and_cuda_and_mps():
+    return cpu_and_cuda() + (pytest.param("mps", marks=pytest.mark.needs_mps),)
+
+
 def needs_cuda(test_func):
     import pytest  # noqa
 
     return pytest.mark.needs_cuda(test_func)
 
 
+def needs_mps(test_func):
+    import pytest  # noqa
+
+    return pytest.mark.needs_mps(test_func)
+
+
 def _create_data(height=3, width=3, channels=3, device="cpu"):
     # TODO: When all relevant tests are ported to pytest, turn this into a module-level fixture
     tensor = torch.randint(0, 256, (channels, height, width), dtype=torch.uint8, device=device)

test/conftest.py

@@ -8,12 +8,20 @@
 
 torchvision.disable_beta_transforms_warning()
 
-from common_utils import CUDA_NOT_AVAILABLE_MSG, IN_FBCODE, IN_OSS_CI, IN_RE_WORKER, OSS_CI_GPU_NO_CUDA_MSG
+from common_utils import (
+    CUDA_NOT_AVAILABLE_MSG,
+    IN_FBCODE,
+    IN_OSS_CI,
+    IN_RE_WORKER,
+    MPS_NOT_AVAILABLE_MSG,
+    OSS_CI_GPU_NO_CUDA_MSG,
+)
 
 
 def pytest_configure(config):
     # register an additional marker (see pytest_collection_modifyitems)
     config.addinivalue_line("markers", "needs_cuda: mark for tests that rely on a CUDA device")
+    config.addinivalue_line("markers", "needs_mps: mark for tests that rely on a MPS device")
     config.addinivalue_line("markers", "dont_collect: mark for tests that should not be collected")
 
 
@@ -37,12 +45,16 @@ def pytest_collection_modifyitems(items):
         # the "instances" of the tests where device == 'cuda' will have the 'needs_cuda' mark,
         # and the ones with device == 'cpu' won't have the mark.
         needs_cuda = item.get_closest_marker("needs_cuda") is not None
+        needs_mps = item.get_closest_marker("needs_mps") is not None
 
         if needs_cuda and not torch.cuda.is_available():
             # In general, we skip cuda tests on machines without a GPU
             # There are special cases though, see below
             item.add_marker(pytest.mark.skip(reason=CUDA_NOT_AVAILABLE_MSG))
 
+        if needs_mps and not torch.backends.mps.is_available():
+            item.add_marker(pytest.mark.skip(reason=MPS_NOT_AVAILABLE_MSG))
+
         if IN_FBCODE:
             # fbcode doesn't like skipping tests, so instead we  just don't collect the test
             # so that they don't even "exist", hence the continue statements.
@@ -54,6 +66,9 @@ def pytest_collection_modifyitems(items):
                 # TODO: something more robust would be to do that only in a sandcastle instance,
                 # so that we can still see the test being skipped when testing locally from a devvm
                 continue
+            if needs_mps and not torch.backends.mps.is_available():
+                # Same as above, but for MPS
+                continue
         elif IN_OSS_CI:
             # Here we're not in fbcode, so we can safely collect and skip tests.
             if not needs_cuda and torch.cuda.is_available():

test/test_ops.py

@@ -10,7 +10,7 @@
 import torch
 import torch.fx
 import torch.nn.functional as F
-from common_utils import assert_equal, cpu_and_cuda, needs_cuda
+from common_utils import assert_equal, cpu_and_cuda, cpu_and_cuda_and_mps, needs_cuda, needs_mps
 from PIL import Image
 from torch import nn, Tensor
 from torch.autograd import gradcheck
@@ -96,12 +96,33 @@ def forward(self, imgs: Tensor, boxes: List[Tensor]) -> Tensor:
 
 class RoIOpTester(ABC):
     dtype = torch.float64
+    mps_dtype = torch.float32
+    mps_backward_atol = 2e-2
 
-    @pytest.mark.parametrize("device", cpu_and_cuda())
+    @pytest.mark.parametrize("device", cpu_and_cuda_and_mps())
     @pytest.mark.parametrize("contiguous", (True, False))
-    def test_forward(self, device, contiguous, x_dtype=None, rois_dtype=None, deterministic=False, **kwargs):
-        x_dtype = self.dtype if x_dtype is None else x_dtype
-        rois_dtype = self.dtype if rois_dtype is None else rois_dtype
+    @pytest.mark.parametrize(
+        "x_dtype",
+        (
+            torch.float16,
+            torch.float32,
+            torch.float64,
+        ),
+        ids=str,
+    )
+    def test_forward(self, device, contiguous, x_dtype, rois_dtype=None, deterministic=False, **kwargs):
+        if device == "mps" and x_dtype is torch.float64:
+            pytest.skip("MPS does not support float64")
+
+        rois_dtype = x_dtype if rois_dtype is None else rois_dtype
+
+        tol = 1e-5
+        if x_dtype is torch.half:
+            if device == "mps":
+                tol = 5e-3
+            else:
+                tol = 4e-3
+
         pool_size = 5
         # n_channels % (pool_size ** 2) == 0 required for PS operations.
         n_channels = 2 * (pool_size**2)
@@ -120,10 +141,9 @@ def test_forward(self, device, contiguous, x_dtype=None, rois_dtype=None, determ
         # the following should be true whether we're running an autocast test or not.
         assert y.dtype == x.dtype
         gt_y = self.expected_fn(
-            x, rois, pool_h, pool_w, spatial_scale=1, sampling_ratio=-1, device=device, dtype=self.dtype, **kwargs
+            x, rois, pool_h, pool_w, spatial_scale=1, sampling_ratio=-1, device=device, dtype=x_dtype, **kwargs
         )
 
-        tol = 1e-3 if (x_dtype is torch.half or rois_dtype is torch.half) else 1e-5
         torch.testing.assert_close(gt_y.to(y), y, rtol=tol, atol=tol)
 
     @pytest.mark.parametrize("device", cpu_and_cuda())
@@ -155,16 +175,19 @@ def test_torch_fx_trace(self, device, x_dtype=torch.float, rois_dtype=torch.floa
         torch.testing.assert_close(output_gt, output_fx, rtol=tol, atol=tol)
 
     @pytest.mark.parametrize("seed", range(10))
-    @pytest.mark.parametrize("device", cpu_and_cuda())
+    @pytest.mark.parametrize("device", cpu_and_cuda_and_mps())
     @pytest.mark.parametrize("contiguous", (True, False))
     def test_backward(self, seed, device, contiguous, deterministic=False):
+        atol = self.mps_backward_atol if device == "mps" else 1e-05
+        dtype = self.mps_dtype if device == "mps" else self.dtype
+
         torch.random.manual_seed(seed)
         pool_size = 2
-        x = torch.rand(1, 2 * (pool_size**2), 5, 5, dtype=self.dtype, device=device, requires_grad=True)
+        x = torch.rand(1, 2 * (pool_size**2), 5, 5, dtype=dtype, device=device, requires_grad=True)
         if not contiguous:
             x = x.permute(0, 1, 3, 2)
         rois = torch.tensor(
-            [[0, 0, 0, 4, 4], [0, 0, 2, 3, 4], [0, 2, 2, 4, 4]], dtype=self.dtype, device=device  # format is (xyxy)
+            [[0, 0, 0, 4, 4], [0, 0, 2, 3, 4], [0, 2, 2, 4, 4]], dtype=dtype, device=device  # format is (xyxy)
         )
 
         def func(z):
@@ -173,9 +196,25 @@ def func(z):
         script_func = self.get_script_fn(rois, pool_size)
 
         with DeterministicGuard(deterministic):
-            gradcheck(func, (x,))
+            gradcheck(func, (x,), atol=atol)
+
+        gradcheck(script_func, (x,), atol=atol)
 
-        gradcheck(script_func, (x,))
+    @needs_mps
+    def test_mps_error_inputs(self):
+        pool_size = 2
+        x = torch.rand(1, 2 * (pool_size**2), 5, 5, dtype=torch.float16, device="mps", requires_grad=True)
+        rois = torch.tensor(
+            [[0, 0, 0, 4, 4], [0, 0, 2, 3, 4], [0, 2, 2, 4, 4]], dtype=torch.float16, device="mps"  # format is (xyxy)
+        )
+
+        def func(z):
+            return self.fn(z, rois, pool_size, pool_size, spatial_scale=1, sampling_ratio=1)
+
+        with pytest.raises(
+            RuntimeError, match="MPS does not support (?:ps_)?roi_(?:align|pool)? backward with float16 inputs."
+        ):
+            gradcheck(func, (x,))
 
     @needs_cuda
     @pytest.mark.parametrize("x_dtype", (torch.float, torch.half))
@@ -271,6 +310,8 @@ def test_jit_boxes_list(self):
 
 
 class TestPSRoIPool(RoIOpTester):
+    mps_backward_atol = 5e-2
+
     def fn(self, x, rois, pool_h, pool_w, spatial_scale=1, sampling_ratio=-1, **kwargs):
         return ops.PSRoIPool((pool_h, pool_w), 1)(x, rois)
 
@@ -352,6 +393,8 @@ def bilinear_interpolate(data, y, x, snap_border=False):
 
 
 class TestRoIAlign(RoIOpTester):
+    mps_backward_atol = 6e-2
+
     def fn(self, x, rois, pool_h, pool_w, spatial_scale=1, sampling_ratio=-1, aligned=False, **kwargs):
         return ops.RoIAlign(
             (pool_h, pool_w), spatial_scale=spatial_scale, sampling_ratio=sampling_ratio, aligned=aligned
@@ -418,10 +461,11 @@ def test_boxes_shape(self):
         self._helper_boxes_shape(ops.roi_align)
 
     @pytest.mark.parametrize("aligned", (True, False))
-    @pytest.mark.parametrize("device", cpu_and_cuda())
+    @pytest.mark.parametrize("device", cpu_and_cuda_and_mps())
+    @pytest.mark.parametrize("x_dtype", (torch.float16, torch.float32, torch.float64), ids=str)
     @pytest.mark.parametrize("contiguous", (True, False))
     @pytest.mark.parametrize("deterministic", (True, False))
-    def test_forward(self, device, contiguous, deterministic, aligned, x_dtype=None, rois_dtype=None):
+    def test_forward(self, device, contiguous, deterministic, aligned, x_dtype, rois_dtype=None):
         if deterministic and device == "cpu":
             pytest.skip("cpu is always deterministic, don't retest")
         super().test_forward(
@@ -450,7 +494,7 @@ def test_autocast(self, aligned, deterministic, x_dtype, rois_dtype):
             )
 
     @pytest.mark.parametrize("seed", range(10))
-    @pytest.mark.parametrize("device", cpu_and_cuda())
+    @pytest.mark.parametrize("device", cpu_and_cuda_and_mps())
     @pytest.mark.parametrize("contiguous", (True, False))
     @pytest.mark.parametrize("deterministic", (True, False))
     def test_backward(self, seed, device, contiguous, deterministic):
@@ -537,6 +581,8 @@ def test_jit_boxes_list(self):
 
 
 class TestPSRoIAlign(RoIOpTester):
+    mps_backward_atol = 5e-2
+
     def fn(self, x, rois, pool_h, pool_w, spatial_scale=1, sampling_ratio=-1, **kwargs):
         return ops.PSRoIAlign((pool_h, pool_w), spatial_scale=spatial_scale, sampling_ratio=sampling_ratio)(x, rois)
 
@@ -705,40 +751,53 @@ def test_qnms(self, iou, scale, zero_point):
 
         torch.testing.assert_close(qkeep, keep, msg=err_msg.format(iou))
 
-    @needs_cuda
+    @pytest.mark.parametrize(
+        "device",
+        (
+            pytest.param("cuda", marks=pytest.mark.needs_cuda),
+            pytest.param("mps", marks=pytest.mark.needs_mps),
+        ),
+    )
     @pytest.mark.parametrize("iou", (0.2, 0.5, 0.8))
-    def test_nms_cuda(self, iou, dtype=torch.float64):
+    def test_nms_gpu(self, iou, device, dtype=torch.float64):
+        dtype = torch.float32 if device == "mps" else dtype
         tol = 1e-3 if dtype is torch.half else 1e-5
         err_msg = "NMS incompatible between CPU and CUDA for IoU={}"
 
         boxes, scores = self._create_tensors_with_iou(1000, iou)
         r_cpu = ops.nms(boxes, scores, iou)
-        r_cuda = ops.nms(boxes.cuda(), scores.cuda(), iou)
+        r_gpu = ops.nms(boxes.to(device), scores.to(device), iou)
 
-        is_eq = torch.allclose(r_cpu, r_cuda.cpu())
+        is_eq = torch.allclose(r_cpu, r_gpu.cpu())
         if not is_eq:
             # if the indices are not the same, ensure that it's because the scores
             # are duplicate
-            is_eq = torch.allclose(scores[r_cpu], scores[r_cuda.cpu()], rtol=tol, atol=tol)
+            is_eq = torch.allclose(scores[r_cpu], scores[r_gpu.cpu()], rtol=tol, atol=tol)
         assert is_eq, err_msg.format(iou)
 
     @needs_cuda
     @pytest.mark.parametrize("iou", (0.2, 0.5, 0.8))
     @pytest.mark.parametrize("dtype", (torch.float, torch.half))
     def test_autocast(self, iou, dtype):
         with torch.cuda.amp.autocast():
-            self.test_nms_cuda(iou=iou, dtype=dtype)
+            self.test_nms_gpu(iou=iou, dtype=dtype, device="cuda")
 
-    @needs_cuda
-    def test_nms_cuda_float16(self):
+    @pytest.mark.parametrize(
+        "device",
+        (
+            pytest.param("cuda", marks=pytest.mark.needs_cuda),
+            pytest.param("mps", marks=pytest.mark.needs_mps),
+        ),
+    )
+    def test_nms_float16(self, device):
         boxes = torch.tensor(
             [
                 [285.3538, 185.5758, 1193.5110, 851.4551],
                 [285.1472, 188.7374, 1192.4984, 851.0669],
                 [279.2440, 197.9812, 1189.4746, 849.2019],
             ]
-        ).cuda()
-        scores = torch.tensor([0.6370, 0.7569, 0.3966]).cuda()
+        ).to(device)
+        scores = torch.tensor([0.6370, 0.7569, 0.3966]).to(device)
 
         iou_thres = 0.2
         keep32 = ops.nms(boxes, scores, iou_thres)

torchvision/csrc/ops/cpu/nms_kernel.cpp

@@ -11,8 +11,8 @@ at::Tensor nms_kernel_impl(
     const at::Tensor& dets,
     const at::Tensor& scores,
     double iou_threshold) {
-  TORCH_CHECK(!dets.is_cuda(), "dets must be a CPU tensor");
-  TORCH_CHECK(!scores.is_cuda(), "scores must be a CPU tensor");
+  TORCH_CHECK(dets.is_cpu(), "dets must be a CPU tensor");
+  TORCH_CHECK(scores.is_cpu(), "scores must be a CPU tensor");
   TORCH_CHECK(
       dets.scalar_type() == scores.scalar_type(),
       "dets should have the same type as scores");

torchvision/csrc/ops/mps/mps_helpers.h

@@ -0,0 +1,6 @@
+constexpr int threadsPerBlock = 512;
+
+template <typename T>
+constexpr inline T ceil_div(T n, T m) {
+  return (n + m - 1) / m;
+}