[Enhancement] Support MultiScaleDeformableAttention with AMP (open-mm…

…lab#2541)

* [Enhance] Support FP16 for MSDeformAttn

* [Fix] Data type mismatch

* Update mmcv/ops/multi_scale_deform_attn.py

* Add UT

Author:    nijkah <nijkah@gmail.com>

* Add cuda available condition

---------

Co-authored-by: Zaida Zhou <58739961+zhouzaida@users.noreply.github.com>

mmcv/ops/csrc/pytorch/cuda/ms_deform_attn_cuda.cu

@@ -255,7 +255,7 @@ at::Tensor ms_deform_attn_cuda_forward(const at::Tensor &value,
   auto per_attn_weight_size = num_query * num_heads * num_levels * num_point;
   for (int n = 0; n < batch / im2col_step_; ++n) {
     auto columns = output_n.select(0, n);
-    AT_DISPATCH_FLOATING_TYPES(
+    AT_DISPATCH_FLOATING_TYPES_AND_HALF(
         value.scalar_type(), "ms_deform_attn_forward_cuda", ([&] {
           ms_deformable_im2col_cuda(
               at::cuda::getCurrentCUDAStream(),
@@ -326,7 +326,7 @@ void ms_deform_attn_cuda_backward(
 
   for (int n = 0; n < batch / im2col_step_; ++n) {
     auto grad_output_g = grad_output_n.select(0, n);
-    AT_DISPATCH_FLOATING_TYPES(
+    AT_DISPATCH_FLOATING_TYPES_AND_HALF(
         value.scalar_type(), "ms_deform_attn_backward_cuda", ([&] {
           ms_deformable_col2im_cuda(
               at::cuda::getCurrentCUDAStream(),

mmcv/ops/multi_scale_deform_attn.py

@@ -49,6 +49,18 @@ def forward(ctx, value: torch.Tensor, value_spatial_shapes: torch.Tensor,
         """
 
         ctx.im2col_step = im2col_step
+
+        # When pytorch version >= 1.6.0, amp is adopted for fp16 mode;
+        # amp won't cast the type of sampling_locations, attention_weights
+        # (float32), but "value" is cast to float16, leading to the type
+        # mismatch with input (when it is float32) or weight.
+        # The flag for whether to use fp16 or amp is the type of "value",
+        # we cast sampling_locations and attention_weights to
+        # temporarily support fp16 and amp whatever the
+        # pytorch version is.
+        sampling_locations = sampling_locations.type_as(value)
+        attention_weights = attention_weights.type_as(value)
+
         output = ext_module.ms_deform_attn_forward(
             value,
             value_spatial_shapes,

tests/test_ops/test_ms_deformable_attn.py

@@ -8,12 +8,21 @@
 from mmcv.utils import IS_CUDA_AVAILABLE, IS_MLU_AVAILABLE
 
 _USING_PARROTS = True
+_IS_AUTOCAST_AVAILABLE = True
 try:
     from parrots.autograd import gradcheck
 except ImportError:
     from torch.autograd import gradcheck
     _USING_PARROTS = False
 
+try:
+    # If PyTorch version >= 1.6.0 and fp16 is enabled, torch.cuda.amp.autocast
+    # would be imported and used; we should test if our modules support it.
+    from torch.cuda.amp import autocast
+except ImportError:
+    _IS_AUTOCAST_AVAILABLE = False
+    pass
+
 
 @pytest.mark.parametrize('device', [
     'cpu',
@@ -168,6 +177,58 @@ def test_forward_equal_with_pytorch_float(device):
     assert max_rel_err < 1e-6
 
 
+@pytest.mark.skipif(
+    not _IS_AUTOCAST_AVAILABLE, reason='requires autocast support')
+@pytest.mark.skipif(not IS_CUDA_AVAILABLE, reason='requires CUDA support')
+def test_forward_equal_with_autocast():
+    N, M, D = 1, 2, 2
+    Lq, L, P = 2, 2, 2
+    shapes = torch.as_tensor([(6, 4), (3, 2)], dtype=torch.long)
+    level_start_index = torch.cat((shapes.new_zeros(
+        (1, )), shapes.prod(1).cumsum(0)[:-1]))
+    S = sum((H * W).item() for H, W in shapes)
+
+    torch.manual_seed(3)
+    value = torch.rand(N, S, M, D) * 0.01
+    sampling_locations = torch.rand(N, Lq, M, L, P, 2)
+    attention_weights = torch.rand(N, Lq, M, L, P) + 1e-5
+    attention_weights /= attention_weights.sum(
+        -1, keepdim=True).sum(
+            -2, keepdim=True)
+    im2col_step = 2
+    output_pytorch = multi_scale_deformable_attn_pytorch(
+        value, shapes, sampling_locations, attention_weights).detach().cpu()
+
+    # float test
+    dtype = torch.float
+    with autocast(enabled=True):
+        output_device = MultiScaleDeformableAttnFunction.apply(
+            value.cuda().type(dtype), shapes.cuda(), level_start_index.cuda(),
+            sampling_locations.cuda(), attention_weights.cuda(),
+            im2col_step).detach().cpu()
+    assert torch.allclose(output_device, output_pytorch, rtol=1e-2, atol=1e-3)
+    max_abs_err = (output_device - output_pytorch).abs().max()
+    max_rel_err = ((output_device - output_pytorch).abs() /
+                   output_pytorch.abs()).max()
+    assert max_abs_err < 1e-9
+    assert max_rel_err < 1e-6
+
+    # half test
+    dtype = torch.half
+    with autocast(enabled=True):
+        output_device = MultiScaleDeformableAttnFunction.apply(
+            value.cuda().type(dtype), shapes.cuda(), level_start_index.cuda(),
+            sampling_locations.cuda(), attention_weights.cuda(),
+            im2col_step).detach().cpu()
+    assert torch.allclose(
+        output_device, output_pytorch.half(), rtol=1e-2, atol=1e-3)
+    max_abs_err = (output_device - output_pytorch).abs().max()
+    max_rel_err = ((output_device - output_pytorch).abs() /
+                   output_pytorch.abs()).max()
+    assert max_abs_err < 1e-5
+    assert max_rel_err < 1e-2
+
+
 @pytest.mark.parametrize('device', [
     pytest.param(
         'cuda',