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DeformableDetrModel support fp16 (#29013)

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* Update ms_deform_attn_cuda.cu

* Update ms_deform_attn_cuda.cuh

* Update modeling_deformable_detr.py

* Update src/transformers/models/deformable_detr/modeling_deformable_detr.py

Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>

* Update modeling_deformable_detr.py

* python utils/check_copies.py --fix_and_overwrite

* Fix dtype missmatch error

* Update test_modeling_deformable_detr.py

* Update test_modeling_deformable_detr.py

* Update modeling_deformable_detr.py

* Update modeling_deformable_detr.py

---------

Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
This commit is contained in:
Donggeun Yu
2024-02-15 21:31:09 +09:00
committed by GitHub
parent 83e96dc0ab
commit 5b6fa2306a
5 changed files with 29 additions and 16 deletions
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4
src/transformers/kernels/deformable_detr/cuda/ms_deform_attn_cuda.cu
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@@ -64,7 +64,7 @@ at::Tensor ms_deform_attn_cuda_forward(
for (int n = 0; n < batch/im2col_step_; ++n)
{
auto columns = output_n.select(0, n);
AT_DISPATCH_FLOATING_TYPES(value.type(), "ms_deform_attn_forward_cuda", ([&] {
AT_DISPATCH_FLOATING_TYPES_AND_HALF(value.type(), "ms_deform_attn_forward_cuda", ([&] {
ms_deformable_im2col_cuda(at::cuda::getCurrentCUDAStream(),
value.data<scalar_t>() + n * im2col_step_ * per_value_size,
spatial_shapes.data<int64_t>(),
@@ -134,7 +134,7 @@ std::vector<at::Tensor> 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(value.type(), "ms_deform_attn_backward_cuda", ([&] {
AT_DISPATCH_FLOATING_TYPES_AND_HALF(value.type(), "ms_deform_attn_backward_cuda", ([&] {
ms_deformable_col2im_cuda(at::cuda::getCurrentCUDAStream(),
grad_output_g.data<scalar_t>(),
value.data<scalar_t>() + n * im2col_step_ * per_value_size,

4
src/transformers/kernels/deformable_detr/cuda/ms_deform_attn_cuda.cuh
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@@ -72,7 +72,7 @@ at::Tensor ms_deform_attn_cuda_forward(
for (int n = 0; n < batch/im2col_step_; ++n)
{
auto columns = output_n.select(0, n);
AT_DISPATCH_FLOATING_TYPES(value.type(), "ms_deform_attn_forward_cuda", ([&] {
AT_DISPATCH_FLOATING_TYPES_AND_HALF(value.type(), "ms_deform_attn_forward_cuda", ([&] {
ms_deformable_im2col_cuda(at::cuda::getCurrentCUDAStream(),
value.data<scalar_t>() + n * im2col_step_ * per_value_size,
spatial_shapes.data<int64_t>(),
@@ -142,7 +142,7 @@ std::vector<at::Tensor> 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(value.type(), "ms_deform_attn_backward_cuda", ([&] {
AT_DISPATCH_FLOATING_TYPES_AND_HALF(value.type(), "ms_deform_attn_backward_cuda", ([&] {
ms_deformable_col2im_cuda(at::cuda::getCurrentCUDAStream(),
grad_output_g.data<scalar_t>(),
value.data<scalar_t>() + n * im2col_step_ * per_value_size,

17
src/transformers/models/deformable_detr/modeling_deformable_detr.py
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@@ -617,7 +617,8 @@ class DeformableDetrMultiscaleDeformableAttention(nn.Module):
def _reset_parameters(self):
nn.init.constant_(self.sampling_offsets.weight.data, 0.0)
thetas = torch.arange(self.n_heads, dtype=torch.int64).float() * (2.0 * math.pi / self.n_heads)
default_dtype = torch.get_default_dtype()
thetas = torch.arange(self.n_heads, dtype=torch.int64).to(default_dtype) * (2.0 * math.pi / self.n_heads)
grid_init = torch.stack([thetas.cos(), thetas.sin()], -1)
grid_init = (
(grid_init / grid_init.abs().max(-1, keepdim=True)[0])
@@ -1171,8 +1172,8 @@ class DeformableDetrEncoder(DeformableDetrPreTrainedModel):
reference_points_list = []
for level, (height, width) in enumerate(spatial_shapes):
ref_y, ref_x = meshgrid(
torch.linspace(0.5, height - 0.5, height, dtype=torch.float32, device=device),
torch.linspace(0.5, width - 0.5, width, dtype=torch.float32, device=device),
torch.linspace(0.5, height - 0.5, height, dtype=valid_ratios.dtype, device=device),
torch.linspace(0.5, width - 0.5, width, dtype=valid_ratios.dtype, device=device),
indexing="ij",
)
# TODO: valid_ratios could be useless here. check https://github.com/fundamentalvision/Deformable-DETR/issues/36
@@ -1540,15 +1541,15 @@ class DeformableDetrModel(DeformableDetrPreTrainedModel):
for name, param in self.backbone.conv_encoder.model.named_parameters():
param.requires_grad_(True)
def get_valid_ratio(self, mask):
def get_valid_ratio(self, mask, dtype=torch.float32):
"""Get the valid ratio of all feature maps."""
_, height, width = mask.shape
valid_height = torch.sum(mask[:, :, 0], 1)
valid_width = torch.sum(mask[:, 0, :], 1)
valid_ratio_heigth = valid_height.float() / height
valid_ratio_width = valid_width.float() / width
valid_ratio = torch.stack([valid_ratio_width, valid_ratio_heigth], -1)
valid_ratio_height = valid_height.to(dtype) / height
valid_ratio_width = valid_width.to(dtype) / width
valid_ratio = torch.stack([valid_ratio_width, valid_ratio_height], -1)
return valid_ratio
def get_proposal_pos_embed(self, proposals):
@@ -1721,7 +1722,7 @@ class DeformableDetrModel(DeformableDetrPreTrainedModel):
lvl_pos_embed_flatten = torch.cat(lvl_pos_embed_flatten, 1)
spatial_shapes = torch.as_tensor(spatial_shapes, dtype=torch.long, device=source_flatten.device)
level_start_index = torch.cat((spatial_shapes.new_zeros((1,)), spatial_shapes.prod(1).cumsum(0)[:-1]))
valid_ratios = torch.stack([self.get_valid_ratio(m) for m in masks], 1)
valid_ratios = torch.stack([self.get_valid_ratio(m, dtype=source_flatten.dtype) for m in masks], 1)
valid_ratios = valid_ratios.float()
# Fourth, sent source_flatten + mask_flatten + lvl_pos_embed_flatten (backbone + proj layer output) through encoder

8
src/transformers/models/deta/modeling_deta.py
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@@ -1549,15 +1549,15 @@ class DetaModel(DetaPreTrainedModel):
param.requires_grad_(True)
# Copied from transformers.models.deformable_detr.modeling_deformable_detr.DeformableDetrModel.get_valid_ratio
def get_valid_ratio(self, mask):
def get_valid_ratio(self, mask, dtype=torch.float32):
"""Get the valid ratio of all feature maps."""
_, height, width = mask.shape
valid_height = torch.sum(mask[:, :, 0], 1)
valid_width = torch.sum(mask[:, 0, :], 1)
valid_ratio_heigth = valid_height.float() / height
valid_ratio_width = valid_width.float() / width
valid_ratio = torch.stack([valid_ratio_width, valid_ratio_heigth], -1)
valid_ratio_height = valid_height.to(dtype) / height
valid_ratio_width = valid_width.to(dtype) / width
valid_ratio = torch.stack([valid_ratio_width, valid_ratio_height], -1)
return valid_ratio
# Copied from transformers.models.deformable_detr.modeling_deformable_detr.DeformableDetrModel.get_proposal_pos_embed

12
tests/models/deformable_detr/test_modeling_deformable_detr.py
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@@ -583,6 +583,18 @@ class DeformableDetrModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineT
loss = model(**inputs).loss
loss.backward()
def create_and_check_model_fp16_forward(self):
model_class = DeformableDetrForObjectDetection
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
model = model_class(config)
model.to(torch_device)
model.half()
model.eval()
inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=True)
output = model(**inputs)["last_hidden_state"]
self.parent.assertFalse(torch.isnan(output).any().item())
TOLERANCE = 1e-4