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import torch.nn.functional as F
from kernels.benchmark import Benchmark
def ms_deform_attn_reference(
value: torch.Tensor,
spatial_shapes: torch.Tensor,
level_start_index: torch.Tensor,
sampling_locations: torch.Tensor,
attention_weights: torch.Tensor,
) -> torch.Tensor:
batch, _, num_heads, channels = value.shape
_, num_query, _, num_levels, num_points, _ = sampling_locations.shape
# Split value by levels
value_list = []
for level_id in range(num_levels):
H, W = spatial_shapes[level_id]
start_idx = level_start_index[level_id]
end_idx = (
level_start_index[level_id + 1]
if level_id < num_levels - 1
else value.shape[1]
)
# (batch, H*W, num_heads, channels) -> (batch, num_heads, channels, H, W)
value_level = value[:, start_idx:end_idx, :, :].view(
batch, H, W, num_heads, channels
)
value_level = value_level.permute(0, 3, 4, 1, 2).contiguous()
value_list.append(value_level)
# Sample from each level
output = torch.zeros(
batch, num_query, num_heads, channels, device=value.device, dtype=value.dtype
)
for level_id in range(num_levels):
H, W = spatial_shapes[level_id]
value_level = value_list[level_id] # (batch, num_heads, channels, H, W)
# Get sampling locations for this level: (batch, num_query, num_heads, num_points, 2)
sampling_loc_level = sampling_locations[:, :, :, level_id, :, :]
# Convert from [0, 1] to [-1, 1] for grid_sample
grid = (
2.0 * sampling_loc_level - 1.0
) # (batch, num_query, num_heads, num_points, 2)
# Reshape for grid_sample: need (batch * num_heads, channels, H, W) and (batch * num_heads, num_query, num_points, 2)
value_level = value_level.view(batch * num_heads, channels, H.item(), W.item())
grid = grid.permute(
0, 2, 1, 3, 4
).contiguous() # (batch, num_heads, num_query, num_points, 2)
grid = grid.view(batch * num_heads, num_query, num_points, 2)
# Sample: output is (batch * num_heads, channels, num_query, num_points)
sampled = F.grid_sample(
value_level,
grid,
mode="bilinear",
padding_mode="zeros",
align_corners=False,
)
# Reshape back: (batch, num_heads, channels, num_query, num_points)
sampled = sampled.view(batch, num_heads, channels, num_query, num_points)
# -> (batch, num_query, num_heads, num_points, channels)
sampled = sampled.permute(0, 3, 1, 4, 2).contiguous()
# Get attention weights for this level: (batch, num_query, num_heads, num_points)
attn_level = attention_weights[:, :, :, level_id, :]
# Weighted sum over points: (batch, num_query, num_heads, channels)
output += (sampled * attn_level.unsqueeze(-1)).sum(dim=3)
# Reshape to (batch, num_query, num_heads * channels)
output = output.view(batch, num_query, num_heads * channels)
return output
class MSDeformAttnBenchmark(Benchmark):
seed: int = 42
def setup(self):
batch = 2
num_heads = 8
channels = 32 # embed_dim = num_heads * channels = 256
num_levels = 4
num_query = 300
num_points = 4
im2col_step = 64
# Spatial shapes for 4 levels: 64x64, 32x32, 16x16, 8x8
spatial_shapes = torch.tensor(
[[64, 64], [32, 32], [16, 16], [8, 8]],
dtype=torch.int64,
device=self.device,
)
# Calculate spatial_size = sum of H*W for all levels
spatial_size = (64 * 64) + (32 * 32) + (16 * 16) + (8 * 8) # 5440
# Level start indices
level_start_index = torch.tensor(
[0, 64 * 64, 64 * 64 + 32 * 32, 64 * 64 + 32 * 32 + 16 * 16],
dtype=torch.int64,
device=self.device,
)
self.value = torch.randn(
batch,
spatial_size,
num_heads,
channels,
device=self.device,
dtype=torch.float32,
)
self.spatial_shapes = spatial_shapes
self.level_start_index = level_start_index
self.sampling_loc = torch.rand(
batch,
num_query,
num_heads,
num_levels,
num_points,
2,
device=self.device,
dtype=torch.float32,
)
self.attn_weight = torch.rand(
batch,
num_query,
num_heads,
num_levels,
num_points,
device=self.device,
dtype=torch.float32,
)
# Normalize attention weights
self.attn_weight = self.attn_weight / self.attn_weight.sum(-1, keepdim=True)
self.im2col_step = im2col_step
self.out = torch.empty(
batch,
num_query,
num_heads * channels,
device=self.device,
dtype=torch.float32,
)
def benchmark_forward(self):
self.out = self.kernel.ms_deform_attn_forward(
self.value,
self.spatial_shapes,
self.level_start_index,
self.sampling_loc,
self.attn_weight,
self.im2col_step,
)
def verify_forward(self) -> torch.Tensor:
return ms_deform_attn_reference(
self.value,
self.spatial_shapes,
self.level_start_index,
self.sampling_loc,
self.attn_weight,
)
def setup_large(self):
batch = 8
num_heads = 8
channels = 32
num_levels = 4
num_query = 900
num_points = 4
im2col_step = 64
spatial_shapes = torch.tensor(
[[64, 64], [32, 32], [16, 16], [8, 8]],
dtype=torch.int64,
device=self.device,
)
spatial_size = (64 * 64) + (32 * 32) + (16 * 16) + (8 * 8)
level_start_index = torch.tensor(
[0, 64 * 64, 64 * 64 + 32 * 32, 64 * 64 + 32 * 32 + 16 * 16],
dtype=torch.int64,
device=self.device,
)
self.value = torch.randn(
batch,
spatial_size,
num_heads,
channels,
device=self.device,
dtype=torch.float32,
)
self.spatial_shapes = spatial_shapes
self.level_start_index = level_start_index
self.sampling_loc = torch.rand(
batch,
num_query,
num_heads,
num_levels,
num_points,
2,
device=self.device,
dtype=torch.float32,
)
self.attn_weight = torch.rand(
batch,
num_query,
num_heads,
num_levels,
num_points,
device=self.device,
dtype=torch.float32,
)
self.attn_weight = self.attn_weight / self.attn_weight.sum(-1, keepdim=True)
self.im2col_step = im2col_step
self.out = torch.empty(
batch,
num_query,
num_heads * channels,
device=self.device,
dtype=torch.float32,
)
def benchmark_large(self):
self.out = self.kernel.ms_deform_attn_forward(
self.value,
self.spatial_shapes,
self.level_start_index,
self.sampling_loc,
self.attn_weight,
self.im2col_step,
)
def verify_large(self) -> torch.Tensor:
return ms_deform_attn_reference(
self.value,
self.spatial_shapes,
self.level_start_index,
self.sampling_loc,
self.attn_weight,
)
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