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[Feat][Convolution] Support grouped conv2d and conv3d #1568

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84 changes: 54 additions & 30 deletions benchmarks/ops/bench_convolution.py
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Original file line number Diff line number Diff line change
Expand Up @@ -139,6 +139,7 @@ def __init__(
stride: tuple[int, int],
padding: tuple[int, int],
dilation: tuple[int, int],
groups: int,
dtype: torch.dtype,
) -> None:
self.n = n
Expand All @@ -150,12 +151,13 @@ def __init__(
self.stride = stride
self.padding = padding
self.dilation = dilation
self.groups = groups
self.dtype = dtype

def gen_inputs(self) -> tuple[torch.Tensor, torch.Tensor, Optional[torch.Tensor]]:
x = torch.randn(self.n, self.c_in, self.h, self.w, device="cuda", dtype=self.dtype).contiguous()
weight = torch.randn(
self.c_out, self.c_in, self.kernel_size[0], self.kernel_size[1],
self.c_out, self.c_in // self.groups, self.kernel_size[0], self.kernel_size[1],
device="cuda", dtype=self.dtype,
).contiguous()
bias = torch.zeros(self.c_out, device="cuda", dtype=self.dtype).contiguous()
Expand All @@ -174,7 +176,7 @@ def ref_program(
stride=self.stride,
padding=self.padding,
dilation=self.dilation,
groups=1,
groups=self.groups,
)

class Conv2dBenchmark(BenchmarkBase[Conv2dBenchCase]):
Expand All @@ -183,41 +185,47 @@ def calculate_flops(self) -> Optional[float]:
t = self.workload
out_h = (t.h + 2 * t.padding[0] - t.dilation[0] * (t.kernel_size[0] - 1) - 1) // t.stride[0] + 1
out_w = (t.w + 2 * t.padding[1] - t.dilation[1] * (t.kernel_size[1] - 1) - 1) // t.stride[1] + 1
return 2.0 * t.n * t.c_out * out_h * out_w * t.c_in * t.kernel_size[0] * t.kernel_size[1]
c_in_g = t.c_in // t.groups
return 2.0 * t.n * t.c_out * out_h * out_w * c_in_g * t.kernel_size[0] * t.kernel_size[1]

def calculate_memory(self) -> Optional[float]:
t = self.workload
out_h = (t.h + 2 * t.padding[0] - t.dilation[0] * (t.kernel_size[0] - 1) - 1) // t.stride[0] + 1
out_w = (t.w + 2 * t.padding[1] - t.dilation[1] * (t.kernel_size[1] - 1) - 1) // t.stride[1] + 1
c_in_g = t.c_in // t.groups
bytes_ = (
t.n * t.c_in * t.h * t.w
+ t.c_out * t.c_in * t.kernel_size[0] * t.kernel_size[1]
+ t.c_out * c_in_g * t.kernel_size[0] * t.kernel_size[1]
+ t.n * t.c_out * out_h * out_w
) * t.dtype.itemsize
return bytes_


_CONV2D_BENCH_PARAMS = [
pytest.param(2, 64, 56, 56, 64, (3, 3), (1, 1), (1, 1), (1, 1), torch.float16, True, id="resnet-3x3-fp16"),
pytest.param(1, 3, 112, 112, 64, (3, 3), (2, 2), (1, 1), (1, 1), torch.float16, True, id="stem-3x3-s2-fp16"),
pytest.param(1, 128, 56, 56, 256, (3, 3), (2, 2), (1, 1), (1, 1), torch.float16, True, id="stage-transition-3x3-s2-fp16"),
pytest.param(1, 256, 112, 112, 512, (3, 3), (1, 1), (1, 1), (1, 1), torch.float16, True, id="highres-3x3-s1-fp16"),
pytest.param(1, 64, 56, 56, 128, (5, 5), (1, 1), (2, 2), (1, 1), torch.float16, True, id="midres-5x5-s1-fp16"),
pytest.param(1, 128, 56, 56, 256, (5, 5), (2, 2), (2, 2), (1, 1), torch.float16, True, id="stage-transition-5x5-s2-fp16"),
pytest.param(1, 128, 28, 28, 128, (3, 3), (2, 2), (1, 1), (1, 1), torch.bfloat16, True, id="stride2-bf16"),
pytest.param(2, 64, 56, 56, 256, (1, 1), (1, 1), (0, 0), (1, 1), torch.float16, True, id="resnet-1x1-fp16"),
pytest.param(2, 128, 28, 28, 512, (1, 1), (1, 1), (0, 0), (1, 1), torch.float16, True, id="bottleneck-expand-1x1-fp16"),
pytest.param(2, 512, 28, 28, 128, (1, 1), (1, 1), (0, 0), (1, 1), torch.float16, True, id="bottleneck-reduce-1x1-fp16"),
pytest.param(1, 256, 14, 14, 1024, (1, 1), (1, 1), (0, 0), (1, 1), torch.float16, True, id="late-stage-1x1-fp16"),
pytest.param(1, 512, 7, 7, 2048, (1, 1), (1, 1), (0, 0), (1, 1), torch.float16, True, id="classifier-1x1-fp16"),
pytest.param(2, 64, 56, 56, 256, (1, 1), (1, 1), (0, 0), (1, 1), torch.bfloat16, True, id="resnet-1x1-bf16"),
pytest.param(2, 64, 56, 56, 64, (3, 3), (1, 1), (1, 1), (1, 1), 1, torch.float16, True, id="resnet-3x3-fp16"),
pytest.param(1, 3, 112, 112, 64, (3, 3), (2, 2), (1, 1), (1, 1), 1, torch.float16, True, id="stem-3x3-s2-fp16"),
pytest.param(1, 128, 56, 56, 256, (3, 3), (2, 2), (1, 1), (1, 1), 1, torch.float16, True, id="stage-transition-3x3-s2-fp16"),
pytest.param(1, 256, 112, 112, 512, (3, 3), (1, 1), (1, 1), (1, 1), 1, torch.float16, True, id="highres-3x3-s1-fp16"),
pytest.param(1, 64, 56, 56, 128, (5, 5), (1, 1), (2, 2), (1, 1), 1, torch.float16, True, id="midres-5x5-s1-fp16"),
pytest.param(1, 128, 56, 56, 256, (5, 5), (2, 2), (2, 2), (1, 1), 1, torch.float16, True, id="stage-transition-5x5-s2-fp16"),
pytest.param(1, 128, 28, 28, 128, (3, 3), (2, 2), (1, 1), (1, 1), 1, torch.bfloat16, True, id="stride2-bf16"),
pytest.param(2, 64, 56, 56, 256, (1, 1), (1, 1), (0, 0), (1, 1), 1, torch.float16, True, id="resnet-1x1-fp16"),
pytest.param(2, 128, 28, 28, 512, (1, 1), (1, 1), (0, 0), (1, 1), 1, torch.float16, True, id="bottleneck-expand-1x1-fp16"),
pytest.param(2, 512, 28, 28, 128, (1, 1), (1, 1), (0, 0), (1, 1), 1, torch.float16, True, id="bottleneck-reduce-1x1-fp16"),
pytest.param(1, 256, 14, 14, 1024, (1, 1), (1, 1), (0, 0), (1, 1), 1, torch.float16, True, id="late-stage-1x1-fp16"),
pytest.param(1, 512, 7, 7, 2048, (1, 1), (1, 1), (0, 0), (1, 1), 1, torch.float16, True, id="classifier-1x1-fp16"),
pytest.param(2, 64, 56, 56, 256, (1, 1), (1, 1), (0, 0), (1, 1), 1, torch.bfloat16, True, id="resnet-1x1-bf16"),
# DeepLabV3/DeepLabV3+ ASPP branch: 3x3 atrous conv on stride-16 encoder features.
pytest.param(1, 2048, 32, 32, 256, (3, 3), (1, 1), (12, 12), (12, 12), torch.float16, True, id="deeplabv3-aspp-3x3-rate12-fp16"),
pytest.param(1, 2048, 32, 32, 256, (3, 3), (1, 1), (12, 12), (12, 12), 1, torch.float16, True, id="deeplabv3-aspp-3x3-rate12-fp16"),
# MobileNetV2 inverted residual depthwise 3x3 convolution.
pytest.param(1, 32, 56, 56, 32, (3, 3), (1, 1), (1, 1), (1, 1), 32, torch.float16, True, id="mobilenetv2-depthwise-fp16"),
# ResNeXt bottleneck grouped 3x3 convolution.
pytest.param(1, 128, 28, 28, 256, (3, 3), (1, 1), (1, 1), (1, 1), 32, torch.float16, True, id="resnext-grouped-3x3-fp16"),
]


@pytest.mark.parametrize(
"n, c_in, h, w, c_out, kernel_size, stride, padding, dilation, dtype, tune",
"n, c_in, h, w, c_out, kernel_size, stride, padding, dilation, groups, dtype, tune",
_CONV2D_BENCH_PARAMS,
)
def test_conv2d_bench(
Expand All @@ -230,10 +238,11 @@ def test_conv2d_bench(
stride: tuple[int, int],
padding: tuple[int, int],
dilation: tuple[int, int],
groups: int,
dtype: torch.dtype,
tune: bool,
) -> None:
test = Conv2dBenchCase(n, c_in, h, w, c_out, kernel_size, stride, padding, dilation, dtype)
test = Conv2dBenchCase(n, c_in, h, w, c_out, kernel_size, stride, padding, dilation, groups, dtype)
bm = Conv2dBenchmark(test)
inputs = test.gen_inputs()
x, weight, bias = inputs
Expand All @@ -248,6 +257,7 @@ def test_conv2d_bench(
stride=stride,
padding=padding,
dilation=dilation,
groups=groups,
dtype=dtype,
tune=tune,
)
Expand All @@ -272,6 +282,7 @@ def __init__(
stride: tuple[int, int, int],
padding: tuple[int, int, int],
dilation: tuple[int, int, int],
groups: int,
dtype: torch.dtype,
) -> None:
self.n = n
Expand All @@ -284,6 +295,7 @@ def __init__(
self.stride = stride
self.padding = padding
self.dilation = dilation
self.groups = groups
self.dtype = dtype

def gen_inputs(self) -> tuple[torch.Tensor, torch.Tensor, Optional[torch.Tensor]]:
Expand All @@ -292,7 +304,11 @@ def gen_inputs(self) -> tuple[torch.Tensor, torch.Tensor, Optional[torch.Tensor]
device="cuda", dtype=self.dtype,
).contiguous()
weight = torch.randn(
self.c_out, self.c_in, self.kernel_size[0], self.kernel_size[1], self.kernel_size[2],
self.c_out,
self.c_in // self.groups,
self.kernel_size[0],
self.kernel_size[1],
self.kernel_size[2],
device="cuda", dtype=self.dtype,
).contiguous()
bias = torch.zeros(self.c_out, device="cuda", dtype=self.dtype).contiguous()
Expand All @@ -311,7 +327,7 @@ def ref_program(
stride=self.stride,
padding=self.padding,
dilation=self.dilation,
groups=1,
groups=self.groups,
)

class Conv3dBenchmark(BenchmarkBase[Conv3dBenchCase]):
Expand All @@ -321,32 +337,38 @@ def calculate_flops(self) -> Optional[float]:
out_d = (t.d + 2 * t.padding[0] - t.dilation[0] * (t.kernel_size[0] - 1) - 1) // t.stride[0] + 1
out_h = (t.h + 2 * t.padding[1] - t.dilation[1] * (t.kernel_size[1] - 1) - 1) // t.stride[1] + 1
out_w = (t.w + 2 * t.padding[2] - t.dilation[2] * (t.kernel_size[2] - 1) - 1) // t.stride[2] + 1
return 2.0 * t.n * t.c_out * out_d * out_h * out_w * t.c_in * t.kernel_size[0] * t.kernel_size[1] * t.kernel_size[2]
c_in_g = t.c_in // t.groups
return 2.0 * t.n * t.c_out * out_d * out_h * out_w * c_in_g * t.kernel_size[0] * t.kernel_size[1] * t.kernel_size[2]

def calculate_memory(self) -> Optional[float]:
t = self.workload
out_d = (t.d + 2 * t.padding[0] - t.dilation[0] * (t.kernel_size[0] - 1) - 1) // t.stride[0] + 1
out_h = (t.h + 2 * t.padding[1] - t.dilation[1] * (t.kernel_size[1] - 1) - 1) // t.stride[1] + 1
out_w = (t.w + 2 * t.padding[2] - t.dilation[2] * (t.kernel_size[2] - 1) - 1) // t.stride[2] + 1
c_in_g = t.c_in // t.groups
bytes_ = (
t.n * t.c_in * t.d * t.h * t.w
+ t.c_out * t.c_in * t.kernel_size[0] * t.kernel_size[1] * t.kernel_size[2]
+ t.c_out * c_in_g * t.kernel_size[0] * t.kernel_size[1] * t.kernel_size[2]
+ t.n * t.c_out * out_d * out_h * out_w
) * t.dtype.itemsize
return bytes_


_CONV3D_BENCH_PARAMS = [
pytest.param(1, 3, 16, 112, 112, 64, (3, 3, 3), (1, 1, 1), (1, 1, 1), (1, 1, 1), torch.float16, True, id="r3d-stem-k3-s1-fp16"),
pytest.param(1, 64, 8, 56, 56, 128, (3, 3, 3), (2, 2, 2), (1, 1, 1), (1, 1, 1), torch.float16, True, id="video-stage-downsample-k3-s2-fp16"),
pytest.param(1, 32, 32, 64, 64, 64, (3, 3, 3), (1, 1, 1), (1, 1, 1), (1, 1, 1), torch.bfloat16, True, id="unet-encoder-k3-s1-bf16"),
pytest.param(1, 3, 16, 112, 112, 64, (3, 3, 3), (1, 1, 1), (1, 1, 1), (1, 1, 1), 1, torch.float16, True, id="r3d-stem-k3-s1-fp16"),
pytest.param(1, 64, 8, 56, 56, 128, (3, 3, 3), (2, 2, 2), (1, 1, 1), (1, 1, 1), 1, torch.float16, True, id="video-stage-downsample-k3-s2-fp16"),
pytest.param(1, 32, 32, 64, 64, 64, (3, 3, 3), (1, 1, 1), (1, 1, 1), (1, 1, 1), 1, torch.bfloat16, True, id="unet-encoder-k3-s1-bf16"),
# 3D U-Net + 3D ASPP medical segmentation branch: 3x3x3 atrous conv on low-resolution volume features.
pytest.param(1, 256, 8, 16, 16, 256, (3, 3, 3), (1, 1, 1), (6, 6, 6), (6, 6, 6), torch.float16, True, id="3d-unet-aspp-3x3x3-rate6-fp16"),
pytest.param(1, 256, 8, 16, 16, 256, (3, 3, 3), (1, 1, 1), (6, 6, 6), (6, 6, 6), 1, torch.float16, True, id="3d-unet-aspp-3x3x3-rate6-fp16"),
# 3D-ResNeXt/video backbone grouped 3x3x3 convolution.
pytest.param(1, 64, 8, 28, 28, 128, (3, 3, 3), (1, 1, 1), (1, 1, 1), (1, 1, 1), 32, torch.float16, True, id="3d-resnext-grouped-k3-fp16"),
# 3D-ResNeXt/video backbone grouped 3x3x3 convolution, larger batch for throughput.
pytest.param(8, 64, 8, 28, 28, 128, (3, 3, 3), (1, 1, 1), (1, 1, 1), (1, 1, 1), 32, torch.float16, True, id="3d-resnext-grouped-k3-b8-fp16"),
]


@pytest.mark.parametrize(
"n, c_in, d, h, w, c_out, kernel_size, stride, padding, dilation, dtype, tune",
"n, c_in, d, h, w, c_out, kernel_size, stride, padding, dilation, groups, dtype, tune",
_CONV3D_BENCH_PARAMS,
)
def test_conv3d_bench(
Expand All @@ -360,10 +382,11 @@ def test_conv3d_bench(
stride: tuple[int, int, int],
padding: tuple[int, int, int],
dilation: tuple[int, int, int],
groups: int,
dtype: torch.dtype,
tune: bool,
) -> None:
test = Conv3dBenchCase(n, c_in, d, h, w, c_out, kernel_size, stride, padding, dilation, dtype)
test = Conv3dBenchCase(n, c_in, d, h, w, c_out, kernel_size, stride, padding, dilation, groups, dtype)
bm = Conv3dBenchmark(test)
inputs = test.gen_inputs()
x, weight, bias = inputs
Expand All @@ -379,6 +402,7 @@ def test_conv3d_bench(
stride=stride,
padding=padding,
dilation=dilation,
groups=groups,
dtype=dtype,
tune=tune,
)
Expand Down
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