mirror of https://github.com/open-mmlab/mmcv.git
[Feature] Add FusedBiasLeakyRelu npu adapter (#2474)
* init npu * add npu extension and focal loss adapter * clean code * clean code * clean code * clean code * fix autocast bugs on npu (#2273) fix autocast bugs on npu (#2273) * code format * code format * code format * bug fix * pytorch_npu_helper.hpp clean code * Npu dev (#2306) * fix autocast bugs on npu * using scatter_kwargs in mmcv.device.scatter_gather * raise ImportError when compile with npu * add npu test case (#2307) * add npu test case * Update focal_loss.py * add comment * clean lint * update dtype assert * update DDP forward and comment * fix bug Co-authored-by: Zaida Zhou <58739961+zhouzaida@users.noreply.github.com> * sigmoidfocalloss npu adapter bug fix * BugFix: modify softmaxFocalLoss adapter * BugFix: remove equal sign in the code * add npu install information in README * add modulatedDeformConv npu adapter * init npu * add npu extension and focal loss adapter * clean code * clean code * clean code * add modulatedDeformConv npu adapter * merge master branch 20221103 * Add masked_ Conv2d operator in NPU * add nms_npu * fix bug * fix code check * fix code check * fix code check * Masked_conv2d NPU * Masked_conv2d NPU * Masked_conv2d NPU * remove npu-install-info in README.md * annotate the clang-format in pre-commit-config-zh-ch.yaml * Clean code: fix the clean code problem in masked_conv2d and modulated_deform_conv * Create fused_bias_leakyrelu_npu.cpp Add NPU adapter for fused_bias_leaky_relu operator * Update fused_bias_leakyrelu_npu.cpp * Update fused_bias_leakyrelu_npu.cpp * Update ops.md * Update ops.md * Update fused_bias_leakyrelu_npu.cpp * Update fused_bias_leakyrelu_npu.cpp * Update test_fused_bias_leakyrelu.py * Update fused_bias_leakyrelu.py * Update test_fused_bias_leakyrelu.py * Update fused_bias_leakyrelu.py * Update test_fused_bias_leakyrelu.py * Update ops.md * amend for CI * bugfix * amend ops.md * Update test_fused_bias_leakyrelu.py * clean code * bugfix * clean code * Update fused_bias_leakyrelu_npu.cpp * Update fused_bias_leakyrelu_npu.cpp Co-authored-by: wangjiangben <wangjiangben_hw@126.com> Co-authored-by: ckirchhoff <515629648@qq.com> Co-authored-by: wangjiangben-hw <111729245+wangjiangben-hw@users.noreply.github.com> Co-authored-by: Zaida Zhou <58739961+zhouzaida@users.noreply.github.com> Co-authored-by: zcc-zjut <zcczxy2019@163.com> Co-authored-by: wangxiaoxin_sherie <wangxiaoxin7@huawei.com> Co-authored-by: momo609 <963372609@qq.com>pull/2477/head
jayggh
GitHub
parent
4c4ac6b43f
commit
4c51afce2a
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@ -24,7 +24,7 @@ We implement common ops used in detection, segmentation, etc.
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| DynamicScatter | | √ | | | |
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| FurthestPointSample | | √ | | | |
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| FurthestPointSampleWithDist | | √ | | | |
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| FusedBiasLeakyrelu | | √ | | | |
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| FusedBiasLeakyrelu | | √ | | | √ |
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| GatherPoints | | √ | | | |
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| GroupPoints | | √ | | | |
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| Iou3d | | √ | √ | | |
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@ -24,7 +24,7 @@ MMCV 提供了检测、分割等任务中常用的算子
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| DynamicScatter | | √ | | | |
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| FurthestPointSample | | √ | | | |
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| FurthestPointSampleWithDist | | √ | | | |
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| FusedBiasLeakyrelu | | √ | | | |
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| FusedBiasLeakyrelu | | √ | | | √ |
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| GatherPoints | | √ | | | |
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| GroupPoints | | √ | | | |
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| Iou3d | | √ | √ | | |
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@ -0,0 +1,54 @@
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#include "pytorch_npu_helper.hpp"
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using namespace NPU_NAME_SPACE;
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using namespace std;
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Tensor fused_bias_leakyrelu_op_impl(const Tensor &input, const Tensor &bias,
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const Tensor &refer, int act, int grad,
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float alpha, float scale);
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Tensor fused_bias_leakyrelu_npu(const Tensor &input, const Tensor &bias,
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const Tensor &refer, int act, int grad,
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float alpha, float scale) {
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at::Tensor py = at::empty_like(input);
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// forward
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if (grad == 0) {
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auto input_size = input.sizes();
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int input_length = input_size.size();
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c10::SmallVector<int64_t, SIZE> input_size_tmp;
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input_size_tmp = array_to_small_vector(input_size);
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if (input_length > 1) {
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for (int i = 0; i < input_length; i++) {
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if (i != 1) {
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input_size_tmp[i] = 1;
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}
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}
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}
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at::Tensor bias_tmp = at::reshape(bias, input_size_tmp);
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at::Tensor bias_ = at_npu::native::NPUNativeFunctions::npu_broadcast(
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bias_tmp, input.sizes());
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OpCommand cmd;
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cmd.Name("FusedBiasLeakyRelu")
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.Input(input)
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.Input(bias_)
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.Output(py)
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.Attr("scale", scale)
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.Attr("negative_slope", alpha)
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.Run();
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}
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// backward
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if (grad == 1) {
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OpCommand cmd;
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cmd.Name("FusedBiasLeakyReluGrad")
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.Input(input)
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.Input(refer)
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.Output(py)
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.Attr("scale", scale)
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.Attr("negative_slope", alpha)
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.Run();
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}
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return py;
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}
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REGISTER_NPU_IMPL(fused_bias_leakyrelu_op_impl, fused_bias_leakyrelu_npu);
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@ -258,7 +258,7 @@ def fused_bias_leakyrelu(input: torch.Tensor,
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torch.Tensor: Feature map after non-linear activation.
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"""
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if not input.is_cuda:
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if not input.is_cuda and input.device.type != 'npu':
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return bias_leakyrelu_ref(input, bias, negative_slope, scale)
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return FusedBiasLeakyReLUFunction.apply(input, bias.to(input.dtype),
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@ -2,6 +2,8 @@
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import pytest
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import torch
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from mmcv.utils import IS_CUDA_AVAILABLE, IS_NPU_AVAILABLE
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_USING_PARROTS = True
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try:
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from parrots.autograd import gradcheck
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@ -14,36 +16,59 @@ class TestFusedBiasLeakyReLU:
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@classmethod
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def setup_class(cls):
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if not torch.cuda.is_available():
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if not IS_CUDA_AVAILABLE and not IS_NPU_AVAILABLE:
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return
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cls.input_tensor = torch.randn((2, 2, 2, 2), requires_grad=True).cuda()
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cls.bias = torch.zeros(2, requires_grad=True).cuda()
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if IS_CUDA_AVAILABLE:
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cls.input_tensor = torch.randn((2, 2, 2, 2),
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requires_grad=True).cuda()
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cls.bias = torch.zeros(2, requires_grad=True).cuda()
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elif IS_NPU_AVAILABLE:
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cls.input_tensor = torch.randn((2, 2, 2, 2),
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requires_grad=True).npu()
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cls.bias = torch.zeros(2, requires_grad=True).npu()
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@pytest.mark.skipif(not torch.cuda.is_available(), reason='requires cuda')
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def test_gradient(self):
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@pytest.mark.parametrize('device', [
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pytest.param(
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'cuda',
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marks=pytest.mark.skipif(
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not IS_CUDA_AVAILABLE, reason='requires CUDA support')),
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pytest.param(
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'npu',
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marks=pytest.mark.skipif(
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not IS_NPU_AVAILABLE, reason='requires NPU support'))
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])
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def test_gradient(self, device):
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from mmcv.ops import FusedBiasLeakyReLU
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if _USING_PARROTS:
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gradcheck(
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FusedBiasLeakyReLU(2).cuda(),
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self.input_tensor,
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delta=1e-4,
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pt_atol=1e-3)
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if IS_CUDA_AVAILABLE:
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gradcheck(
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FusedBiasLeakyReLU(2).cuda(),
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self.input_tensor,
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delta=1e-4,
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pt_atol=1e-3)
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else:
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gradcheck(
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FusedBiasLeakyReLU(2).cuda(),
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FusedBiasLeakyReLU(2).to(device),
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self.input_tensor,
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eps=1e-4,
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atol=1e-3)
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@pytest.mark.skipif(
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not torch.cuda.is_available() or _USING_PARROTS,
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reason='requires cuda')
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def test_gradgradient(self):
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@pytest.mark.parametrize('device', [
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pytest.param(
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'cuda',
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marks=pytest.mark.skipif(
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not IS_CUDA_AVAILABLE, reason='requires CUDA support')),
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pytest.param(
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'npu',
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marks=pytest.mark.skipif(
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not IS_NPU_AVAILABLE, reason='requires NPU support'))
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])
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def test_gradgradient(self, device):
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from mmcv.ops import FusedBiasLeakyReLU
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gradgradcheck(
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FusedBiasLeakyReLU(2).cuda(),
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FusedBiasLeakyReLU(2).to(device),
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self.input_tensor,
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eps=1e-4,
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atol=1e-3)
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