diff --git a/paddle/fluid/operators/group_norm_op.h b/paddle/fluid/operators/group_norm_op.h index afe70ea64a999..2f0edd0451a3b 100644 --- a/paddle/fluid/operators/group_norm_op.h +++ b/paddle/fluid/operators/group_norm_op.h @@ -14,6 +14,8 @@ limitations under the License. */ #pragma once #include +#include +#include #include #include "paddle/fluid/framework/data_layout.h" #include "paddle/fluid/framework/eigen.h" @@ -73,6 +75,11 @@ class GroupNormKernel : public framework::OpKernel { auto* iter_y_data = y_data; for (int bid = 0; bid < x_dims[0]; bid++) { for (int gid = 0; gid < groups; gid++) { + const int64_t M = 8; + std::array x_mean_arr; + std::array x_var_arr; + std::fill(x_mean_arr.begin(), x_mean_arr.end(), T(0)); + std::fill(x_var_arr.begin(), x_var_arr.end(), T(0)); T x_mean = 0, x_var = 0; int number = std::min(group_size, static_cast(C - gid * group_size)); @@ -83,7 +90,37 @@ class GroupNormKernel : public framework::OpKernel { if (data_layout == DataLayout::kNCHW) { for (int cid = 0; cid < number; cid++) { - for (int imid = 0; imid < imsize; imid++, iter_x_data++) { + int imid; + for (imid = 0; imid < imsize - (imsize % M); + imid += M, iter_x_data += M) { + // TODO(gaoxiang) ：Because AVX/AVX2/AVX512 can not directly used + // in template class/function, before we complete high + // performance cpu vector extension, temporarily unrolling + // loop to get high precision and performance + x_mean_arr[0] += iter_x_data[0]; + x_var_arr[0] += iter_x_data[0] * iter_x_data[0]; + x_mean_arr[1] += iter_x_data[1]; + x_var_arr[1] += iter_x_data[1] * iter_x_data[1]; + x_mean_arr[2] += iter_x_data[2]; + x_var_arr[2] += iter_x_data[2] * iter_x_data[2]; + x_mean_arr[3] += iter_x_data[3]; + x_var_arr[3] += iter_x_data[3] * iter_x_data[3]; + x_mean_arr[4] += iter_x_data[4]; + x_var_arr[4] += iter_x_data[4] * iter_x_data[4]; + x_mean_arr[5] += iter_x_data[5]; + x_var_arr[5] += iter_x_data[5] * iter_x_data[5]; + x_mean_arr[6] += iter_x_data[6]; + x_var_arr[6] += iter_x_data[6] * iter_x_data[6]; + x_mean_arr[7] += iter_x_data[7]; + x_var_arr[7] += iter_x_data[7] * iter_x_data[7]; + } + x_mean = + std::accumulate(x_mean_arr.cbegin(), x_mean_arr.cend(), x_mean); + x_var = + std::accumulate(x_var_arr.cbegin(), x_var_arr.cend(), x_var); + std::fill(x_mean_arr.begin(), x_mean_arr.end(), T(0)); + std::fill(x_var_arr.begin(), x_var_arr.end(), T(0)); + for (; imid < imsize; imid++, iter_x_data++) { x_mean += iter_x_data[0]; x_var += iter_x_data[0] * iter_x_data[0]; } @@ -91,7 +128,37 @@ class GroupNormKernel : public framework::OpKernel { } else { for (int cid = 0; cid < number; cid++) { iter_x_data = tmp_x + cid; - for (int imid = 0; imid < imsize; imid++, iter_x_data += C) { + int imid; + for (imid = 0; imid < imsize - (imsize % M); + imid += M, iter_x_data += M * C) { + // TODO(gaoxiang) ：Because AVX/AVX2/AVX512 can not directly used + // in template class/function, before we complete high + // performance cpu vector extension, temporarily unrolling + // loop to get high precision and performance + x_mean_arr[0] += iter_x_data[0 * C]; + x_var_arr[0] += iter_x_data[0 * C] * iter_x_data[0 * C]; + x_mean_arr[1] += iter_x_data[1 * C]; + x_var_arr[1] += iter_x_data[1 * C] * iter_x_data[1 * C]; + x_mean_arr[2] += iter_x_data[2 * C]; + x_var_arr[2] += iter_x_data[2 * C] * iter_x_data[2 * C]; + x_mean_arr[3] += iter_x_data[3 * C]; + x_var_arr[3] += iter_x_data[3 * C] * iter_x_data[3 * C]; + x_mean_arr[4] += iter_x_data[4 * C]; + x_var_arr[4] += iter_x_data[4 * C] * iter_x_data[4 * C]; + x_mean_arr[5] += iter_x_data[5 * C]; + x_var_arr[5] += iter_x_data[5 * C] * iter_x_data[5 * C]; + x_mean_arr[6] += iter_x_data[6 * C]; + x_var_arr[6] += iter_x_data[6 * C] * iter_x_data[6 * C]; + x_mean_arr[7] += iter_x_data[7 * C]; + x_var_arr[7] += iter_x_data[7 * C] * iter_x_data[7 * C]; + } + x_mean = + std::accumulate(x_mean_arr.cbegin(), x_mean_arr.cend(), x_mean); + x_var = + std::accumulate(x_var_arr.cbegin(), x_var_arr.cend(), x_var); + std::fill(x_mean_arr.begin(), x_mean_arr.end(), T(0)); + std::fill(x_var_arr.begin(), x_var_arr.end(), T(0)); + for (; imid < imsize; imid++, iter_x_data += C) { x_mean += iter_x_data[0]; x_var += iter_x_data[0] * iter_x_data[0]; } @@ -101,8 +168,8 @@ class GroupNormKernel : public framework::OpKernel { x_mean /= number * imsize; x_var /= number * imsize; - x_var = x_var - x_mean * x_mean; - T var_inv = 1.0 / sqrt(x_var + epsilon); + x_var = std::max(x_var - x_mean * x_mean, T(0)); + T var_inv = T(1) / std::sqrt(x_var + epsilon); mean_data[bid * groups + gid] = x_mean; var_data[bid * groups + gid] = x_var; diff --git a/python/paddle/fluid/tests/unittests/test_group_norm_op_v2.py b/python/paddle/fluid/tests/unittests/test_group_norm_op_v2.py index 2ba79cc9e4396..0e13ca1756202 100644 --- a/python/paddle/fluid/tests/unittests/test_group_norm_op_v2.py +++ b/python/paddle/fluid/tests/unittests/test_group_norm_op_v2.py @@ -53,6 +53,15 @@ def test_weight_bias_false(): weight_attr=False, bias_attr=False) + def test_nn_exception(): + with fluid.dygraph.guard(p): + + def attr_data_format(): + out = paddle.nn.GroupNorm( + num_groups=2, num_channels=2, data_format="NHWC") + + self.assertRaises(ValueError, attr_data_format) + x = np.random.randn(*shape).astype("float32") y1 = compute_v1(x) y2 = compute_v2(x) @@ -61,6 +70,7 @@ def test_weight_bias_false(): print("y1:", y1, "\ty2:", y2) self.assertTrue(result) test_weight_bias_false() + test_nn_exception() def test_static(self): places = [fluid.CPUPlace()] diff --git a/python/paddle/nn/layer/norm.py b/python/paddle/nn/layer/norm.py index 45640a6598e57..d05d6152efaf1 100644 --- a/python/paddle/nn/layer/norm.py +++ b/python/paddle/nn/layer/norm.py @@ -375,7 +375,7 @@ def __init__(self, self._num_channels = num_channels self._num_groups = num_groups if data_format != 'NCHW': - raise ValueError("unsupported data layout:" + data_layout) + raise ValueError("unsupported data layout:" + data_format) param_shape = [self._num_channels]