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* initial implementation of distributed DISCO layer * working distributed convolution * working refactored serial conv transpose with torch kernel * working distributed conv and transposed conv when using the python kernel * working distributed convolution with torch kernel * fixed triton kernel tests * adding print statement to debug CI * adjusting tolerances in local convolution unittest --------- Co-authored-by: Boris Bonev <bbonev@nvidia.com>
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| # coding=utf-8 | ||
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| # SPDX-FileCopyrightText: Copyright (c) 2022 The torch-harmonics Authors. All rights reserved. | ||
| # SPDX-License-Identifier: BSD-3-Clause | ||
| # | ||
| # Redistribution and use in source and binary forms, with or without | ||
| # modification, are permitted provided that the following conditions are met: | ||
| # | ||
| # 1. Redistributions of source code must retain the above copyright notice, this | ||
| # list of conditions and the following disclaimer. | ||
| # | ||
| # 2. Redistributions in binary form must reproduce the above copyright notice, | ||
| # this list of conditions and the following disclaimer in the documentation | ||
| # and/or other materials provided with the distribution. | ||
| # | ||
| # 3. Neither the name of the copyright holder nor the names of its | ||
| # contributors may be used to endorse or promote products derived from | ||
| # this software without specific prior written permission. | ||
| # | ||
| # THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" | ||
| # AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE | ||
| # IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE | ||
| # DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE | ||
| # FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL | ||
| # DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR | ||
| # SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER | ||
| # CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, | ||
| # OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE | ||
| # OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. | ||
| # | ||
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| import os | ||
| import unittest | ||
| from parameterized import parameterized | ||
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| import torch | ||
| import torch.nn.functional as F | ||
| import torch.distributed as dist | ||
| import torch_harmonics as harmonics | ||
| import torch_harmonics.distributed as thd | ||
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| class TestDistributedDiscreteContinuousConvolution(unittest.TestCase): | ||
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| @classmethod | ||
| def setUpClass(cls): | ||
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| # set up distributed | ||
| cls.world_rank = int(os.getenv('WORLD_RANK', 0)) | ||
| cls.grid_size_h = int(os.getenv('GRID_H', 1)) | ||
| cls.grid_size_w = int(os.getenv('GRID_W', 1)) | ||
| port = int(os.getenv('MASTER_PORT', '29501')) | ||
| master_address = os.getenv('MASTER_ADDR', 'localhost') | ||
| cls.world_size = cls.grid_size_h * cls.grid_size_w | ||
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| if torch.cuda.is_available(): | ||
| if cls.world_rank == 0: | ||
| print("Running test on GPU") | ||
| local_rank = cls.world_rank % torch.cuda.device_count() | ||
| cls.device = torch.device(f"cuda:{local_rank}") | ||
| torch.cuda.set_device(local_rank) | ||
| torch.cuda.manual_seed(333) | ||
| proc_backend = 'nccl' | ||
| else: | ||
| if cls.world_rank == 0: | ||
| print("Running test on CPU") | ||
| cls.device = torch.device('cpu') | ||
| proc_backend = 'gloo' | ||
| torch.manual_seed(333) | ||
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| dist.init_process_group(backend = proc_backend, | ||
| init_method = f"tcp://{master_address}:{port}", | ||
| rank = cls.world_rank, | ||
| world_size = cls.world_size) | ||
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| cls.wrank = cls.world_rank % cls.grid_size_w | ||
| cls.hrank = cls.world_rank // cls.grid_size_w | ||
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| # now set up the comm groups: | ||
| #set default | ||
| cls.w_group = None | ||
| cls.h_group = None | ||
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| # do the init | ||
| wgroups = [] | ||
| for w in range(0, cls.world_size, cls.grid_size_w): | ||
| start = w | ||
| end = w + cls.grid_size_w | ||
| wgroups.append(list(range(start, end))) | ||
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| if cls.world_rank == 0: | ||
| print("w-groups:", wgroups) | ||
| for grp in wgroups: | ||
| if len(grp) == 1: | ||
| continue | ||
| tmp_group = dist.new_group(ranks=grp) | ||
| if cls.world_rank in grp: | ||
| cls.w_group = tmp_group | ||
|
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| # transpose: | ||
| hgroups = [sorted(list(i)) for i in zip(*wgroups)] | ||
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| if cls.world_rank == 0: | ||
| print("h-groups:", hgroups) | ||
| for grp in hgroups: | ||
| if len(grp) == 1: | ||
| continue | ||
| tmp_group = dist.new_group(ranks=grp) | ||
| if cls.world_rank in grp: | ||
| cls.h_group = tmp_group | ||
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| if cls.world_rank == 0: | ||
| print(f"Running distributed tests on grid H x W = {cls.grid_size_h} x {cls.grid_size_w}") | ||
|
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| # initializing sht | ||
| thd.init(cls.h_group, cls.w_group) | ||
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| def _split_helper(self, tensor): | ||
| with torch.no_grad(): | ||
| # split in W | ||
| tensor_list_local = thd.split_tensor_along_dim(tensor, dim=-1, num_chunks=self.grid_size_w) | ||
| tensor_local = tensor_list_local[self.wrank] | ||
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| # split in H | ||
| tensor_list_local = thd.split_tensor_along_dim(tensor_local, dim=-2, num_chunks=self.grid_size_h) | ||
| tensor_local = tensor_list_local[self.hrank] | ||
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| return tensor_local | ||
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| def _gather_helper_fwd(self, tensor, B, C, convolution_dist): | ||
| # we need the shapes | ||
| lat_shapes = convolution_dist.lat_out_shapes | ||
| lon_shapes = convolution_dist.lon_out_shapes | ||
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| #print("tensor before gather shape", tensor.shape) | ||
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| # gather in W | ||
| if self.grid_size_w > 1: | ||
| gather_shapes = [(B, C, lat_shapes[self.hrank], w) for w in lon_shapes] | ||
| olist = [torch.empty(shape, dtype=tensor.dtype, device=tensor.device) for shape in gather_shapes] | ||
| olist[self.wrank] = tensor | ||
| dist.all_gather(olist, tensor, group=self.w_group) | ||
| tensor_gather = torch.cat(olist, dim=-1) | ||
| else: | ||
| tensor_gather = tensor | ||
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| #print("tensor_gather shape", tensor_gather.shape) | ||
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| # gather in H | ||
| if self.grid_size_h > 1: | ||
| gather_shapes = [(B, C, h, convolution_dist.nlon_out) for h in lat_shapes] | ||
| olist = [torch.empty(shape, dtype=tensor_gather.dtype, device=tensor_gather.device) for shape in gather_shapes] | ||
| olist[self.hrank] = tensor_gather | ||
| dist.all_gather(olist, tensor_gather, group=self.h_group) | ||
| tensor_gather = torch.cat(olist, dim=-2) | ||
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| return tensor_gather | ||
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| def _gather_helper_bwd(self, tensor, B, C, convolution_dist): | ||
| # we need the shapes | ||
| lat_shapes = convolution_dist.lat_in_shapes | ||
| lon_shapes = convolution_dist.lon_in_shapes | ||
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| # gather in W | ||
| if self.grid_size_w > 1: | ||
| gather_shapes = [(B, C, lat_shapes[self.hrank], w) for w in lon_shapes] | ||
| olist = [torch.empty(shape, dtype=tensor.dtype, device=tensor.device) for shape in gather_shapes] | ||
| olist[self.wrank] = tensor | ||
| dist.all_gather(olist, tensor, group=self.w_group) | ||
| tensor_gather = torch.cat(olist, dim=-1) | ||
| else: | ||
| tensor_gather = tensor | ||
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| # gather in H | ||
| if self.grid_size_h > 1: | ||
| gather_shapes = [(B, C, h, convolution_dist.nlon_in) for h in lat_shapes] | ||
| olist = [torch.empty(shape, dtype=tensor_gather.dtype, device=tensor_gather.device) for shape in gather_shapes] | ||
| olist[self.hrank] = tensor_gather | ||
| dist.all_gather(olist, tensor_gather, group=self.h_group) | ||
| tensor_gather = torch.cat(olist, dim=-2) | ||
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| return tensor_gather | ||
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| @parameterized.expand([ | ||
| [128, 256, 128, 256, 32, 8, [3 ], 1, "equiangular", "equiangular", False, 1e-6], | ||
| [129, 256, 128, 256, 32, 8, [3 ], 1, "equiangular", "equiangular", False, 1e-6], | ||
| [128, 256, 128, 256, 32, 8, [3, 2], 1, "equiangular", "equiangular", False, 1e-6], | ||
| [128, 256, 64, 128, 32, 8, [3 ], 1, "equiangular", "equiangular", False, 1e-6], | ||
| [128, 256, 128, 256, 32, 8, [3 ], 2, "equiangular", "equiangular", False, 1e-6], | ||
| [128, 256, 128, 256, 32, 5, [3 ], 1, "equiangular", "equiangular", False, 1e-6], | ||
| [128, 256, 128, 256, 32, 8, [3 ], 1, "equiangular", "equiangular", True, 1e-6], | ||
| [129, 256, 128, 256, 32, 8, [3 ], 1, "equiangular", "equiangular", True, 1e-6], | ||
| [128, 256, 128, 256, 32, 8, [3, 2], 1, "equiangular", "equiangular", True, 1e-6], | ||
| [ 64, 128, 128, 256, 32, 8, [3 ], 1, "equiangular", "equiangular", True, 1e-6], | ||
| [128, 256, 128, 256, 32, 8, [3 ], 2, "equiangular", "equiangular", True, 1e-6], | ||
| [128, 256, 128, 256, 32, 5, [3 ], 1, "equiangular", "equiangular", True, 1e-6], | ||
| ]) | ||
| def test_distributed_disco_conv(self, nlat_in, nlon_in, nlat_out, nlon_out, batch_size, num_chan, | ||
| kernel_shape, groups, grid_in, grid_out, transpose, tol): | ||
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| B, C, H, W = batch_size, num_chan, nlat_in, nlon_in | ||
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| disco_args = dict(in_channels=C, out_channels=C, | ||
| in_shape=(nlat_in, nlon_in), out_shape=(nlat_out, nlon_out), | ||
| kernel_shape=kernel_shape, groups=groups, | ||
| grid_in=grid_in, grid_out=grid_out, bias=True) | ||
|
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| # set up handles | ||
| if transpose: | ||
| conv_local = harmonics.DiscreteContinuousConvTransposeS2(**disco_args).to(self.device) | ||
| conv_dist = thd.DistributedDiscreteContinuousConvTransposeS2(**disco_args).to(self.device) | ||
| else: | ||
| conv_local = harmonics.DiscreteContinuousConvS2(**disco_args).to(self.device) | ||
| conv_dist = thd.DistributedDiscreteContinuousConvS2(**disco_args).to(self.device) | ||
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| # copy the weights from the local conv into the dist conv | ||
| with torch.no_grad(): | ||
| conv_dist.weight.copy_(conv_local.weight) | ||
| conv_dist.bias.copy_(conv_local.bias) | ||
|
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| # create tensors | ||
| inp_full = torch.randn((B, C, H, W), dtype=torch.float32, device=self.device) | ||
|
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| ############################################################# | ||
| # local conv | ||
| ############################################################# | ||
| # FWD pass | ||
| inp_full.requires_grad = True | ||
| out_full = conv_local(inp_full, use_triton_kernel=True) | ||
|
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| # create grad for backward | ||
| with torch.no_grad(): | ||
| # create full grad | ||
| ograd_full = torch.randn_like(out_full) | ||
|
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| # BWD pass | ||
| out_full.backward(ograd_full) | ||
| igrad_full = inp_full.grad.clone() | ||
|
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| ############################################################# | ||
| # distributed conv | ||
| ############################################################# | ||
| # FWD pass | ||
| inp_local = self._split_helper(inp_full) | ||
| inp_local.requires_grad = True | ||
| out_local = conv_dist(inp_local, use_triton_kernel=True) | ||
|
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| # BWD pass | ||
| ograd_local = self._split_helper(ograd_full) | ||
| out_local = conv_dist(inp_local, use_triton_kernel=True) | ||
| out_local.backward(ograd_local) | ||
| igrad_local = inp_local.grad.clone() | ||
|
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| ############################################################# | ||
| # evaluate FWD pass | ||
| ############################################################# | ||
| with torch.no_grad(): | ||
| out_gather_full = self._gather_helper_fwd(out_local, B, C, conv_dist) | ||
| err = torch.mean(torch.norm(out_full-out_gather_full, p='fro', dim=(-1,-2)) / torch.norm(out_full, p='fro', dim=(-1,-2)) ) | ||
| if self.world_rank == 0: | ||
| print(f"final relative error of output: {err.item()}") | ||
| self.assertTrue(err.item() <= tol) | ||
|
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| ############################################################# | ||
| # evaluate BWD pass | ||
| ############################################################# | ||
| with torch.no_grad(): | ||
| igrad_gather_full = self._gather_helper_bwd(igrad_local, B, C, conv_dist) | ||
| err = torch.mean(torch.norm(igrad_full-igrad_gather_full, p='fro', dim=(-1,-2)) / torch.norm(igrad_full, p='fro', dim=(-1,-2)) ) | ||
| if self.world_rank == 0: | ||
| print(f"final relative error of gradients: {err.item()}") | ||
| self.assertTrue(err.item() <= tol) | ||
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| if __name__ == '__main__': | ||
| unittest.main() |
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