├── .gitignore ├── LICENSE ├── MANIFEST ├── README.md ├── diffdist ├── __init__.py ├── extra_collectives.py ├── functional.py ├── functions.py ├── modules.py └── testing.py ├── dist └── diffdist-0.1.tar.gz ├── setup.cfg └── setup.py /.gitignore: -------------------------------------------------------------------------------- 1 | __pycache__ 2 | -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | GNU GENERAL PUBLIC LICENSE 2 | Version 3, 29 June 2007 3 | 4 | Copyright (C) 2007 Free Software Foundation, Inc. 5 | Everyone is permitted to copy and distribute verbatim copies 6 | of this license document, but changing it is not allowed. 7 | 8 | Preamble 9 | 10 | The GNU General Public License is a free, copyleft license for 11 | software and other kinds of works. 12 | 13 | The licenses for most software and other practical works are designed 14 | to take away your freedom to share and change the works. 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Of course, your program's commands 662 | might be different; for a GUI interface, you would use an "about box". 663 | 664 | You should also get your employer (if you work as a programmer) or school, 665 | if any, to sign a "copyright disclaimer" for the program, if necessary. 666 | For more information on this, and how to apply and follow the GNU GPL, see 667 | . 668 | 669 | The GNU General Public License does not permit incorporating your program 670 | into proprietary programs. If your program is a subroutine library, you 671 | may consider it more useful to permit linking proprietary applications with 672 | the library. If this is what you want to do, use the GNU Lesser General 673 | Public License instead of this License. But first, please read 674 | . 675 | -------------------------------------------------------------------------------- /MANIFEST: -------------------------------------------------------------------------------- 1 | # file GENERATED by distutils, do NOT edit 2 | README 3 | setup.cfg 4 | setup.py 5 | diffdist/__init__.py 6 | diffdist/extra_collectives.py 7 | diffdist/functional.py 8 | diffdist/functions.py 9 | diffdist/modules.py 10 | diffdist/testing.py 11 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # diffdist 2 | 3 | ## NOTE: PyTorch now includes the distributed RPC framework (https://pytorch.org/docs/stable/rpc.html). Most tasks can now be achieved using RPC. If you need a simpler MPI-like alternative, `diffdist` is for you! 4 | 5 | `diffdist` is a python library for pytorch. It extends the default functionality of `torch.autograd` and adds support for differentiable communication between processes. This enables backpropagation to work in distributed settings and makes it super easy to use distributed model parallelism! `diffdist` achieves that by simply implementing the backward passes for most common communication primitives of `torch.distributed`. Processes that communicate during the forward pass, will automatically communicate during the backward pass to exchange gradients. 6 | 7 | ### Installation 8 | 9 | After installing `pytorch` install simply using: 10 | 11 | ```sh 12 | $ pip install diffdist 13 | ``` 14 | 15 | ### Examples 16 | You can find examples on how to use `diffdist` in `diffdist/testing.py` 17 | 18 | License 19 | ---- 20 | 21 | GNU GPLv3 22 | 23 | -------------------------------------------------------------------------------- /diffdist/__init__.py: -------------------------------------------------------------------------------- 1 | from diffdist import extra_collectives 2 | from diffdist import functional 3 | from diffdist import modules 4 | -------------------------------------------------------------------------------- /diffdist/extra_collectives.py: -------------------------------------------------------------------------------- 1 | import torch.distributed as dist 2 | from torch.distributed import ReduceOp 3 | 4 | 5 | class AsyncOpList(object): 6 | def __init__(self, ops): 7 | self.ops = ops 8 | 9 | def wait(self): 10 | for op in self.ops: 11 | op.wait() 12 | 13 | def is_completed(self): 14 | for op in self.ops: 15 | if not op.is_completed(): 16 | return False 17 | return True 18 | 19 | 20 | def reduce_scatter(tensor, 21 | tensor_list, 22 | op=ReduceOp.SUM, 23 | group=dist.group.WORLD, 24 | async_op=False): 25 | rank = dist.get_rank(group) 26 | if tensor is None: 27 | tensor = tensor_list[rank] 28 | if tensor.dim() == 0: 29 | tensor = tensor.view(-1) 30 | tensor[:] = tensor_list[rank] 31 | ops = [] 32 | for i in range(dist.get_world_size(group)): 33 | if i == rank: 34 | tmp = dist.reduce(tensor, rank, op, group, async_op=True) 35 | else: 36 | tmp = dist.reduce(tensor_list[i], i, op, group, async_op=True) 37 | ops.append(tmp) 38 | 39 | oplist = AsyncOpList(ops) 40 | if async_op: 41 | return oplist 42 | else: 43 | oplist.wait() 44 | -------------------------------------------------------------------------------- /diffdist/functional.py: -------------------------------------------------------------------------------- 1 | import diffdist.modules as mods 2 | import torch.distributed as dist 3 | 4 | 5 | def consume_variable(tensor_to_consume, tensors_to_return, set_ones_grad=True): 6 | return mods.ConsumeVariable(set_ones_grad)(tensor_to_consume, 7 | *tensors_to_return) 8 | 9 | 10 | def send(tensor, dst, group=dist.group.WORLD, tag=0): 11 | return mods.Send(dst, group, tag)(tensor) 12 | 13 | 14 | def recv(tensor, 15 | src=None, 16 | group=dist.group.WORLD, 17 | tag=0, 18 | next_backprop=None, 19 | inplace=True): 20 | return mods.Recv(src, group, tag, next_backprop, inplace)(tensor) 21 | 22 | 23 | def broadcast(tensor, 24 | src, 25 | group=dist.group.WORLD, 26 | next_backprop=None, 27 | inplace=True): 28 | return mods.Broadcast(src, group, next_backprop, inplace)(tensor) 29 | 30 | 31 | def gather(tensor, 32 | gather_list=None, 33 | dst=None, 34 | group=dist.group.WORLD, 35 | next_backprop=None, 36 | inplace=True): 37 | return mods.Gather(dst, group, next_backprop, inplace)(tensor, gather_list) 38 | 39 | 40 | def scatter(tensor, 41 | scatter_list=None, 42 | src=None, 43 | group=dist.group.WORLD, 44 | next_backprop=None, 45 | inplace=True): 46 | return mods.Scatter(src, group, next_backprop, inplace)(tensor, 47 | scatter_list) 48 | 49 | 50 | def all_gather(gather_list, 51 | tensor, 52 | group=dist.group.WORLD, 53 | next_backprop=None, 54 | inplace=True): 55 | return mods.AllGather(group, next_backprop, inplace)(gather_list, tensor) 56 | -------------------------------------------------------------------------------- /diffdist/functions.py: -------------------------------------------------------------------------------- 1 | from torch.autograd import Function 2 | import diffdist.extra_collectives as dist_extra 3 | import torch.distributed as dist 4 | import torch 5 | 6 | 7 | class ConsumeVariableFunc(Function): 8 | @staticmethod 9 | def forward(ctx, tensor_to_consume, set_ones_grad, *tensors_to_return): 10 | ctx.save_for_backward(tensor_to_consume) 11 | ctx.set_ones_grad = set_ones_grad 12 | return tensors_to_return 13 | 14 | @staticmethod 15 | def backward(ctx, *grad_outputs): 16 | tensor_to_consume, = ctx.saved_tensors 17 | if ctx.set_ones_grad: 18 | fake_grad = torch.ones_like(tensor_to_consume) 19 | else: 20 | fake_grad = torch.zeros_like(tensor_to_consume) 21 | 22 | return (fake_grad, None) + grad_outputs 23 | 24 | 25 | class SendFunc(Function): 26 | @staticmethod 27 | def forward(ctx, tensor, dst, group=dist.group.WORLD, tag=0): 28 | ctx.save_for_backward(tensor) 29 | ctx.dst = dst 30 | ctx.group = group 31 | ctx.tag = tag 32 | dist.send(tensor, dst, group, tag) 33 | return tensor.new_tensor([]) 34 | 35 | @staticmethod 36 | def backward(ctx, grad_output): 37 | tensor, = ctx.saved_tensors 38 | # TODO: Add ctx.needs_input_grad check 39 | grad_tensor = torch.zeros_like(tensor) 40 | dist.recv(grad_tensor, ctx.dst, ctx.group, ctx.tag) 41 | 42 | return grad_tensor, None, None, None 43 | 44 | 45 | class RecvFunc(Function): 46 | @staticmethod 47 | def forward(ctx, 48 | tensor, 49 | src=None, 50 | group=dist.group.WORLD, 51 | tag=0, 52 | inplace=True): 53 | if not inplace: 54 | tensor = torch.zeros_like(tensor).requires_grad_(False) 55 | ctx.src = src 56 | ctx.group = group 57 | ctx.tag = tag 58 | sender = dist.recv(tensor, src, group, tag) 59 | if src: 60 | assert sender == src 61 | else: 62 | ctx.src = sender 63 | sender = torch.tensor(sender) 64 | ctx.mark_non_differentiable(sender) 65 | return tensor, sender 66 | 67 | @staticmethod 68 | def backward(ctx, grad_tensor, grad_sender): 69 | dist.send(grad_tensor, ctx.src, ctx.group, ctx.tag) 70 | return grad_tensor, None, None, None, None 71 | 72 | 73 | class BroadcastFunc(Function): 74 | @staticmethod 75 | def forward(ctx, tensor, src, group=dist.group.WORLD, inplace=True): 76 | ctx.src = src 77 | ctx.group = group 78 | if dist.get_rank(group) == src: 79 | if not inplace: 80 | with torch.no_grad(): 81 | tensor = tensor.clone().requires_grad_(False) 82 | else: 83 | if not inplace: 84 | tensor = torch.zeros_like(tensor).requires_grad_(False) 85 | dist.broadcast(tensor, src, group) 86 | return tensor 87 | 88 | @staticmethod 89 | def backward(ctx, grad_output): 90 | dist.reduce(grad_output, 91 | ctx.src, 92 | op=dist.ReduceOp.SUM, 93 | group=ctx.group) 94 | return grad_output, None, None, None 95 | 96 | 97 | class AllReduceFunc(Function): 98 | @staticmethod 99 | def forward(ctx, i): 100 | raise NotImplementedError 101 | 102 | @staticmethod 103 | def backward(ctx, grad_output): 104 | raise NotImplementedError 105 | 106 | 107 | class ReduceFunc(Function): 108 | @staticmethod 109 | def forward(ctx, i): 110 | raise NotImplementedError 111 | 112 | @staticmethod 113 | def backward(ctx, grad_output): 114 | raise NotImplementedError 115 | 116 | 117 | class AllGatherFunc(Function): 118 | @staticmethod 119 | def forward(ctx, tensor, group, inplace, *gather_list): 120 | ctx.save_for_backward(tensor) 121 | ctx.group = group 122 | gather_list = list(gather_list) 123 | if not inplace: 124 | gather_list = [torch.zeros_like(g) for g in gather_list] 125 | dist.all_gather(gather_list, tensor, group) 126 | return tuple(gather_list) 127 | 128 | @staticmethod 129 | def backward(ctx, *grads): 130 | input, = ctx.saved_tensors 131 | grad_out = torch.zeros_like(input) 132 | dist_extra.reduce_scatter(grad_out, list(grads), group=ctx.group) 133 | return (grad_out, None, None) + grads 134 | 135 | 136 | class GatherFunc(Function): 137 | @staticmethod 138 | def forward(ctx, input, dst, group, inplace, *gather_list): 139 | ctx.dst = dst 140 | ctx.group = group 141 | ctx.save_for_backward(input) 142 | if dist.get_rank(group) == dst: 143 | gather_list = list(gather_list) 144 | if not inplace: 145 | gather_list = [torch.zeros_like(g) for g in gather_list] 146 | dist.gather(input, gather_list=gather_list, dst=dst, group=group) 147 | return tuple(gather_list) 148 | else: 149 | dist.gather(input, [], dst=dst, group=group) 150 | return input.new_tensor([]) 151 | 152 | @staticmethod 153 | def backward(ctx, *grads): 154 | input, = ctx.saved_tensors 155 | grad_input = torch.zeros_like(input) 156 | if dist.get_rank(ctx.group) == ctx.dst: 157 | grad_outputs = list(grads) 158 | dist.scatter(grad_input, 159 | grad_outputs, 160 | src=ctx.dst, 161 | group=ctx.group) 162 | return (grad_input, None, None, None) + grads 163 | else: 164 | dist.scatter(grad_input, [], src=ctx.dst, group=ctx.group) 165 | return grad_input, None, None, None, None 166 | 167 | 168 | class ScatterFunc(Function): 169 | @staticmethod 170 | def forward(ctx, 171 | tensor, 172 | src, 173 | group=dist.group.WORLD, 174 | inplace=True, 175 | *scatter_list): 176 | ctx.src = src 177 | ctx.group = group 178 | if not inplace: 179 | tensor = torch.zeros_like(tensor) 180 | if dist.get_rank(group) == src: 181 | ctx.save_for_backward(*scatter_list) 182 | scatter_list = list(scatter_list) 183 | dist.scatter(tensor, scatter_list, src=src, group=group) 184 | else: 185 | dist.scatter(tensor, [], src=src, group=group) 186 | return tensor 187 | 188 | @staticmethod 189 | def backward(ctx, grad_tensor): 190 | if dist.get_rank(ctx.group) == ctx.src: 191 | grad_outputs = [torch.zeros_like(g) for g in ctx.saved_tensors] 192 | dist.gather(grad_tensor, grad_outputs, ctx.src, group=ctx.group) 193 | return (grad_tensor, None, None, None) + tuple(grad_outputs) 194 | else: 195 | dist.gather(grad_tensor, [], ctx.src, group=ctx.group) 196 | return grad_tensor, None, None, None, None 197 | -------------------------------------------------------------------------------- /diffdist/modules.py: -------------------------------------------------------------------------------- 1 | import torch.nn as nn 2 | import torch.distributed as dist 3 | import diffdist.functions as funcs 4 | 5 | 6 | class ConsumeVariable(nn.Module): 7 | def __init__(self, set_ones_grad=True): 8 | """ 9 | If set_ones_grad=True then the gradient w.r.t tensor_to_consume 10 | is set to 1 during backprop. Otherwise, it is set to 0. 11 | """ 12 | super(ConsumeVariable, self).__init__() 13 | self.set_ones_grad = set_ones_grad 14 | 15 | def forward(self, tensor_to_consume, *tensors_to_return): 16 | tensors_to_return = funcs.ConsumeVariableFunc.apply( 17 | tensor_to_consume, self.set_ones_grad, *tensors_to_return) 18 | return tensors_to_return 19 | 20 | 21 | class Send(nn.Module): 22 | def __init__(self, dst, group=dist.group.WORLD, tag=0): 23 | super(Send, self).__init__() 24 | self.dst = dst 25 | self.group = group 26 | self.tag = tag 27 | 28 | def forward(self, tensor): 29 | return funcs.SendFunc.apply(tensor, self.dst, self.group, self.tag) 30 | 31 | 32 | class Recv(nn.Module): 33 | def __init__(self, 34 | src=None, 35 | group=dist.group.WORLD, 36 | tag=0, 37 | next_backprop=None, 38 | inplace=True): 39 | super(Recv, self).__init__() 40 | self.next_backprop = next_backprop 41 | self.src = src 42 | self.group = group 43 | self.tag = tag 44 | self.inplace = inplace 45 | 46 | self.consume = None 47 | if self.next_backprop is not None: 48 | self.consume = ConsumeVariable() 49 | 50 | def forward(self, tensor): 51 | if self.consume: 52 | tensor, = self.consume(self.next_backprop, tensor) 53 | tensor, sender = funcs.RecvFunc.apply(tensor, self.src, self.group, 54 | self.tag, self.inplace) 55 | return tensor, sender.item() 56 | 57 | 58 | class Broadcast(nn.Module): 59 | def __init__(self, 60 | src, 61 | group=dist.group.WORLD, 62 | next_backprop=None, 63 | inplace=True): 64 | super(Broadcast, self).__init__() 65 | self.src = src 66 | self.group = group 67 | self.next_backprop = next_backprop 68 | self.inplace = inplace 69 | 70 | self.consume = None 71 | if self.next_backprop is not None: 72 | self.consume = ConsumeVariable() 73 | 74 | def forward(self, tensor): 75 | if self.consume: 76 | tensor, = self.consume(self.next_backprop, tensor) 77 | return funcs.BroadcastFunc.apply(tensor, self.src, self.group, 78 | self.inplace) 79 | 80 | 81 | class Gather(nn.Module): 82 | def __init__(self, 83 | dst=None, 84 | group=dist.group.WORLD, 85 | next_backprop=None, 86 | inplace=True): 87 | super(Gather, self).__init__() 88 | self.dst = dst 89 | self.group = group 90 | self.next_backprop = next_backprop 91 | self.inplace = inplace 92 | 93 | self.consume = None 94 | if self.next_backprop is not None: 95 | self.consume = ConsumeVariable() 96 | 97 | def forward(self, tensor, gather_list=None): 98 | if self.consume: 99 | tensor, = self.consume(self.next_backprop, tensor) 100 | if dist.get_rank(self.group) == self.dst: 101 | return list( 102 | funcs.GatherFunc.apply(tensor, self.dst, self.group, 103 | self.inplace, *gather_list)) 104 | else: 105 | return funcs.GatherFunc.apply(tensor, self.dst, self.group, 106 | self.inplace, None) 107 | 108 | 109 | class Scatter(nn.Module): 110 | def __init__(self, 111 | src=None, 112 | group=dist.group.WORLD, 113 | next_backprop=None, 114 | inplace=True): 115 | super(Scatter, self).__init__() 116 | self.src = src 117 | self.group = group 118 | self.next_backprop = next_backprop 119 | self.inplace = inplace 120 | 121 | self.consume = None 122 | if self.next_backprop is not None: 123 | self.consume = ConsumeVariable() 124 | 125 | def forward(self, tensor, scatter_list=None): 126 | if self.consume: 127 | tensor, = self.consume(self.next_backprop, tensor) 128 | if dist.get_rank(self.group) == self.src: 129 | return funcs.ScatterFunc.apply(tensor, self.src, self.group, 130 | self.inplace, *scatter_list) 131 | else: 132 | return funcs.ScatterFunc.apply(tensor, self.src, self.group, 133 | self.inplace, None) 134 | 135 | 136 | class AllGather(nn.Module): 137 | def __init__(self, 138 | group=dist.group.WORLD, 139 | next_backprop=None, 140 | inplace=True): 141 | super(AllGather, self).__init__() 142 | self.group = group 143 | self.next_backprop = next_backprop 144 | self.inplace = inplace 145 | 146 | self.consume = None 147 | if self.next_backprop is not None: 148 | self.consume = ConsumeVariable() 149 | 150 | def forward(self, gather_list, tensor): 151 | if self.consume: 152 | tensor, = self.consume(self.next_backprop, tensor) 153 | return list( 154 | funcs.AllGatherFunc.apply(tensor, self.group, self.inplace, 155 | *gather_list)) 156 | -------------------------------------------------------------------------------- /diffdist/testing.py: -------------------------------------------------------------------------------- 1 | import diffdist.functional as distops 2 | import torch.distributed as dist 3 | import torch 4 | import diffdist.extra_collectives as extra_comm 5 | 6 | 7 | def test_reduce_scatter(): 8 | if dist.get_rank() == 0: 9 | print("REDUCE_SCATTER TEST\n") 10 | x = torch.arange(dist.get_world_size()).float().split(1) 11 | buff = torch.tensor(0.) 12 | extra_comm.reduce_scatter(buff, x) 13 | print(dist.get_rank(), x) 14 | print(dist.get_rank(), buff) 15 | dist.barrier() 16 | if dist.get_rank() == 0: 17 | print('-' * 50) 18 | 19 | 20 | def test_all_gather(): 21 | if dist.get_rank() == 0: 22 | print("ALL GATHER TEST\n") 23 | dist.barrier() 24 | x = torch.tensor(3., requires_grad=True) 25 | y = (dist.get_rank() + 1) * x 26 | 27 | print(dist.get_rank(), "Sending y:", y) 28 | z = distops.all_gather(list(torch.zeros(dist.get_world_size())), 29 | y, 30 | next_backprop=None, 31 | inplace=True) 32 | print(dist.get_rank(), "Received tensor:", z) 33 | l = torch.sum(torch.stack(z)) 34 | l = l * (dist.get_rank() + 1) 35 | l.backward() 36 | 37 | print(dist.get_rank(), "Gradient with MPI:", x.grad) 38 | dist.barrier() 39 | if dist.get_rank() == 0: 40 | print() 41 | x = [ 42 | torch.tensor(3., requires_grad=True) 43 | for i in range(dist.get_world_size()) 44 | ] 45 | res = [] 46 | for i in range(1, dist.get_world_size() + 1): 47 | res.append(i * x[i - 1]) 48 | 49 | res2 = [] 50 | for i in range(dist.get_world_size()): 51 | temp = [] 52 | for j in range(dist.get_world_size()): 53 | temp.append(torch.clone(res[j])) 54 | res2.append(temp) 55 | l_s = [torch.sum(torch.stack(i)) for i in res2] 56 | final = [(i + 1) * k for i, k in enumerate(l_s)] 57 | for i in range(dist.get_world_size() - 1): 58 | final[i].backward(retain_graph=True) 59 | final[-1].backward() 60 | for i, x_i in enumerate(x): 61 | print(i, "Gradient in single process:", x_i.grad) 62 | print('-' * 50) 63 | 64 | 65 | def test_scatter(): 66 | if dist.get_rank() == 0: 67 | print("SCATTER TEST\n") 68 | x = [ 69 | torch.tensor(3., requires_grad=True) 70 | for i in range(dist.get_world_size()) 71 | ] 72 | y = [2 * x_i for x_i in x] 73 | 74 | print("Sending y:", y) 75 | buffer = torch.tensor(0.) 76 | z = distops.scatter(buffer, y, src=0, inplace=False) 77 | else: 78 | buffer = torch.tensor(0., requires_grad=True) 79 | z = distops.scatter(buffer, src=0, inplace=False) 80 | 81 | print(dist.get_rank(), "Received tensor:", z) 82 | # Computation 83 | k = (dist.get_rank() + 1) * z 84 | k.backward() 85 | 86 | if dist.get_rank() == 0: 87 | print("Gradient with MPI:", [x_i.grad for x_i in x]) 88 | 89 | if dist.get_rank() == 0: 90 | print() 91 | x = [ 92 | torch.tensor(3., requires_grad=True) 93 | for i in range(dist.get_world_size()) 94 | ] 95 | y = [2 * x_i for x_i in x] 96 | res = [] 97 | for i in range(dist.get_world_size()): 98 | res.append((i + 1) * y[i]) 99 | 100 | for i, k in enumerate(res): 101 | k.backward() 102 | print("Gradient in single process:", [x_i.grad for x_i in x]) 103 | dist.barrier() 104 | if dist.get_rank() == 0: 105 | print('-' * 50) 106 | 107 | 108 | def test_gather(): 109 | if dist.get_rank() == 0: 110 | print("GATHER TEST\n") 111 | dist.barrier() 112 | x = torch.tensor(3., requires_grad=True) 113 | y = (dist.get_rank() + 1) * x 114 | 115 | print(dist.get_rank(), "Sending y:", y) 116 | if dist.get_rank() == 0: 117 | z = distops.gather(y, 118 | torch.zeros(dist.get_world_size()).split(1), 119 | dst=0, 120 | next_backprop=None, 121 | inplace=True) 122 | print(dist.get_rank(), "Received tensor:", z) 123 | l = torch.sum(torch.stack(z)) 124 | l.backward() 125 | else: 126 | dummy = distops.gather(y, dst=0, next_backprop=None, inplace=True) 127 | dummy.backward(torch.tensor([])) 128 | print(dist.get_rank(), "Gradient with MPI:", x.grad) 129 | dist.barrier() 130 | if dist.get_rank() == 0: 131 | print() 132 | x = [ 133 | torch.tensor(3., requires_grad=True) 134 | for i in range(dist.get_world_size()) 135 | ] 136 | res = [] 137 | for i in range(1, dist.get_world_size() + 1): 138 | res.append(i * x[i - 1]) 139 | 140 | z = torch.stack(res) 141 | l = torch.sum(z) 142 | l.backward() 143 | for i, x_i in enumerate(x): 144 | print(i, "Gradient in single process:", x_i.grad) 145 | print('-' * 50) 146 | 147 | 148 | def test_broadcast(): 149 | if dist.get_rank() == 0: 150 | print("BROADCAST TEST\n") 151 | x = torch.tensor(3., requires_grad=True) 152 | y = 2 * x 153 | 154 | print(dist.get_rank(), "Sending y:", y) 155 | z = distops.broadcast(y, src=0, inplace=False) 156 | print(dist.get_rank(), "Received tensor:", z) 157 | 158 | # Computation 159 | k = 3 * z 160 | k.backward() 161 | print("Gradient with MPI:", x.grad) 162 | 163 | print() 164 | x = torch.tensor(3., requires_grad=True) 165 | y = 2 * x 166 | res = [3 * y] 167 | for i in range(1, dist.get_world_size()): 168 | res.append(9 * y) 169 | 170 | for i, k in enumerate(res): 171 | if i == (len(res) - 1): 172 | k.backward() 173 | else: 174 | k.backward(retain_graph=True) 175 | print("Gradient in single process:", x.grad) 176 | else: 177 | x = torch.tensor(5., requires_grad=True) 178 | y = 7 * x 179 | 180 | buffer = torch.tensor(0.) 181 | z = distops.broadcast(buffer, src=0, next_backprop=y) 182 | print(dist.get_rank(), "Received tensor:", z) 183 | k = 9 * z 184 | k.backward() 185 | print(dist.get_rank(), "Grad of disconnected part:", x.grad) 186 | dist.barrier() 187 | if dist.get_rank() == 0: 188 | print('-' * 50) 189 | 190 | 191 | def test_consume_variable(): 192 | x = torch.tensor(5., requires_grad=True) 193 | y = 2 * x 194 | 195 | z = 3 * y 196 | j = 4 * y 197 | 198 | z = distops.consume_variable(j, [z], set_ones_grad=True)[0] 199 | print(z) 200 | z.backward() 201 | print(x.grad) 202 | print() 203 | x = torch.tensor(5., requires_grad=True) 204 | y = 2 * x 205 | 206 | z = 3 * y 207 | j = 4 * y 208 | 209 | z.backward(retain_graph=True) 210 | j.backward() 211 | print(x.grad) 212 | 213 | 214 | def test_send_recv(): 215 | if dist.get_rank() == 0: 216 | print("SEND/RECV TEST\n") 217 | x = torch.tensor(3., requires_grad=True) 218 | y = 2 * x 219 | 220 | print("Before sending y:", y) 221 | connector = distops.send(y, dst=1) 222 | # Computation happens in process 1 223 | buffer = torch.tensor(0.) 224 | z, _ = distops.recv(buffer, src=1, next_backprop=connector) 225 | print("After receiving:", z) 226 | 227 | k = 3 * z 228 | k.backward() 229 | print("Gradient with MPI:", x.grad) 230 | 231 | print() 232 | x = torch.tensor(3., requires_grad=True) 233 | y = 2 * x 234 | l = y * 10 235 | k = 3 * l 236 | k.backward() 237 | print("Gradient in single process:", x.grad) 238 | print('-' * 50) 239 | elif dist.get_rank() == 1: 240 | buffer = torch.tensor(0., requires_grad=True) 241 | y, _ = distops.recv(buffer, src=0) 242 | 243 | l = y * 10 244 | 245 | connector = distops.send(l, dst=0) 246 | connector.backward(torch.tensor([])) 247 | 248 | 249 | if __name__ == '__main__': 250 | dist.init_process_group('mpi') 251 | 252 | print(f'I am {dist.get_rank()}') 253 | dist.barrier() 254 | if dist.get_rank() == 0: 255 | print('-' * 50) 256 | 257 | if dist.get_rank() == 0: 258 | print("EXTRA COLLECTIVES") 259 | 260 | test_reduce_scatter() 261 | 262 | if dist.get_rank() == 0: 263 | print('-' * 50) 264 | 265 | test_send_recv() 266 | 267 | test_broadcast() 268 | 269 | test_gather() 270 | 271 | test_scatter() 272 | 273 | test_all_gather() 274 | -------------------------------------------------------------------------------- /dist/diffdist-0.1.tar.gz: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ag14774/diffdist/b5c17c7354bbbe98b6e8a791ea78614861b4997a/dist/diffdist-0.1.tar.gz -------------------------------------------------------------------------------- /setup.cfg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ag14774/diffdist/b5c17c7354bbbe98b6e8a791ea78614861b4997a/setup.cfg -------------------------------------------------------------------------------- /setup.py: -------------------------------------------------------------------------------- 1 | from distutils.core import setup 2 | setup( 3 | name = 'diffdist', 4 | packages = ['diffdist'], 5 | version = '0.1', 6 | license='GNU GPLv3', 7 | description = 'Provides differentiable communication primitives such as Send/Recv/Gather etc for pytorch. Autograd can backpropagate through those, making distributed model parallel models easy to implement.', 8 | author = 'Andreas Georgiou', 9 | author_email = 'geandrea@ethz.ch', 10 | url = 'https://github.com/ag14774/diffdist', 11 | download_url = 'https://github.com/ag14774/diffdist/archive/v0.1.tar.gz', 12 | keywords = ['torch', 'distributed', 'autograd', 'model', 'parallel', 'differentiable', 'backpropagate'], 13 | install_requires=[], 14 | classifiers=[ 15 | 'Development Status :: 3 - Alpha', 16 | 'Intended Audience :: Developers', 17 | 'Topic :: Software Development :: Libraries', 18 | 'License :: OSI Approved :: GNU General Public License v3 or later (GPLv3+)', 19 | 'Programming Language :: Python :: 3', 20 | 'Programming Language :: Python :: 3.4', 21 | 'Programming Language :: Python :: 3.5', 22 | 'Programming Language :: Python :: 3.6', 23 | 'Programming Language :: Python :: 3.7' 24 | ], 25 | ) 26 | --------------------------------------------------------------------------------