46 lines
2.6 KiB
Python
46 lines
2.6 KiB
Python
import os
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import process_group_manager as pgm
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import torch, torch.distributed as dist
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import process_group_manager as pgm
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STEP, VERBOSE = 0, os.environ.get("VERBOSE", "0") == "1"
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def communicate(operation='send_forward', tensor=None, shapes=None, dtype=None):
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global STEP
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global VERBOSE
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if operation == 'recv_forward':
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if pgm.process_group_manager.pp_is_first_stage: return None
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tensor = torch.empty(shapes, requires_grad=True, device='cuda', dtype=dtype)
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src = pgm.process_group_manager.pp_prev_rank
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elif operation == 'send_forward':
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if pgm.process_group_manager.pp_is_last_stage: return
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dest = pgm.process_group_manager.pp_next_rank
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elif operation == 'recv_backward':
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if pgm.process_group_manager.pp_is_last_stage: return None
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tensor = torch.empty(shapes, requires_grad=True, device='cuda', dtype=dtype)
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src = pgm.process_group_manager.pp_next_rank
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elif operation == 'send_backward':
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if pgm.process_group_manager.pp_is_first_stage: return
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dest = pgm.process_group_manager.pp_prev_rank
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is_send = operation.startswith('send')
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peer_rank = dest if is_send else src
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op = dist.P2POp(dist.isend if is_send else dist.irecv, tensor, peer_rank)
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if VERBOSE: print(f"{operation} | {'sending' if is_send else 'receiving'} {operation.split('_')[1]} {pgm.process_group_manager.pp_rank} {'→' if is_send else '←'} {peer_rank} | STEP:{STEP} | RANK:{pgm.process_group_manager.pp_rank}", flush=True)
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[req.wait() for req in dist.batch_isend_irecv([op])]
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torch.cuda.synchronize()
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if VERBOSE: STEP += 1
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return tensor if not is_send else None
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def bidirectional_communicate(operation, send_tensor, recv_shapes, dtype, device):
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global STEP
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global VERBOSE
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is_fwd = (operation == 'send_fwd_recv_bwd')
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if (is_fwd and pgm.process_group_manager.pp_is_last_stage) or (not is_fwd and pgm.process_group_manager.pp_is_first_stage): return None
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peer_rank = pgm.process_group_manager.pp_next_rank if is_fwd else pgm.process_group_manager.pp_prev_rank
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recv_tensor = torch.empty(recv_shapes, requires_grad=True, device=device, dtype=dtype)
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reqs = dist.batch_isend_irecv([dist.P2POp(dist.isend, send_tensor, peer_rank), dist.P2POp(dist.irecv, recv_tensor, peer_rank)])
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if VERBOSE: print(f"{operation} | sending {'next' if is_fwd else 'prev'} {pgm.process_group_manager.pp_rank} -> {peer_rank} | "f"receiving {'next' if is_fwd else 'prev'} {peer_rank} -> {pgm.process_group_manager.pp_rank} | "f"STEP {STEP=} | RANK:{pgm.process_group_manager.pp_rank}", flush=True)
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[req.wait() for req in reqs]
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torch.cuda.synchronize()
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if VERBOSE: STEP += 1
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return recv_tensor |