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Distributed communication abstraction layer.
This module provides a high-level interface for distributed communication operations,
encapsulating the low-level PyTorch distributed primitives.
"""
import torch
import torch.distributed as dist
from typing import List, Tuple, Optional, Any
import logging
import time
from .utils import CommunicationTags, get_next_rank, get_prev_rank, CommunicationTimer
from .data_containers import CommunicationConfig
class DistributedCommunicator:
"""
High-level interface for distributed communication operations.
This class encapsulates all distributed communication operations, providing
a clean interface for sending and receiving tensors between ranks.
"""
def __init__(self, rank: int, world_size: int, device: torch.device,
config: Optional[CommunicationConfig] = None):
"""
Initialize the distributed communicator.
Args:
rank: Current rank
world_size: Total number of ranks
device: GPU device for communication
config: Communication configuration
"""
self.rank = rank
self.world_size = world_size
self.device = device
self.config = config or CommunicationConfig()
# Track outstanding operations
self.outstanding_operations: List[Any] = []
# Setup logging
self.logger = logging.getLogger(f"DistributedCommunicator_rank_{rank}")
self.logger.propagate = False
if not self.logger.handlers:
handler = logging.StreamHandler()
formatter = logging.Formatter(
f'[Rank {rank}] %(asctime)s - %(name)s - %(levelname)s - %(message)s'
)
handler.setFormatter(formatter)
self.logger.addHandler(handler)
# Validate distributed is initialized
if not dist.is_initialized():
raise RuntimeError("Distributed not initialized. Call init_distributed() first.")
def send_tensor_async(self, tensor: torch.Tensor, dst: int, tag: int) -> Any:
"""
Asynchronously send a tensor to the specified destination.
Args:
tensor: Tensor to send
dst: Destination rank
tag: Communication tag
Returns:
Work object for the send operation
"""
if tensor.device != self.device:
raise ValueError(f"Tensor device {tensor.device} doesn't match communicator device {self.device}")
work = dist.isend(tensor, dst=dst, tag=tag)
self.outstanding_operations.append(work)
self.logger.debug(f"Started async send to rank {dst} with tag {tag}, tensor shape: {tensor.shape}")
return work
def recv_tensor(self, src: int, tag: int, shape: Tuple[int, ...],
dtype: torch.dtype) -> torch.Tensor:
"""
Receive a tensor from the specified source.
Args:
src: Source rank
tag: Communication tag
shape: Expected tensor shape
dtype: Expected tensor dtype
Returns:
Received tensor
"""
tensor = torch.empty(shape, dtype=dtype, device=self.device)
with CommunicationTimer(f"recv_tensor from rank {src}", self.logger):
dist.recv(tensor, src=src, tag=tag)
self.logger.debug(f"Received tensor from rank {src} with tag {tag}, shape: {tensor.shape}")
return tensor
def send_header_and_tensor_async(self, header: torch.Tensor, tensor: torch.Tensor,
dst: int, tag_header: int, tag_tensor: int) -> Tuple[Any, Any]:
"""
Asynchronously send a header and tensor pair.
Args:
header: Header tensor containing metadata
tensor: Data tensor
dst: Destination rank
tag_header: Tag for header
tag_tensor: Tag for tensor
Returns:
Tuple of (header_work, tensor_work)
"""
if header.device != self.device or tensor.device != self.device:
raise ValueError("Header and tensor must be on the same device as communicator")
header_work = dist.isend(header, dst=dst, tag=tag_header)
tensor_work = dist.isend(tensor, dst=dst, tag=tag_tensor)
self.outstanding_operations.extend([header_work, tensor_work])
self.logger.debug(f"Started async send of header+tensor to rank {dst}, "
f"header shape: {header.shape}, tensor shape: {tensor.shape}")
return header_work, tensor_work
def recv_header_and_tensor(self, src: int, tag_header: int, tag_tensor: int, header_len: int) -> Tuple[torch.Tensor, torch.Tensor]:
"""
Receive a header and tensor pair.
Args:
src: Source rank
tag_header: Tag for header
tag_tensor: Tag for tensor
header_len: Length of header tensor to receive
Returns:
Tuple of (header, tensor)
"""
with CommunicationTimer(f"recv_header_and_tensor from rank {src}", self.logger):
# First receive the header to get tensor shape (length can vary)
header = torch.empty(header_len, dtype=torch.int64, device=self.device)
dist.recv(header, src=src, tag=tag_header)
# Parse header to get tensor shape
chunk_idx, shape = self._parse_header(header)
# Receive the tensor
tensor = torch.empty(shape, dtype=torch.bfloat16, device=self.device)
dist.recv(tensor, src=src, tag=tag_tensor)
self.logger.debug(f"Received header+tensor from rank {src}, "
f"header: {header.tolist()}, tensor shape: {tensor.shape}")
return header, tensor
def send_latent_data_async(self, chunk_idx: int, latents: torch.Tensor,
original_latents: torch.Tensor, patched_x_shape: torch.Tensor,
current_start: torch.Tensor, current_end: torch.Tensor,
current_step: int) -> List[Any]:
"""
Asynchronously send all latent data components.
Args:
chunk_idx: Chunk index
latents: Latent tensor
original_latents: Original latent tensor
patched_x_shape: Patched x shape tensor
current_start: Current start indices
current_end: Current end indices
current_step: Current step
Returns:
List of work objects for all send operations
"""
dst = get_next_rank(self.rank, self.world_size)
work_objects = []
# Create headers
latent_header = self._create_header(chunk_idx, latents.shape)
origin_header = self._create_header(chunk_idx, original_latents.shape)
# Create start/end/step tensor
start_end_step = torch.cat([
current_start,
current_end,
torch.tensor([current_step], dtype=torch.int64, device=self.device)
], dim=0)
# Send all components asynchronously
work_objects.extend(self.send_header_and_tensor_async(
latent_header, latents, dst, CommunicationTags.LATENT_HDR, CommunicationTags.LATENT_PAY
))
work_objects.extend(self.send_header_and_tensor_async(
origin_header, original_latents, dst,
CommunicationTags.LATENT_ORIGIN_HDR, CommunicationTags.LATENT_ORIGIN_PAY
))
work_objects.append(self.send_tensor_async(
patched_x_shape, dst, CommunicationTags.PATCHED_X_SHAPE
))
work_objects.append(self.send_tensor_async(
start_end_step, dst, CommunicationTags.START_END_STEP
))
self.logger.debug(f"Started async send of latent data to rank {dst}, chunk_idx: {chunk_idx}")
return work_objects
def recv_latent_data_async(self, num_steps: int, buffer_manager) -> Tuple[int, torch.Tensor, torch.Tensor,
torch.Tensor, torch.Tensor, int, torch.Tensor]:
"""
Asynchronously receive all latent data components.
Args:
num_steps: Number of denoising steps
buffer_manager: Buffer manager for tensor allocation
Returns:
Tuple of (chunk_idx, latents, original_latents, current_start, current_end, current_step, patched_x_shape)
"""
src = get_prev_rank(self.rank, self.world_size)
with CommunicationTimer(f"recv_latent_data_async from rank {src}", self.logger):
# Receive latent header (length 4): [i, bsz, slen, cch]
latent_header = buffer_manager.get_buffer((4,), torch.int64, "misc")
dist.recv(latent_header, src=src, tag=CommunicationTags.LATENT_HDR)
chunk_idx, latent_shape = self._parse_header(latent_header)
# header no longer needed
buffer_manager.return_buffer(latent_header, "misc")
# Allocate or reuse buffer for latents: shape (bsz, slen, cch)
latents = buffer_manager.get_buffer(tuple(latent_shape), torch.bfloat16, "latent")
dist.recv(latents, src=src, tag=CommunicationTags.LATENT_PAY)
# Receive original latent header (length 6): [i, bsz, cch, tlen, hh, ww]
origin_header = buffer_manager.get_buffer((6,), torch.int64, "misc")
dist.recv(origin_header, src=src, tag=CommunicationTags.LATENT_ORIGIN_HDR)
_, origin_shape = self._parse_header(origin_header)
# header no longer needed
buffer_manager.return_buffer(origin_header, "misc")
# Allocate or reuse buffer for original latents: shape (bsz, cch, tlen, hh, ww)
original_latents = buffer_manager.get_buffer(tuple(origin_shape), torch.bfloat16, "origin")
dist.recv(original_latents, src=src, tag=CommunicationTags.LATENT_ORIGIN_PAY)
# Receive patched_x_shape (length 5, int64)
patched_x_shape = buffer_manager.get_buffer((5,), torch.int64, "misc")
dist.recv(patched_x_shape, src=src, tag=CommunicationTags.PATCHED_X_SHAPE)
# Receive start_end_step (length 2*num_steps+1, int64)
start_end_step = buffer_manager.get_buffer((2 * num_steps + 1,), torch.int64, "misc")
dist.recv(start_end_step, src=src, tag=CommunicationTags.START_END_STEP)
# Parse start/end/step into dedicated misc buffers, then release the combined vector
current_start = buffer_manager.get_buffer((num_steps,), torch.int64, "misc")
current_end = buffer_manager.get_buffer((num_steps,), torch.int64, "misc")
current_start.copy_(start_end_step[:num_steps])
current_end.copy_(start_end_step[num_steps:-1])
current_step = int(start_end_step[-1].item())
# Release the temporary combined buffer
buffer_manager.return_buffer(start_end_step, "misc")
self.logger.debug(f"Received latent data from rank {src}, chunk_idx: {chunk_idx}")
return chunk_idx, latents, original_latents, current_start, current_end, current_step, patched_x_shape
def send_prompt_async(self, prompt: str, device: torch.device) -> List[Any]:
work_objects = []
dst = get_next_rank(self.rank, self.world_size)
# Encode to bytes
encoded = prompt.encode("utf-8")
data = torch.ByteTensor(list(encoded)).to(device)
# Send length first
length = torch.tensor([len(data)], dtype=torch.int64, device=data.device)
work_objects.append(dist.isend(length, dst=dst, tag=CommunicationTags.UPDATED_PROMPT_LENGTH))
# Then send the content
work_objects.append(dist.isend(data, dst=dst, tag=CommunicationTags.UPDATED_PROMPT))
return work_objects
def recv_prompt_async(self) -> str:
src = get_prev_rank(self.rank, self.world_size)
# Receive length first
length = torch.empty(1, dtype=torch.int64, device=self.device)
dist.recv(length, src=src, tag=CommunicationTags.UPDATED_PROMPT_LENGTH)
# Then receive the content
prompt = torch.empty(length.item(), dtype=torch.uint8, device=self.device)
dist.recv(prompt, src=src, tag=CommunicationTags.UPDATED_PROMPT)
return bytes(prompt.cpu().tolist()).decode("utf-8")
def broadcast_tensor(self, tensor: torch.Tensor, src: int) -> None:
"""
Broadcast a tensor from source to all ranks.
Args:
tensor: Tensor to broadcast
src: Source rank
"""
with CommunicationTimer(f"broadcast_tensor from rank {src}", self.logger):
dist.broadcast(tensor, src=src)
self.logger.debug(f"Broadcasted tensor from rank {src}, shape: {tensor.shape}")
def all_gather_tensors(self, tensor: torch.Tensor) -> List[torch.Tensor]:
"""
Gather tensors from all ranks.
Args:
tensor: Local tensor to gather
Returns:
List of tensors from all ranks
"""
with CommunicationTimer("all_gather_tensors", self.logger):
gather_list = [torch.zeros_like(tensor) for _ in range(self.world_size)]
dist.all_gather(gather_list, tensor)
self.logger.debug(f"Gathered tensors from all ranks, local shape: {tensor.shape}")
return gather_list
def wait_for_outstanding(self, max_outstanding: Optional[int] = None) -> None:
"""
Wait for outstanding operations to complete.
Args:
max_outstanding: Maximum number of outstanding operations to keep
"""
max_outstanding = max_outstanding or self.config.max_outstanding
while len(self.outstanding_operations) >= max_outstanding:
if not self.outstanding_operations:
break
# Wait for the oldest operation
oldest_operations = self.outstanding_operations.pop(0)
# Handle both single work objects and lists of work objects
if isinstance(oldest_operations, (list, tuple)):
for work in oldest_operations:
try:
work.wait()
except Exception as e:
self.logger.error(f"Error waiting for outstanding operation: {e}")
raise
else:
try:
oldest_operations.wait()
except Exception as e:
self.logger.error(f"Error waiting for outstanding operation: {e}")
raise
self.logger.debug(f"Outstanding operations: {len(self.outstanding_operations)}")
def barrier(self) -> None:
"""Synchronize all ranks."""
with CommunicationTimer("barrier", self.logger):
dist.barrier()
def _create_header(self, chunk_idx: int, shape: Tuple[int, ...]) -> torch.Tensor:
"""Create a header tensor for communication."""
header_data = [chunk_idx] + list(shape)
return torch.tensor(header_data, dtype=torch.int64, device=self.device)
def _parse_header(self, header: torch.Tensor) -> Tuple[int, Tuple[int, ...]]:
"""Parse a header tensor to extract metadata."""
header_list = header.tolist()
chunk_idx = int(header_list[0])
shape = tuple(int(x) for x in header_list[1:])
return chunk_idx, shape
def get_statistics(self) -> dict:
"""Get communication statistics."""
return {
"rank": self.rank,
"world_size": self.world_size,
"outstanding_operations": len(self.outstanding_operations),
"max_outstanding": self.config.max_outstanding,
"device": str(self.device)
}
def print_statistics(self) -> None:
"""Print communication statistics."""
stats = self.get_statistics()
self.logger.info("Distributed Communicator Statistics:")
for key, value in stats.items():
self.logger.info(f" {key}: {value}") |