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from __future__ import annotations
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import asyncio
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import contextlib
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from itertools import chain
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from typing import Any, AsyncIterator, Dict, Iterable, List, Optional, Sequence, Tuple, Union
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import torch
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from async_timeout import timeout
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from hivemind import (
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DHT,
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MSGPackSerializer,
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P2PContext,
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deserialize_tensor_stream,
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deserialize_torch_tensor,
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nested_flatten,
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nested_pack,
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serialize_torch_tensor,
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)
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from hivemind.moe.server.connection_handler import ConnectionHandler
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from hivemind.p2p.p2p_daemon import DEFAULT_MAX_MSG_SIZE
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from hivemind.proto import runtime_pb2
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from hivemind.utils.asyncio import amap_in_executor, anext
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from hivemind.utils.logging import get_logger
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from hivemind.utils.streaming import split_for_streaming
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import petals
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from petals.data_structures import CHAIN_DELIMITER, InferenceMetadata, ModuleUID
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from petals.server.backend import TransformerBackend
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from petals.server.memory_cache import Handle
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from petals.server.task_pool import PrioritizedTaskPool
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from petals.server.task_prioritizer import DummyTaskPrioritizer, TaskPrioritizerBase
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from petals.utils.misc import DUMMY, is_dummy
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logger = get_logger(__file__)
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CACHE_TOKENS_AVAILABLE = "cache_tokens_available"
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class TransformerConnectionHandler(ConnectionHandler):
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"""Handles three request types: forward, backward and forward-incremental (inference)"""
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module_backends: Dict[ModuleUID, TransformerBackend]
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def __init__(
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self,
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dht: DHT,
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module_backends: Dict[str, TransformerBackend],
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*,
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inference_max_length: int,
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request_timeout: float,
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session_timeout: float,
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step_timeout: float,
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task_prioritizer: TaskPrioritizerBase = DummyTaskPrioritizer(),
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):
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super().__init__(dht, module_backends)
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for module_backend in self.module_backends.values():
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assert isinstance(module_backend, TransformerBackend)
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self.inference_max_length = inference_max_length
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self.request_timeout = request_timeout
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self.session_timeout, self.step_timeout = session_timeout, step_timeout
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self._prioritizer = task_prioritizer
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def shutdown(self):
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if self.is_alive():
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self._outer_pipe.send("_shutdown")
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self.join(self.shutdown_timeout)
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if self.is_alive():
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logger.warning(f"{self.__class__.__name__} failed to shut down gracefully, sending SIGTERM")
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self.terminate()
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async def _gather_inputs(
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self, requests: AsyncIterator[runtime_pb2.ExpertRequest], context: P2PContext
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) -> Tuple[str, List[torch.Tensor], Dict]:
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block_uid, metadata = None, None
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def _unpack(req: runtime_pb2.ExpertRequest) -> Iterable[runtime_pb2.Tensor]:
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nonlocal block_uid, metadata
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if block_uid is None:
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block_uid = req.uid
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elif block_uid != req.uid:
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raise ValueError("Block uids differ in one request")
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if metadata is None:
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metadata = MSGPackSerializer.loads(req.metadata) if req.metadata else {}
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return req.tensors
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tensors_stream = amap_in_executor(_unpack, requests)
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inputs = await deserialize_tensor_stream(tensors_stream)
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assert isinstance(block_uid, str) and isinstance(metadata, dict)
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return block_uid, inputs, metadata
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async def rpc_inference(
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self,
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requests: AsyncIterator[runtime_pb2.ExpertRequest],
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context: P2PContext,
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) -> AsyncIterator[runtime_pb2.ExpertRequest]:
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"""Compute a single step of inference using attention cache; update attention cache accordingly."""
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async with timeout(self.session_timeout):
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try:
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request = await asyncio.wait_for(anext(requests), self.step_timeout)
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except asyncio.TimeoutError:
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self._log_request("rpc_inference.open", None, context, warning="timed out")
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return
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requested_uids = self._check_uids(request.uid)
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self._log_request("rpc_inference.open", requested_uids, context)
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try:
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metadata = MSGPackSerializer.loads(request.metadata) if request.metadata else {}
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requested_backends = tuple(self.module_backends[uid] for uid in requested_uids)
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max_length = metadata.get("max_length")
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points = metadata.get("points", 0)
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if not requested_uids:
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raise ValueError("User must specify at least one block for inference, but got none")
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assert isinstance(
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max_length, int
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), f"rpc_inference metadata must contain int max_length, got {max_length}"
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assert isinstance(
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points, (float, int)
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), f"rpc_inference should have number of points as a number or None, got {points}"
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if not 0 <= max_length <= self.inference_max_length:
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raise ValueError(
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f"Cannot allocate KV cache for {max_length} tokens, max = {self.inference_max_length}"
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)
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point_per_piece = points / max_length if max_length > 0 else 0.0
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batch_size = request.tensors[0].size[0] if request.tensors else 1
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prefix_length = 0
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async with self._allocate_cache(requested_backends, batch_size, max_length) as cache_handles:
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assert len(cache_handles) == len(requested_backends)
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while request.tensors: # iterate while user is willing to supply tensors
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hidden_states, prompts, hypo_ids = map(deserialize_torch_tensor, request.tensors)
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# Cast inputs to backend dtype
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hidden_states = hidden_states.to(requested_backends[0].dtype)
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assert hypo_ids.dtype == torch.int64, f"hypo ids must be int64, got {hypo_ids.dtype}"
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# parse deep prompts (optional argument)
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if prompts is None or is_dummy(prompts):
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prompts = [None] * len(requested_backends)
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else:
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prompts = [p.squeeze(0) for p in prompts.to(requested_backends[0].dtype).split(1, dim=0)]
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prompts = [prompt if not is_dummy(prompt) else None for prompt in prompts]
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if not (len(requested_backends) == len(prompts)):
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raise ValueError(f"Received {len(prompts)} prompts for {len(requested_backends)} backends")
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length_increment = hidden_states.shape[1] # how many tokens are added this step (in each seq)
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if prefix_length + length_increment > max_length:
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raise ValueError(
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f"Maximum length exceeded: prefix {prefix_length} + current {length_increment}"
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f" exceeds pre-allocated maximum {max_length}"
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)
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priority = self._prioritizer.prioritize(
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hidden_states,
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hypo_ids,
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points=point_per_piece,
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requested_uids=requested_uids,
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type="inference",
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)
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inference_infos = tuple(
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InferenceMetadata(uid, prefix_length, tuple(handles))
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for uid, handles in zip(requested_uids, cache_handles)
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)
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if hidden_states.numel() == 0:
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pass # user passed a tensor with 0 tokens. This is a special case that occurs, e.g.
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# when user wants to pre-allocate cache or check that server *can* allocate that cache
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else:
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assert hidden_states.ndim == 3, f"hidden states must be a single 3d tensor"
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(hidden_states,) = await self.module_backends[requested_uids[0]].inference_pool.submit_task(
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hidden_states, hypo_ids, inference_infos, *prompts, priority=priority
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)
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# serialize and send last layer outputs
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yield runtime_pb2.ExpertResponse(
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tensors=[
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serialize_torch_tensor(result.to(proto.dtype), proto.compression, allow_inplace=True)
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for result, proto in zip(
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(hidden_states,), nested_flatten(requested_backends[-1].outputs_schema)
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)
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]
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)
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# prepare for next step
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prefix_length += hidden_states.shape[1]
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try:
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request = await asyncio.wait_for(anext(requests), self.step_timeout)
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except asyncio.TimeoutError:
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self._log_request("rpc_inference.step", requested_uids, context, warning="timed out")
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return
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finally:
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self._log_request("rpc_inference.close", requested_uids, context)
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async def rpc_forward(self, request: runtime_pb2.ExpertRequest, context: P2PContext) -> runtime_pb2.ExpertResponse:
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async with timeout(self.request_timeout):
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# Parse request and prepare backends
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flat_inputs = [deserialize_torch_tensor(tensor) for tensor in request.tensors]
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requested_uids = self._check_uids(request.uid)
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self._log_request("rpc_forward", requested_uids, context)
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requested_backends = tuple(self.module_backends[uid] for uid in requested_uids)
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metadata = MSGPackSerializer.loads(request.metadata) if request.metadata else {}
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points = metadata.get("points", 0)
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assert isinstance(
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points, (float, int)
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), f"rpc_forward should have number of points as number or None, got {points}"
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hidden_states = await _rpc_forward(
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*flat_inputs, requested_backends=requested_backends, prioritizer=self._prioritizer, points=points
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)
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return runtime_pb2.ExpertResponse(
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tensors=self._serialize_outputs(hidden_states, requested_backends, metadata)
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)
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async def rpc_forward_stream(
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self, requests: AsyncIterator[runtime_pb2.ExpertRequest], context: P2PContext
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) -> AsyncIterator[runtime_pb2.ExpertRequest]:
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async with timeout(self.request_timeout):
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# Parse requests and prepare backends
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uid_str, flat_inputs, metadata = await self._gather_inputs(requests, context)
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requested_uids = self._check_uids(uid_str)
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self._log_request("rpc_forward_stream", requested_uids, context)
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requested_backends = tuple(self.module_backends[uid] for uid in requested_uids)
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points = metadata.get("points", 0)
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assert isinstance(
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points, (float, int)
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), f"rpc_forward_stream should have number of points as number or None, got {points}"
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hidden_states = await _rpc_forward(
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*flat_inputs, requested_backends=requested_backends, prioritizer=self._prioritizer, points=points
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)
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# Split the serialized_output for streaming and respond to client
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for tensor in self._serialize_outputs(hidden_states, requested_backends, metadata):
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for part in split_for_streaming(tensor, DEFAULT_MAX_MSG_SIZE):
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yield runtime_pb2.ExpertResponse(tensors=[part])
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def _serialize_outputs(
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self,
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hidden_states: torch.Tensor,
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requested_backends: Sequence[TransformerBackend],
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metadata: Dict[str, Any],
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) -> Sequence[runtime_pb2.Tensor]:
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"""Serialize forward outputs using either outputs_schema or custom user-specified schema"""
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assert isinstance(hidden_states, torch.Tensor) and hidden_states.ndim == 3, "hidden_states must be a 3d tensor"
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outputs_schema = requested_backends[-1].outputs_schema
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if metadata.get("output_compression") is not None:
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assert isinstance(metadata["output_compression"], (list, tuple)), "output_compression must be a tuple/list"
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output_compression = tuple(metadata["output_compression"])
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assert all(isinstance(c, int) for c in output_compression), "output_compression must contain integers"
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assert len(output_compression) == 1, f"output_compression tuple should have 1 element"
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else:
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output_compression = tuple(tensor.compression for tensor in outputs_schema)
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return [
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serialize_torch_tensor(result.to(proto.dtype), compression, allow_inplace=True)
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for result, proto, compression in zip([hidden_states], outputs_schema, output_compression)
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]
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async def rpc_backward(self, request: runtime_pb2.ExpertRequest, context: P2PContext) -> runtime_pb2.ExpertResponse:
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async with timeout(self.request_timeout):
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# Parse requests and prepare backends
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flat_tensors = [deserialize_torch_tensor(tensor) for tensor in request.tensors]
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requested_uids = self._check_uids(request.uid)
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self._log_request("rpc_backward", requested_uids, context)
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requested_backends = tuple(self.module_backends[uid] for uid in requested_uids)
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metadata = MSGPackSerializer.loads(request.metadata) if request.metadata else {}
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points = metadata.get("points", 0)
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assert isinstance(
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points, (float, int)
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), f"rpc_backward should have number of points as number or None, got {points}"
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grads = await _rpc_backward(
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*flat_tensors, requested_backends=requested_backends, prioritizer=self._prioritizer, points=points
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)
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return runtime_pb2.ExpertResponse(tensors=self._serialize_grads(grads, requested_backends, metadata))
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async def rpc_backward_stream(
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self, requests: AsyncIterator[runtime_pb2.ExpertRequest], context: P2PContext
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) -> AsyncIterator[runtime_pb2.ExpertResponse]:
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async with timeout(self.request_timeout):
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uids_header, flat_tensors, metadata = await self._gather_inputs(requests, context)
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requested_uids = self._check_uids(uids_header)
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self._log_request("rpc_backward_stream", requested_uids, context)
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requested_backends = tuple(self.module_backends[uid] for uid in requested_uids)
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points = metadata.get("points", 0)
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assert isinstance(
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points, (float, int)
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), f"rpc_backward_stream should have number of points as number or None, got {points}"
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grads = await _rpc_backward(
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*flat_tensors, requested_backends=requested_backends, prioritizer=self._prioritizer, points=points
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)
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# Split the serialized_grad_inputs for streaming and respond
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for tensor in self._serialize_grads(grads, requested_backends, metadata):
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for part in split_for_streaming(tensor, DEFAULT_MAX_MSG_SIZE):
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yield runtime_pb2.ExpertResponse(tensors=[part])
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def _serialize_grads(
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self,
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grads: Sequence[torch.Tensor],
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requested_backends: Sequence[TransformerBackend],
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metadata: Dict[str, Any],
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) -> Sequence[runtime_pb2.Tensor]:
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"""Serialize backward gradients w.r.t. inputs using either default schema or custom user-specified schema"""
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# Modify grad_inputs_schema to support grad_prompts
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assert len(requested_backends[0].args_schema) == 1 and len(grads) in (1, 2) # TODO generalize
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flat_grads_schema = tuple(
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nested_flatten((requested_backends[0].args_schema * len(grads), requested_backends[0].kwargs_schema))
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) # TODO generalize
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if metadata.get("output_compression") is not None:
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assert isinstance(metadata["output_compression"], (list, tuple)), "output_compression must be a tuple/list"
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output_compression = tuple(metadata["output_compression"])
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assert all(isinstance(c, int) for c in output_compression), "output_compression must contain integers"
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assert len(output_compression) == len(grads), f"output_compression should have {len(grads)} elements"
|
|
|
|
else:
|
|
|
|
output_compression = tuple(tensor.compression for tensor in flat_grads_schema)
|
|
|
|
|
|
|
|
return [
|
|
|
|
serialize_torch_tensor(result.to(proto.dtype), compression, allow_inplace=True)
|
|
|
|
for result, proto, compression in zip(grads, flat_grads_schema, output_compression)
|
|
|
|
]
|
|
|
|
|
|
|
|
def _check_uids(self, uids: str) -> Tuple[ModuleUID, ...]:
|
|
|
|
"""Check that the first request to rpc_inference is valid"""
|
|
|
|
uids = (uids or "").split(CHAIN_DELIMITER)
|
|
|
|
if not uids:
|
|
|
|
raise RuntimeError("User did not provide any uids")
|
|
|
|
for uid in uids:
|
|
|
|
if uid not in self.module_backends:
|
|
|
|
raise RuntimeError(f"Remote peer does not serve {uid}")
|
|
|
|
return tuple(uids)
|
|
|
|
|
|
|
|
@contextlib.asynccontextmanager
|
|
|
|
async def _allocate_cache(
|
|
|
|
self, backends: Sequence[TransformerBackend], batch_size: int, max_length: int
|
|
|
|
) -> Sequence[Sequence[Handle, ...]]:
|
|
|
|
"""
|
|
|
|
Allocate memory cache for all transformer blocks, return cache handle
|
|
|
|
:returns: a list of {len(backends)} elements, where i-th element is a tuple of cache handles for i-th backend
|
|
|
|
"""
|
|
|
|
descriptors = [backend.get_inference_cache_descriptors(batch_size, max_length) for backend in backends]
|
|
|
|
async with backends[0].memory_cache.allocate_cache(*chain(*descriptors)) as handles:
|
|
|
|
yield nested_pack(handles, descriptors)
|
|
|
|
|
|
|
|
def _log_request(
|
|
|
|
self, method: str, uids: Optional[Sequence[ModuleUID]], context: P2PContext, *, warning: Optional[str] = None
|
|
|
|
) -> None:
|
|
|
|
if uids is not None:
|
|
|
|
friendly_uids = [uid.split(".")[-1] for uid in uids if "." in uid]
|
|
|
|
friendly_uids = [int(uid) for uid in friendly_uids if uid.isdigit()]
|
|
|
|
friendly_uids = f"{min(friendly_uids)}:{max(friendly_uids) + 1}" if friendly_uids else uids
|
|
|
|
else:
|
|
|
|
friendly_uids = "n/a"
|
|
|
|
|
|
|
|
friendly_remote_id = "..." + str(context.remote_id)[-6:]
|
|
|
|
|
|
|
|
message = f"{method}(blocks={friendly_uids}, remote_peer={friendly_remote_id})"
|
|
|
|
if warning is None:
|
|
|
|
logger.info(message)
|
|
|
|
else:
|
|
|
|
logger.warning(f"{message}: {warning}")
|
|
|
|
|
|
|
|
async def rpc_info(self, request: runtime_pb2.ExpertUID, context: P2PContext) -> runtime_pb2.ExpertInfo:
|
|
|
|
"""Return metadata about stored block uids and current load"""
|
|
|
|
|
|
|
|
backend = self.module_backends[request.uid] if request.uid else next(iter(self.module_backends.values()))
|
|
|
|
cache_bytes_left = max(0, backend.memory_cache.max_size_bytes - backend.memory_cache.current_size_bytes)
|
|
|
|
result = {
|
|
|
|
"version": petals.__version__,
|
|
|
|
"dht_client_mode": self.dht.client_mode,
|
|
|
|
CACHE_TOKENS_AVAILABLE: cache_bytes_left // max(backend.cache_bytes_per_token.values()),
|
|
|
|
}
|
|
|
|
|
|
|
|
if request.uid:
|
|
|
|
block_info = self.module_backends[request.uid].get_info()
|
|
|
|
common_keys = set(result.keys()) & set(block_info.keys())
|
|
|
|
if common_keys:
|
|
|
|
raise RuntimeError(f"The block's rpc_info has keys reserved for the server's rpc_info: {common_keys}")
|
|
|
|
result.update(block_info)
|
|
|
|
|
|
|
|
return runtime_pb2.ExpertInfo(serialized_info=MSGPackSerializer.dumps(result))
|
|
|
|
|
|
|
|
|
|
|
|
async def _rpc_forward(
|
|
|
|
*flat_tensors: torch.Tensor,
|
|
|
|
requested_backends: Sequence[TransformerBackend],
|
|
|
|
prioritizer: TaskPrioritizerBase,
|
|
|
|
points: int = 0,
|
|
|
|
) -> torch.Tensor:
|
|
|
|
"""
|
|
|
|
Run forward pass on deserialized inputs and prompts, used by rpc_forward and rpc_forward_stream
|
|
|
|
|
|
|
|
:param flat_tensors: a list of tensors that includes first layer inputs, optional prompts and extra tensors
|
|
|
|
:note: some input tensors can be missing, in which case they will be replaced with dummy tensors (see is_dummy)
|
|
|
|
:param requested_backends: a sequence of transformer blocks in the same order as they appear in forward pass
|
|
|
|
:returns: hidden states after the last layer [batch_size, seq_length, hid_size]
|
|
|
|
"""
|
|
|
|
hidden_states, prompts = flat_tensors
|
|
|
|
dtype = requested_backends[0].dtype
|
|
|
|
# check parse input tensors and cast dtypes
|
|
|
|
hidden_states = hidden_states.to(dtype)
|
|
|
|
assert hidden_states.ndim == 3
|
|
|
|
if prompts is None or is_dummy(prompts):
|
|
|
|
prompts = [DUMMY] * len(requested_backends)
|
|
|
|
else:
|
|
|
|
prompts = [p.squeeze(0) for p in prompts.to(requested_backends[0].dtype).split(1, dim=0)]
|
|
|
|
|
|
|
|
# Run a chain of requested backends
|
|
|
|
for backend, prompt in zip(requested_backends, prompts):
|
|
|
|
if not is_dummy(prompt):
|
|
|
|
hidden_states[:, : prompt.shape[1]] += prompt
|
|
|
|
|
|
|
|
assert isinstance(backend.inference_pool, PrioritizedTaskPool), "petals support only prioritized pools"
|
|
|
|
priority = prioritizer.prioritize(
|
|
|
|
hidden_states, points=points / len(requested_backends), backend=backend, type="forward"
|
|
|
|
)
|
|
|
|
(hidden_states,) = await backend.forward_pool.submit_task(
|
|
|
|
hidden_states,
|
|
|
|
priority=priority,
|
|
|
|
)
|
|
|
|
assert isinstance(hidden_states, torch.Tensor)
|
|
|
|
assert (
|
|
|
|
hidden_states.ndim == 3
|
|
|
|
), f"inputs to {type(backend)} must be a list with a single 3d tensor of hidden states"
|
|
|
|
|
|
|
|
return hidden_states
|
|
|
|
|
|
|
|
|
|
|
|
async def _rpc_backward(
|
|
|
|
*flat_tensors: torch.Tensor,
|
|
|
|
requested_backends: Sequence[TransformerBackend],
|
|
|
|
prioritizer: TaskPrioritizerBase,
|
|
|
|
points: int = 0,
|
|
|
|
) -> Union[torch.Tensor, Sequence[torch.Tensor]]:
|
|
|
|
inputs, grad_outputs, prompts = flat_tensors
|
|
|
|
# Cast inputs & grad outputs to backend dtype
|
|
|
|
inputs = inputs.to(requested_backends[0].dtype)
|
|
|
|
grad_outputs = grad_outputs.to(requested_backends[-1].dtype)
|
|
|
|
|
|
|
|
if prompts is None or is_dummy(prompts):
|
|
|
|
prompts = [DUMMY] * len(requested_backends)
|
|
|
|
else:
|
|
|
|
prompts = [p.squeeze(0) for p in prompts.to(requested_backends[0].dtype).split(1, dim=0)]
|
|
|
|
|
|
|
|
# Run a forward chain to collect intermediate inputs
|
|
|
|
# Note that we do not forward for the last module since we do not need its output
|
|
|
|
inter_inputs = []
|
|
|
|
for backend, prompt in zip(requested_backends[:-1], prompts[:-1]):
|
|
|
|
assert inputs.ndim == 3, f"inputs to {type(backend)} must be a single 3d tensor of hidden states"
|
|
|
|
if not is_dummy(prompt):
|
|
|
|
inputs[:, : prompt.shape[1]] += prompt
|
|
|
|
inter_inputs.append(inputs)
|
|
|
|
assert isinstance(backend.inference_pool, PrioritizedTaskPool), "petals support only prioritized pools"
|
|
|
|
priority = prioritizer.prioritize(
|
|
|
|
inputs, points=points / len(requested_backends), backend=backend, type="forward_in_backward"
|
|
|
|
)
|
|
|
|
(inputs,) = await backend.forward_pool.submit_task(inputs, priority=priority)
|
|
|
|
|
|
|
|
assert isinstance(inputs, torch.Tensor)
|
|
|
|
|
|
|
|
if not is_dummy(prompts[-1]):
|
|
|
|
inputs[:, : prompts[-1].shape[1]] += prompts[-1]
|
|
|
|
inter_inputs.append(inputs)
|
|
|
|
|
|
|
|
assert len(inter_inputs) == len(prompts) == len(requested_backends), "internal shape error during backward"
|
|
|
|
grad_prompts_reversed = []
|
|
|
|
# Run a chain of requested backends
|
|
|
|
for inp, prompt, backend in zip(*map(reversed, (inter_inputs, prompts, requested_backends))):
|
|
|
|
assert isinstance(backend.inference_pool, PrioritizedTaskPool), "petals support only prioritized pools"
|
|
|
|
priority = prioritizer.prioritize(
|
|
|
|
inp, grad_outputs, points=points / len(requested_backends), backend=backend, type="backward"
|
|
|
|
)
|
|
|
|
(grad_outputs,) = await backend.backward_pool.submit_task(inp, grad_outputs, priority=priority)
|
|
|
|
|
|
|
|
assert isinstance(grad_outputs, torch.Tensor)
|
|
|
|
if not is_dummy(prompt):
|
|
|
|
grad_prompts_reversed.append(grad_outputs[:, : prompt.shape[1]].unsqueeze(0))
|
|
|
|
|
|
|
|
grad_prompts = torch.cat(grad_prompts_reversed[::-1], dim=0) if grad_prompts_reversed else DUMMY
|
|
|
|
return [grad_outputs] if is_dummy(grad_prompts) else [grad_outputs, grad_prompts] # TODO un-duct-tape
|