petals/src/server/handler.py

import contextlib
from typing import AsyncIterator, Dict, Iterable, List, Sequence, Tuple, Union

import torch
from hivemind import (
    DHT,
    MSGPackSerializer,
    P2PContext,
    TensorDescriptor,
    deserialize_tensor_stream,
    deserialize_torch_tensor,
    nested_flatten,
    serialize_torch_tensor,
)
from hivemind.moe.server.connection_handler import ConnectionHandler
from hivemind.p2p.p2p_daemon import DEFAULT_MAX_MSG_SIZE
from hivemind.proto import runtime_pb2
from hivemind.utils.asyncio import amap_in_executor, anext, as_aiter
from hivemind.utils.streaming import split_for_streaming

from src.data_structures import CHAIN_DELIMITER, ModuleUID
from src.server.backend import TransformerBackend
from src.server.task_pool import PrioritizedTaskPool
from src.server.task_prioritizer import DummyTaskPrioritizer, TaskPrioritizerBase
from src.utils.misc import DUMMY, is_dummy


class TransformerConnectionHandler(ConnectionHandler):
    """Handles three request types: forward, backward and forward-incremental (inference)"""

    module_backends: Dict[ModuleUID, TransformerBackend]

    def __init__(
        self,
        dht: DHT,
        module_backends: Dict[str, TransformerBackend],
        inference_max_length: int,
        task_prioritizer: TaskPrioritizerBase = DummyTaskPrioritizer(),
    ):
        super().__init__(dht, module_backends)
        for module_backend in self.module_backends.values():
            assert isinstance(module_backend, TransformerBackend)
        self.inference_max_length = inference_max_length
        self._prioritizer = task_prioritizer

    async def _gather_inputs(
        self, requests: AsyncIterator[runtime_pb2.ExpertRequest], context: P2PContext
    ) -> Tuple[str, List[torch.Tensor], Dict]:
        expert_uid, metadata = None, None

        def _unpack(req: runtime_pb2.ExpertRequest) -> Iterable[runtime_pb2.Tensor]:
            nonlocal expert_uid, metadata

            if expert_uid is None:
                expert_uid = req.uid
            elif expert_uid != req.uid:
                raise ValueError("Expert uids differ in one request")

            if metadata is None:
                metadata = MSGPackSerializer.loads(req.metadata) if req.metadata else {}

            return req.tensors

        tensors_stream = amap_in_executor(_unpack, requests)
        inputs = await deserialize_tensor_stream(tensors_stream)
        return expert_uid, inputs, metadata

    async def rpc_inference(
        self,
        requests: AsyncIterator[runtime_pb2.ExpertRequest],
        context: P2PContext,
    ) -> AsyncIterator[runtime_pb2.ExpertRequest]:
        """Compute a single step of inference using attention cache; update attention cache accordingly."""
        try:
            print("OPENED RPC_INFERENCE")
            request = await anext(requests)
            requested_uids = self._check_uids(request.uid)
            metadata = MSGPackSerializer.loads(request.metadata) if request.metadata else {}
            requested_backends = tuple(self.module_backends[uid] for uid in requested_uids)
            max_length = metadata.get("max_length")
            points = metadata.get("points", 0)

            if not requested_uids:
                raise ValueError("User must specify at least one block for inference, but got none")
            assert isinstance(max_length, int), f"rpc_inference metadata must contain int max_length, got {max_length}"
            assert isinstance(
                points, (float, int)
            ), f"rpc_inference should have number of points as a number or None, got {points}"
            if not 0 <= max_length <= self.inference_max_length:
                raise ValueError(f"Cannot allocate KV cache for {max_length} tokens, max = {self.inference_max_length}")

            point_per_piece = points / max_length if max_length > 0 else 0.0
            batch_size = request.tensors[0].size[0] if request.tensors else 1

            cache_metadata = torch.tensor(
                [[-1, -1] for _ in range(batch_size)], dtype=torch.int64
            )  # [cache_handle, prefix_length]
            prefix_length = 0

            async with self._allocate_caches(requested_backends, batch_size, max_length) as cache_handles:
                assert len(cache_handles) == len(requested_backends)
                while request.tensors:  # iterate while user is willing to supply tensors
                    hidden_states, prompts, hypo_ids = [deserialize_torch_tensor(tensor) for tensor in request.tensors]

                    # Cast inputs to backend dtype
                    hidden_states = hidden_states.to(requested_backends[0].dtype)
                    assert hypo_ids.dtype == torch.int64, f"hypo ids must be int64, got {hypo_ids.dtype}"

                    # parse deep prompts (optional argument)
                    if prompts is None or is_dummy(prompts) 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)]

                    if not (len(requested_backends) == len(prompts)):
                        raise ValueError(f"Received {len(prompts)} prompts for {len(requested_backends)} backends")

                    length_increment = hidden_states.shape[1]  # how many tokens are added this step (in each seq)
                    if prefix_length + length_increment > max_length:
                        raise ValueError(
                            f"Maximum length exceeded: prefix {prefix_length} + current {length_increment}"
                            f" exceeds pre-allocated maximum {max_length}"
                        )

                    # run request tensors through all requested modules, update caches
                    for backend, prompt, cache_handle in zip(requested_backends, prompts, cache_handles):
                        if not is_dummy(prompt):
                            hidden_states[:, : prompt.shape[1]] += prompt

                        cache_metadata[:, 0], cache_metadata[:, 1] = cache_handle, prefix_length
                        assert isinstance(
                            hidden_states, torch.Tensor
                        ), f"hidden states must be tensor, got {type(hidden_states)}"
                        assert (
                            hidden_states.ndim == 3
                        ), f"inputs to {type(backend)} must be a list with a single 3d tensor of hidden states"
                        assert isinstance(
                            backend.inference_pool, PrioritizedTaskPool
                        ), "petals support only prioritized pools"
                        priority = self._prioritizer.prioritize(
                            cache_metadata,
                            hidden_states,
                            hypo_ids,
                            points=point_per_piece / len(requested_backends),
                            backend=backend,
                            type="inference",
                        )
                        (hidden_states,) = await backend.inference_pool.submit_task(
                            cache_metadata, hidden_states, hypo_ids, priority=priority
                        )

                    # serialize and send last layer outputs
                    yield runtime_pb2.ExpertResponse(
                        tensors=[
                            serialize_torch_tensor(result.to(proto.dtype), proto.compression, allow_inplace=True)
                            for result, proto in zip(
                                (hidden_states,), nested_flatten(requested_backends[-1].outputs_schema)
                            )
                        ]
                    )

                    # prepare for next step
                    prefix_length += hidden_states.shape[1]
                    request = await (anext(requests))
        finally:
            print("CLOSED RPC_INFERENCE")

    async def rpc_forward(self, request: runtime_pb2.ExpertRequest, context: P2PContext) -> runtime_pb2.ExpertResponse:
        # Parse request and prepare backends
        flat_inputs = [deserialize_torch_tensor(tensor) for tensor in request.tensors]
        requested_uids = self._check_uids(request.uid)
        requested_backends = tuple(self.module_backends[uid] for uid in requested_uids)
        metadata = MSGPackSerializer.loads(request.metadata) if request.metadata else {}
        points = metadata.get("points", 0)
        assert isinstance(
            points, (float, int)
        ), f"rpc_forward should have number of points as number or None, got {points}"

        hidden_states = await _rpc_forward(
            *flat_inputs, requested_backends=requested_backends, prioritizer=self._prioritizer, points=points
        )
        assert isinstance(hidden_states, torch.Tensor) and hidden_states.ndim == 3

        # Serialize output and respond to client
        return runtime_pb2.ExpertResponse(
            tensors=[
                serialize_torch_tensor(result.to(proto.dtype), proto.compression, allow_inplace=True)
                for result, proto in zip((hidden_states,), nested_flatten(requested_backends[-1].outputs_schema))
            ]
        )

    async def rpc_forward_stream(
        self, requests: AsyncIterator[runtime_pb2.ExpertRequest], context: P2PContext
    ) -> AsyncIterator[runtime_pb2.ExpertRequest]:
        # Parse requests and prepare backends
        uid_str, flat_inputs, metadata = await self._gather_inputs(requests, context)
        requested_uids = self._check_uids(uid_str)
        requested_backends = tuple(self.module_backends[uid] for uid in requested_uids)
        points = metadata.get("points", 0)
        assert isinstance(
            points, (float, int)
        ), f"rpc_forward_stream should have number of points as number or None, got {points}"

        hidden_states = await _rpc_forward(
            *flat_inputs, requested_backends=requested_backends, prioritizer=self._prioritizer, points=points
        )
        assert isinstance(hidden_states, torch.Tensor) and hidden_states.ndim == 3, "hidden_states must be a 3d tensor"

        # Serialize the overall output
        serialized_output = [
            serialize_torch_tensor(result.to(proto.dtype), proto.compression, allow_inplace=True)
            for result, proto in zip((hidden_states,), nested_flatten(requested_backends[-1].outputs_schema))
        ]

        # Split the serialized_output for streaming and respond to client
        output_split = [
            part for tensor in serialized_output for part in split_for_streaming(tensor, DEFAULT_MAX_MSG_SIZE)
        ]
        async for part in as_aiter(*output_split):
            yield runtime_pb2.ExpertResponse(tensors=[part])

    async def rpc_backward(self, request: runtime_pb2.ExpertRequest, context: P2PContext) -> runtime_pb2.ExpertResponse:
        # Parse requests and prepare backends
        flat_tensors = [deserialize_torch_tensor(tensor) for tensor in request.tensors]
        requested_uids = self._check_uids(request.uid)
        requested_backends = tuple(self.module_backends[uid] for uid in requested_uids)
        metadata = MSGPackSerializer.loads(request.metadata) if request.metadata else {}
        points = metadata.get("points", 0)
        assert isinstance(
            points, (float, int)
        ), f"rpc_backward should have number of points as number or None, got {points}"

        grads = await _rpc_backward(
            *flat_tensors, requested_backends=requested_backends, prioritizer=self._prioritizer, points=points
        )

        # Modify grad_inputs_schema to support grad_prompts
        assert len(requested_backends[0].args_schema) == 1 and len(grads) in (1, 2)  # TODO generalize

        grad_inputs_schema_with_prompts = (
            requested_backends[0].args_schema * len(grads),
            requested_backends[0].kwargs_schema,
        )  # TODO generalize

        # Serialize the overall grad_input and respond
        return runtime_pb2.ExpertResponse(
            tensors=[
                serialize_torch_tensor(result.to(proto.dtype), proto.compression, allow_inplace=True)
                for result, proto in zip(grads, nested_flatten(grad_inputs_schema_with_prompts))
            ]
        )

    async def rpc_backward_stream(
        self, requests: AsyncIterator[runtime_pb2.ExpertRequest], context: P2PContext
    ) -> AsyncIterator[runtime_pb2.ExpertResponse]:

        uids_header, flat_tensors, metadata = await self._gather_inputs(requests, context)
        requested_uids = self._check_uids(uids_header)
        requested_backends = tuple(self.module_backends[uid] for uid in requested_uids)
        points = metadata.get("points", 0)
        assert isinstance(
            points, (float, int)
        ), f"rpc_backward_stream should have number of points as number or None, got {points}"

        grads = await _rpc_backward(
            *flat_tensors, requested_backends=requested_backends, prioritizer=self._prioritizer, points=points
        )

        # Modify grad_inputs_schema to support grad_prompts
        assert len(requested_backends[0].args_schema) == 1 and len(grads) in (1, 2)  # TODO generalize
        grad_inputs_schema_with_prompts = (
            requested_backends[0].args_schema * len(grads),
            requested_backends[0].kwargs_schema,
        )  # TODO generalize

        # Serialize the overall grad_inputs
        serialized_grad_inputs = [
            serialize_torch_tensor(result.to(proto.dtype), proto.compression, allow_inplace=True)
            for result, proto in zip(grads, nested_flatten(grad_inputs_schema_with_prompts))
        ]
        # Split the serialized_grad_inputs for streaming and respond
        output_split = [
            part for tensor in serialized_grad_inputs for part in split_for_streaming(tensor, DEFAULT_MAX_MSG_SIZE)
        ]

        async for part in as_aiter(*output_split):
            yield runtime_pb2.ExpertResponse(tensors=[part])

    def _check_uids(self, uids: str) -> Sequence[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_caches(
        self, backends: Sequence[TransformerBackend], batch_size: int, max_length: int
    ) -> Sequence[int]:
        """Allocate memory caches for each transformer block, return cache handles"""
        async with contextlib.AsyncExitStack() as stack:
            handles = []
            for backend in backends:
                num_heads = backend.module.self_attention.num_heads
                head_dim = backend.module.self_attention.head_dim

                cache_descriptor = TensorDescriptor(
                    size=(2, batch_size, max_length, num_heads, head_dim), dtype=backend.dtype
                )
                # [key_or_value, batch_size, max_length, num_heads, head_dim]

                handles.append(await stack.enter_async_context(backend.memory_cache.allocate_cache(cache_descriptor)))

            yield handles


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"

    # Serialize the overall output
    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