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158 lines
7.0 KiB
Python
158 lines
7.0 KiB
Python
"""
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Utility functions that call RPC forward or backward on a single remote server
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"""
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import asyncio
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from typing import Iterable, List, Optional, Sequence, Tuple
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import torch
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from hivemind import nested_compare, nested_flatten, nested_pack, serialize_torch_tensor
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from hivemind.compression.serialization import deserialize_tensor_stream, deserialize_torch_tensor
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from hivemind.p2p import StubBase
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from hivemind.p2p.p2p_daemon_bindings.control import DEFAULT_MAX_MSG_SIZE, MAX_UNARY_PAYLOAD_SIZE
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from hivemind.proto import runtime_pb2
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from hivemind.utils.asyncio import aiter_with_timeout, iter_as_aiter
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from hivemind.utils.streaming import split_for_streaming
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from petals.data_structures import ModuleUID, RPCInfo
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async def _forward_unary(
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uid: str, serialized_tensors: Iterable[runtime_pb2.Tensor], stub, timeout: float, **kwargs
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) -> List[torch.Tensor]:
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outputs: runtime_pb2.ExpertResponse = await stub.rpc_forward(
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runtime_pb2.ExpertRequest(uid=uid, tensors=list(serialized_tensors), **kwargs),
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timeout=timeout,
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)
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return [deserialize_torch_tensor(t) for t in outputs.tensors]
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async def _backward_unary(
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uid: str, serialized_tensors: Iterable[runtime_pb2.Tensor], stub, timeout: float, **kwargs
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) -> List[torch.Tensor]:
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grad_inputs: runtime_pb2.ExpertResponse = await stub.rpc_backward(
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runtime_pb2.ExpertRequest(uid=uid, tensors=list(serialized_tensors), **kwargs),
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timeout=timeout,
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)
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return [deserialize_torch_tensor(t) for t in grad_inputs.tensors]
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async def _forward_stream(
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uid: str, serialized_tensors: Iterable[runtime_pb2.Tensor], stub, timeout: float, **kwargs
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) -> List[torch.Tensor]:
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parts = (
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runtime_pb2.ExpertRequest(uid=uid, tensors=[part], **kwargs)
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for tensor in serialized_tensors
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for part in split_for_streaming(tensor, DEFAULT_MAX_MSG_SIZE)
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)
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outputs = await asyncio.wait_for(stub.rpc_forward_stream(iter_as_aiter(parts)), timeout)
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outputs = aiter_with_timeout(outputs, timeout)
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return await deserialize_tensor_stream(msg.tensors async for msg in outputs)
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async def _backward_stream(
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uid: str, serialized_tensors: Iterable[runtime_pb2.Tensor], stub, timeout: float, **kwargs
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) -> List[torch.Tensor]:
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parts = (
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runtime_pb2.ExpertRequest(uid=uid, tensors=[part], **kwargs)
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for tensor in serialized_tensors
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for part in split_for_streaming(tensor, DEFAULT_MAX_MSG_SIZE)
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)
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grad_inputs = await asyncio.wait_for(stub.rpc_backward_stream(iter_as_aiter(parts)), timeout)
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grad_inputs = aiter_with_timeout(grad_inputs, timeout)
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return await deserialize_tensor_stream(msg.tensors async for msg in grad_inputs)
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async def run_remote_forward(
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uid: ModuleUID,
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stub: StubBase,
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rpc_info: RPCInfo,
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*inputs: torch.Tensor,
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timeout: float,
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metadata: Optional[bytes] = None,
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**kwargs,
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) -> Tuple[torch.Tensor, ...]:
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"""
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Serializes input tensors and calls "rpc_forward" on a remote server.
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Mostly adapted from https://github.com/learning-at-home/hivemind/blob/7a7c93aefffc9494c39e7b170c07cb06d8c09c4c/hivemind/moe/client/expert.py#L198
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but without RemoteExpertWorker.run_coroutine() call that leads to deadlock here.
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"""
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# Note: *inputs are flattened input tensors that follow the expert's info['input_schema']
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# detach to avoid pickling the computation graph
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assert len(kwargs) == len(rpc_info["keyword_names"]), f"Keyword args should be {rpc_info['keyword_names']}"
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kwargs = {key: kwargs[key] for key in rpc_info["keyword_names"]}
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# Note: we put keyword arguments in the same order as on a server to prevent f(a=1, b=2) != f(b=2, a=1) errors
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forward_inputs = (inputs, kwargs)
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# Modify forward_schema to support prompts
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args_schema, kwargs_schema = rpc_info["forward_schema"]
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# TODO: rm this assert when support arbitrary number of input tensors
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assert len(args_schema) == 1 and len(inputs) == 2
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forward_schema_with_prompts = (tuple(args_schema * len(inputs)), kwargs_schema)
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if not nested_compare(forward_inputs, forward_schema_with_prompts):
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raise TypeError(f"Inputs do not match expert input schema. Did you pass the right number of parameters?")
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forward_inputs = nested_flatten(forward_inputs)
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inputs = tuple(tensor.cpu().detach() for tensor in forward_inputs)
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# Asynchronous serialization
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loop = asyncio.get_running_loop()
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serialized_tensors = await asyncio.gather(
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*(
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loop.run_in_executor(None, serialize_torch_tensor, tensor.to(proto.dtype), proto.compression)
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for tensor, proto in zip(inputs, nested_flatten(forward_schema_with_prompts))
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)
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)
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# call RPC on remote server
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size = sum(t.element_size() * t.nelement() for t in inputs)
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forward_fn = _forward_stream if size > MAX_UNARY_PAYLOAD_SIZE // 2 else _forward_unary
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# Hotfix: we use "// 2" since hivemind==1.1.5 serializes bfloat16 tensors in float32, so they take 2x more space
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deserialized_outputs = await forward_fn(uid, serialized_tensors, stub, timeout, metadata=metadata, **kwargs)
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return nested_pack(deserialized_outputs, structure=rpc_info["outputs_schema"])
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async def run_remote_backward(
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uid: ModuleUID,
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stub: StubBase,
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rpc_info: RPCInfo,
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inputs: torch.Tensor,
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grad_outputs: List[torch.Tensor],
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*extra_tensors: torch.Tensor,
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timeout: float,
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metadata: Optional[bytes] = None,
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**kwargs,
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) -> Sequence[torch.Tensor]:
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"""
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Serializes grad outputs and calls "rpc_backward" on a remote server.
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Mostly adapted from https://github.com/learning-at-home/hivemind/blob/7a7c93aefffc9494c39e7b170c07cb06d8c09c4c/hivemind/moe/client/expert.py#L221
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but without RemoteExpertWorker.run_coroutine() call that leads to deadlock here.
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"""
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grad_outputs_cpu = tuple(tensor.cpu() for tensor in grad_outputs)
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inputs_and_grad_outputs = tuple(nested_flatten((inputs, grad_outputs_cpu, *extra_tensors)))
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# Modify forward_schema to support prompts
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args_schema, kwargs_schema = rpc_info["forward_schema"]
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assert len(args_schema) == 1 and isinstance(inputs, torch.Tensor)
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# TODO generalize this
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prompts_schema = next(iter(args_schema))
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backward_schema = tuple(nested_flatten((rpc_info["forward_schema"], rpc_info["outputs_schema"], prompts_schema)))
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# Asynchronous serialization
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loop = asyncio.get_running_loop()
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serialized_tensors = await asyncio.gather(
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*(
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loop.run_in_executor(None, serialize_torch_tensor, tensor.to(proto.dtype), proto.compression)
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for tensor, proto in zip(inputs_and_grad_outputs, backward_schema)
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)
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)
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size = sum(t.element_size() * t.nelement() for t in inputs_and_grad_outputs)
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backward_fn = _backward_stream if size > MAX_UNARY_PAYLOAD_SIZE // 2 else _backward_unary
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# Hotfix: we use "// 2" since hivemind==1.1.5 serializes bfloat16 tensors in float32, so they take 2x more space
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deserialized_grad_inputs = await backward_fn(uid, serialized_tensors, stub, timeout, metadata=metadata, **kwargs)
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return deserialized_grad_inputs
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