[Core] Introduce SPMD worker execution using Ray accelerated DAG

[ruisearch42]

This introduces an SPMD execution mode for Worker. In this mode, there is no longer a driver worker and the rank 0 worker is moved to a separate process. All workers are expected to take an ExecuteModelRequest input, instead of using NCCL as a control plane to receive inputs.

To keep the changes contained, for now, this path needs to be used with the new Ray accelerated DAG feature. Compared to Ray Core, this feature reduces system performance overheads for task execution and args passing, by using an execution loop and shared memory, respectively.

This PR is based on top of #5980 , and added the following:

• Added e2e correctness tests for VLLM_USE_SPMD_WORKER=1 VLLM_USE_RAY_COMPILED_DAG=1
• Fixed test failures
• Resolved conflicts with master
• Update the required Ray version
• Add some benchmarks

Benchmarking
TP = 4, requests = 500
Latency column format: latency_with_spmd_change / latency_without_spmd_change

GPU	Model	input_len	output_len	qps	avg latency	% comparison	median latency	% comparison
A10	Mistral-7B-v0.1	128	128	3	22.7 / 23.4	97.0%	19 / 18.4	103.2%
V100	Llama-2-7b-chat-hf	32	128	3	13.8 / 13.7	100.7%	13.7 / 13.7	100.0%
V100	Llama-2-7b-chat-hf	128	128	3	13.9 / 13.7	101.5%	13.9 / 13.7	101.5%
V100	Llama-2-7b-hf	256	128	3	14.7 / 13.7	107.3%	14.2 / 13.7	103.6%
A100	Meta-Llama-3-70B-Instruct	32	32	6	53.8 / 54.1	99.4%	53.5 / 53.6	99.8%

Summary
For smaller input lengths, the latency is better than or the same as before. For larger input lengths, the latency has small overhead. For larger input lengths, it is expected to have better latency when delta optimization is built on top (work starting soon).

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[youkaichao]

I'd like to support this, but currently the problem is we need to serialize ExecuteModelRequest and SamplerOutput. They have redundant data and can contain on-device data that are expensive to serialize.

I think the first step should be simplify these two structure.

[cadedaniel]

Can you help me understand the problem better @youkaichao ? I want to understand if it's something we can solve with deltas, plus moving the on-device fields to worker state (like what Jamba modeling does).

[youkaichao]

@cadedaniel I think #6241 should be a starting point.

And, if this PR can achieve the same performance as the main, then I would be glad to accept it. My current impression is this would be slow because of the inefficient serialization overhead.

[cadedaniel]

OK. @ruisearch42 will collect numbers and report here.

[rkooo567]

This changes the semantic of existing env var USE_RAY_COMPILED_DAG completely. Maybe we should just deprecate this env var (just raise an exception) and replace it to USE_SPMD_WORKER?

[rkooo567]

And, if this PR can achieve the same performance as the main, then I would be glad to accept it. My current impression is this would be slow because of the inefficient serialization overhead.

this is correct. Our old fork shows that doing input delta optimization can match the perf with the master. Do you think it makes sense to merge the PR and follow up after given the feature is isolated using an env var?

[rkooo567]

LGTM if tests pass!

[youkaichao]

sorry for the long wait.

I did some benchmarking for this branch on 4 H100:

without spmd (using mp backend):

$ python benchmarks/benchmark_throughput.py --output-len 256 --input 256 --model meta-llama/Llama-2-7b-hf -tp 4
Throughput: 32.98 requests/s, 16883.88 tokens/s

with spmd:

$ VLLM_USE_SPMD_WORKER=1 VLLM_USE_RAY_COMPILED_DAG=1 python benchmarks/benchmark_throughput.py --output-len 256 --input 256 --model meta-llama/Llama-2-7b-hf -tp 4 --distributed-executor-backend ray
Throughput: 17.78 requests/s, 9102.25 tokens/s

the throughput is only a half. I might be wrong in the benchmarking, please help me investigate or reproduce.

there is also a shutdown error, although it is benign:

Exception ignored in: <function RayGPUExecutor.del at 0x7fb0ba6d6160>
Traceback (most recent call last):
File "/data/youkaichao/vllm/vllm/executor/ray_gpu_executor.py", line 373, in del
self.forward_dag.teardown()
File "/data/youkaichao/miniconda/envs/vllm/lib/python3.9/site-packages/ray/dag/compiled_dag_node.py", line 1402, in teardown
monitor.teardown(wait=True)
File "/data/youkaichao/miniconda/envs/vllm/lib/python3.9/site-packages/ray/dag/compiled_dag_node.py", line 1204, in teardown
outer._dag_submitter.close()
File "/data/youkaichao/miniconda/envs/vllm/lib/python3.9/site-packages/ray/experimental/channel/common.py", line 383, in close
self._output_channel.close()
File "/data/youkaichao/miniconda/envs/vllm/lib/python3.9/site-packages/ray/experimental/channel/shared_memory_channel.py", line 629, in close
channel.close()
File "/data/youkaichao/miniconda/envs/vllm/lib/python3.9/site-packages/ray/experimental/channel/shared_memory_channel.py", line 512, in close
self._worker.core_worker.experimental_channel_set_error(self._writer_ref)
AttributeError: 'Worker' object has no attribute 'core_worker'

In general, this is the direction I want to push in the future. However, I would say this implementation is quick and dirty. It is too specialized, and would leave much tech debit for the future. We have two control-plane execution pattern in the same codebase, and the code can be very confusing.

By "quick and dirty", I mean, this PR only specializes to execute_model, and a lot of methods are left untouched. For example, in spmd worker, the driver (engine) does not hold the model anymore, but if we call add_lora, it will still call the driver (engine), which will lead to error. For a full spmd style worker, we should consider all possible functions.

My original plan, is to analyze which objects should live in the engine process and which objects should live in the worker process, and then minimize the data transfer between engine process and worker process. Then we can confidently remove the non-spmd style code completely.

[rkooo567]

@youkaichao we will take a look at the benchmark. I am 99% sure it is due to that we send all tokens to workers at each batch. The overhead increases with more batch size. So this requires delta input optimization.

[youkaichao]

why the benchmark of latency shown in #6032 (comment) is so different from benchmark of throughput then?

"we send all tokens to workers at each batch"

I assume this would also affect benchmark of latency.

[rkooo567]

Btw, we are confirming the theory now! Latency benchmark has lower batch size in general compared to throughput benchmark, and I assume that's why. (so with higher batch, serialization overhead is much higher without delta optimization). But 2X is pretty big, and rui is taking a look at this.

[ruisearch42]

Looking into the benchmarks. Some quick responses:

there is also a shutdown error, although it is benign

Thanks for reporting. This is likely some Ray/config issue, I happen to see the same error yesterday where ADAG is not used. I didn't run into it last time in testing. Will take a look.

By "quick and dirty", I mean, this PR only specializes to execute_model, and a lot of methods are left untouched. For example, in spmd worker, the driver (engine) does not hold the model anymore, but if we call add_lora, it will still call the driver (engine), which will lead to error. For a full spmd style worker, we should consider all possible functions.

Hmm, I think in SPMD mode add_lora will be called on the driver worker (which holds the model), not the driver itself. And it looks straightforward to adapt the code if there is a need.

My original plan, is to analyze which objects should live in the engine process and which objects should live in the worker process, and then minimize the data transfer between engine process and worker process. Then we can confidently remove the non-spmd style code completely.

Great thought. We are probably moving towards the same direction. In this PR, SPMD is config guarded and the plan is to remove non-SPMD path in future without being blocked.

[rkooo567]

thanks for another review @youkaichao !