[PoC]: Support encode only models by Workflow Defined Engine

demo_temporary/benchmarks/benchmark_attention_impl.py

@@ -0,0 +1,102 @@
+import os
+import random
+import time
+
+
+def benchmark_vllm(args):
+    random.seed(args.seed)
+    os.environ["VLLM_ATTENTION_BACKEND"] = args.attention_impl
+
+    import gc
+
+    import torch
+
+    from vllm.wde.encode_only.arg_utils import (  # noqa: E501
+        EncodeOnlyEngineArgs as EngineArgs)
+    from vllm.wde.entrypoints.llm import LLMEngine
+
+    prompt = "if" * args.input_len
+    requests = [prompt for _ in range(args.num_prompts)]
+
+    engine_args = EngineArgs(model=args.model,
+                             tokenizer=args.tokenizer,
+                             seed=args.seed,
+                             trust_remote_code=args.trust_remote_code,
+                             dtype=args.dtype,
+                             max_model_len=args.max_model_len,
+                             device=args.device,
+                             max_num_seqs=32,
+                             scheduling=args.scheduling)
+
+    engine = LLMEngine.from_engine_args(engine_args)
+
+    for batchsize in args.batchsize:
+        engine.engine_config.scheduler_config.set_args(max_num_seqs=batchsize)
+
+        start = time.perf_counter()
+        for request_id, prompt in enumerate(requests):
+            engine.add_request(str(request_id), prompt)
+
+        n_step = 0
+        while engine.has_unfinished_requests():
+            engine.step()
+            n_step += 1
+        end = time.perf_counter()
+
+        elapsed_time = end - start
+        delay = elapsed_time / n_step
+
+        print(f"Batchsize {batchsize}, Throughput: "
+              f"{len(requests) / elapsed_time:.4f} requests/s, "
+              f"Delay {delay * 1000:0.2f} ms, n_step {n_step}")
+
+        engine.executor.shutdown_execute_loop()
+        gc.collect()
+        torch.cuda.empty_cache()
+
+
+if __name__ == '__main__':
+    from easydict import EasyDict as edict
+    args = edict()
+
+    args.input_len = 256
+    args.num_prompts = 10000
+
+    args.model = 'BAAI/bge-m3'
+
+    args.trust_remote_code = False
+    args.tokenizer = args.model
+    args.seed = 0
+    args.max_model_len = None
+    args.device = "cuda"
+    args.batchsize = [1, 2, 4, 8, 16, 32, 64]
+    args.scheduling = "double_buffer"
+
+    from concurrent.futures import ProcessPoolExecutor
+
+    def run_vllm(args):
+        with ProcessPoolExecutor(1) as executor:
+            f = executor.submit(benchmark_vllm, args)
+            f.result()
+
+    AttentionImpls_fp32 = ["TORCH_SDPA", "XFORMERS", "TORCH_NAIVE"]
+    AttentionImpls_fp16 = [
+        "FLASH_ATTN", "TORCH_SDPA", "XFORMERS", "FLASHINFER", "TORCH_NAIVE"
+    ]
+    AttentionImpls_bf16 = [
+        "FLASH_ATTN", "TORCH_SDPA", "XFORMERS", "FLASHINFER", "TORCH_NAIVE"
+    ]
+
+    AttentionImpls = {
+        "float": AttentionImpls_fp32,
+        "half": AttentionImpls_fp16,
+        "bfloat16": AttentionImpls_bf16,
+    }
+
+    for dtype, attention_impls in AttentionImpls.items():
+        print("dtype:", dtype)
+        for attention_impl in attention_impls:
+            print("attention_impl:", attention_impl)
+            args.attention_impl = attention_impl
+            args.dtype = dtype
+            run_vllm(args)

demo_temporary/benchmarks/benchmark_bge-m3.py

@@ -0,0 +1,119 @@
+import random
+import time
+
+
+def benchmark_hf(args):
+    random.seed(args.seed)
+
+    import torch
+    from FlagEmbedding import BGEM3FlagModel
+
+    model = BGEM3FlagModel(args.model, use_fp16=True)
+
+    prompt = "if" * args.input_len
+    requests = [prompt for _ in range(args.num_prompts)]
+
+    with torch.no_grad():
+        for batchsize in args.batchsize:
+            start = time.perf_counter()
+            n_step = 0
+            for i in range(0, len(requests), batchsize):
+                batch = requests[i:i + batchsize]
+                model.encode(batch, batch_size=batchsize)
+                n_step += 1
+            end = time.perf_counter()
+
+            elapsed_time = end - start
+            delay = elapsed_time / n_step
+
+            print(f"Batchsize {batchsize}, Throughput: "
+                  f"{len(requests) / elapsed_time:.4f} requests/s, "
+                  f"Delay {delay * 1000:0.2f} ms, n_step {n_step}")
+
+
+def benchmark_vllm(args):
+    random.seed(args.seed)
+
+    import gc
+
+    import torch
+
+    from vllm.wde.encode_only.arg_utils import (  # noqa: E501
+        EncodeOnlyEngineArgs as EngineArgs)
+    from vllm.wde.entrypoints.llm import LLMEngine
+
+    prompt = "if" * args.input_len
+    requests = [prompt for _ in range(args.num_prompts)]
+
+    engine_args = EngineArgs(model=args.model,
+                             tokenizer=args.tokenizer,
+                             seed=args.seed,
+                             trust_remote_code=args.trust_remote_code,
+                             dtype=args.dtype,
+                             max_model_len=args.max_model_len,
+                             device=args.device,
+                             max_num_seqs=32,
+                             scheduling=args.scheduling)
+
+    engine = LLMEngine.from_engine_args(engine_args)
+
+    for batchsize in args.batchsize:
+        engine.engine_config.scheduler_config.set_args(max_num_seqs=batchsize)
+
+        start = time.perf_counter()
+        for request_id, prompt in enumerate(requests):
+            engine.add_request(str(request_id), prompt)
+
+        n_step = 0
+        while engine.has_unfinished_requests():
+            engine.step()
+            n_step += 1
+        end = time.perf_counter()
+
+        elapsed_time = end - start
+        delay = elapsed_time / n_step
+
+        print(f"Batchsize {batchsize}, Throughput: "
+              f"{len(requests) / elapsed_time:.4f} requests/s, "
+              f"Delay {delay * 1000:0.2f} ms, n_step {n_step}")
+
+        engine.executor.shutdown_execute_loop()
+        gc.collect()
+        torch.cuda.empty_cache()
+
+
+if __name__ == '__main__':
+    from easydict import EasyDict as edict
+    args = edict()
+
+    args.input_len = 256
+    args.num_prompts = 10000
+
+    args.model = 'BAAI/bge-m3'
+
+    args.trust_remote_code = False
+    args.tokenizer = args.model
+    args.seed = 0
+    args.max_model_len = None
+    args.dtype = "half"
+    args.device = "cuda"
+    args.batchsize = [1, 2, 4, 8, 16, 32, 64]
+
+    from concurrent.futures import ProcessPoolExecutor
+
+    def run_hf(args):
+        with ProcessPoolExecutor(1) as executor:
+            f = executor.submit(benchmark_hf, args)
+            f.result()
+
+    run_hf(args)
+
+    def run_vllm(args):
+        with ProcessPoolExecutor(1) as executor:
+            f = executor.submit(benchmark_vllm, args)
+            f.result()
+
+    for scheduling in ["sync", "async", "double_buffer"]:
+        print(scheduling)
+        args.scheduling = scheduling
+        run_vllm(args)

demo_temporary/benchmarks/benchmark_data_parallelism.py

@@ -0,0 +1,89 @@
+import random
+import time
+
+
+def benchmark(args):
+    random.seed(args.seed)
+
+    import gc
+
+    import torch
+
+    from vllm.wde.encode_only.arg_utils import (  # noqa: E501
+        EncodeOnlyEngineArgs as EngineArgs)
+    from vllm.wde.entrypoints.llm import LLMEngine
+
+    prompt = "if" * args.input_len
+    requests = [prompt for _ in range(args.num_prompts)]
+
+    engine_args = EngineArgs(model=args.model,
+                             tokenizer=args.tokenizer,
+                             seed=args.seed,
+                             trust_remote_code=args.trust_remote_code,
+                             dtype=args.dtype,
+                             max_model_len=args.max_model_len,
+                             device=args.device,
+                             max_num_seqs=32,
+                             scheduling=args.scheduling,
+                             data_parallel_size=args.data_parallel_size)
+
+    engine = LLMEngine.from_engine_args(engine_args)
+
+    for batchsize in args.batchsize:
+        engine.engine_config.scheduler_config.set_args(max_num_seqs=batchsize)
+        engine.executor.ensure_start_execute_loop()
+
+        # Because each thread has to load the model separately,
+        # the loading may not be completed here.
+        # If it is run only once, the measured data parallel speed will be low.
+
+        for i in range(3):
+            start = time.perf_counter()
+            for request_id, prompt in enumerate(requests):
+                engine.add_request(str(request_id), prompt)
+
+            while engine.has_unfinished_requests():
+                engine.step()
+            end = time.perf_counter()
+            elapsed_time = end - start
+
+        print(f"Batchsize {batchsize}, Throughput: "
+              f"{len(requests) / elapsed_time:.4f} requests/s")
+
+        engine.executor.shutdown_execute_loop()
+        gc.collect()
+        torch.cuda.empty_cache()
+
+
+if __name__ == '__main__':
+    from easydict import EasyDict as edict
+    args = edict()
+
+    args.input_len = 256
+    args.num_prompts = 10000
+
+    args.model = 'BAAI/bge-m3'
+
+    args.trust_remote_code = False
+    args.tokenizer = args.model
+    args.seed = 0
+    args.max_model_len = None
+    args.dtype = "half"
+    args.device = "cuda"
+    args.batchsize = [1, 2, 4, 8, 16]
+    args.max_data_parallel_size = 1
+
+    from concurrent.futures import ProcessPoolExecutor
+
+    def run_vllm(args):
+        with ProcessPoolExecutor(1) as executor:
+            f = executor.submit(benchmark, args)
+            f.result()
+
+    for scheduling in ["async", "double_buffer"]:
+        for data_parallel_size in range(args.max_data_parallel_size + 1):
+            print("scheduling:", scheduling, "data_parallel_size",
+                  data_parallel_size)
+            args.data_parallel_size = data_parallel_size
+            args.scheduling = scheduling
+            run_vllm(args)