Add throughput benchmarking script (#133)

benchmark/benchmark_attention.py

@@ -1,165 +0,0 @@
-import functools
-import random
-import time
-from typing import List
-
-from flash_attn.flash_attn_interface import _flash_attn_forward
-import torch
-
-from cacheflow import attention_ops
-
-
-def benchmark(name, f, num_warmup = 10, num_iters = 100):
-    for _ in range(num_warmup):
-        f()
-    torch.cuda.synchronize()
-
-    start = time.time()
-    for _ in range(num_iters):
-        f()
-    torch.cuda.synchronize()
-    end = time.time()
-    print(f'{name}: {(end - start) / num_iters * 1000:.3f} ms')
-
-
-@torch.inference_mode()
-def benchmark_multi_query_cached_kv_attention(
-    query_lens: List[int],
-    context_lens: List[int],
-    num_heads: int,
-    head_size: int,
-    block_size: int,
-    num_blocks: int,
-    dtype: torch.dtype,
-) -> None:
-    print(f'query_lens: {query_lens}, context_lens: {context_lens}, '
-          f'num_heads: {num_heads}, head_size: {head_size}, block_size: '
-          f'{block_size}, num_blocks: {num_blocks}, dtype: {dtype}')
-    # Create query tensor.
-    num_queries = len(query_lens)
-    cu_query_lens = [0]
-    for query_len in query_lens:
-        cu_query_lens.append(cu_query_lens[-1] + query_len)
-    num_total_tokens = cu_query_lens[-1]
-    qkv = torch.randn(
-        num_total_tokens, 3, num_heads, head_size, dtype=dtype, device='cuda')
-    query, _, _ = qkv.unbind(dim=1)
-
-    # Create key and value cache.
-    x = 16 // torch.tensor([], dtype=dtype).element_size()
-    key_block_shape = (num_heads, head_size // x, block_size, x)
-    key_cache = torch.randn(
-        size=(num_blocks, *key_block_shape), dtype=dtype, device='cuda')
-    value_block_shape = (num_heads, head_size, block_size)
-    value_cache = torch.randn(
-        size=(num_blocks, *value_block_shape), dtype=dtype, device='cuda')
-
-    # Create block tables.
-    max_context_len = max(context_lens)
-    max_num_blocks_per_seq = (max_context_len + block_size - 1) // block_size
-    block_tables = []
-    for _ in range(num_queries):
-        block_table = [
-            random.randint(0, num_blocks - 1)
-            for _ in range(max_num_blocks_per_seq)
-        ]
-        block_tables.append(block_table)
-    block_tables = torch.tensor(block_tables, dtype=torch.int, device='cuda')
-
-    # Create input and output data structures.
-    cu_query_lens = torch.tensor(cu_query_lens, dtype=torch.int, device='cuda')
-    context_len_tensor = torch.tensor(context_lens, dtype=torch.int, device='cuda')
-    scale = float(1.0 / (head_size ** 0.5))
-    output = torch.empty(
-        num_total_tokens, num_heads, head_size, dtype=dtype, device='cuda')
-
-    # Run our implementation.
-    def run_ours():
-        attention_ops.multi_query_cached_kv_attention(
-            cu_query_lens,
-            output,
-            query,
-            key_cache,
-            value_cache,
-            scale,
-            block_tables,
-            context_len_tensor,
-            block_size,
-            max_context_len,
-        )
-    benchmark('Ours', run_ours)
-
-    # Upper bound: Flash attention.
-    # Becuase Flash attention cannot read our own cache,
-    # we make key and value tensors contiguous.
-    num_kv_tokens = sum(context_lens)
-    cu_context_lens = [0]
-    for context_len in context_lens:
-        cu_context_lens.append(cu_context_lens[-1] + context_len)
-    cu_context_lens = torch.tensor(cu_context_lens, dtype=torch.int, device='cuda')
-    qkv = torch.randn(
-        num_kv_tokens, 3, num_heads, head_size, dtype=dtype, device='cuda')
-    _, key, value = qkv.unbind(dim=1)
-    ref_output = torch.empty_like(output)
-
-    # Run Flash attention.
-    def run_flash_attn():
-        _flash_attn_forward(
-            query,
-            key,
-            value,
-            ref_output,
-            cu_query_lens,
-            cu_context_lens,
-            max(query_lens),
-            max_context_len,
-            dropout_p=0.0,
-            softmax_scale=scale,
-            causal=True,
-            return_softmax=False,
-        )
-    benchmark('Flash attention', run_flash_attn)
-
-
-if __name__ == '__main__':
-    BLOCK_SIZE = 8
-    NUM_BLOCKS = 1024
-    DTYPE = torch.half
-
-    # LLaMA-13B and OPT-13B
-    NUM_HEADS = 40
-    HEAD_SIZE = 128
-
-    run_benchmark = functools.partial(
-        benchmark_multi_query_cached_kv_attention,
-        num_heads=NUM_HEADS,
-        head_size=HEAD_SIZE,
-        block_size=BLOCK_SIZE,
-        num_blocks=NUM_BLOCKS,
-        dtype=DTYPE,
-    )
-
-    run_benchmark(
-        query_lens=[64] * 1,
-        context_lens=[64] * 1,
-    )
-    run_benchmark(
-        query_lens=[128] * 1,
-        context_lens=[128] * 1,
-    )
-    run_benchmark(
-        query_lens=[64] * 8,
-        context_lens=[64] * 8,
-    )
-    run_benchmark(
-        query_lens=[128] * 8,
-        context_lens=[128] * 8,
-    )
-    run_benchmark(
-        query_lens=[64, 32, 16],
-        context_lens=[128, 256, 64],
-    )
-    run_benchmark(
-        query_lens=[1024],
-        context_lens=[1024],
-    )

benchmark/benchmark_cache.py

@@ -1,81 +0,0 @@
-import functools
-import random
-import time
-
-import torch
-
-from cacheflow import cache_ops
-
-
-def benchmark(name, f, size: int, num_warmup = 10, num_iters = 100):
-    for _ in range(num_warmup):
-        f()
-    torch.cuda.synchronize()
-
-    start = time.time()
-    for _ in range(num_iters):
-        f()
-    torch.cuda.synchronize()
-    end = time.time()
-    avg_time = (end - start) / num_iters
-    print(f'[Latency] {name}: {avg_time * 1000:.3f} ms')
-    print(f'[Throughput] {name}: {size / avg_time / 2 ** 30:.3f} GB/s')
-
-
-@torch.inference_mode()
-def test_gather_cached_kv(
-    num_tokens: int,
-    num_heads: int,
-    head_size: int,
-    block_size: int,
-    num_blocks: int,
-    dtype: torch.dtype,
-) -> None:
-    print(f'num_tokens: {num_tokens}, num_heads: {num_heads}, '
-          f'head_size: {head_size}, block_size: {block_size}, '
-          f'num_blocks: {num_blocks}, dtype: {dtype}')
-
-    num_slots = block_size * num_blocks
-    slot_mapping = random.sample(range(num_slots), num_tokens)
-    slot_mapping = torch.tensor(slot_mapping, dtype=torch.int, device='cuda')
-
-    qkv = torch.randn(
-        num_tokens, 3, num_heads, head_size, dtype=dtype, device='cuda')
-    _, key, value = qkv.unbind(dim=1)
-
-    x = 16 // torch.tensor([], dtype=dtype).element_size()
-    key_cache_shape = (num_blocks, num_heads, head_size // x, block_size, x)
-    key_cache = torch.randn(size=key_cache_shape, dtype=dtype, device='cuda')
-
-    value_cache_shape = (num_blocks, num_heads, head_size, block_size)
-    value_cache = torch.randn(
-        size=value_cache_shape, dtype=dtype, device='cuda')
-
-    # Run Flash attention.
-    def run():
-        cache_ops.gather_cached_kv(key, value, key_cache, value_cache, slot_mapping)
-
-    benchmark('gather_cached_kv', run,
-              size=num_tokens * num_heads * head_size * 2 * qkv.element_size())
-
-
-if __name__ == '__main__':
-    BLOCK_SIZE = 8
-    NUM_BLOCKS = 1024
-    DTYPE = torch.half
-
-    # LLaMA-13B and OPT-13B
-    NUM_HEADS = 40
-    HEAD_SIZE = 128
-
-    run_benchmark = functools.partial(
-        test_gather_cached_kv,
-        num_heads=NUM_HEADS,
-        head_size=HEAD_SIZE,
-        block_size=BLOCK_SIZE,
-        num_blocks=NUM_BLOCKS,
-        dtype=DTYPE,
-    )
-
-    for i in range(6, 12):
-        run_benchmark(num_tokens=2 ** i)

benchmarks/README.md

@@ -0,0 +1,8 @@
+# Benchmarking CacheFlow
+
+## Downloading the ShareGPT dataset
+
+You can download the dataset by running:
+```bash
+wget https://huggingface.co/datasets/anon8231489123/ShareGPT_Vicuna_unfiltered/resolve/main/ShareGPT_V3_unfiltered_cleaned_split.json
+```

benchmarks/benchmark_throughput.py

@@ -0,0 +1,104 @@
+import argparse
+import json
+import random
+import time
+from typing import List, Tuple
+
+from cacheflow import LLM, SamplingParams
+from transformers import PreTrainedTokenizerBase
+
+
+def sample_requests(
+    dataset_path: str,
+    num_requests: int,
+    tokenizer: PreTrainedTokenizerBase,
+) -> List[Tuple[List[int], int]]:
+    # Load the dataset.
+    with open(dataset_path) as f:
+        dataset = json.load(f)
+    # Filter out the conversations with less than 2 turns.
+    dataset = [
+        data for data in dataset
+        if len(data["conversations"]) >= 2
+    ]
+    # Only keep the first two turns of each conversation.
+    dataset = [
+        (data["conversations"][0]["value"], data["conversations"][1]["value"])
+        for data in dataset
+    ]
+
+    # Tokenize the prompts and completions.
+    prompts = [prompt for prompt, _ in dataset]
+    prompt_token_ids = tokenizer(prompts).input_ids
+    completions = [completion for _, completion in dataset]
+    completion_token_ids = tokenizer(completions).input_ids
+    tokenized_dataset = []
+    for i in range(len(dataset)):
+        output_len = len(completion_token_ids[i])
+        tokenized_dataset.append((prompt_token_ids[i], output_len))
+    # Filter out if the prompt length + output length is greater than 2048.
+    tokenized_dataset = [
+        (prompt_token_ids, output_len)
+        for prompt_token_ids, output_len in tokenized_dataset
+        if len(prompt_token_ids) + output_len <= 2048
+    ]
+
+    # Sample the requests.
+    sampled_requests = random.sample(tokenized_dataset, num_requests)
+    return sampled_requests
+
+
+def main(args: argparse.Namespace):
+    print(args)
+    random.seed(args.seed)
+
+    llm = LLM(
+        model=args.model,
+        tensor_parallel_size=args.tensor_parallel_size,
+        seed=args.seed,
+    )
+    tokenizer = llm.get_tokenizer()
+    requests = sample_requests(args.dataset, args.num_prompts, tokenizer)
+
+    # Add the requests to the server.
+    for prompt_token_ids, output_len in requests:
+        sampling_params = SamplingParams(
+            n=args.n,
+            temperature=0.0 if args.use_beam_search else 1.0,
+            top_p=1.0,
+            use_beam_search=args.use_beam_search,
+            ignore_eos=True,
+            max_tokens=output_len,
+        )
+        # FIXME(woosuk): Do not use internal method.
+        llm._add_request(
+            prompt="",
+            sampling_params=sampling_params,
+            prompt_token_ids=prompt_token_ids,
+        )
+
+    start = time.time()
+    # FIXME(woosuk): Do use internal method.
+    llm._run_server(use_tqdm=True)
+    end = time.time()
+    total_num_tokens = sum(
+        len(prompt_token_ids) + output_len
+        for prompt_token_ids, output_len in requests
+    )
+    print(f"Throughput: {total_num_tokens / (end - start):.2f} tokens/s")
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser(description="Benchmark the throughput.")
+    parser.add_argument("--dataset", type=str, required=True,
+                        help="Path to the dataset.")
+    parser.add_argument("--model", type=str, default="facebook/opt-125m")
+    parser.add_argument("--tensor-parallel-size", "-tp", type=int, default=1)
+    parser.add_argument("--n", type=int, default=1,
+                        help="Number of generated sequences per prompt.")
+    parser.add_argument("--use-beam-search", action="store_true")
+    parser.add_argument("--num-prompts", type=int, default=1000,
+                        help="Number of prompts to process.")
+    parser.add_argument("--seed", type=int, default=0)
+    args = parser.parse_args()
+    main(args)

cacheflow/__init__.py

@@ -1,5 +1,5 @@
 from cacheflow.entrypoints.llm import LLM
-from cacheflow.outputs import RequestOutput
+from cacheflow.outputs import RequestOutput, CompletionOutput
 from cacheflow.sampling_params import SamplingParams
 from cacheflow.server.arg_utils import ServerArgs
 from cacheflow.server.llm_server import LLMServer
@@ -9,6 +9,7 @@ __all__ = [
     "LLM",
     "SamplingParams",
     "RequestOutput",
+    "CompletionOutput",
     "LLMServer",
     "ServerArgs",
     "initialize_cluster",

cacheflow/core/scheduler.py

@@ -87,6 +87,9 @@ class Scheduler:
     def has_unfinished_seqs(self) -> bool:
         return self.waiting or self.running or self.swapped
 
+    def get_num_unfinished_seq_groups(self) -> int:
+        return len(self.waiting) + len(self.running) + len(self.swapped)
+
     def _schedule(self) -> Tuple[SchedulerOutputs, List[str]]:
         # Blocks that need to be swaped or copied before model execution.
         blocks_to_swap_in: Dict[int, int] = {}

cacheflow/entrypoints/llm.py

@@ -1,5 +1,6 @@
-from typing import List, Optional
+from typing import List, Optional, Union
 
+from transformers import PreTrainedTokenizer, PreTrainedTokenizerFast
 from tqdm import tqdm
 
 from cacheflow.outputs import RequestOutput
@@ -31,6 +32,11 @@ class LLM:
         self.llm_server = LLMServer.from_server_args(server_args)
         self.request_counter = Counter()
 
+    def get_tokenizer(
+        self,
+    ) -> Union[PreTrainedTokenizer, PreTrainedTokenizerFast]:
+        return self.llm_server.tokenizer
+
     def generate(
         self,
         prompts: List[str],
@@ -41,10 +47,6 @@ class LLM:
         if sampling_params is None:
             # Use default sampling params.
             sampling_params = SamplingParams()
-        # Initialize tqdm.
-        if use_tqdm:
-            pbar = tqdm(total=len(prompts), desc="Processed prompts")
-
         # Add requests to the server.
         for i in range(len(prompts)):
             prompt = prompts[i]
@@ -52,10 +54,24 @@ class LLM:
                 token_ids = None
             else:
                 token_ids = prompt_token_ids[i]
-            request_id = str(next(self.request_counter))
+            self._add_request(prompt, sampling_params, token_ids)
-            self.llm_server.add_request(request_id, prompt, sampling_params,
+        return self._run_server(use_tqdm)
-                                        token_ids)
 
+    def _add_request(
+        self,
+        prompt: str,
+        sampling_params: SamplingParams,
+        prompt_token_ids: Optional[List[int]],
+    ) -> None:
+        request_id = str(next(self.request_counter))
+        self.llm_server.add_request(request_id, prompt, sampling_params,
+                                    prompt_token_ids)
+
+    def _run_server(self, use_tqdm: bool) -> List[RequestOutput]:
+        # Initialize tqdm.
+        if use_tqdm:
+            num_requests = self.llm_server.get_num_unfinished_requests()
+            pbar = tqdm(total=num_requests, desc="Processed prompts")
         # Run the server.
         outputs: List[RequestOutput] = []
         while self.llm_server.has_unfinished_requests():

cacheflow/server/llm_server.py

@@ -151,6 +151,9 @@ class LLMServer:
         # Add the sequence group to the scheduler.
         self.scheduler.add_seq_group(seq_group)
 
+    def get_num_unfinished_requests(self) -> int:
+        return self.scheduler.get_num_unfinished_seq_groups()
+
     def has_unfinished_requests(self) -> bool:
         return self.scheduler.has_unfinished_seqs()
 

examples/simple_server.py

@@ -19,9 +19,8 @@ def main(args: argparse.Namespace):
          SamplingParams(n=3, best_of=3, use_beam_search=True, temperature=0.0)),
     ]
 
-    request_id = 0
-
     # Run the server.
+    request_id = 0
     while True:
         # To test iteration-level scheduling, we add one request at each step.
         if test_prompts: