Support FP8-E5M2 KV Cache

benchmarks/benchmark_latency.py

@@ -24,6 +24,7 @@ def main(args: argparse.Namespace):
         trust_remote_code=args.trust_remote_code,
         dtype=args.dtype,
         enforce_eager=args.enforce_eager,
+        kv_cache_dtype=args.kv_cache_dtype,
     )
 
     sampling_params = SamplingParams(
@@ -117,6 +118,13 @@ def run_to_completion(profile_dir: Optional[str] = None):
     parser.add_argument('--enforce-eager',
                         action='store_true',
                         help='enforce eager mode and disable CUDA graph')
+    parser.add_argument(
+        "--kv-cache-dtype",
+        type=str,
+        choices=['auto', 'fp8_e5m2'],
+        default='auto',
+        help=
+        'Data type for kv cache storage. If "auto", will use model data type.')
     parser.add_argument(
         '--profile',
         action='store_true',

benchmarks/benchmark_throughput.py

@@ -71,6 +71,7 @@ def run_vllm(
     dtype: str,
     max_model_len: Optional[int],
     enforce_eager: bool,
+    kv_cache_dtype: str,
 ) -> float:
     from vllm import LLM, SamplingParams
     llm = LLM(
@@ -83,6 +84,7 @@ def run_vllm(
         dtype=dtype,
         max_model_len=max_model_len,
         enforce_eager=enforce_eager,
+        kv_cache_dtype=kv_cache_dtype,
     )
 
     # Add the requests to the engine.
@@ -206,7 +208,8 @@ def main(args: argparse.Namespace):
                                 args.quantization, args.tensor_parallel_size,
                                 args.seed, args.n, args.use_beam_search,
                                 args.trust_remote_code, args.dtype,
-                                args.max_model_len, args.enforce_eager)
+                                args.max_model_len, args.enforce_eager,
+                                args.kv_cache_dtype)
     elif args.backend == "hf":
         assert args.tensor_parallel_size == 1
         elapsed_time = run_hf(requests, args.model, tokenizer, args.n,
@@ -284,6 +287,13 @@ def main(args: argparse.Namespace):
     parser.add_argument("--enforce-eager",
                         action="store_true",
                         help="enforce eager execution")
+    parser.add_argument(
+        "--kv-cache-dtype",
+        type=str,
+        choices=["auto", "fp8_e5m2"],
+        default="auto",
+        help=
+        'Data type for kv cache storage. If "auto", will use model data type.')
     args = parser.parse_args()
     if args.tokenizer is None:
         args.tokenizer = args.model

benchmarks/kernels/benchmark_paged_attention.py

@@ -1,9 +1,11 @@
+from typing import Optional
 import argparse
 import random
 import time
 
 import torch
 
+from vllm.utils import create_kv_caches
 from vllm._C import ops
 
 NUM_BLOCKS = 1024
@@ -23,6 +25,7 @@ def main(
     dtype: torch.dtype,
     seed: int,
     do_profile: bool,
+    kv_cache_dtype: Optional[str] = None,
 ) -> None:
     random.seed(seed)
     torch.random.manual_seed(seed)
@@ -59,15 +62,10 @@ def main(
     block_tables = torch.tensor(block_tables, dtype=torch.int, device="cuda")
 
     # Create the KV cache.
-    x = 16 // torch.tensor([], dtype=dtype).element_size()
-    key_cache_shape = (NUM_BLOCKS, num_kv_heads, head_size // x, block_size, x)
-    key_cache = torch.empty(size=key_cache_shape, dtype=dtype, device="cuda")
-    key_cache.uniform_(-scale, scale)
-    value_cache_shape = (NUM_BLOCKS, num_kv_heads, head_size, block_size)
-    value_cache = torch.empty(size=value_cache_shape,
-                              dtype=dtype,
-                              device="cuda")
-    value_cache.uniform_(-scale, scale)
+    key_caches, value_caches = create_kv_caches(NUM_BLOCKS, block_size, 1,
+                                                num_kv_heads, head_size,
+                                                kv_cache_dtype, dtype)
+    key_cache, value_cache = key_caches[0], value_caches[0]
 
     # Prepare for the paged attention kernel.
     output = torch.empty_like(query)
@@ -106,6 +104,7 @@ def run_benchmark(num_iters: int, profile: bool = False) -> float:
                     block_size,
                     max_context_len,
                     alibi_slopes,
+                    kv_cache_dtype,
                 )
             elif version == "v2":
                 ops.paged_attention_v2(
@@ -123,6 +122,7 @@ def run_benchmark(num_iters: int, profile: bool = False) -> float:
                     block_size,
                     max_context_len,
                     alibi_slopes,
+                    kv_cache_dtype,
                 )
             else:
                 raise ValueError(f"Invalid version: {version}")
@@ -168,6 +168,13 @@ def run_benchmark(num_iters: int, profile: bool = False) -> float:
                         default="half")
     parser.add_argument("--seed", type=int, default=0)
     parser.add_argument("--profile", action="store_true")
+    parser.add_argument(
+        "--kv-cache-dtype",
+        type=str,
+        choices=["auto", "fp8_e5m2"],
+        default="auto",
+        help=
+        'Data type for kv cache storage. If "auto", will use model data type.')
     args = parser.parse_args()
     print(args)
 
@@ -190,4 +197,5 @@ def run_benchmark(num_iters: int, profile: bool = False) -> float:
         dtype=dtype_to_torch_dtype[args.dtype],
         seed=args.seed,
         do_profile=args.profile,
+        kv_cache_dtype=args.kv_cache_dtype,
     )

csrc/attention/attention_dtypes.h

@@ -4,3 +4,4 @@
 #include "dtype_float16.cuh"
 #include "dtype_float32.cuh"
 #include "dtype_bfloat16.cuh"
+#include "dtype_fp8_e5m2.cuh"