Support block size 32 (#35)

cacheflow/master/block_manager.py

@@ -15,9 +15,9 @@ class BlockAllocator:
         block_size: int,
         num_blocks: int,
     ) -> None:
-        if block_size not in [8, 16]:
+        if block_size not in [8, 16, 32]:
             raise ValueError(f'Unsupported block size: {block_size}'
-                             'The block size must be either 8 or 16.')
+                             'The block size must be one of {8, 16, 32}.')
         self.device = device
         self.block_size = block_size
         self.num_blocks = num_blocks

cacheflow/master/server.py

@@ -174,7 +174,7 @@ def add_server_arguments(parser: argparse.ArgumentParser):
     parser.add_argument('--pipeline-parallel-size', '-pp', type=int, default=1, help='number of pipeline stages')
     parser.add_argument('--tensor-parallel-size', '-tp', type=int, default=1, help='number of tensor parallel replicas')
     # KV cache arguments
-    parser.add_argument('--block-size', type=int, default=8, choices=[8, 16], help='token block size')
+    parser.add_argument('--block-size', type=int, default=8, choices=[8, 16, 32], help='token block size')
     # NOTE(woosuk): If FlashAttention is used, the float data type is not supported.
     parser.add_argument('--dtype', type=str, default='half', choices=['half', 'float'], help='data type')
     # TODO(woosuk): Support fine-grained seeds (e.g., seed per request).

csrc/attention_kernels.cu

@@ -654,6 +654,16 @@ void single_query_cached_kv_attention(
         block_tables,
         context_lens,
         max_context_len);
+    } else if (block_size == 32) {
+      single_query_cached_kv_attention_launcher<uint16_t, 32>(
+        out,
+        query,
+        key_cache,
+        value_cache,
+        scale,
+        block_tables,
+        context_lens,
+        max_context_len);
     } else {
       assert(false);
     }
@@ -679,6 +689,16 @@ void single_query_cached_kv_attention(
         block_tables,
         context_lens,
         max_context_len);
+    } else if (block_size == 32) {
+      single_query_cached_kv_attention_launcher<float, 32>(
+        out,
+        query,
+        key_cache,
+        value_cache,
+        scale,
+        block_tables,
+        context_lens,
+        max_context_len);
     } else {
       assert(false);
     }
@@ -834,6 +854,18 @@ void multi_query_cached_kv_attention(
         block_tables,
         context_lens,
         max_context_len);
+    } else if (block_size == 32) {
+      multi_query_cached_kv_attention_launcher<uint16_t, 32>(
+        cu_query_lens,
+        seq_prompt_mapping,
+        out,
+        query,
+        key_cache,
+        value_cache,
+        scale,
+        block_tables,
+        context_lens,
+        max_context_len);
     } else {
       assert(false);
     }
@@ -863,6 +895,18 @@ void multi_query_cached_kv_attention(
         block_tables,
         context_lens,
         max_context_len);
+    } else if (block_size == 32) {
+      multi_query_cached_kv_attention_launcher<float, 32>(
+        cu_query_lens,
+        seq_prompt_mapping,
+        out,
+        query,
+        key_cache,
+        value_cache,
+        scale,
+        block_tables,
+        context_lens,
+        max_context_len);
     } else {
       assert(false);
     }

tests/kernels/attention.py

@@ -350,7 +350,7 @@ def test_attention(seed: int) -> None:
     torch.random.manual_seed(seed)
     torch.cuda.manual_seed(seed)
     for dtype in [torch.half, torch.float]:
-        for block_size in [8, 16]:
+        for block_size in [8, 16, 32]:
             for head_size in [32, 64, 80, 96, 128, 160, 192, 256]:
                 print(f'Testing single_query_cached_kv_attention with '
                       f'dtype={dtype}, block_size={block_size}, '
@@ -368,7 +368,7 @@ def test_attention(seed: int) -> None:
     # note that the test is also more likely to fail due to the much
     # larger amount of tokens in the input may increase the variance.
     for dtype in [torch.half, torch.float]:
-        for block_size in [8, 16]:
+        for block_size in [8, 16, 32]:
             for head_size in [32, 64, 80, 96, 128, 160, 192, 256]:
                 print(f'Testing multi_query_cached_kv_attention with '
                       f'dtype={dtype}, block_size={block_size}, '