Contiguous PA

requirements-hpu.txt

@@ -8,4 +8,4 @@ pandas
 tabulate
 setuptools>=61
 setuptools-scm>=8
-vllm-hpu-extension @ git+https://github.com/HabanaAI/vllm-hpu-extension.git@341a77f
+vllm-hpu-extension @ git+https://github.com/HabanaAI/vllm-hpu-extension.git@6cb6e19

vllm/worker/hpu_model_runner.py

@@ -199,10 +199,11 @@ def generate_decode_buckets(bs_bucket_config, blocks_bucket_config,
     bs_buckets = warmup_range(bs_bucket_config)
     block_buckets = warmup_range(blocks_bucket_config)
     bmin, bstep, bmax = blocks_bucket_config
-    last_bucket = round_up(max_blocks, bstep)
+    last_bucket = max_blocks
     for bs in bs_buckets:
         for blocks in block_buckets:
             if blocks > last_bucket:
+                buckets.append((bs, last_bucket))
                 break
             buckets.append((bs, blocks))
     return list(sorted(buckets, key=lambda b: (b[0] * b[1], b[1], b[0])))
@@ -1002,39 +1003,40 @@ def _prepare_decode(
 
         num_decode_tokens = sum(seq_lens)
 
-        blocks_used = [len(bt) for bt in block_tables if bt]
-        block_list = []
-        block_scales = []
+        block_list = list(itertools.chain(*block_tables))
+
+        max_idx = max(block_list)
+        max_blocks = max(max_idx + 1, len(block_list))
+        block_bucket_size = find_bucket(
+            max_blocks, self.bucketing_global_state.decode_block_bucket_cfg)
+        block_bucket_size = min(block_bucket_size,
+                                self.cache_config.num_gpu_blocks)
+
+        block_mapping: List[Union[None, int]] = [None] * block_bucket_size
+        block_usage: List[Union[None, int]] = [None] * block_bucket_size
+        block_scales: List[Union[None, float]] = [None] * block_bucket_size
+
         for i, bt in enumerate(block_tables):
-            block_list.extend(bt)
-            blocks_in_group = len(bt)
-            if blocks_in_group > 0:
+            if bt:
+                blocks_in_group = len(bt)
                 scale = 1.0 / blocks_in_group
-                block_scales.extend([scale] * blocks_in_group)
+                for b in bt:
+                    if block_mapping[b] is None:
+                        block_mapping[b] = i
+                        block_usage[b] = self.block_size
+                        block_scales[b] = scale
 
-        block_mapping_nested: List[List[int]] = [
-            [i] * b_u for i, b_u in enumerate(blocks_used)
-        ]
-        block_mapping: List[int] = list(
-            itertools.chain.from_iterable(block_mapping_nested))
+        block_mapping = [b if b is not None else -1 for b in block_mapping]
+        block_scales = [b if b is not None else 0.0 for b in block_scales]
 
-        last_block = [
-            sl % self.block_size + 1 for sl in itertools.chain(*slot_mapping)
-        ]
-        block_usage = [[self.block_size] * (b_u - 1) + [lb]
-                       for b_u, lb in zip(blocks_used, last_block)]
-        block_usage = list(itertools.chain(*block_usage))
+        for bt, sl in zip(block_tables, slot_mapping):
+            if bt:
+                block_usage[bt[-1]] = sl[-1] % self.block_size + 1
+        block_usage = [u if u is not None else 1 for u in block_usage]
 
-        block_bucket_size = find_bucket(
-            len(block_list),
-            self.bucketing_global_state.decode_block_bucket_cfg)
         block_list = pad_list(block_list, block_bucket_size, _PAD_BLOCK_ID)
         block_groups = pad_list(block_mapping, block_bucket_size,
                                 len(block_tables))
-        block_mapping = pad_list(block_mapping, block_bucket_size, -1)
-        block_usage = pad_list(block_usage, block_bucket_size, 1)
-        block_scales = pad_list(block_scales, block_bucket_size, 0.0)
-
         block_list = torch.tensor(block_list,
                                   dtype=torch.int,
                                   device=self.device)