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fix(model_runner): all_reduce num_kvcache_blocks to MIN across TP ranks #215

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fix(model_runner): all_reduce num_kvcache_blocks to MIN across TP ranks #215

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6 changes: 6 additions & 0 deletions nanovllm/engine/model_runner.py
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Original file line number Diff line number Diff line change
Expand Up @@ -111,6 +111,12 @@ def allocate_kv_cache(self):
head_dim = getattr(hf_config, "head_dim", hf_config.hidden_size // hf_config.num_attention_heads)
block_bytes = 2 * hf_config.num_hidden_layers * self.block_size * num_kv_heads * head_dim * hf_config.dtype.itemsize
config.num_kvcache_blocks = int(total * config.gpu_memory_utilization - used - peak + current) // block_bytes
if self.world_size > 1:
# Under TP all ranks must agree on the same block count so block IDs are
# consistent. Take the minimum to avoid over-allocating on any rank.
t = torch.tensor(config.num_kvcache_blocks, dtype=torch.int64, device="cuda")
dist.all_reduce(t, op=dist.ReduceOp.MIN)
config.num_kvcache_blocks = int(t.item())
assert config.num_kvcache_blocks > 0
self.kv_cache = torch.empty(2, hf_config.num_hidden_layers, config.num_kvcache_blocks, self.block_size, num_kv_heads, head_dim)
layer_id = 0
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