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Ensure buckets do not exceed the batch token limit (#206)
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This PR ensures we don't capture buckets that are above the specified
token budget (as set by `max_num_batched_tokens` argument)

Example for token budget of 2048 (`--max-num-batched-tokens 2048`):
```
$ python vllm_test.py --max-num-batched-tokens 2048
WARNING 08-27 14:48:55 _custom_ops.py:14] Failed to import from vllm._C with ModuleNotFoundError("No module named 'vllm._C'")
/usr/local/lib/python3.10/dist-packages/torch/distributed/distributed_c10d.py:366: UserWarning: torch.distributed.reduce_op is deprecated, please use torch.distributed.ReduceOp instead
  warnings.warn(
No chat template is set for this tokenizer, falling back to a default class-level template. This is very error-prone, because models are often trained with templates different from the class default! Default chat templates are a legacy feature and will be removed in Transformers v4.43, at which point any code depending on them will stop working. We recommend setting a valid chat template before then to ensure that this model continues working without issues.
INFO 08-27 14:48:56 llm_engine.py:176] Initializing an LLM engine (v0.5.3.post1) with config: model='facebook/opt-125m', speculative_config=None, tokenizer='facebook/opt-125m', skip_tokenizer_init=False, tokenizer_mode=auto, revision=None, rope_scaling=None, rope_theta=None, tokenizer_revision=None, trust_remote_code=False, dtype=torch.float16, max_seq_len=2048, download_dir=None, load_format=LoadFormat.AUTO, tensor_parallel_size=1, pipeline_parallel_size=1, disable_custom_all_reduce=False, quantization=None, weights_load_device=hpu, enforce_eager=False, kv_cache_dtype=auto, quantization_param_path=None, device_config=hpu, decoding_config=DecodingConfig(guided_decoding_backend='outlines'), observability_config=ObservabilityConfig(otlp_traces_endpoint=None), seed=0, served_model_name=facebook/opt-125m, use_v2_block_manager=False, enable_prefix_caching=False)
generation_config.json: 100%|███████████████████████████████████████████████████████████████████████████████████████| 137/137 [00:00<00:00, 1.91MB/s]
INFO 08-27 14:48:57 profiler.py:62] Profiler enabled for: vllm-instance-d356a015eeb349f7a4650e00bf6ce976
WARNING 08-27 14:48:57 utils.py:566] Pin memory is not supported on HPU.
INFO 08-27 14:48:57 selector.py:85] Using HabanaAttention backend.
INFO 08-27 14:48:57 habana_model_runner.py:532] Prompt bucket config (min, step, max_warmup) bs:[1, 32, 64], seq:[128, 128, 1024]
INFO 08-27 14:48:57 habana_model_runner.py:545] Generated 23 prompt buckets: [(1, 128), (1, 256), (1, 384), (1, 512), (1, 640), (1, 768), (1, 896), (1, 1024), (2, 128), (2, 256), (2, 384), (2, 512), (2, 640), (2, 768), (2, 896), (2, 1024), (4, 128), (4, 256), (4, 384), (4, 512), (8, 128), (8, 256), (16, 128)]
INFO 08-27 14:48:57 habana_model_runner.py:550] Decode bucket config (min, step, max_warmup) bs:[1, 128, 256], seq:[128, 128, 2048]
INFO 08-27 14:48:57 habana_model_runner.py:561] Generated 31 decode buckets: [(1, 128), (1, 256), (1, 384), (1, 512), (1, 640), (1, 768), (1, 896), (1, 1024), (1, 1152), (1, 1280), (1, 1408), (1, 1536), (1, 1664), (1, 1792), (1, 1920), (1, 2048), (2, 128), (2, 256), (2, 384), (2, 512), (2, 640), (2, 768), (2, 896), (2, 1024), (4, 128), (4, 256), (4, 384), (4, 512), (8, 128), (8, 256), (16, 128)]
============================= HABANA PT BRIDGE CONFIGURATION =========================== 
 PT_HPU_LAZY_MODE = 1
 PT_RECIPE_CACHE_PATH = 
 PT_CACHE_FOLDER_DELETE = 0
 PT_HPU_RECIPE_CACHE_CONFIG = 
 PT_HPU_MAX_COMPOUND_OP_SIZE = 9223372036854775807
 PT_HPU_LAZY_ACC_PAR_MODE = 1
 PT_HPU_ENABLE_REFINE_DYNAMIC_SHAPES = 0
---------------------------: System Configuration :---------------------------
Num CPU Cores : 160
CPU RAM       : 1056398260 KB
------------------------------------------------------------------------------
INFO 08-27 14:49:00 selector.py:85] Using HabanaAttention backend.
INFO 08-27 14:49:00 loader.py:284] Loading weights on hpu ...
INFO 08-27 14:49:00 weight_utils.py:224] Using model weights format ['*.bin']
pytorch_model.bin: 100%|██████████████████████████████████████████████████████████████████████████████████████████| 251M/251M [00:06<00:00, 35.9MB/s]
Loading pt checkpoint shards:   0% Completed | 0/1 [00:00<?, ?it/s]
Loading pt checkpoint shards: 100% Completed | 1/1 [00:00<00:00,  4.15it/s]
Loading pt checkpoint shards: 100% Completed | 1/1 [00:00<00:00,  4.15it/s]

INFO 08-27 14:49:08 habana_model_runner.py:441] Pre-loading model weights on hpu:0 took 238.9 MiB of device memory (244.4 MiB/94.62 GiB used) and 298.9 MiB of host memory (485.6 GiB/1007 GiB used)
INFO 08-27 14:49:08 habana_model_runner.py:486] Wrapping in HPU Graph took 0 B of device memory (244.4 MiB/94.62 GiB used) and 0 B of host memory (485.6 GiB/1007 GiB used)
INFO 08-27 14:49:08 habana_model_runner.py:490] Loading model weights took in total 238.9 MiB of device memory (244.4 MiB/94.62 GiB used) and 298.2 MiB of host memory (485.6 GiB/1007 GiB used)
```
We can see that no bucket exceeds 2048 tokens, and we have `(16, 128)`
as well as `(1, 2048)`. Previously, with default bucket settings, we'd
also capture `(16, 2048)`, and `(64, 2048)` cases, which should not be
allowed.


With `--max-num-batched-tokens 32768`:
```
$ python vllm_test.py --max-num-batched-tokens 32768
WARNING 08-27 14:54:39 _custom_ops.py:14] Failed to import from vllm._C with ModuleNotFoundError("No module named 'vllm._C'")
/usr/local/lib/python3.10/dist-packages/torch/distributed/distributed_c10d.py:366: UserWarning: torch.distributed.reduce_op is deprecated, please use torch.distributed.ReduceOp instead
  warnings.warn(
No chat template is set for this tokenizer, falling back to a default class-level template. This is very error-prone, because models are often trained with templates different from the class default! Default chat templates are a legacy feature and will be removed in Transformers v4.43, at which point any code depending on them will stop working. We recommend setting a valid chat template before then to ensure that this model continues working without issues.
INFO 08-27 14:54:41 llm_engine.py:176] Initializing an LLM engine (v0.5.3.post1) with config: model='facebook/opt-125m', speculative_config=None, tokenizer='facebook/opt-125m', skip_tokenizer_init=False, tokenizer_mode=auto, revision=None, rope_scaling=None, rope_theta=None, tokenizer_revision=None, trust_remote_code=False, dtype=torch.float16, max_seq_len=2048, download_dir=None, load_format=LoadFormat.AUTO, tensor_parallel_size=1, pipeline_parallel_size=1, disable_custom_all_reduce=False, quantization=None, weights_load_device=hpu, enforce_eager=False, kv_cache_dtype=auto, quantization_param_path=None, device_config=hpu, decoding_config=DecodingConfig(guided_decoding_backend='outlines'), observability_config=ObservabilityConfig(otlp_traces_endpoint=None), seed=0, served_model_name=facebook/opt-125m, use_v2_block_manager=False, enable_prefix_caching=False)
INFO 08-27 14:54:41 profiler.py:62] Profiler enabled for: vllm-instance-be8ab3101609425ba60df601dc9de3a6
WARNING 08-27 14:54:41 utils.py:566] Pin memory is not supported on HPU.
INFO 08-27 14:54:41 selector.py:85] Using HabanaAttention backend.
INFO 08-27 14:54:41 habana_model_runner.py:533] Prompt bucket config (min, step, max_warmup) bs:[1, 32, 64], seq:[128, 128, 1024]
INFO 08-27 14:54:41 habana_model_runner.py:546] Generated 52 prompt buckets: [(1, 128), (1, 256), (1, 384), (1, 512), (1, 640), (1, 768), (1, 896), (1, 1024), (2, 128), (2, 256), (2, 384), (2, 512), (2, 640), (2, 768), (2, 896), (2, 1024), (4, 128), (4, 256), (4, 384), (4, 512), (4, 640), (4, 768), (4, 896), (4, 1024), (8, 128), (8, 256), (8, 384), (8, 512), (8, 640), (8, 768), (8, 896), (8, 1024), (16, 128), (16, 256), (16, 384), (16, 512), (16, 640), (16, 768), (16, 896), (16, 1024), (32, 128), (32, 256), (32, 384), (32, 512), (32, 640), (32, 768), (32, 896), (32, 1024), (64, 128), (64, 256), (64, 384), (64, 512)]
INFO 08-27 14:54:41 habana_model_runner.py:551] Decode bucket config (min, step, max_warmup) bs:[1, 128, 256], seq:[128, 128, 2048]
INFO 08-27 14:54:41 habana_model_runner.py:562] Generated 95 decode buckets: [(1, 128), (1, 256), (1, 384), (1, 512), (1, 640), (1, 768), (1, 896), (1, 1024), (1, 1152), (1, 1280), (1, 1408), (1, 1536), (1, 1664), (1, 1792), (1, 1920), (1, 2048), (2, 128), (2, 256), (2, 384), (2, 512), (2, 640), (2, 768), (2, 896), (2, 1024), (2, 1152), (2, 1280), (2, 1408), (2, 1536), (2, 1664), (2, 1792), (2, 1920), (2, 2048), (4, 128), (4, 256), (4, 384), (4, 512), (4, 640), (4, 768), (4, 896), (4, 1024), (4, 1152), (4, 1280), (4, 1408), (4, 1536), (4, 1664), (4, 1792), (4, 1920), (4, 2048), (8, 128), (8, 256), (8, 384), (8, 512), (8, 640), (8, 768), (8, 896), (8, 1024), (8, 1152), (8, 1280), (8, 1408), (8, 1536), (8, 1664), (8, 1792), (8, 1920), (8, 2048), (16, 128), (16, 256), (16, 384), (16, 512), (16, 640), (16, 768), (16, 896), (16, 1024), (16, 1152), (16, 1280), (16, 1408), (16, 1536), (16, 1664), (16, 1792), (16, 1920), (16, 2048), (32, 128), (32, 256), (32, 384), (32, 512), (32, 640), (32, 768), (32, 896), (32, 1024), (64, 128), (64, 256), (64, 384), (64, 512), (128, 128), (128, 256), (256, 128)]
============================= HABANA PT BRIDGE CONFIGURATION =========================== 
 PT_HPU_LAZY_MODE = 1
 PT_RECIPE_CACHE_PATH = 
 PT_CACHE_FOLDER_DELETE = 0
 PT_HPU_RECIPE_CACHE_CONFIG = 
 PT_HPU_MAX_COMPOUND_OP_SIZE = 9223372036854775807
 PT_HPU_LAZY_ACC_PAR_MODE = 1
 PT_HPU_ENABLE_REFINE_DYNAMIC_SHAPES = 0
---------------------------: System Configuration :---------------------------
Num CPU Cores : 160
CPU RAM       : 1056398260 KB
------------------------------------------------------------------------------
INFO 08-27 14:54:45 selector.py:85] Using HabanaAttention backend.
INFO 08-27 14:54:45 loader.py:284] Loading weights on hpu ...
INFO 08-27 14:54:45 weight_utils.py:224] Using model weights format ['*.bin']
Loading pt checkpoint shards:   0% Completed | 0/1 [00:00<?, ?it/s]
Loading pt checkpoint shards: 100% Completed | 1/1 [00:00<00:00,  3.99it/s]
Loading pt checkpoint shards: 100% Completed | 1/1 [00:00<00:00,  3.99it/s]

INFO 08-27 14:54:45 habana_model_runner.py:442] Pre-loading model weights on hpu:0 took 238.9 MiB of device memory (244.4 MiB/94.62 GiB used) and 279.7 MiB of host memory (485.8 GiB/1007 GiB used)
INFO 08-27 14:54:46 habana_model_runner.py:487] Wrapping in HPU Graph took 0 B of device memory (244.4 MiB/94.62 GiB used) and 48 KiB of host memory (485.8 GiB/1007 GiB used)
INFO 08-27 14:54:46 habana_model_runner.py:491] Loading model weights took in total 238.9 MiB of device memory (244.4 MiB/94.62 GiB used) and 279.6 MiB of host memory (485.8 GiB/1007 GiB used)
```

Max model length (2048) is not exceeded for low batch, as seen in `(1,
2048)` bucket, but we can still get high batch sizes captured up to 32k
tokens, as seen in `(256, 128)` bucket.
  • Loading branch information
@kzawora-intel
kzawora-intel authored Aug 27, 2024
1 parent fdf3fd8 commit aefd336
Showing 1 changed file with 12 additions and 4 deletions.
16 changes: 12 additions & 4 deletions vllm/worker/habana_model_runner.py
View file
Original file line number Diff line number Diff line change
Expand Up @@ -94,10 +94,16 @@ def warmup_range(config: Tuple[int, int, int]):
return list(ramp_up_tw) + list(stable)


def warmup_buckets(bs_bucket_config, seq_bucket_config):
def warmup_buckets(bs_bucket_config, seq_bucket_config,
max_num_batched_tokens):
buckets = itertools.product(warmup_range(bs_bucket_config),
warmup_range(seq_bucket_config))
return list(sorted(buckets, key=lambda b: (b[0] * b[1], b[1], b[0])))
# Remove buckets exceeding batch token budget
filtered_buckets = filter(
lambda bucket: bucket[0] * bucket[1] <= max_num_batched_tokens,
buckets)
return list(
sorted(filtered_buckets, key=lambda b: (b[0] * b[1], b[1], b[0])))


def next_pow2(value: int):
Expand Down Expand Up @@ -526,7 +532,8 @@ def _setup_buckets(self) -> None:
f"seq:{self.prompt_seq_bucket_cfg}")
logger.info(msg)
self.prompt_buckets = warmup_buckets(self.prompt_bs_bucket_cfg,
self.prompt_seq_bucket_cfg)
self.prompt_seq_bucket_cfg,
self.max_num_batched_tokens)

if self.lora_config:
self.prompt_buckets[:] = [
Expand All @@ -543,7 +550,8 @@ def _setup_buckets(self) -> None:
f"seq:{self.decode_seq_bucket_cfg}")
logger.info(msg)
self.decode_buckets = warmup_buckets(self.decode_bs_bucket_cfg,
self.decode_seq_bucket_cfg)
self.decode_seq_bucket_cfg,
self.max_num_batched_tokens)
if self.lora_config:
self.decode_buckets[:] = [
bucket for bucket in self.decode_buckets
Expand Down

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