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Fix oom issues with fp8 for llama (#1454)

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This commit is contained in:
Lianmin Zheng
2024-09-18 03:45:19 -07:00
committed by GitHub
parent aa2750beb3
commit 1acccb364a
8 changed files with 33 additions and 21 deletions
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12
.github/workflows/pr-test.yml vendored
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@@ -144,18 +144,18 @@ jobs:
cd test/srt
python3 -m unittest test_bench_serving.TestBenchServing.test_offline_throughput_without_radix_cache
- name: Benchmark Offline Throughput (w/o ChunkedPrefill)
timeout-minutes: 10
run: |
cd test/srt
python3 -m unittest test_bench_serving.TestBenchServing.test_offline_throughput_without_chunked_prefill
- name: Benchmark Offline Throughput (w/ Triton)
timeout-minutes: 10
run: |
cd test/srt
python3 -m unittest test_bench_serving.TestBenchServing.test_offline_throughput_with_triton_attention_backend
- name: Benchmark Offline Throughput (w/ FP8)
timeout-minutes: 10
run: |
cd test/srt
python3 -m unittest test_bench_serving.TestBenchServing.test_offline_throughput_default_fp8
performance-test-2-gpu:
if: github.repository == 'sgl-project/sglang' || github.event_name == 'pull_request'
runs-on: 2-gpu-runner

4
python/sglang/srt/models/llama.py
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@@ -305,8 +305,6 @@ class LlamaForCausalLM(nn.Module):
self.lm_head = ParallelLMHead(config.vocab_size, config.hidden_size)
self.logits_processor = LogitsProcessor(config)
self.param_dict = dict(self.named_parameters())
@torch.no_grad()
def forward(
self,
@@ -374,7 +372,7 @@ class LlamaForCausalLM(nn.Module):
(".gate_up_proj", ".gate_proj", 0),
(".gate_up_proj", ".up_proj", 1),
]
params_dict = self.param_dict
params_dict = dict(self.named_parameters())
for name, loaded_weight in weights:
if "rotary_emb.inv_freq" in name or "projector" in name:

5
python/sglang/srt/models/llama_classification.py
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@@ -36,6 +36,7 @@ class LlamaForClassification(nn.Module):
) -> None:
super().__init__()
self.config = config
self.torchao_config = None
self.quant_config = quant_config
self.model = LlamaModel(config, quant_config=quant_config)
@@ -44,8 +45,6 @@ class LlamaForClassification(nn.Module):
)
self.eos_token_id = config.eos_token_id
self.param_dict = dict(self.named_parameters())
@torch.no_grad()
def forward(
self,
@@ -77,7 +76,7 @@ class LlamaForClassification(nn.Module):
return logits_output
def load_weights(self, weights: Iterable[Tuple[str, torch.Tensor]]):
params_dict = self.param_dict
params_dict = dict(self.named_parameters())
for name, loaded_weight in weights:
if "classification_head" in name:

4
python/sglang/srt/models/xverse.py
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@@ -307,8 +307,6 @@ class XverseForCausalLM(nn.Module):
self.lm_head = ParallelLMHead(config.vocab_size, config.hidden_size)
self.logits_processor = LogitsProcessor(config)
self.param_dict = dict(self.named_parameters())
@torch.no_grad()
def forward(
self,
@@ -333,7 +331,7 @@ class XverseForCausalLM(nn.Module):
("gate_up_proj", "gate_proj", 0),
("gate_up_proj", "up_proj", 1),
]
params_dict = self.param_dict
params_dict = dict(self.named_parameters())
def load_weights_per_param(name, loaded_weight):
if "rotary_emb.inv_freq" in name or "projector" in name:

5
python/sglang/srt/models/xverse_moe.py
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@@ -383,8 +383,6 @@ class XverseMoeForCausalLM(nn.Module):
)
self.logits_processor = LogitsProcessor(config)
self.param_dict = dict(self.named_parameters())
@torch.no_grad()
def forward(
self,
@@ -406,8 +404,7 @@ class XverseMoeForCausalLM(nn.Module):
("gate_up_proj", "gate_proj", 0),
("gate_up_proj", "up_proj", 1),
]
params_dict = self.param_dict
params_dict = dict(self.named_parameters())
for name, loaded_weight in weights:
if "rotary_emb.inv_freq" in name:

1
python/sglang/test/test_utils.py
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@@ -22,6 +22,7 @@ from sglang.lang.backend.runtime_endpoint import RuntimeEndpoint
from sglang.srt.utils import kill_child_process
from sglang.utils import get_exception_traceback
DEFAULT_FP8_MODEL_NAME_FOR_TEST = "neuralmagic/Meta-Llama-3.1-8B-FP8"
DEFAULT_MODEL_NAME_FOR_TEST = "meta-llama/Meta-Llama-3.1-8B-Instruct"
DEFAULT_MOE_MODEL_NAME_FOR_TEST = "mistralai/Mixtral-8x7B-Instruct-v0.1"
DEFAULT_TIMEOUT_FOR_SERVER_LAUNCH = 600

12
test/srt/test_bench_serving.py
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@@ -1,6 +1,7 @@
import unittest
from sglang.test.test_utils import (
DEFAULT_FP8_MODEL_NAME_FOR_TEST,
DEFAULT_MODEL_NAME_FOR_TEST,
DEFAULT_MOE_MODEL_NAME_FOR_TEST,
is_in_ci,
@@ -59,6 +60,17 @@ class TestBenchServing(unittest.TestCase):
if is_in_ci():
assert res["output_throughput"] > 2600
def test_offline_throughput_default_fp8(self):
res = run_bench_serving(
model=DEFAULT_FP8_MODEL_NAME_FOR_TEST,
num_prompts=500,
request_rate=float("inf"),
other_server_args=[],
)
if is_in_ci():
assert res["output_throughput"] > 3100
def test_online_latency_default(self):
res = run_bench_serving(
model=DEFAULT_MODEL_NAME_FOR_TEST,

11
test/srt/test_chunked_prefill.py
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@@ -12,8 +12,10 @@ from sglang.test.test_utils import (
class TestChunkedPrefill(unittest.TestCase):
def run_mmlu(self, disable_radix_cache, enable_mixed_chunk):
other_args = ["--chunked-prefill-size", "32"]
def run_mmlu(
self, disable_radix_cache, enable_mixed_chunk, chunked_prefill_size=32
):
other_args = ["--chunked-prefill-size", str(chunked_prefill_size)]
if disable_radix_cache:
other_args += ["--disable-radix-cache"]
@@ -55,6 +57,11 @@ class TestChunkedPrefill(unittest.TestCase):
def test_mixed_chunked_prefill_without_radix_cache(self):
self.run_mmlu(disable_radix_cache=True, enable_mixed_chunk=True)
def test_no_chunked_prefill(self):
self.run_mmlu(
disable_radix_cache=False, enable_mixed_chunk=False, chunked_prefill_size=-1
)
if __name__ == "__main__":
unittest.main()