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[Model] Adding support for MSFT Phi-3.5-MoE (vllm-project#7729)
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Co-authored-by: Your Name <you@example.com>
Co-authored-by: Zeqi Lin <zelin@microsoft.com>
Co-authored-by: Zeqi Lin <Zeqi.Lin@microsoft.com>
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@wenxcs @zelinms @linzeqipku
4 people authored Aug 30, 2024
1 parent 2684efc commit 1248e85
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4 changes: 4 additions & 0 deletions docs/source/models/supported_models.rst
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Expand Up @@ -147,6 +147,10 @@ Decoder-only Language Models
- Phi-3-Small
- :code:`microsoft/Phi-3-small-8k-instruct`, :code:`microsoft/Phi-3-small-128k-instruct`, etc.
-
* - :code:`PhiMoEForCausalLM`
- Phi-3.5-MoE
- :code:`microsoft/Phi-3.5-MoE-instruct`, etc.
-
* - :code:`PersimmonForCausalLM`
- Persimmon
- :code:`adept/persimmon-8b-base`, :code:`adept/persimmon-8b-chat`, etc.
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111 changes: 111 additions & 0 deletions tests/models/test_phimoe.py
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"""Compare the outputs of HF and vLLM for moe models using greedy sampling.
Run `pytest tests/models/test_phimoe.py`.
"""
import pytest
import torch

from vllm.utils import is_cpu

from .utils import check_logprobs_close

MODELS = [
"microsoft/Phi-3.5-MoE-instruct",
]


def test_phimoe_routing_function():
from vllm.model_executor.models.phimoe import phimoe_routing_function
test_case = {
0: {
"hidden_states":
torch.tensor([1, 2, 3, 4, 5, 6, 7, 8],
dtype=torch.float32,
requires_grad=False).view(4, 2),
"gating_output":
torch.tensor([0.1, 0.2, 0.3, 0.4],
dtype=torch.float32,
requires_grad=False),
"topk":
2,
"renormalize":
False,
},
1: {
"hidden_states":
torch.tensor([1, 2, 3, 4, 5, 6, 7, 8],
dtype=torch.float32,
requires_grad=False).view(4, 2),
"gating_output":
torch.tensor([0.4, 0.2, 0.3, 0.4],
dtype=torch.float32,
requires_grad=False),
"topk":
2,
"renormalize":
False,
}
}

ground_truth = {
0: {
"topk_weights":
torch.tensor([1., 1.], dtype=torch.float32, requires_grad=False),
"topk_ids":
torch.tensor([3, 2], dtype=torch.long, requires_grad=False),
},
1: {
"topk_weights":
torch.tensor([0.5, 1.], dtype=torch.float32, requires_grad=False),
"topk_ids":
torch.tensor([0, 3], dtype=torch.long, requires_grad=False),
}
}

for test_id in test_case:
topk_weights, topk_ids = phimoe_routing_function(**test_case[test_id])
assert torch.allclose(topk_weights,
ground_truth[test_id]["topk_weights"])
assert torch.equal(topk_ids, ground_truth[test_id]["topk_ids"])


def get_gpu_memory():
try:
props = torch.cuda.get_device_properties(torch.cuda.current_device())
gpu_memory = props.total_memory / (1024**3)
return gpu_memory
except Exception:
return 0


@pytest.mark.skipif(condition=is_cpu(),
reason="This test takes a lot time to run on CPU, "
"and vllm CI's disk space is not enough for this model.")
@pytest.mark.skipif(condition=get_gpu_memory() < 100,
reason="Skip this test if GPU memory is insufficient.")
@pytest.mark.parametrize("model", MODELS)
@pytest.mark.parametrize("dtype", ["bfloat16"])
@pytest.mark.parametrize("max_tokens", [64])
@pytest.mark.parametrize("num_logprobs", [5])
def test_models(
hf_runner,
vllm_runner,
example_prompts,
model: str,
dtype: str,
max_tokens: int,
num_logprobs: int,
) -> None:
with hf_runner(model, dtype=dtype) as hf_model:
hf_outputs = hf_model.generate_greedy_logprobs_limit(
example_prompts, max_tokens, num_logprobs)

with vllm_runner(model, dtype=dtype) as vllm_model:
vllm_outputs = vllm_model.generate_greedy_logprobs(
example_prompts, max_tokens, num_logprobs)
check_logprobs_close(
outputs_0_lst=hf_outputs,
outputs_1_lst=vllm_outputs,
name_0="hf",
name_1="vllm",
)
130 changes: 130 additions & 0 deletions ...ayers/fused_moe/configs/E=16,N=3200,device_name=NVIDIA_H100_80GB_HBM3,dtype=fp8_w8a8.json
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{
"3328": {
"BLOCK_SIZE_M": 64,
"BLOCK_SIZE_N": 256,
"BLOCK_SIZE_K": 64,
"GROUP_SIZE_M": 16,
"num_warps": 4,
"num_stages": 2
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"1024": {
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"BLOCK_SIZE_N": 256,
"BLOCK_SIZE_K": 32,
"GROUP_SIZE_M": 32,
"num_warps": 4,
"num_stages": 4
},
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"256": {
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},
"768": {
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}
}
130 changes: 130 additions & 0 deletions ...ayers/fused_moe/configs/E=16,N=6400,device_name=NVIDIA_H100_80GB_HBM3,dtype=fp8_w8a8.json
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{
"3840": {
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"BLOCK_SIZE_K": 64,
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},
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"GROUP_SIZE_M": 1,
"num_warps": 4,
"num_stages": 4
}
}
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