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92 lines
3.7 KiB
Python
92 lines
3.7 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
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"""Test ModelOpt quantization method setup and weight loading.
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Run `pytest tests/quantization/test_modelopt.py`.
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"""
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import os
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import pytest
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import torch
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from tests.quantization.utils import is_quant_method_supported
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from vllm.platforms import current_platform
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@pytest.fixture(scope="function", autouse=True)
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def use_v0_only(monkeypatch):
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"""
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This module relies on V0 internals, so set VLLM_USE_V1=0.
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"""
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if not current_platform.is_cpu():
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monkeypatch.setenv('VLLM_USE_V1', '0')
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@pytest.mark.skipif(not is_quant_method_supported("modelopt"),
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reason="ModelOpt FP8 is not supported on this GPU type.")
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def test_modelopt_fp8_checkpoint_setup(vllm_runner):
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"""Test ModelOpt FP8 checkpoint loading and structure validation."""
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# TODO: provide a small publicly available test checkpoint
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model_path = ("/home/scratch.omniml_data_1/zhiyu/ckpts/test_ckpts/"
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"TinyLlama-1.1B-Chat-v1.0-fp8-0710")
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# Skip test if checkpoint doesn't exist
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if not os.path.exists(model_path):
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pytest.skip(f"Test checkpoint not found at {model_path}. "
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"This test requires a local ModelOpt FP8 checkpoint.")
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with vllm_runner(model_path, quantization="modelopt",
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enforce_eager=True) as llm:
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def check_model(model):
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layer = model.model.layers[0]
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qkv_proj = layer.self_attn.qkv_proj
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o_proj = layer.self_attn.o_proj
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gate_up_proj = layer.mlp.gate_up_proj
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down_proj = layer.mlp.down_proj
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# Check that ModelOpt quantization method is properly applied
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from vllm.model_executor.layers.quantization.modelopt import (
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ModelOptFp8LinearMethod)
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assert isinstance(qkv_proj.quant_method, ModelOptFp8LinearMethod)
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assert isinstance(o_proj.quant_method, ModelOptFp8LinearMethod)
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assert isinstance(gate_up_proj.quant_method,
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ModelOptFp8LinearMethod)
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assert isinstance(down_proj.quant_method, ModelOptFp8LinearMethod)
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# Check weight dtype is FP8
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assert qkv_proj.weight.dtype == torch.float8_e4m3fn
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assert o_proj.weight.dtype == torch.float8_e4m3fn
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assert gate_up_proj.weight.dtype == torch.float8_e4m3fn
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assert down_proj.weight.dtype == torch.float8_e4m3fn
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# Check scales are present and have correct dtype
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assert hasattr(qkv_proj, 'weight_scale')
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assert hasattr(qkv_proj, 'input_scale')
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assert qkv_proj.weight_scale.dtype == torch.float32
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assert qkv_proj.input_scale.dtype == torch.float32
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assert hasattr(o_proj, 'weight_scale')
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assert hasattr(o_proj, 'input_scale')
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assert o_proj.weight_scale.dtype == torch.float32
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assert o_proj.input_scale.dtype == torch.float32
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assert hasattr(gate_up_proj, 'weight_scale')
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assert hasattr(gate_up_proj, 'input_scale')
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assert gate_up_proj.weight_scale.dtype == torch.float32
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assert gate_up_proj.input_scale.dtype == torch.float32
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assert hasattr(down_proj, 'weight_scale')
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assert hasattr(down_proj, 'input_scale')
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assert down_proj.weight_scale.dtype == torch.float32
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assert down_proj.input_scale.dtype == torch.float32
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llm.apply_model(check_model)
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# Run a simple generation test to ensure the model works
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output = llm.generate_greedy(["Hello my name is"], max_tokens=20)
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assert output
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print(f"ModelOpt FP8 output: {output}")
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