129 lines
4.0 KiB
Python
129 lines
4.0 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
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"""Unit tests for diffusion GGUF quantization config and linear method."""
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from __future__ import annotations
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from types import SimpleNamespace
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import pytest
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import torch
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from vllm.model_executor.layers.linear import LinearBase, UnquantizedLinearMethod
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from vllm_omni.quantization import build_quant_config
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from vllm_omni.quantization.gguf_config import (
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UNQUANTIZED_TYPES,
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DiffusionGGUFConfig,
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DiffusionGGUFLinearMethod,
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dequant_gemm_gguf,
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)
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pytestmark = [pytest.mark.core_model, pytest.mark.diffusion, pytest.mark.cpu]
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def test_gguf_config_creation_and_delegation():
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config = DiffusionGGUFConfig(
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gguf_model="weights.gguf",
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unquantized_modules=["proj_out"],
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)
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assert config.gguf_model == "weights.gguf"
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assert config.unquantized_modules == ["proj_out"]
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assert config.get_name() == "gguf"
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def test_build_quant_config_builds_gguf_config():
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config = build_quant_config(
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"gguf",
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gguf_model="repo/model-Q4_0.gguf",
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unquantized_modules=["norm_out"],
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)
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assert isinstance(config, DiffusionGGUFConfig)
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assert config.gguf_model == "repo/model-Q4_0.gguf"
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assert config.unquantized_modules == ["norm_out"]
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def test_gguf_vllm_config_returns_diffusion_linear_method_for_linear_layers():
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linear = object.__new__(LinearBase)
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method = DiffusionGGUFConfig(unquantized_modules=[]).get_quant_method(linear, "transformer.img_in")
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assert isinstance(method, DiffusionGGUFLinearMethod)
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def test_gguf_vllm_config_respects_unquantized_modules():
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linear = object.__new__(LinearBase)
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method = DiffusionGGUFConfig(unquantized_modules=["proj_out"]).get_quant_method(linear, "transformer.proj_out")
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assert isinstance(method, UnquantizedLinearMethod)
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def test_gguf_vllm_config_returns_none_for_non_linear_layers():
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method = DiffusionGGUFConfig(unquantized_modules=[]).get_quant_method(torch.nn.LayerNorm(4), "norm")
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assert method is None
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def test_dequant_gemm_gguf_uses_plain_matmul_for_unquantized_types():
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qweight_type = next(iter(UNQUANTIZED_TYPES))
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x = torch.tensor([[1.0, 2.0]], dtype=torch.float32)
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qweight = torch.tensor([[3.0, 4.0], [5.0, 6.0]], dtype=torch.float32)
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out = dequant_gemm_gguf(x, qweight, qweight_type)
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assert torch.allclose(out, x @ qweight.T)
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def test_diffusion_gguf_linear_method_applies_bias_on_unquantized_weight():
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qweight_type = next(iter(UNQUANTIZED_TYPES))
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method = DiffusionGGUFLinearMethod(DiffusionGGUFConfig(unquantized_modules=[]))
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layer = SimpleNamespace(
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qweight=torch.tensor([[1.0, 2.0], [3.0, 4.0]], dtype=torch.float32),
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qweight_type=SimpleNamespace(weight_type=qweight_type),
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)
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x = torch.tensor([[[1.0, 0.5]]], dtype=torch.float32)
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bias = torch.tensor([0.25, -0.5], dtype=torch.float32)
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out = method.apply(layer, x, bias)
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expected = x @ layer.qweight.T + bias
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assert torch.allclose(out, expected)
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def test_diffusion_gguf_linear_method_concatenates_sharded_outputs():
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qweight_type = next(iter(UNQUANTIZED_TYPES))
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method = DiffusionGGUFLinearMethod(DiffusionGGUFConfig(unquantized_modules=[]))
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qweight = torch.nn.Parameter(
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torch.tensor(
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[
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[1.0, 0.0],
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[0.0, 1.0],
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[2.0, 0.0],
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[0.0, 2.0],
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],
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dtype=torch.float32,
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),
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requires_grad=False,
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)
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qweight.shard_id = ["left", "right"]
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qweight.shard_offset_map = {
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"left": (0, 2, 2),
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"right": (2, 4, 2),
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}
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layer = SimpleNamespace(
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qweight=qweight,
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qweight_type=SimpleNamespace(
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shard_weight_type={
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"left": qweight_type,
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"right": qweight_type,
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}
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),
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)
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x = torch.tensor([[1.5, 2.0]], dtype=torch.float32)
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out = method.apply(layer, x)
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expected_q = x @ qweight[:2].T
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expected_k = x @ qweight[2:4].T
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assert torch.allclose(out, torch.cat([expected_q, expected_k], dim=-1))
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