# SPDX-License-Identifier: Apache-2.0 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project """Test model set-up and weight loading for quark-quantized models. Run `pytest tests/quantization/test_quark.py`. See also `tests/kernels/moe/test_ocp_mx_moe.py`. """ import importlib.metadata from dataclasses import dataclass from importlib.util import find_spec from types import SimpleNamespace from unittest.mock import MagicMock, Mock, patch import huggingface_hub import lm_eval import pytest import torch from packaging import version from tests.quantization.utils import load_model_without_vllm_runner from vllm._aiter_ops import is_aiter_found_and_supported, rocm_aiter_ops from vllm.config import VllmConfig, set_current_vllm_config from vllm.config.cache import CacheConfig from vllm.forward_context import set_forward_context from vllm.model_executor import parameter from vllm.model_executor.kernels.linear.scaled_mm.aiter import ( AiterHipbMMPerTokenFp8ScaledMMLinearKernel, AiterPerTokenFp8ScaledMMLinearKernel, AiterPreshuffledPerTokenFp8ScaledMMLinearKernel, ) from vllm.model_executor.kernels.linear.scaled_mm.ScaledMMLinearKernel import ( FP8ScaledMMLinearLayerConfig, ) from vllm.model_executor.layers.attention import Attention from vllm.model_executor.layers.fused_moe import ( FusedMoeWeightScaleSupported, RoutedExperts, UnquantizedFusedMoEMethod, ) from vllm.model_executor.layers.fused_moe.activation import MoEActivation from vllm.model_executor.layers.fused_moe.config import ( FusedMoEConfig, FusedMoEParallelConfig, RoutingMethodType, ) from vllm.model_executor.layers.linear import LinearBase, UnquantizedLinearMethod from vllm.model_executor.layers.quantization.quark.quark import ( # noqa: E501 QuarkConfig, QuarkLinearMethod, QuarkNVFP4, QuarkOCP_MX, QuarkW8A8Fp8, QuarkW8A8Fp8PerBlock, QuarkW8A8Int8, ) from vllm.model_executor.layers.quantization.quark.quark_moe import ( # noqa: E501 QuarkMoEMethod, QuarkW4A8Fp8MoEMethod, QuarkW4A16Int4MoEMethod, QuarkW8A8Fp8MoEMethod, QuarkW8A8Int8MoEMethod, ) from vllm.model_executor.layers.quantization.quark.schemes import ( QuarkScheme, QuarkW4A16Int4, ) from vllm.model_executor.layers.quantization.quark.utils import ( QuarkQTensorHint, canonicalize_quark_packed_int4, should_ignore_layer, ) from vllm.model_executor.layers.quantization.utils.mxfp4_utils import ( quant_dequant_mxfp4, ) from vllm.model_executor.layers.quantization.utils.quant_utils import ( QuantKey, is_layer_skipped, kFp8Dynamic128Sym, kFp8DynamicTensorSym, kFp8DynamicTokenSym, kFp8Static128BlockE8M0Sym, kFp8Static128BlockSym, kFp8StaticChannelSym, kFp8StaticTensorSym, kInt4W4A8StaticChannelSym, kInt8DynamicTensorAsym, kInt8DynamicTensorSym, kInt8DynamicTokenAsym, kInt8DynamicTokenSym, kInt8StaticChannelSym, kInt8StaticTensorAsym, kInt8StaticTensorSym, kMxfp4Dynamic, kMxfp4Static, kMxfp6E2M3Dynamic, kMxfp6E2M3Static, kMxfp6E3M2Dynamic, kMxfp6E3M2Static, kNvfp4Dynamic, kNvfp4Static, ) from vllm.model_executor.models.llama import LlamaForCausalLM from vllm.model_executor.models.utils import WeightsMapper from vllm.platforms import current_platform from vllm.transformers_utils.repo_utils import hf_api if current_platform.is_rocm(): from vllm.platforms.rocm import on_gfx942, on_gfx950 else: def on_gfx942() -> bool: return False def on_gfx950() -> bool: return False from .reference_mxfp4 import dq_mxfp4_torch, qdq_mxfp4_torch # Minimum amd-quark version for MXFP4/OCP_MX tests (single source of truth). QUARK_MXFP4_MIN_VERSION = "0.12" QUARK_MXFP4_AVAILABLE = find_spec("quark") is not None and version.parse( importlib.metadata.version("amd-quark") ) >= version.parse(QUARK_MXFP4_MIN_VERSION) AITER_AVAILABLE = is_aiter_found_and_supported() AITER_PTPC_KERNELS = ( AiterHipbMMPerTokenFp8ScaledMMLinearKernel, AiterPreshuffledPerTokenFp8ScaledMMLinearKernel, AiterPerTokenFp8ScaledMMLinearKernel, ) DEVICE_TYPE = current_platform.device_type @dataclass(frozen=True) class QTensorConfig: name: str weight: QuarkQTensorHint input_tensors: QuarkQTensorHint weight_quant_key: QuantKey | None = None act_quant_key: QuantKey | None = None dispatch_cls: type[QuarkScheme] | type[QuarkMoEMethod] | None = None expected_error: tuple[type[Exception], str] | None = None QTENSOR_CONFIGS = [ QTensorConfig( name="fp8_w8a8_static_tensor", weight={"dtype": "fp8_e4m3", "qscheme": "per_tensor", "is_dynamic": False}, input_tensors={ "dtype": "fp8_e4m3", "qscheme": "per_tensor", "is_dynamic": False, }, weight_quant_key=kFp8StaticTensorSym, act_quant_key=kFp8StaticTensorSym, dispatch_cls=QuarkW8A8Fp8, ), QTensorConfig( name="fp8_w8a8_static_tensor_single_entry_lists", weight=[ { "dtype": "fp8_e4m3", "qscheme": "per_tensor", "is_dynamic": False, } ], input_tensors=[ { "dtype": "fp8_e4m3", "qscheme": "per_tensor", "is_dynamic": False, } ], weight_quant_key=kFp8StaticTensorSym, act_quant_key=kFp8StaticTensorSym, dispatch_cls=QuarkW8A8Fp8, ), QTensorConfig( name="fp8_w8a8_dynamic_tensor", weight={"dtype": "fp8_e4m3", "qscheme": "per_tensor", "is_dynamic": False}, input_tensors={ "dtype": "fp8_e4m3", "qscheme": "per_tensor", "is_dynamic": True, }, weight_quant_key=kFp8StaticTensorSym, act_quant_key=kFp8DynamicTensorSym, dispatch_cls=QuarkW8A8Fp8, ), QTensorConfig( name="fp8_w8a8_dynamic_token", weight={"dtype": "fp8_e4m3", "qscheme": "per_channel", "is_dynamic": False}, input_tensors={ "dtype": "fp8_e4m3", "qscheme": "per_channel", "is_dynamic": True, }, weight_quant_key=kFp8StaticChannelSym, act_quant_key=kFp8DynamicTokenSym, dispatch_cls=QuarkW8A8Fp8, ), QTensorConfig( name="fp8_w8a8_channel_static_tensor", weight={"dtype": "fp8_e4m3", "qscheme": "per_channel", "is_dynamic": False}, input_tensors={ "dtype": "fp8_e4m3", "qscheme": "per_tensor", "is_dynamic": False, }, weight_quant_key=kFp8StaticChannelSym, act_quant_key=kFp8StaticTensorSym, dispatch_cls=QuarkW8A8Fp8, ), QTensorConfig( name="fp8_w8a8_tensor_dynamic_token", weight={"dtype": "fp8_e4m3", "qscheme": "per_tensor", "is_dynamic": False}, input_tensors={ "dtype": "fp8_e4m3", "qscheme": "per_channel", "is_dynamic": True, }, weight_quant_key=kFp8StaticTensorSym, act_quant_key=kFp8DynamicTokenSym, dispatch_cls=QuarkW8A8Fp8, ), QTensorConfig( name="fp8_w8a8_dynamic_block_fp32", weight={ "dtype": "fp8_e4m3", "qscheme": "per_block", "is_dynamic": False, "block_size": [128, 128], "symmetric": True, }, input_tensors={ "dtype": "fp8_e4m3", "qscheme": "per_group", "is_dynamic": True, "group_size": 128, "symmetric": True, }, weight_quant_key=kFp8Static128BlockSym, act_quant_key=kFp8Dynamic128Sym, dispatch_cls=QuarkW8A8Fp8PerBlock, ), QTensorConfig( name="fp8_w8a8_dynamic_block_e8m0", weight={ "dtype": "fp8_e4m3", "qscheme": "per_block", "is_dynamic": False, "block_size": [128, 128], "symmetric": True, "scale_type": "float8_e8m0fnu", }, input_tensors={ "dtype": "fp8_e4m3", "qscheme": "per_group", "is_dynamic": True, "group_size": 128, "symmetric": True, }, weight_quant_key=kFp8Static128BlockE8M0Sym, act_quant_key=kFp8Dynamic128Sym, dispatch_cls=QuarkW8A8Fp8PerBlock, ), QTensorConfig( name="fp8_w8a8_dynamic_block_fp32_moe", weight={ "dtype": "fp8_e4m3", "qscheme": "per_block", "is_dynamic": False, "block_size": [128, 128], "symmetric": True, }, input_tensors={ "dtype": "fp8_e4m3", "qscheme": "per_group", "is_dynamic": True, "group_size": 128, "symmetric": True, }, weight_quant_key=kFp8Static128BlockSym, act_quant_key=kFp8Dynamic128Sym, dispatch_cls=QuarkW8A8Fp8MoEMethod, ), QTensorConfig( name="fp8_w8a8_block_static_input", weight={ "dtype": "fp8_e4m3", "qscheme": "per_block", "is_dynamic": False, "block_size": [128, 128], "symmetric": True, }, input_tensors={ "dtype": "fp8_e4m3", "qscheme": "per_group", "is_dynamic": False, "group_size": 128, "symmetric": True, }, expected_error=(NotImplementedError, "No quark compatible scheme"), ), QTensorConfig( name="fp8_w8a8_block_group_size_mismatch", weight={ "dtype": "fp8_e4m3", "qscheme": "per_block", "is_dynamic": False, "block_size": [128, 128], "symmetric": True, }, input_tensors={ "dtype": "fp8_e4m3", "qscheme": "per_group", "is_dynamic": True, "group_size": 64, "symmetric": True, }, expected_error=(NotImplementedError, "No quark compatible scheme"), ), QTensorConfig( name="fp8_w8a8_block_missing_block_size", weight={ "dtype": "fp8_e4m3", "qscheme": "per_block", "is_dynamic": False, "symmetric": True, }, input_tensors={ "dtype": "fp8_e4m3", "qscheme": "per_group", "is_dynamic": True, "group_size": 128, "symmetric": True, }, expected_error=(ValueError, "requires `block_size`"), ), QTensorConfig( name="int8_w8a8_static_symmetric", weight={ "dtype": "int8", "qscheme": "per_tensor", "is_dynamic": False, "symmetric": True, }, input_tensors={ "dtype": "int8", "qscheme": "per_tensor", "is_dynamic": False, "symmetric": True, }, weight_quant_key=kInt8StaticTensorSym, act_quant_key=kInt8StaticTensorSym, dispatch_cls=QuarkW8A8Int8, ), QTensorConfig( name="int8_w8a8_static_asymmetric", weight={ "dtype": "int8", "qscheme": "per_tensor", "is_dynamic": False, "symmetric": True, }, input_tensors={ "dtype": "int8", "qscheme": "per_tensor", "is_dynamic": False, "symmetric": False, }, weight_quant_key=kInt8StaticTensorSym, act_quant_key=kInt8StaticTensorAsym, dispatch_cls=QuarkW8A8Int8, ), QTensorConfig( name="int8_w8a8_channel_static_symmetric", weight={ "dtype": "int8", "qscheme": "per_channel", "is_dynamic": False, "symmetric": True, }, input_tensors={ "dtype": "int8", "qscheme": "per_tensor", "is_dynamic": False, "symmetric": True, }, weight_quant_key=kInt8StaticChannelSym, act_quant_key=kInt8StaticTensorSym, dispatch_cls=QuarkW8A8Int8, ), QTensorConfig( name="int8_w8a8_channel_static_asymmetric", weight={ "dtype": "int8", "qscheme": "per_channel", "is_dynamic": False, "symmetric": True, }, input_tensors={ "dtype": "int8", "qscheme": "per_tensor", "is_dynamic": False, "symmetric": False, }, weight_quant_key=kInt8StaticChannelSym, act_quant_key=kInt8StaticTensorAsym, dispatch_cls=QuarkW8A8Int8, ), QTensorConfig( name="int8_w8a8_dynamic_tensor_symmetric", weight={ "dtype": "int8", "qscheme": "per_tensor", "is_dynamic": False, "symmetric": True, }, input_tensors={ "dtype": "int8", "qscheme": "per_channel", "is_dynamic": True, "symmetric": True, }, weight_quant_key=kInt8StaticTensorSym, act_quant_key=kInt8DynamicTensorSym, dispatch_cls=QuarkW8A8Int8, ), QTensorConfig( name="int8_w8a8_dynamic_tensor_asymmetric", weight={ "dtype": "int8", "qscheme": "per_tensor", "is_dynamic": False, "symmetric": True, }, input_tensors={ "dtype": "int8", "qscheme": "per_channel", "is_dynamic": True, "symmetric": False, }, weight_quant_key=kInt8StaticTensorSym, act_quant_key=kInt8DynamicTensorAsym, dispatch_cls=QuarkW8A8Int8, ), QTensorConfig( name="int8_w8a8_dynamic_token", weight={ "dtype": "int8", "qscheme": "per_channel", "is_dynamic": False, "symmetric": True, }, input_tensors={ "dtype": "int8", "qscheme": "per_channel", "is_dynamic": True, "symmetric": True, }, weight_quant_key=kInt8StaticChannelSym, act_quant_key=kInt8DynamicTokenSym, dispatch_cls=QuarkW8A8Int8, ), QTensorConfig( name="int8_w8a8_dynamic_token_asymmetric", weight={ "dtype": "int8", "qscheme": "per_channel", "is_dynamic": False, "symmetric": True, }, input_tensors={ "dtype": "int8", "qscheme": "per_channel", "is_dynamic": True, "symmetric": False, }, weight_quant_key=kInt8StaticChannelSym, act_quant_key=kInt8DynamicTokenAsym, dispatch_cls=QuarkW8A8Int8, ), QTensorConfig( name="ocp_mx_mxfp4_weight_only", weight={ "dtype": "fp4", "qscheme": "per_group", "group_size": 32, "scale_format": "e8m0", "is_dynamic": False, }, input_tensors=None, weight_quant_key=kMxfp4Static, act_quant_key=None, dispatch_cls=QuarkOCP_MX, ), QTensorConfig( name="ocp_mx_mxfp4_activation", weight={ "dtype": "fp4", "qscheme": "per_group", "group_size": 32, "scale_format": "e8m0", "is_dynamic": False, }, input_tensors={ "dtype": "fp4", "qscheme": "per_group", "group_size": 32, "scale_format": "e8m0", "is_dynamic": True, }, weight_quant_key=kMxfp4Static, act_quant_key=kMxfp4Dynamic, dispatch_cls=QuarkOCP_MX, ), QTensorConfig( name="ocp_mx_mxfp6_e3m2", weight={ "dtype": "fp6_e3m2", "qscheme": "per_group", "group_size": 32, "scale_format": "e8m0", "is_dynamic": False, }, input_tensors={ "dtype": "fp6_e3m2", "qscheme": "per_group", "group_size": 32, "scale_format": "e8m0", "is_dynamic": True, }, weight_quant_key=kMxfp6E3M2Static, act_quant_key=kMxfp6E3M2Dynamic, dispatch_cls=QuarkOCP_MX, ), QTensorConfig( name="ocp_mx_mxfp4_mxfp6_e3m2_activation", weight={ "dtype": "fp4", "qscheme": "per_group", "group_size": 32, "scale_format": "e8m0", "is_dynamic": False, }, input_tensors={ "dtype": "fp6_e3m2", "qscheme": "per_group", "group_size": 32, "scale_format": "e8m0", "is_dynamic": True, }, weight_quant_key=kMxfp4Static, act_quant_key=kMxfp6E3M2Dynamic, dispatch_cls=QuarkOCP_MX, ), QTensorConfig( name="ocp_mx_mxfp4_mxfp6_e2m3_activation", weight={ "dtype": "fp4", "qscheme": "per_group", "group_size": 32, "scale_format": "e8m0", "is_dynamic": False, }, input_tensors={ "dtype": "fp6_e2m3", "qscheme": "per_group", "group_size": 32, "scale_format": "e8m0", "is_dynamic": True, }, weight_quant_key=kMxfp4Static, act_quant_key=kMxfp6E2M3Dynamic, dispatch_cls=QuarkOCP_MX, ), QTensorConfig( name="ocp_mx_mxfp6_e2m3", weight={ "dtype": "fp6_e2m3", "qscheme": "per_group", "group_size": 32, "scale_format": "e8m0", "is_dynamic": False, }, input_tensors={ "dtype": "fp6_e2m3", "qscheme": "per_group", "group_size": 32, "scale_format": "e8m0", "is_dynamic": True, }, weight_quant_key=kMxfp6E2M3Static, act_quant_key=kMxfp6E2M3Dynamic, dispatch_cls=QuarkOCP_MX, ), QTensorConfig( name="nvfp4", weight=[ { "dtype": "fp4", "qscheme": "per_group", "group_size": 16, "is_dynamic": False, }, {"dtype": "fp8_e4m3", "qscheme": "per_tensor", "is_dynamic": False}, ], input_tensors=[ { "dtype": "fp4", "qscheme": "per_group", "group_size": 16, "is_dynamic": True, }, {"dtype": "fp8_e4m3", "qscheme": "per_tensor", "is_dynamic": False}, ], weight_quant_key=kNvfp4Static, act_quant_key=kNvfp4Dynamic, dispatch_cls=QuarkNVFP4, ), QTensorConfig( name="w4a8_fp8_static", weight=[ {"dtype": "fp8_e4m3", "qscheme": "per_tensor", "is_dynamic": False}, { "dtype": "int4", "qscheme": "per_channel", "is_dynamic": False, "symmetric": True, "ch_axis": 0, }, ], input_tensors={ "dtype": "fp8_e4m3", "qscheme": "per_tensor", "is_dynamic": False, }, weight_quant_key=kInt4W4A8StaticChannelSym, act_quant_key=kFp8StaticTensorSym, dispatch_cls=QuarkW4A8Fp8MoEMethod, ), QTensorConfig( name="w4a8_fp8_dynamic", weight=[ {"dtype": "fp8_e4m3", "qscheme": "per_tensor", "is_dynamic": False}, { "dtype": "int4", "qscheme": "per_channel", "is_dynamic": False, "symmetric": True, "ch_axis": 0, }, ], input_tensors={ "dtype": "fp8_e4m3", "qscheme": "per_channel", "is_dynamic": True, }, weight_quant_key=kInt4W4A8StaticChannelSym, act_quant_key=kFp8DynamicTokenSym, dispatch_cls=QuarkW4A8Fp8MoEMethod, ), QTensorConfig( name="w4a8_fp8_static_single_entry_input", weight=[ {"dtype": "fp8_e4m3", "qscheme": "per_tensor", "is_dynamic": False}, { "dtype": "int4", "qscheme": "per_channel", "is_dynamic": False, "symmetric": True, "ch_axis": 0, }, ], input_tensors=[ { "dtype": "fp8_e4m3", "qscheme": "per_tensor", "is_dynamic": False, } ], weight_quant_key=kInt4W4A8StaticChannelSym, act_quant_key=kFp8StaticTensorSym, dispatch_cls=QuarkW4A8Fp8MoEMethod, ), ] def _make_qtensor_config( weight: QuarkQTensorHint, input_tensors: QuarkQTensorHint, exclude: list[str] | None = None, ) -> QuarkConfig: return QuarkConfig( { "global_quant_config": { "weight": weight, "input_tensors": input_tensors, }, "layer_type_quant_config": {}, "exclude": exclude or [], } ) def _make_test_moe_config() -> FusedMoEConfig: return FusedMoEConfig( num_experts=8, experts_per_token=2, hidden_dim=256, intermediate_size=256, num_local_experts=8, num_logical_experts=8, activation=MoEActivation.SILU, device=current_platform.device_type, routing_method=RoutingMethodType.Renormalize, moe_parallel_config=FusedMoEParallelConfig.make_no_parallel(), in_dtype=torch.bfloat16, ) if QUARK_MXFP4_AVAILABLE: from quark.torch.export.nn.modules.realquantizer import StaticScaledRealQuantizer from quark.torch.kernel import mx as mx_kernel from quark.torch.quantization.config.config import FP4PerGroupSpec try: hf_api().list_repo_refs( "amd/Llama-3.3-70B-Instruct-WMXFP4-AMXFP4-KVFP8-Scale-UINT8-SQ" ) HF_HUB_AMD_ORG_ACCESS = True except huggingface_hub.errors.RepositoryNotFoundError: HF_HUB_AMD_ORG_ACCESS = False @pytest.fixture(scope="function", autouse=True) def enable_pickle(monkeypatch): """`LLM.apply_model` requires pickling a function.""" monkeypatch.setenv("VLLM_ALLOW_INSECURE_SERIALIZATION", "1") def test_quark_w8a8_fp8_per_block_registers_weight_scale(monkeypatch): from vllm.model_executor.layers.quantization.utils.fp8_utils import ( get_fp8_block_weight_scale, ) monkeypatch.setattr( "vllm.model_executor.layers.quantization.quark.schemes." "quark_w8a8_fp8.get_current_vllm_config", lambda: SimpleNamespace(model_config=SimpleNamespace(dtype=torch.bfloat16)), ) scheme = QuarkW8A8Fp8PerBlock(kFp8Static128BlockSym, kFp8Dynamic128Sym) layer = torch.nn.Module() layer.weight_scale = torch.tensor([2.0]) assert get_fp8_block_weight_scale(layer) is None layer.scheme = scheme assert get_fp8_block_weight_scale(layer) is layer.weight_scale layer.weight_scale_inv = torch.tensor([3.0]) assert get_fp8_block_weight_scale(layer) is layer.weight_scale layer.scheme = None assert get_fp8_block_weight_scale(layer) is layer.weight_scale_inv loaded = torch.nn.Module() def weight_loader(param, loaded_weight): return None dummy_param = torch.nn.Parameter(torch.empty(1), requires_grad=False) with ( patch( "vllm.model_executor.layers.quantization.quark.schemes.quark_w8a8_fp8." "validate_fp8_block_shape" ), patch( "vllm.model_executor.layers.quantization.quark.schemes.quark_w8a8_fp8." "create_fp8_weight_parameter", return_value=dummy_param, ), patch( "vllm.model_executor.layers.quantization.quark.schemes.quark_w8a8_fp8." "create_fp8_scale_parameter", return_value=dummy_param, ), patch( "vllm.model_executor.layers.quantization.quark.schemes.quark_w8a8_fp8." "init_fp8_linear_kernel", return_value=MagicMock(), ), ): scheme.create_weights( loaded, output_partition_sizes=[256], input_size_per_partition=256, params_dtype=torch.bfloat16, weight_loader=weight_loader, input_size=256, output_size=256, ) assert hasattr(loaded, "weight_scale") assert not hasattr(loaded, "weight_scale_inv") def test_quark_config_has_no_model_specific_fused_mappings(): config = QuarkConfig({}) assert "gate_up_proj" not in config.packed_modules_mapping assert "fused_wqa_wkv" not in config.packed_modules_mapping def test_quark_config_preserves_existing_packed_modules_mapping(): class CustomQuarkConfig(QuarkConfig): packed_modules_mapping = {"custom_proj": ["a", "b"]} config = CustomQuarkConfig({}) assert config.packed_modules_mapping["custom_proj"] == ["a", "b"] def test_quant_method_dispatch_ignored(default_vllm_config): config = _make_qtensor_config(None, None, exclude=["linear", "experts"]) class TestLinear(LinearBase): def __init__(self): torch.nn.Module.__init__(self) class TestRoutedExperts(RoutedExperts): def __init__(self): torch.nn.Module.__init__(self) self.moe_config = _make_test_moe_config() assert config.get_quant_method_target("linear", LinearBase) == ( None, None, UnquantizedLinearMethod, ) assert isinstance( config.get_quant_method(TestLinear(), "linear"), UnquantizedLinearMethod ) assert config.get_quant_method_target("experts", RoutedExperts) == ( None, None, UnquantizedFusedMoEMethod, ) assert isinstance( config.get_quant_method(TestRoutedExperts(), "experts"), UnquantizedFusedMoEMethod, ) dynamic_mxfp4_config = _make_qtensor_config( { "dtype": "fp4", "qscheme": "per_group", "group_size": 32, "scale_format": "e8m0", "is_dynamic": False, }, None, exclude=["self_attn.q_proj", "mlp.down_proj"], ) dynamic_mxfp4_config.dynamic_mxfp4_quant = True assert dynamic_mxfp4_config.get_quant_method_target( "self_attn.q_proj", LinearBase ) == (kMxfp4Static, None, QuarkLinearMethod) attention_proj = TestLinear() assert isinstance( dynamic_mxfp4_config.get_quant_method(attention_proj, "self_attn.q_proj"), QuarkLinearMethod, ) assert isinstance(attention_proj.scheme, QuarkOCP_MX) assert attention_proj.scheme.dynamic_mxfp4_quant assert dynamic_mxfp4_config.get_quant_method_target( "mlp.down_proj", LinearBase ) == (None, None, UnquantizedLinearMethod) assert isinstance( dynamic_mxfp4_config.get_quant_method(TestLinear(), "mlp.down_proj"), UnquantizedLinearMethod, ) @pytest.mark.parametrize("case", QTENSOR_CONFIGS, ids=lambda case: case.name) def test_quant_method_dispatch_target(case): config = _make_qtensor_config(case.weight, case.input_tensors) if case.expected_error is not None: error_type, error_message = case.expected_error with pytest.raises(error_type, match=error_message): config.get_quant_method_target("linear", LinearBase) return assert case.dispatch_cls is not None is_linear = issubclass(case.dispatch_cls, QuarkScheme) weight_quant_key, act_quant_key, method_cls = config.get_quant_method_target( "linear" if is_linear else "experts", LinearBase if is_linear else RoutedExperts, ) assert weight_quant_key == case.weight_quant_key assert act_quant_key == case.act_quant_key assert method_cls is (QuarkLinearMethod if is_linear else case.dispatch_cls) @pytest.mark.parametrize( ("weight", "input_tensors"), [ pytest.param( { "dtype": "int8", "qscheme": "per_group", "is_dynamic": False, "symmetric": True, }, { "dtype": "int8", "qscheme": "per_tensor", "is_dynamic": False, "symmetric": True, }, id="single_entry", ), pytest.param( [ {"dtype": "int8", "qscheme": "per_tensor"}, {"dtype": "int8", "qscheme": "per_tensor"}, ], [ {"dtype": "int8", "qscheme": "per_tensor"}, {"dtype": "int8", "qscheme": "per_tensor"}, ], id="multi_entry", ), ], ) def test_quant_method_dispatch_unsupported(weight, input_tensors): config = _make_qtensor_config(weight, input_tensors) class TestRoutedExperts(RoutedExperts): def __init__(self): torch.nn.Module.__init__(self) with pytest.raises(RuntimeError, match="^Unsupported FusedMoe scheme$"): config.get_quant_method_target("experts", RoutedExperts) with pytest.raises(RuntimeError, match="^Unsupported FusedMoe scheme$"): config.get_quant_method(TestRoutedExperts(), "experts") @pytest.mark.parametrize( "case", [case for case in QTENSOR_CONFIGS if case.expected_error is None], ids=lambda case: case.name, ) def test_quant_method_dispatch_instantiation(case, monkeypatch, default_vllm_config): config = _make_qtensor_config(case.weight, case.input_tensors) assert case.dispatch_cls is not None if issubclass(case.dispatch_cls, QuarkScheme): class TestLinear(LinearBase): def __init__(self): torch.nn.Module.__init__(self) monkeypatch.setattr( "vllm.model_executor.layers.quantization.quark.schemes." "quark_w8a8_fp8.get_current_vllm_config", lambda: SimpleNamespace(model_config=SimpleNamespace(dtype=torch.bfloat16)), ) layer = TestLinear() method = config.get_quant_method(layer, "linear") assert isinstance(method, QuarkLinearMethod) assert isinstance(layer.scheme, case.dispatch_cls) if case.weight_quant_key == kFp8Static128BlockE8M0Sym: # TODO: Remove once E8M0 quant key is properly handled in oracle assert layer.scheme.weight_quant_key == kFp8Static128BlockSym else: assert layer.scheme.weight_quant_key == case.weight_quant_key assert layer.scheme.activation_quant_key == case.act_quant_key else: class TestRoutedExperts(RoutedExperts): def __init__(self): torch.nn.Module.__init__(self) self.moe_config = _make_test_moe_config() for target in ( "select_fp8_moe_backend", "select_int8_moe_backend", "select_mxfp4_moe_backend", "backend_to_kernel_cls", "select_nvfp4_moe_backend", ): monkeypatch.setattr( f"vllm.model_executor.layers.quantization.quark.quark_moe.{target}", lambda *args, **kwargs: (object(), object()), ) # AssertionError: W4A8 FP8 MoE requires ROCm AITER fused MoE support monkeypatch.setattr( "vllm.model_executor.layers.quantization.quark.quark_moe." "rocm_aiter_ops.is_fused_moe_enabled", lambda: True, ) # default_vllm_config carries no model, but the FP8 and OCP MX methods # read the model type off the HF config. monkeypatch.setattr( "vllm.model_executor.layers.quantization.quark.quark_moe." "get_current_vllm_config", lambda: SimpleNamespace( model_config=SimpleNamespace(hf_config=SimpleNamespace()) ), ) layer = TestRoutedExperts() method = config.get_quant_method(layer, "experts") assert isinstance(method, case.dispatch_cls) QUARK_MOE_MODULE = "vllm.model_executor.layers.quantization.quark.quark_moe" # validate_fp8_block_shape_moe imports this from vllm.distributed when called, # so the patch has to target the source module rather than a local binding. TP_WORLD_SIZE = "vllm.distributed.get_tensor_model_parallel_world_size" def _make_per_block_fp8_moe_method( activation_quant_key: QuantKey = kFp8Dynamic128Sym, ) -> QuarkW8A8Fp8MoEMethod: return QuarkW8A8Fp8MoEMethod( _make_test_moe_config(), kFp8Static128BlockSym, activation_quant_key, ) def test_quark_w8a8_fp8_moe_per_block_requires_dynamic_group_input(): with ( patch( f"{QUARK_MOE_MODULE}.select_fp8_moe_backend", return_value=(Mock(), Mock()), ), patch(f"{QUARK_MOE_MODULE}.get_current_vllm_config"), pytest.raises(ValueError, match="per-block scales"), ): _make_per_block_fp8_moe_method(kFp8StaticTensorSym) def test_quark_w8a8_fp8_moe_per_block_weight_shapes(): with ( patch( f"{QUARK_MOE_MODULE}.select_fp8_moe_backend", return_value=(Mock(), Mock()), ), patch(f"{QUARK_MOE_MODULE}.get_current_vllm_config"), patch(TP_WORLD_SIZE, return_value=1), ): method = _make_per_block_fp8_moe_method() assert method.block_quant assert method.weight_block_size == [128, 128] layer = torch.nn.Module() method.create_weights( layer, num_experts=4, hidden_size=512, intermediate_size_per_partition=256, params_dtype=torch.bfloat16, ) w13_num_shards = method.moe.w13_num_shards assert layer.weight_block_size == [128, 128] assert layer.w13_weight.shape == (4, w13_num_shards * 256, 512) assert layer.w2_weight.shape == (4, 512, 256) # Quark exports block scales as `weight_scale`, like the per-tensor and # per-channel schemes, so all schemes register the same parameter name. assert not hasattr(layer, "w13_weight_scale_inv") assert not hasattr(layer, "w2_weight_scale_inv") # One scale per 128x128 tile of each expert's weight. assert layer.w13_weight_scale.shape == ( 4, w13_num_shards * (256 // 128), 512 // 128, ) assert layer.w2_weight_scale.shape == (4, 512 // 128, 256 // 128) # The loader shards block scales on the block grid, not per row. for scale in (layer.w13_weight_scale, layer.w2_weight_scale): assert scale.quant_method == FusedMoeWeightScaleSupported.BLOCK.value def test_quark_w8a8_fp8_moe_per_block_rejects_misaligned_partition(): with ( patch( f"{QUARK_MOE_MODULE}.select_fp8_moe_backend", return_value=(Mock(), Mock()), ), patch(f"{QUARK_MOE_MODULE}.get_current_vllm_config"), patch(TP_WORLD_SIZE, return_value=1), pytest.raises(ValueError, match="not divisible by"), ): _make_per_block_fp8_moe_method().create_weights( torch.nn.Module(), num_experts=4, hidden_size=512, intermediate_size_per_partition=192, params_dtype=torch.bfloat16, ) def test_quark_fp8_ptpc_exposes_kernel_input_quant_key(monkeypatch): """QuarkW8A8Fp8 must advertise the key its kernel consumes pre-quantized. Only kernel selection runs, no GEMM. The shape matters: AITER PTPC kernels decline untuned shapes, leaving a torch kernel that has no key. """ # Llama-3.1-70B qkv_proj at TP1 (aiter-tuned N, K on gfx950). N, K = 10240, 8192 dtype = torch.bfloat16 kernel_config = FP8ScaledMMLinearLayerConfig( weight_quant_key=kFp8StaticChannelSym, activation_quant_key=kFp8DynamicTokenSym, weight_shape=(N, K), input_dtype=dtype, out_dtype=dtype, ) if not any( cls.is_supported()[0] and cls.can_implement(kernel_config)[0] for cls in AITER_PTPC_KERNELS ): pytest.skip("no AITER PTPC kernel is usable for this shape and environment") monkeypatch.setattr(parameter, "get_tensor_model_parallel_rank", lambda: 0) monkeypatch.setattr(parameter, "get_tensor_model_parallel_world_size", lambda: 1) scheme = QuarkW8A8Fp8.__new__(QuarkW8A8Fp8) scheme.weight_qscheme = "per_channel" scheme.is_static_input_scheme = False scheme.input_qscheme = "per_channel" scheme.activation_quant_key = kFp8DynamicTokenSym scheme.weight_quant_key = kFp8StaticChannelSym scheme.out_dtype = dtype scheme.input_dtype = dtype layer = torch.nn.Module() with set_current_vllm_config(VllmConfig()): scheme.create_weights( layer, output_partition_sizes=[N], input_size_per_partition=K, params_dtype=dtype, weight_loader=lambda *args, **kwargs: None, ) assert isinstance(scheme.fp8_linear, AITER_PTPC_KERNELS) assert layer.input_quant_key == kFp8DynamicTokenSym @pytest.mark.parametrize("kv_cache_dtype", ["auto", "fp8"]) def test_quark_fp8_w_per_tensor_a_per_tensor( kv_cache_dtype: str, monkeypatch, dist_init, workspace_init ): model_path = "amd/Llama-3.1-8B-Instruct-FP8-KV-Quark-test" checkpoint_scales = {} scale_names = { "model.layers.0.self_attn.k_proj.output_scale", "model.layers.0.self_attn.v_proj.output_scale", } original_load_weights = LlamaForCausalLM.load_weights def load_weights(self, weights): def capture_scales(): for name, weight in weights: if name in scale_names: checkpoint_scales[name] = weight.detach().cpu() yield name, weight return original_load_weights(self, capture_scales()) monkeypatch.setattr(LlamaForCausalLM, "load_weights", load_weights) model, vllm_config = load_model_without_vllm_runner( model_path, model_config_kwargs={"hf_overrides": {"num_hidden_layers": 3}}, vllm_config_kwargs={"cache_config": CacheConfig(cache_dtype=kv_cache_dtype)}, ) qkv_proj = model.model.layers[0].self_attn.qkv_proj assert isinstance(qkv_proj.quant_method, QuarkLinearMethod) assert isinstance(qkv_proj.scheme, QuarkW8A8Fp8) assert len(qkv_proj.input_scale.shape) == 0 assert qkv_proj.weight.dtype is current_platform.fp8_dtype() assert len(qkv_proj.weight_scale.shape) == 0 attn = model.model.layers[0].self_attn.attn if kv_cache_dtype == "fp8": assert checkpoint_scales.keys() == scale_names scale_multiplier = 2 if current_platform.is_fp8_fnuz() else 1 assert attn._k_scale_float == ( checkpoint_scales["model.layers.0.self_attn.k_proj.output_scale"].item() * scale_multiplier ) assert attn._v_scale_float == ( checkpoint_scales["model.layers.0.self_attn.v_proj.output_scale"].item() * scale_multiplier ) else: assert attn._k_scale_float == 1.0 assert attn._v_scale_float == 1.0 monkeypatch.setattr(Attention, "forward", lambda _, q, k, v: q.contiguous()) input_ids = torch.tensor([1, 2, 3, 4], device=DEVICE_TYPE) positions = torch.arange(input_ids.numel(), device=DEVICE_TYPE) with ( set_current_vllm_config(vllm_config), set_forward_context(None, vllm_config, num_tokens=input_ids.numel()), ): hidden_states = model(input_ids, positions, None) logits = model.compute_logits(hidden_states) assert torch.isfinite(logits).all() def test_quark_fp8_w_per_channel_a_per_token(monkeypatch, dist_init, workspace_init): model_path = "amd/Qwen2.5-1.5B-Instruct-ptpc-Quark-ts" model, vllm_config = load_model_without_vllm_runner( model_path, model_config_kwargs={"hf_overrides": {"num_hidden_layers": 3}}, ) qkv_proj = model.model.layers[0].self_attn.qkv_proj assert isinstance(qkv_proj.quant_method, QuarkLinearMethod) assert isinstance(qkv_proj.scheme, QuarkW8A8Fp8) assert qkv_proj.weight.dtype is current_platform.fp8_dtype() assert qkv_proj.weight_scale.shape[0] == qkv_proj.weight.shape[1] assert qkv_proj.weight_scale.shape[1] == 1 monkeypatch.setattr(Attention, "forward", lambda _, q, k, v: q.contiguous()) input_ids = torch.tensor([1, 2, 3, 4], device=DEVICE_TYPE) positions = torch.arange(input_ids.numel(), device=DEVICE_TYPE) with ( set_current_vllm_config(vllm_config), set_forward_context(None, vllm_config, num_tokens=input_ids.numel()), ): hidden_states = model(input_ids, positions, None) logits = model.compute_logits(hidden_states) assert torch.isfinite(logits).all() def test_quark_int8_w_per_tensor_a_per_tensor(monkeypatch, dist_init, workspace_init): model_path = "amd/Llama-3.1-8B-Instruct-w-int8-a-int8-sym-test" model, vllm_config = load_model_without_vllm_runner( model_path, model_config_kwargs={"hf_overrides": {"num_hidden_layers": 3}}, ) with set_current_vllm_config(vllm_config): qkv_proj = model.model.layers[0].self_attn.qkv_proj assert isinstance(qkv_proj.quant_method, QuarkLinearMethod) assert isinstance(qkv_proj.scheme, QuarkW8A8Int8) monkeypatch.setattr(Attention, "forward", lambda _, q, k, v: q.contiguous()) input_ids = torch.tensor([1, 2, 3, 4], device=DEVICE_TYPE) positions = torch.arange(input_ids.numel(), device=DEVICE_TYPE) with set_forward_context(None, vllm_config, num_tokens=input_ids.numel()): hidden_states = model(input_ids, positions, None) logits = model.compute_logits(hidden_states) assert torch.isfinite(logits).all() @pytest.mark.parametrize("tp", [1]) def test_quark_int8_w8a8_moe(vllm_runner, tp): """Test W8A8 INT8 MoE quantization with a tiny Qwen3 MoE model.""" model_path = "amd/tiny-qwen3-moe-w8a8-int8" with vllm_runner( model_path, enforce_eager=True, tensor_parallel_size=tp, gpu_memory_utilization=0.1, ) as llm: def check_model(model): layer = model.model.layers[0] # MoE experts should use QuarkW8A8Int8MoEMethod moe = layer.mlp.experts assert isinstance(moe._quant_method, QuarkW8A8Int8MoEMethod), ( f"Expected QuarkW8A8Int8MoEMethod, got {type(moe._quant_method)}" ) # Non-MoE linear layers should use QuarkW8A8Int8 qkv_proj = layer.self_attn.qkv_proj assert isinstance(qkv_proj.scheme, QuarkW8A8Int8) llm.apply_model(check_model) output = llm.generate_greedy("Hello", max_tokens=4) assert output @pytest.mark.parametrize("tp", [1]) def test_quark_fp8_w8a8_per_block_moe(vllm_runner, tp): """Test per-block (128x128) FP8 MoE quantization with a tiny Qwen3 MoE model.""" model_path = "Adamji/tiny-qwen3-moe-fp8-per-block" with vllm_runner( model_path, enforce_eager=True, tensor_parallel_size=tp, gpu_memory_utilization=0.1, ) as llm: def check_model(model): experts = model.model.layers[0].mlp.experts method = experts._quant_method assert isinstance(method, QuarkW8A8Fp8MoEMethod), ( f"Expected QuarkW8A8Fp8MoEMethod, got {type(method)}" ) assert method.weight_qscheme == "per_block" assert method.weight_block_size == [128, 128] # hidden_size=128, moe_intermediate_size=256 and 4 experts, so one # scale per 128x128 tile, with w13 stacking gate on top of up. routed_experts = experts.routed_experts assert routed_experts.w13_weight_scale.shape == (4, 4, 1) assert routed_experts.w2_weight_scale.shape == (4, 1, 2) # Quark exports the block scales under the same name as the # per-tensor and per-channel schemes. assert not hasattr(routed_experts, "w13_weight_scale_inv") assert not hasattr(routed_experts, "w2_weight_scale_inv") llm.apply_model(check_model) output = llm.generate_greedy("Hello", max_tokens=4) assert output @pytest.mark.skipif( not (on_gfx950() or on_gfx942()), reason="Quark W4A8 (INT4-FP8) MoE requires the AITER kernel on gfx942/gfx950", ) def test_quark_w4a8_fp8_moe(monkeypatch, dist_init, workspace_init): """Test W4A8 (INT4 weight + FP8 activation) MoE with a tiny Qwen3 MoE model. W4A8 dispatches through the AITER fused MoE kernel, so AITER must be on. """ monkeypatch.setenv("VLLM_ROCM_USE_AITER", "1") monkeypatch.setenv("VLLM_ROCM_USE_AITER_MOE", "1") rocm_aiter_ops.refresh_env_variables() model_path = "amd/tiny-qwen3-moe-w4a8" model, vllm_config = load_model_without_vllm_runner( model_path, ) with set_current_vllm_config(vllm_config): moe = model.model.layers[0].mlp.experts assert isinstance(moe._quant_method, QuarkW4A8Fp8MoEMethod), ( f"Expected QuarkW4A8Fp8MoEMethod, got {type(moe._quant_method)}" ) monkeypatch.setattr(Attention, "forward", lambda _, q, k, v: q.contiguous()) input_ids = torch.tensor([1, 2, 3, 4], device=DEVICE_TYPE) positions = torch.arange(input_ids.numel(), device=DEVICE_TYPE) with set_forward_context(None, vllm_config, num_tokens=input_ids.numel()): hidden_states = model(input_ids, positions, None) logits = model.compute_logits(hidden_states) assert torch.isfinite(logits).all() def test_quark_fp8_parity(dist_init, workspace_init): quark_model_id = "amd-quark/llama-tiny-fp8-quark-quant-method" fp8_model_id = "amd-quark/llama-tiny-fp8-quant-method" def load_state_dict(model_id: str) -> dict[str, torch.Tensor]: model, _ = load_model_without_vllm_runner(model_id) return {k: v.cpu() for k, v in model.state_dict().items()} quark_state_dict = load_state_dict(quark_model_id) fp8_state_dict = load_state_dict(fp8_model_id) assert fp8_state_dict.keys() == quark_state_dict.keys() for key in fp8_state_dict: assert torch.equal(fp8_state_dict[key], quark_state_dict[key]) @dataclass class AccuracyTestConfig: model_name: str excepted_value: float def get_model_args( self, tp_size: int, model_max_len: int | None = None, kwargs: dict | None = None, ) -> dict: if kwargs is None: kwargs = {} model_args = { "pretrained": self.model_name, "dtype": "auto", "add_bos_token": True, "tensor_parallel_size": tp_size, "gpu_memory_utilization": 0.7, **kwargs, } if model_max_len is not None: model_args["max_model_len"] = model_max_len return model_args WIKITEXT_ACCURACY_CONFIGS = [ AccuracyTestConfig( model_name="fxmarty/qwen1.5_moe_a2.7b_chat_w_fp4_a_fp6_e2m3", excepted_value=11.3, ), AccuracyTestConfig( model_name="fxmarty/qwen1.5_moe_a2.7b_chat_w_fp6_e3m2_a_fp6_e3m2", excepted_value=10.6, ), ] @pytest.mark.skipif( not QUARK_MXFP4_AVAILABLE, reason=f"amd-quark>={QUARK_MXFP4_MIN_VERSION} is not available", ) @pytest.mark.parametrize( "config", WIKITEXT_ACCURACY_CONFIGS, ids=lambda config: config.model_name ) @pytest.mark.parametrize("tp_size", [1, 2]) def test_ocp_mx_wikitext_correctness(config: AccuracyTestConfig, tp_size: int): device_count = torch.accelerator.device_count() if device_count < tp_size: pytest.skip(f"This test requires >={tp_size} gpus, got only {device_count}") results = lm_eval.simple_evaluate( model="vllm", model_args=config.get_model_args( tp_size=tp_size, kwargs={"cudagraph_capture_sizes": [16]} ), tasks="wikitext", batch_size=64, ) measured_value = results["results"]["wikitext"]["word_perplexity,none"] assert measured_value == pytest.approx(config.excepted_value, abs=0.1) GSM8K_ACCURACY_CONFIGS = [ # Private model. AccuracyTestConfig( model_name="amd/DeepSeek-R1-WMXFP4-AMXFP4-Scale-UINT8-MoE-Quant", excepted_value=0.96, ), ] @pytest.mark.parametrize("config", GSM8K_ACCURACY_CONFIGS) @pytest.mark.skipif( not QUARK_MXFP4_AVAILABLE, reason=f"amd-quark>={QUARK_MXFP4_MIN_VERSION} is not available", ) @pytest.mark.skipif( not HF_HUB_AMD_ORG_ACCESS, reason="Read access to huggingface.co/amd is required for this test.", ) def test_mxfp4_gsm8k_correctness(config: AccuracyTestConfig): device_count = torch.accelerator.device_count() if device_count < 8: pytest.skip(f"This test requires >=8 gpus, got only {device_count}") task = "gsm8k" rtol = 0.03 results = lm_eval.simple_evaluate( model="vllm", model_args=config.get_model_args(tp_size=8, model_max_len=38768), tasks=task, batch_size=64, num_fewshot=8, ) EXPECTED_VALUE = config.excepted_value measured_value = results["results"][task]["exact_match,strict-match"] assert ( measured_value - rtol < EXPECTED_VALUE and measured_value + rtol > EXPECTED_VALUE ), f"Expected: {EXPECTED_VALUE} | Measured: {measured_value}" @pytest.mark.skipif( not QUARK_MXFP4_AVAILABLE, reason=f"amd-quark>={QUARK_MXFP4_MIN_VERSION} is not available", ) @pytest.mark.parametrize("float_dtype", [torch.bfloat16, torch.float16]) @pytest.mark.parametrize("scalings", [[2.3, 0.03, 7.3, 0.1, 0.004, 17.3, 1e4, 1e-4]]) def test_mxfp4_fused_qdq_match_quark(float_dtype: torch.dtype, scalings: list[int]): torch.manual_seed(0) hidden_size = 64 * 32 inp = (torch.rand(1, hidden_size, dtype=float_dtype, device=DEVICE_TYPE) - 0.5) * 2 for i in range(hidden_size // 32): inp[:, i * 32 : (i + 1) * 32] = ( inp[:, i * 32 : (i + 1) * 32] * scalings[i % len(scalings)] ) inp_kernel = inp.clone() inp_kernel_clone = inp_kernel.clone() res_hip = mx_kernel.qdq_mxfp4_hip(inp_kernel_clone, "even") res_torch = qdq_mxfp4_torch(inp_kernel, "even") for i in range(hidden_size // 32): assert torch.all(torch.isfinite(res_hip[:, i * 32 : (i + 1) * 32])) assert torch.all(torch.isfinite(res_torch[:, i * 32 : (i + 1) * 32])) torch.testing.assert_close( res_hip[:, i * 32 : (i + 1) * 32], res_torch[:, i * 32 : (i + 1) * 32] ) @pytest.mark.skipif( not QUARK_MXFP4_AVAILABLE, reason=f"amd-quark>={QUARK_MXFP4_MIN_VERSION} is not available", ) @pytest.mark.parametrize("float_dtype", [torch.bfloat16, torch.float16]) @pytest.mark.parametrize("scalings", [[2.3, 0.03, 7.3, 0.1, 0.004, 17.3, 1e4, 1e-4]]) def test_mxfp4_dequant_kernel_match_quark( float_dtype: torch.dtype, scalings: list[int] ): qspec = FP4PerGroupSpec( ch_axis=-1, group_size=32, scale_format="e8m0", scale_calculation_mode="even", is_dynamic=False, ).to_quantization_spec() weight_quantizer = StaticScaledRealQuantizer( qspec=qspec, quantizer=None, reorder=False, real_quantized=True, float_dtype=float_dtype, device=DEVICE_TYPE, ) observer = qspec.observer_cls(qspec, device=DEVICE_TYPE) hidden_size = 512 shape = (11008, hidden_size) w = (torch.rand(shape, device=DEVICE_TYPE, dtype=float_dtype) - 0.5) * 2 # Make it so that different groups have different scales. for i in range(hidden_size // 32): w[:, i * 32 : (i + 1) * 32] = ( w[:, i * 32 : (i + 1) * 32] * scalings[i % len(scalings)] ) observer(w) scale, _ = observer._calculate_qparams() weight_quantizer.scale = scale w_mxfp4 = weight_quantizer.to_real_quantize_params(w).to(DEVICE_TYPE) weight_quantizer.maybe_convert_and_transpose_scale() scale = weight_quantizer.scale out_hip = mx_kernel.dq_mxfp4_hip(w_mxfp4, scale, float_dtype) out_torch = dq_mxfp4_torch(w_mxfp4, scale, float_dtype) assert torch.equal(out_hip, out_torch) @pytest.mark.skipif( not QUARK_MXFP4_AVAILABLE, reason=f"amd-quark>={QUARK_MXFP4_MIN_VERSION} is not available", ) @pytest.mark.skipif( not AITER_AVAILABLE, reason="AITER is not found or not supported on the current platform", ) @pytest.mark.parametrize("float_dtype", [torch.bfloat16, torch.float16]) @pytest.mark.parametrize("scalings", [[2.3, 0.03, 7.3, 0.1, 0.004, 17.3, 1e4, 1e-4]]) def test_mxfp4_dynamic_quant_match_quark( float_dtype: torch.dtype, scalings: list[float] ): """`AiterMxfp4LinearKernel` quantizes weights dynamically through AITER's `dynamic_mxfp4_quant`, while the emulation path quantizes/dequantizes through Quark's `qdq_mxfp4`. Check that both agree on the same input. """ from aiter.ops.triton.quant import dynamic_mxfp4_quant torch.manual_seed(0) hidden_size = 32 * 64 inp = (torch.rand(48, hidden_size, dtype=float_dtype, device=DEVICE_TYPE) - 0.5) * 2 for i in range(hidden_size // 32): inp[:, i * 32 : (i + 1) * 32] = ( inp[:, i * 32 : (i + 1) * 32] * scalings[i % len(scalings)] ) x_q, x_s = dynamic_mxfp4_quant(inp) out_dynamic_quant = dq_mxfp4_torch(x_q, x_s, float_dtype) out_quark_qdq = quant_dequant_mxfp4(inp) assert torch.equal(out_dynamic_quant, out_quark_qdq) # Unit tests for ``is_layer_skipped`` fused-name handling. FUSED_MAPPING = { "qkv_proj": ["q_proj", "k_proj", "v_proj"], "gate_up_proj": ["gate_proj", "up_proj"], } def test_quark_should_ignore_layer_checks_children(): assert should_ignore_layer( "model.layers.78.mlp.experts", ["model.layers.78.mlp.experts.0.down_proj"], check_children=True, ) def test_quark_should_ignore_layer_rejects_partial_fused_matches(): with pytest.raises(ValueError, match="different quantization schemes"): should_ignore_layer( "model.layers.0.self_attn.qkv_proj", ["model.layers.0.self_attn.q_proj"], FUSED_MAPPING, ) def test_fused_name_listed_directly_is_skipped(): # Regression for Step-3.5-Flash-FP8: the checkpoint lists the fused # name (``qkv_proj``) directly in ``modules_to_not_convert``. When a # ``packed_modules_mapping`` is registered on the model, the fused # match must still win over per-shard expansion. ignored = ["model.layers.0.self_attn.qkv_proj"] assert is_layer_skipped( prefix="model.layers.0.self_attn.qkv_proj", ignored_layers=ignored, fused_mapping=FUSED_MAPPING, ) assert is_layer_skipped( prefix="model.layers.0.mlp.gate_up_proj", ignored_layers=["model.layers.0.mlp.gate_up_proj"], fused_mapping=FUSED_MAPPING, ) def test_unfused_shards_listed_is_skipped(): # Quark INT8 style: per-shard names listed; all shards present means # the fused layer is skipped via expansion. ignored = [ "model.layers.0.self_attn.q_proj", "model.layers.0.self_attn.k_proj", "model.layers.0.self_attn.v_proj", ] assert is_layer_skipped( prefix="model.layers.0.self_attn.qkv_proj", ignored_layers=ignored, fused_mapping=FUSED_MAPPING, ) def test_partial_shards_raises(): # Only some shards listed -> ambiguous, must raise. Fused name is # not in ignored_layers, so we fall through to per-shard expansion. ignored = ["model.layers.0.self_attn.q_proj"] with pytest.raises(ValueError): is_layer_skipped( prefix="model.layers.0.self_attn.qkv_proj", ignored_layers=ignored, fused_mapping=FUSED_MAPPING, ) def test_not_skipped_when_nothing_listed(): assert not is_layer_skipped( prefix="model.layers.0.self_attn.qkv_proj", ignored_layers=["model.layers.0.mlp.gate_up_proj"], fused_mapping=FUSED_MAPPING, ) def test_non_fused_layer_unaffected(): assert is_layer_skipped( prefix="model.layers.0.self_attn.o_proj", ignored_layers=["model.layers.0.self_attn.o_proj"], fused_mapping=FUSED_MAPPING, ) assert not is_layer_skipped( prefix="model.layers.0.self_attn.o_proj", ignored_layers=["model.layers.1.self_attn.o_proj"], fused_mapping=FUSED_MAPPING, ) def test_substr_match_on_fused_name(): # Substring matching: a fused-name match should also # short-circuit before shard expansion. assert is_layer_skipped( prefix="model.layers.0.self_attn.qkv_proj", ignored_layers=["self_attn.qkv_proj"], fused_mapping=FUSED_MAPPING, match_mode="substring", ) @pytest.mark.parametrize( ("prefix", "ignored_layer", "expected"), [ ("model.layers.0.self_attn.b_proj", "b_proj", True), ("model.layers.0.self_attn.q_b_proj", "b_proj", False), ("model.layers.0.self_attn.kv_b_proj", "b_proj", False), ("model.layers.5.self_attn.g_proj", "5.self_attn.g_proj", True), ("model.layers.6.self_attn.g_proj", "5.self_attn.g_proj", False), ], ) def test_suffix_match_at_module_boundary(prefix, ignored_layer, expected): assert ( is_layer_skipped( prefix=prefix, ignored_layers=[ignored_layer], match_mode="suffix", ) is expected ) _GLM5_MXFP4_WEIGHT = { "dtype": "fp4", "qscheme": "per_group", "group_size": 32, "scale_format": "e8m0", "is_dynamic": False, } _GLM5_BLOCK_FP8_WEIGHT = { "dtype": "fp8_e4m3", "qscheme": "per_block", "is_dynamic": False, "block_size": [128, 128], "symmetric": True, } _GLM5_BLOCK_FP8_INPUT = { "dtype": "fp8_e4m3", "qscheme": "per_group", "is_dynamic": True, "group_size": 128, "symmetric": True, } _GLM5_GATE_UP = "language_model.model.layers.0.mlp.gate_up_proj" def _glm5_mixed_precision_config() -> QuarkConfig: """Global MXFP4 with the dense-MLP gate/up shards marked block-FP8.""" fp8 = {"weight": _GLM5_BLOCK_FP8_WEIGHT, "input_tensors": _GLM5_BLOCK_FP8_INPUT} return QuarkConfig( { "global_quant_config": { "weight": _GLM5_MXFP4_WEIGHT, "input_tensors": None, }, "layer_type_quant_config": {}, "layer_quant_config": {"*mlp.gate_proj": fp8, "*mlp.up_proj": fp8}, "exclude": [], } ) class _GLM5RecordingParam: """Stand-in for a BF16 target param that records what gets loaded.""" def __init__(self): self.loaded_weight: torch.Tensor | None = None self.loaded_shard: object = "unset" def weight_loader(self, param, loaded_weight, shard_id=None): self.loaded_weight = loaded_weight self.loaded_shard = shard_id def _glm5_block_fp8(out_dim: int, in_dim: int, block: int = 128): """Return a (fp8 weight, f32 per-block scale) pair.""" weight = (torch.randn(out_dim, in_dim) * 0.1).to(torch.float8_e4m3fn) scale = torch.rand(out_dim // block, in_dim // block, dtype=torch.float32) + 0.5 return weight, scale def test_glm5next_gate_up_proj_mapping_is_genuine_fusion(): from vllm.models.glm5next.common.model import Glm5NextForConditionalGeneration mapping = Glm5NextForConditionalGeneration.packed_modules_mapping assert mapping["gate_up_proj"] == ["gate_proj", "up_proj"] def test_glm5next_genuine_fusion_resolves_gate_up_to_block_fp8(): # The fused module must expand to its real shard names so each resolves to # the per-layer block-FP8 entry (rather than the global MXFP4 scheme). config = _glm5_mixed_precision_config() config.packed_modules_mapping = {"gate_up_proj": ["gate_proj", "up_proj"]} _, _, scheme_cls = config.get_scheme_cls(LinearBase, _GLM5_GATE_UP) assert scheme_cls is QuarkW8A8Fp8PerBlock def test_glm5next_attn_loader_accepts_quark_weight_scale(): # Regression for KeyError on '...kv_a_proj_with_mqa.weight_scale': the fused # q_a/kv_a projection is kept BF16 and dequantized on load; the Quark scale # name must be recognized and routed to the correct fused shard. from vllm.models.glm5next.common.model import ( _dequant_fp8_block, _try_load_fp8_attn_proj, ) prefix = "layers.3.self_attn" target = _GLM5RecordingParam() params_dict = {f"{prefix}.fused_qkv_a_proj.weight": target} buf: dict = {} loaded: set = set() weight, scale = _glm5_block_fp8(256, 256) # fp8 weight arrives first -> buffered, nothing loaded yet. assert ( _try_load_fp8_attn_proj( f"{prefix}.kv_a_proj_with_mqa.weight", weight, buf, params_dict, loaded, 0 ) is True ) assert target.loaded_weight is None # Quark-named scale completes the pair -> dequantize + load. assert ( _try_load_fp8_attn_proj( f"{prefix}.kv_a_proj_with_mqa.weight_scale", scale, buf, params_dict, loaded, 0, ) is True ) assert target.loaded_weight is not None assert target.loaded_weight.dtype == torch.bfloat16 assert target.loaded_shard == 1 # kv_a is shard 1 of fused_qkv_a_proj assert torch.equal(target.loaded_weight, _dequant_fp8_block(weight, scale, 128)) assert f"{prefix}.fused_qkv_a_proj.weight" in loaded def test_glm5next_attn_loader_accepts_deepseek_weight_scale_inv(): # DeepSeek scale name keeps working unchanged. from vllm.models.glm5next.common.model import ( _dequant_fp8_block, _try_load_fp8_attn_proj, ) prefix = "layers.7.self_attn" target = _GLM5RecordingParam() params_dict = {f"{prefix}.o_proj.weight": target} buf: dict = {} loaded: set = set() weight, scale = _glm5_block_fp8(128, 256) _try_load_fp8_attn_proj( f"{prefix}.o_proj.weight", weight, buf, params_dict, loaded, 0 ) assert ( _try_load_fp8_attn_proj( f"{prefix}.o_proj.weight_scale_inv", scale, buf, params_dict, loaded, 0 ) is True ) assert target.loaded_shard is None # o_proj is a direct (non-fused) proj assert torch.equal(target.loaded_weight, _dequant_fp8_block(weight, scale, 128)) _REVERSE_AWQ_PACK_ORDER = [0, 4, 1, 5, 2, 6, 3, 7] def _quark_int4_config( *, pack_method: str = "reorder", symmetric: bool = True, exclude: list[str] | None = None, ) -> dict: return { "quant_method": "quark", "export": {"pack_method": pack_method, "kv_cache_group": []}, "global_quant_config": { "weight": { "dtype": "int4", "group_size": 128, "symmetric": symmetric, } }, "exclude": exclude or [], } def _sign_extend_int4_nibbles(t: torch.Tensor) -> torch.Tensor: mask = (t & 0x8).bool() t = t.clone() t[mask] = t[mask] | 0xF0 return t def _dequantize_quark_signed_awq_torch( qweight: torch.Tensor, scales: torch.Tensor, qzeros: torch.Tensor, group_size: int, *, pack_reorder: bool = True, ) -> torch.Tensor: bits = 4 shifts = torch.arange(0, 32, bits, device=qweight.device) iweights = ((qweight[:, :, None] >> shifts[None, None, :]) & 0xF).to(torch.int8) iweights = iweights.view(qweight.shape[0], -1) zeros = ((qzeros[:, :, None] >> shifts[None, None, :]) & 0xF).to(torch.int8) zeros = zeros.view(qzeros.shape[0], -1) if pack_reorder: order = torch.tensor(_REVERSE_AWQ_PACK_ORDER, device=qweight.device) else: order = torch.arange(8, device=qweight.device) iweights = iweights.view(qweight.shape[0], -1, 8)[:, :, order].reshape( qweight.shape[0], -1 ) zeros = zeros.view(qzeros.shape[0], -1, 8)[:, :, order].reshape(qzeros.shape[0], -1) iweights = _sign_extend_int4_nibbles(iweights & 0xF) zeros = _sign_extend_int4_nibbles(zeros & 0xF) scales = scales.repeat_interleave(group_size, dim=0) zeros = zeros.repeat_interleave(group_size, dim=0) return (iweights - zeros) * scales def _dequantize_awq_unsigned_torch( qweight: torch.Tensor, scales: torch.Tensor, qzeros: torch.Tensor, group_size: int, *, pack_reorder: bool = True, ) -> torch.Tensor: bits = 4 shifts = torch.arange(0, 32, bits, device=qweight.device) iweights = ((qweight[:, :, None] >> shifts[None, None, :]) & 0xF).to(torch.int8) iweights = iweights.view(qweight.shape[0], -1) zeros = ((qzeros[:, :, None] >> shifts[None, None, :]) & 0xF).to(torch.int8) zeros = zeros.view(qzeros.shape[0], -1) if pack_reorder: order = torch.tensor(_REVERSE_AWQ_PACK_ORDER, device=qweight.device) else: order = torch.arange(8, device=qweight.device) iweights = iweights.view(qweight.shape[0], -1, 8)[:, :, order].reshape( qweight.shape[0], -1 ) zeros = zeros.view(qzeros.shape[0], -1, 8)[:, :, order].reshape(qzeros.shape[0], -1) scales = scales.repeat_interleave(group_size, dim=0) zeros = zeros.repeat_interleave(group_size, dim=0) return (iweights - zeros) * scales def _pack_int4_nibbles(nibbles: torch.Tensor, *, pack_reorder: bool) -> torch.Tensor: pack_order = ( torch.tensor(_REVERSE_AWQ_PACK_ORDER, device=nibbles.device) if pack_reorder else torch.arange(8, device=nibbles.device) ) shifts = pack_order * 4 return ((nibbles.to(torch.int64) & 0xF) << shifts).sum(dim=-1).to(torch.int32) class TestQuarkInt4Format: """Tests for Quark INT4 export format compatibility.""" def test_quark_order_pack_method_uses_native_int4_scheme(self): quant_config = QuarkConfig.from_config(_quark_int4_config(pack_method="order")) weight_config = quant_config.quant_config["global_quant_config"] weight_key, act_key, scheme_cls = quant_config._get_scheme_cls_from_config( weight_config ) assert scheme_cls is QuarkW4A16Int4 scheme = quant_config.init_scheme( scheme_cls, weight_quant_key=weight_key, activation_quant_key=act_key, weight_config=weight_config["weight"], ) assert isinstance(scheme, QuarkW4A16Int4) assert not scheme.pack_reorder def test_quark_int4_scheme_supports_asymmetric_weights(self): quant_config = QuarkConfig.from_config(_quark_int4_config(symmetric=False)) weight_config = quant_config.quant_config["global_quant_config"] weight_key, act_key, scheme_cls = quant_config._get_scheme_cls_from_config( weight_config ) assert scheme_cls is QuarkW4A16Int4 scheme = quant_config.init_scheme( scheme_cls, weight_quant_key=weight_key, activation_quant_key=act_key, weight_config=weight_config["weight"], ) assert isinstance(scheme, QuarkW4A16Int4) assert not scheme.is_symmetric @pytest.mark.parametrize("missing_field", ["group_size", "symmetric"]) def test_quark_int4_scheme_requires_weight_config_fields(self, missing_field): from vllm.model_executor.layers.quantization.utils.quant_utils import ( kInt4Static, ) weight_config = { "dtype": "int4", "group_size": 128, "symmetric": True, } weight_config.pop(missing_field) quant_config = QuarkConfig.from_config( { "quant_method": "quark", "export": {"pack_method": "reorder", "kv_cache_group": []}, "global_quant_config": {"weight": weight_config}, "exclude": [], } ) with pytest.raises(ValueError, match=missing_field): quant_config.init_scheme( QuarkW4A16Int4, weight_quant_key=kInt4Static, activation_quant_key=None, weight_config=weight_config, ) def test_quark_int4_moe_uses_native_moe_method(self): from unittest.mock import MagicMock, patch from vllm.model_executor.layers.quantization.utils.quant_utils import ( kInt4StaticAsym, ) quant_config = QuarkConfig.from_config(_quark_int4_config(symmetric=False)) moe_config = type("MoeConfig", (), {})() moe_config.has_bias = False mock_backend = MagicMock() mock_experts_cls = MagicMock() with patch( "vllm.model_executor.layers.quantization.quark.quark_moe" ".select_wna16_moe_backend", return_value=(mock_backend, mock_experts_cls), ): method = QuarkW4A16Int4MoEMethod( kInt4StaticAsym, None, quant_config.quant_config["global_quant_config"]["weight"], quant_config.pack_method, moe_config, ) assert method.group_size == 128 assert method.pack_reorder assert method.use_wna16_backend def test_triton_wna16_experts_supports_asymmetric_int4(self): from vllm.model_executor.layers.fused_moe.experts.triton_moe import ( TritonWNA16Experts, ) from vllm.model_executor.layers.quantization.utils.quant_utils import ( kInt4Static, kInt4Static32, kInt4Static32Asym, kInt4StaticAsym, ) for key in [kInt4Static, kInt4Static32, kInt4StaticAsym, kInt4Static32Asym]: assert TritonWNA16Experts._supports_quant_scheme(key, None), ( f"{key} should be supported by TritonWNA16Experts" ) def test_quark_moe_loader_shards_w2_zero_point_across_tp_ranks(self): """w2 zero-points are sharded along the intermediate dim per TP rank. Loading the same Quark checkpoint tensor at tensor_parallel_size=2 for both ranks must jointly reconstruct the tensor_parallel_size=1 (full) result, with no overlap or gap. This guards the custom loader's TP split for the packed zero-point layout. """ class _FakeMoEConfig: has_bias = False def __init__(self, tp_size, tp_rank): self.tp_size = tp_size self.tp_rank = tp_rank class _FakeLayer: def __init__(self, moe_config): self.moe_config = moe_config self.intermediate_size_per_partition = 64 self.group_size_div_factor = 1 def load_w2_zero_point(raw_zp, tp_size, tp_rank): from unittest.mock import MagicMock, patch from vllm.model_executor.layers.quantization.utils.quant_utils import ( kInt4StaticAsym, ) quant_config = QuarkConfig.from_config(_quark_int4_config(symmetric=False)) moe_config = _FakeMoEConfig(tp_size, tp_rank) with patch( "vllm.model_executor.layers.quantization.quark.quark_moe" ".select_wna16_moe_backend", return_value=(MagicMock(), MagicMock()), ): method = QuarkW4A16Int4MoEMethod( kInt4StaticAsym, None, quant_config.quant_config["global_quant_config"]["weight"], quant_config.pack_method, moe_config, ) layer = _FakeLayer(moe_config) # The loader converts the packed zero-point, then writes the # per-rank slice into param.data[expert_id]. A single expert dim is # enough; the last dim shrinks by tp_size after sharding. param = torch.nn.Parameter( torch.zeros(1, 16, 8 // tp_size, dtype=torch.uint8), requires_grad=False, ) loader = method.get_weight_loader(layer, weight_loader=None) loader( param, raw_zp.clone(), weight_name="w2_weight_zero_point", shard_id="w2", expert_id=0, ) return param.data[0] # Quark-packed zero-point for one expert: rows = hidden // pack_factor, # cols carry the (packed) intermediate dim, split evenly across ranks. raw_zp = torch.randint(0, 256, (8, 16), dtype=torch.uint8) full = load_w2_zero_point(raw_zp, tp_size=1, tp_rank=0) shard0 = load_w2_zero_point(raw_zp, tp_size=2, tp_rank=0) shard1 = load_w2_zero_point(raw_zp, tp_size=2, tp_rank=1) expected = full.view(full.size(0), 2, -1) torch.testing.assert_close(shard0, expected[:, 0]) torch.testing.assert_close(shard1, expected[:, 1]) def test_quark_shared_expert_gate_keeps_quantized_tensors(self): quant_config = QuarkConfig.from_config(_quark_int4_config()) mapper = quant_config.get_cache_scale_mapper() output_names = { name for name, _ in mapper.apply( [ ( "model.language_model.layers.0.mlp.shared_expert_gate.weight", torch.zeros(1), ), ( "model.language_model.layers.0.mlp.shared_expert_gate" ".weight_scale", torch.zeros(1), ), ( "model.language_model.layers.0.mlp.shared_expert_gate" ".weight_zero_point", torch.zeros(1), ), ] ) } assert ( "model.language_model.layers.0.mlp.shared_expert_gate.weight" in output_names ) assert ( "model.language_model.layers.0.mlp.shared_expert_gate.weight_scale" in output_names ) assert ( "model.language_model.layers.0.mlp.shared_expert_gate.weight_zero_point" in output_names ) def test_quark_apply_mapper_updates_exclude_and_layer_quant_config(self): quant_config = QuarkConfig.from_config( { **_quark_int4_config(), "exclude": ["lm_head"], "layer_quant_config": { "model.language_model.layers.0.mlp.gate": { "weight": {"dtype": "float16"}, }, }, } ) quant_config.apply_vllm_mapper( WeightsMapper( orig_to_new_prefix={ "lm_head": "language_model.lm_head", "model.language_model.": "language_model.model.", }, ) ) layer_quant_config = quant_config.quant_config["layer_quant_config"] assert "language_model.model.layers.0.mlp.gate" in layer_quant_config assert "model.language_model.layers.0.mlp.gate" not in layer_quant_config assert quant_config.quant_config["exclude"] == [ "language_model.lm_head", ] def test_quark_apply_mapper_ignores_non_string_list_entries(self): quant_config = QuarkConfig.from_config( { **_quark_int4_config(), "algo_config": [ {"name": "qronos", "inside_layer_modules": ["self_attn.q_proj"]} ], } ) quant_config.apply_vllm_mapper(WeightsMapper()) assert quant_config.quant_config["algo_config"] == [ {"name": "qronos", "inside_layer_modules": ["self_attn.q_proj"]} ] def test_quark_exclude_matches_via_mapper_prefix(self): quant_config = QuarkConfig.from_config(_quark_int4_config(exclude=["lm_head"])) quant_config.apply_vllm_mapper( WeightsMapper(orig_to_new_prefix={"lm_head": "language_model.lm_head"}) ) exclude_layers = quant_config.quant_config["exclude"] assert should_ignore_layer("language_model.lm_head", ignore=exclude_layers) assert not should_ignore_layer( "language_model.model.layers.0.mlp.gate", ignore=exclude_layers ) @pytest.mark.parametrize("symmetric", [False, True]) @pytest.mark.parametrize("pack_method", ["order", "reorder"]) def test_quark_int4_canonicalizes_pack_for_kernel_layout(pack_method, symmetric): """Quark pack order is normalized to the layout expected by awq_* ops. This reuses the existing AWQ dequant/gemm kernels for compute only; loading still goes through the native Quark quantization path, not AutoAWQ. """ pack_reorder = pack_method == "reorder" group_size = 2 packed_values = torch.tensor( [ [0, 1, 7, 8, 9, 15, 2, 14], [15, 8, 0, 3, 12, 7, 1, 9], ], dtype=torch.int32, ) packed_zeros = torch.zeros((1, 8), dtype=torch.int32) qweight = _pack_int4_nibbles(packed_values, pack_reorder=pack_reorder).view(2, 1) qzeros = _pack_int4_nibbles(packed_zeros, pack_reorder=pack_reorder).view(1, 1) scales = torch.ones((1, 8), dtype=torch.float16) dequantize = ( _dequantize_quark_signed_awq_torch if symmetric else _dequantize_awq_unsigned_torch ) expected = dequantize( qweight, scales, qzeros, group_size, pack_reorder=pack_reorder, ) canonical_weight = canonicalize_quark_packed_int4( qweight, pack_reorder=pack_reorder, is_symmetric=symmetric, ) canonical_zero = canonicalize_quark_packed_int4( qzeros, pack_reorder=pack_reorder, is_symmetric=symmetric, ) actual = _dequantize_awq_unsigned_torch( canonical_weight, scales, canonical_zero, group_size ) assert torch.equal(actual, expected)