[Kernel] add triton fused moe kernel for gptq/awq (#12185)

tests/kernels/test_moe.py

@@ -18,6 +18,8 @@ from vllm.model_executor.layers.fused_moe.moe_torch_iterative import (
     fused_moe as iterative_moe)
 from vllm.model_executor.layers.quantization.utils.marlin_utils_test import (
     marlin_quantize)
+from vllm.model_executor.layers.quantization.utils.quant_utils import (
+    quantize_weights)
 from vllm.model_executor.models.mixtral import MixtralMoE
 from vllm.platforms import current_platform
 from vllm.scalar_type import scalar_types
@@ -55,6 +57,95 @@ def test_fused_moe(
                                rtol=0)
 
 
+@pytest.mark.parametrize("m", [1, 32, 222])
+@pytest.mark.parametrize("n", [128, 1024, 2048])
+@pytest.mark.parametrize("k", [128, 1024])
+@pytest.mark.parametrize("e", NUM_EXPERTS)
+@pytest.mark.parametrize("topk", TOP_KS)
+@pytest.mark.parametrize("dtype", [torch.float16, torch.bfloat16])
+@pytest.mark.parametrize("group_size", [64, 128])
+@pytest.mark.parametrize("has_zp", [True, False])
+@pytest.mark.parametrize("weight_bits", [4, 8])
+def test_fused_moe_wn16(m: int, n: int, k: int, e: int, topk: int,
+                        dtype: torch.dtype, group_size: int, has_zp: bool,
+                        weight_bits: int):
+    print(m, n, k, e, topk, dtype, group_size, has_zp, weight_bits)
+    a = torch.randn((m, k), device="cuda", dtype=dtype) / 10
+    w1 = torch.randn((e, 2 * n, k), device="cuda", dtype=dtype) / 10
+    w2 = torch.randn((e, k, n), device="cuda", dtype=dtype) / 10
+    score = torch.randn((m, e), device="cuda", dtype=dtype)
+
+    if weight_bits == 4:
+        pack_factor = 2
+        quant_type = scalar_types.uint4 if has_zp else scalar_types.uint4b8
+    elif weight_bits == 8:
+        pack_factor = 1
+        quant_type = scalar_types.uint8 if has_zp else scalar_types.uint8b128
+
+    w1_ref = w1.clone()
+    w2_ref = w2.clone()
+    w1_qweight = torch.empty((e, 2 * n, k // pack_factor),
+                             device="cuda",
+                             dtype=torch.uint8)
+    w2_qweight = torch.empty((e, k, n // pack_factor),
+                             device="cuda",
+                             dtype=torch.uint8)
+    w1_scales = torch.empty((e, 2 * n, k // group_size),
+                            device="cuda",
+                            dtype=dtype)
+    w2_scales = torch.empty((e, k, n // group_size),
+                            device="cuda",
+                            dtype=dtype)
+    w1_qzeros = torch.empty((e, 2 * n // pack_factor, k // group_size),
+                            device="cuda",
+                            dtype=torch.uint8)
+    w2_qzeros = torch.empty((e, k // pack_factor, n // group_size),
+                            device="cuda",
+                            dtype=torch.uint8)
+
+    for i in range(e * 2):
+        expert_id = i % e
+        if i // e == 0:
+            w, w_ref, w_qweight, w_scales, w_qzeros = \
+                w1, w1_ref, w1_qweight, w1_scales, w1_qzeros
+        else:
+            w, w_ref, w_qweight, w_scales, w_qzeros = \
+                w2, w2_ref, w2_qweight, w2_scales, w2_qzeros
+        weight, qweight, scales, qzeros = quantize_weights(
+            w[expert_id].T, quant_type, group_size, has_zp, False)
+        weight = weight.T
+        qweight = qweight.T.contiguous().to(torch.uint8)
+        scales = scales.T
+        if has_zp:
+            qzeros = qzeros.T.contiguous().to(torch.uint8)
+        if weight_bits == 4:
+            qweight = qweight[:, 1::2] * 16 + qweight[:, ::2]
+            if has_zp:
+                qzeros = qzeros[1::2, :] * 16 + qzeros[::2, :]
+
+        w_ref[expert_id] = weight
+        w_qweight[expert_id] = qweight
+        w_scales[expert_id] = scales
+        if has_zp:
+            w_qzeros[expert_id] = qzeros
+
+    triton_output = fused_moe(a,
+                              w1_qweight,
+                              w2_qweight,
+                              score,
+                              topk,
+                              renormalize=False,
+                              use_int4_w4a16=weight_bits == 4,
+                              use_int8_w8a16=weight_bits == 8,
+                              w1_scale=w1_scales,
+                              w2_scale=w2_scales,
+                              w1_zp=w1_qzeros if has_zp else None,
+                              w2_zp=w2_qzeros if has_zp else None,
+                              block_shape=[0, group_size])
+    torch_output = torch_moe(a, w1_ref, w2_ref, score, topk)
+    torch.testing.assert_close(triton_output, torch_output, atol=2e-2, rtol=0)
+
+
 @pytest.mark.parametrize("dtype",
                          [torch.float32, torch.float16, torch.bfloat16])
 @torch.inference_mode()

vllm/model_executor/layers/quantization/__init__.py

@@ -26,7 +26,8 @@ QUANTIZATION_METHODS: List[str] = [
     "experts_int8",
     "neuron_quant",
     "ipex",
-    "quark"
+    "quark",
+    "moe_wna16"
 ]
 
 # The customized quantization methods which will be added to this dict.
@@ -94,6 +95,7 @@ def get_quantization_config(quantization: str) -> Type[QuantizationConfig]:
     from .ipex_quant import IPEXConfig
     from .marlin import MarlinConfig
     from .modelopt import ModelOptFp8Config
+    from .moe_wna16 import MoeWNA16Config
     from .neuron_quant import NeuronQuantConfig
     from .qqq import QQQConfig
     from .tpu_int8 import Int8TpuConfig
@@ -121,7 +123,8 @@ def get_quantization_config(quantization: str) -> Type[QuantizationConfig]:
         "experts_int8": ExpertsInt8Config,
         "neuron_quant": NeuronQuantConfig,
         "ipex": IPEXConfig,
-        "quark": QuarkConfig
+        "quark": QuarkConfig,
+        "moe_wna16": MoeWNA16Config,
     }
     # Update the `method_to_config` with customized quantization methods.
     method_to_config.update(_CUSTOMIZED_METHOD_TO_QUANT_CONFIG)

vllm/model_executor/layers/quantization/moe_wna16.py

+from typing import Any, Callable, Dict, List, Optional
+
+import torch
+
+from vllm.distributed import get_tensor_model_parallel_rank, get_tp_group
+from vllm.model_executor.layers.fused_moe.layer import (
+    FusedMoE, FusedMoEMethodBase, FusedMoeWeightScaleSupported)
+from vllm.model_executor.layers.linear import UnquantizedLinearMethod
+from vllm.model_executor.layers.quantization.awq import (AWQConfig,
+                                                         AWQLinearMethod)
+from vllm.model_executor.layers.quantization.awq_marlin import (
+    AWQMarlinConfig, AWQMarlinLinearMethod)
+from vllm.model_executor.layers.quantization.base_config import (
+    QuantizationConfig, QuantizeMethodBase)
+from vllm.model_executor.layers.quantization.gptq import (GPTQConfig,
+                                                          GPTQLinearMethod)
+from vllm.model_executor.layers.quantization.gptq_marlin import (
+    GPTQMarlinConfig, GPTQMarlinLinearMethod)
+from vllm.model_executor.utils import set_weight_attrs
+from vllm.platforms import current_platform
+
+
+class MoeWNA16Config(QuantizationConfig):
+    """Config class for MOE WNA16 (W8A16/W4A16) quantization."""
+
+    def __init__(self, linear_quant_method: str, weight_bits: int,
+                 group_size: int, has_zp: bool, lm_head_quantized: bool,
+                 modules_to_not_convert: Optional[List[str]],
+                 full_config: Dict[str, Any]) -> None:
+        self.weight_bits = weight_bits
+        self.group_size = group_size
+        self.has_zp = has_zp
+        self.bit8_pack_factor = 8 // self.weight_bits
+        self.lm_head_quantized = lm_head_quantized
+        self.linear_quant_method = linear_quant_method
+        self.full_config = full_config
+        self.use_marlin = False
+        if self.linear_quant_method == "gptq":
+            self.use_marlin = GPTQMarlinConfig.is_gptq_marlin_compatible(
+                full_config)
+        elif self.linear_quant_method == "awq":
+            capability_tuple = current_platform.get_device_capability()
+            device_capability = (-1 if capability_tuple is None else
+                                 capability_tuple.to_int())
+            awq_min_capability = AWQConfig.get_min_capability()
+            if device_capability < awq_min_capability:
+                raise ValueError(
+                    "The quantization method moe_wna16 + awq is not supported "
+                    "for the current GPU. "
+                    f"Minimum capability: {awq_min_capability}. "
+                    f"Current capability: {device_capability}.")
+            self.use_marlin = AWQMarlinConfig.is_awq_marlin_compatible(
+                full_config)
+        else:
+            raise ValueError("moe_wna16 only support gptq and awq.")
+
+        if modules_to_not_convert is None:
+            self.modules_to_not_convert = []
+        else:
+            self.modules_to_not_convert = modules_to_not_convert
+
+    @classmethod
+    def get_name(cls) -> str:
+        return "moe_wna16"
+
+    @classmethod
+    def get_supported_act_dtypes(cls) -> List[torch.dtype]:
+        return [torch.bfloat16, torch.half]
+
+    @classmethod
+    def get_min_capability(cls) -> int:
+        return 70
+
+    @classmethod
+    def get_config_filenames(cls) -> List[str]:
+        return ["quantize_config.json"]
+
+    @classmethod
+    def from_config(cls, config: Dict[str, Any]) -> "MoeWNA16Config":
+        linear_quant_method = cls.get_from_keys(config, ["quant_method"])
+        weight_bits = cls.get_from_keys(config, ["bits"])
+        group_size = cls.get_from_keys(config, ["group_size"])
+        lm_head_quantized = cls.get_from_keys_or(config, ["lm_head"],
+                                                 default=False)
+        if linear_quant_method == "gptq":
+            has_zp = not cls.get_from_keys(config, ["sym"])
+            modules_to_not_convert = []
+        elif linear_quant_method == "awq":
+            has_zp = cls.get_from_keys(config, ["zero_point"])
+            modules_to_not_convert = cls.get_from_keys(
+                config, ["modules_to_not_convert"])
+        else:
+            raise ValueError("moe_wna16 only support gptq and awq.")
+
+        return cls(linear_quant_method, weight_bits, group_size, has_zp,
+                   lm_head_quantized, modules_to_not_convert, config)
+
+    @classmethod
+    def override_quantization_method(cls, hf_quant_cfg,
+                                     user_quant) -> Optional[str]:
+        can_convert = cls.is_moe_wna16_compatible(hf_quant_cfg)
+        if can_convert and user_quant == "moe_wna16":
+            return cls.get_name()
+        return None
+
+    @classmethod
+    def is_moe_wna16_compatible(cls, quant_config: Dict[str, Any]):
+        # Extract data from quant config.
+        quant_method = quant_config.get("quant_method", "").lower()
+        num_bits = quant_config.get("bits")
+        desc_act = quant_config.get("desc_act")
+
+        capability_tuple = current_platform.get_device_capability()
+        device_capability = (-1 if capability_tuple is None else
+                             capability_tuple.to_int())
+        awq_min_capability = AWQConfig.get_min_capability()
+
+        gptq_compatible = quant_method == "gptq" and \
+                not desc_act and num_bits in [4, 8]
+        awq_compatible = quant_method == "awq" and num_bits == 4 and \
+            device_capability >= awq_min_capability
+
+        return gptq_compatible or awq_compatible
+
+    def get_quant_method(self, layer: torch.nn.Module,
+                         prefix: str) -> Optional["QuantizeMethodBase"]:
+        if is_layer_skipped_quant(prefix, self.modules_to_not_convert):
+            return UnquantizedLinearMethod()
+        elif isinstance(layer, FusedMoE):
+            return MoeWNA16Method(self)
+        else:
+            if self.linear_quant_method == "gptq":
+                if self.use_marlin:
+                    return GPTQMarlinLinearMethod(
+                        GPTQMarlinConfig.from_config(self.full_config))
+                else:
+                    return GPTQLinearMethod(
+                        GPTQConfig.from_config(self.full_config))
+            elif self.linear_quant_method == "awq":
+                if self.use_marlin:
+                    return AWQMarlinLinearMethod(
+                        AWQMarlinConfig.from_config(self.full_config))
+                else:
+                    return AWQLinearMethod(
+                        AWQConfig.from_config(self.full_config))
+            else:
+                raise ValueError("moe_wna16 only support gptq and awq.")
+
+
+def is_layer_skipped_quant(prefix: str, modules_to_not_convert: List[str]):
+    return any(module_name in prefix for module_name in modules_to_not_convert)
+
+
+class MoeWNA16Method(FusedMoEMethodBase):
+    """Linear method for MOE WNA16 (W8A16/W4A16) quantization.
+
+    Args:
+        quant_config: The MOE WNA16 (W8A16/W4A16) quantization config.
+    """
+
+    def __init__(self, quant_config: MoeWNA16Config):
+        self.quant_config = quant_config
+
+    def create_weights(self, layer: torch.nn.Module, num_experts: int,
+                       hidden_size: int, intermediate_size_per_partition: int,
+                       params_dtype: torch.dtype, **extra_weight_attrs):
+
+        layer.quant_config = self.quant_config
+        bit8_pack_factor = self.quant_config.bit8_pack_factor
+        group_size = self.quant_config.group_size
+        group_size_div_factor = 1
+
+        # make intermediate_size and hidden_size diviable by group_size
+        # we reduce the group size to ensure that
+        # and we would repeat the loaded_weight later
+        while intermediate_size_per_partition % group_size or \
+                hidden_size % group_size:
+            group_size = group_size // 2
+            group_size_div_factor *= 2
+            assert group_size >= 32
+        layer.group_size = group_size
+        layer.group_size_div_factor = group_size_div_factor
+
+        strategy = FusedMoeWeightScaleSupported.GROUP.value
+        extra_weight_attrs.update({
+            "quant_method": strategy,
+            "is_transposed": False
+        })
+
+        assert 'weight_loader' in extra_weight_attrs
+        weight_loader = extra_weight_attrs['weight_loader']
+        wrapped_weight_loader = MoeWNA16Method.get_weight_loader(
+            layer, weight_loader)
+        extra_weight_attrs['weight_loader'] = wrapped_weight_loader
+
+        # Fused gate_up_proj (column parallel)
+        w13_qweight = torch.nn.Parameter(torch.empty(
+            num_experts,
+            2 * intermediate_size_per_partition,
+            hidden_size // bit8_pack_factor,
+            dtype=torch.uint8),
+                                         requires_grad=False)
+        layer.register_parameter("w13_qweight", w13_qweight)
+        set_weight_attrs(w13_qweight, extra_weight_attrs)
+
+        # down_proj (row parallel)
+        w2_qweight = torch.nn.Parameter(torch.empty(
+            num_experts,
+            hidden_size,
+            intermediate_size_per_partition // bit8_pack_factor,
+            dtype=torch.uint8),
+                                        requires_grad=False)
+        layer.register_parameter("w2_qweight", w2_qweight)
+        set_weight_attrs(w2_qweight, extra_weight_attrs)
+
+        w13_scales = torch.nn.Parameter(torch.zeros(
+            num_experts,
+            2 * intermediate_size_per_partition,
+            hidden_size // group_size,
+            dtype=params_dtype),
+                                        requires_grad=False)
+        layer.register_parameter("w13_scales", w13_scales)
+        set_weight_attrs(w13_scales, extra_weight_attrs)
+
+        w2_scales = torch.nn.Parameter(torch.zeros(
+            num_experts,
+            hidden_size,
+            intermediate_size_per_partition // group_size,
+            dtype=params_dtype),
+                                       requires_grad=False)
+        layer.register_parameter("w2_scales", w2_scales)
+        set_weight_attrs(w2_scales, extra_weight_attrs)
+
+        if self.quant_config.has_zp:
+            w13_qzeros = torch.nn.Parameter(torch.zeros(
+                num_experts,
+                2 * intermediate_size_per_partition // bit8_pack_factor,
+                hidden_size // group_size,
+                dtype=torch.uint8),
+                                            requires_grad=False)
+            layer.register_parameter("w13_qzeros", w13_qzeros)
+            set_weight_attrs(w13_qzeros, extra_weight_attrs)
+
+            w2_qzeros = torch.nn.Parameter(torch.zeros(
+                num_experts,
+                hidden_size // bit8_pack_factor,
+                intermediate_size_per_partition // group_size,
+                dtype=torch.uint8),
+                                           requires_grad=False)
+            layer.register_parameter("w2_qzeros", w2_qzeros)
+            set_weight_attrs(w2_qzeros, extra_weight_attrs)
+
+        if self.quant_config.linear_quant_method == "gptq":
+            # some param are unused, but we need to init them in order to
+            # load weights
+            invalid_param_keys = ["w13_g_idx", "w2_g_idx"]
+            if not self.quant_config.has_zp:
+                invalid_param_keys += ["w13_qzeros", "w2_qzeros"]
+            for key in invalid_param_keys:
+                param = torch.nn.Parameter(torch.empty((0, ),
+                                                       dtype=torch.int32),
+                                           requires_grad=False)
+                layer.register_parameter(key, param)
+                set_weight_attrs(param, extra_weight_attrs)
+
+    def apply(
+        self,
+        layer: torch.nn.Module,
+        x: torch.Tensor,
+        router_logits: torch.Tensor,
+        top_k: int,
+        renormalize: bool,
+        use_grouped_topk: bool = False,
+        topk_group: Optional[int] = None,
+        num_expert_group: Optional[int] = None,
+        custom_routing_function: Optional[Callable] = None,
+        scoring_func: str = "softmax",
+        e_score_correction_bias: Optional[torch.Tensor] = None,
+    ) -> torch.Tensor:
+        from vllm.model_executor.layers.fused_moe import fused_experts
+
+        topk_weights, topk_ids = FusedMoE.select_experts(
+            hidden_states=x,
+            router_logits=router_logits,
+            use_grouped_topk=use_grouped_topk,
+            top_k=top_k,
+            renormalize=renormalize,
+            topk_group=topk_group,
+            num_expert_group=num_expert_group,
+            custom_routing_function=custom_routing_function,
+            scoring_func=scoring_func,
+            e_score_correction_bias=e_score_correction_bias)
+
+        weight_bits = self.quant_config.weight_bits
+        has_zp = self.quant_config.has_zp
+
+        return fused_experts(x,
+                             layer.w13_qweight,
+                             layer.w2_qweight,
+                             topk_weights=topk_weights,
+                             topk_ids=topk_ids,
+                             inplace=True,
+                             use_int4_w4a16=weight_bits == 4,
+                             use_int8_w8a16=weight_bits == 8,
+                             w1_scale=layer.w13_scales,
+                             w2_scale=layer.w2_scales,
+                             w1_zp=layer.w13_qzeros if has_zp else None,
+                             w2_zp=layer.w2_qzeros if has_zp else None,
+                             block_shape=[0, layer.group_size])
+
+    @staticmethod
+    def get_weight_loader(layer, weight_loader):
+
+        def convert_awq_tensor(tensor, tensor_type):
+            # convert awq qweight/qzeros to a standard format (assume int4)
+            # qweight: (k, n // pack_factor_bit32) -> (n, k // pack_factor_bit8)
+            # qzeros: (k // group_size, n // pack_factor_bit32) ->
+            #         (n // pack_factor_bit8, k // group_size)
+            # pack_factor_bit32 = 32 // weight_bits
+            # pack_factor_bit8 = 8 // weight_bits
+
+            # 0. suppose origin shape (a, b), dtype int32
+            # 1. convert to uint8, shape (a, b) -> (a, 4 * b)
+            size0 = tensor.size(0)
+            tensor = tensor.view(torch.uint8)
+
+            # 2. unpack to uint4 (only when weight_bits == 4)
+            #    shape (a, 4 * b) -> (a, 4 * b, 2)
+            shifter = torch.tensor([0, 4],
+                                   dtype=torch.uint8,
+                                   device=tensor.device)
+            tensor = (tensor[:, :, None] >> shifter) & 0xF
+
+            # 3. change order, see
+            # https://github.com/casper-hansen/AutoAWQ/blob/v0.2.8/awq/utils/quant_utils.py
+            # shape -> (a, 4 * b * pack_factor_bit8)
+            reverse_awq_pack_order = [0, 4, 1, 5, 2, 6, 3, 7]
+            tensor = tensor.view(-1, 8)[:, reverse_awq_pack_order]
+            tensor = tensor.view(size0, -1)
+
+            # 4. transpose, shape -> (4 * b * pack_factor_bit8, a)
+            tensor = tensor.T.contiguous()
+
+            # 5. repack (only when weight_bits == 4)
+            # qweight shape -> (4 * b * pack_factor_bit8, a // pack_factor_bit8)
+            # qzeros shape -> (4 * b, a)
+
+            if tensor_type == "qweight":
+                tensor = tensor[:, 1::2] * 16 + tensor[:, ::2]
+            elif tensor_type == "qzeros":
+                tensor = tensor[1::2, :] * 16 + tensor[::2, :]
+            return tensor
+
+        def convert_gptq_int4_qzeros(tensor):
+            tensor = tensor.view(torch.uint8)
+            shifter = torch.tensor([0, 4],
+                                   dtype=torch.uint8,
+                                   device=tensor.device)
+            tensor = (tensor[:, :, None] >> shifter) & 0xF
+            tensor = tensor + 1
+            tensor = tensor[:, :, 0] + tensor[:, :, 1] * 16
+            return tensor
+
+        def moe_wna16_weight_loader(param: torch.nn.Parameter,
+                                    loaded_weight: torch.Tensor,
+                                    weight_name: str, shard_id: str,
+                                    expert_id: int):
+            if "g_idx" in weight_name:
+                return
+            if not layer.quant_config.has_zp and "qzeros" in weight_name:
+                return
+
+            device = get_tp_group().device
+            tp_rank = get_tensor_model_parallel_rank()
+            loaded_weight = loaded_weight.to(device)
+            shard_size = layer.intermediate_size_per_partition
+
+            # convert gptq and awq weight to a standard format
+            if layer.quant_config.linear_quant_method == "awq":
+                assert layer.quant_config.weight_bits == 4
+                if "weight" in weight_name:
+                    loaded_weight = convert_awq_tensor(loaded_weight,
+                                                       "qweight")
+                elif "zeros" in weight_name:
+                    loaded_weight = convert_awq_tensor(loaded_weight, "qzeros")
+                else:
+                    loaded_weight = loaded_weight.T
+            elif layer.quant_config.linear_quant_method == "gptq":
+                assert layer.quant_config.weight_bits in [4, 8]
+                if "weight" in weight_name:
+                    loaded_weight = loaded_weight.T.contiguous().view(
+                        torch.uint8)
+                elif "zeros" in weight_name:
+                    # add 1 to gptq qzeros to align with awq
+                    loaded_weight = loaded_weight.view(torch.uint8)
+                    if layer.quant_config.weight_bits == 4:
+                        loaded_weight = convert_gptq_int4_qzeros(
+                            loaded_weight).T
+                    else:
+                        loaded_weight = loaded_weight.T + 1
+                else:
+                    loaded_weight = loaded_weight.T
+
+            # repeat the qzeros/scales to fit new group size
+            if layer.group_size_div_factor > 1 and \
+                    "qzeros" in weight_name or "scales" in weight_name:
+                loaded_weight = loaded_weight.repeat_interleave(
+                    layer.group_size_div_factor, 1)
+
+            if "w13_qzeros" in weight_name:
+                tensor = loaded_weight.view(layer.tp_size, -1,
+                                            loaded_weight.size(1))[tp_rank]
+                if shard_id == "w1":
+                    param.data[expert_id, :shard_size // 2] = tensor
+                else:
+                    param.data[expert_id, shard_size // 2:] = tensor
+            elif "w2_qzeros" in weight_name:
+                param.data[expert_id] = loaded_weight.view(
+                    loaded_weight.size(0), layer.tp_size, -1)[:, tp_rank]
+            else:
+                weight_loader(param, loaded_weight, weight_name, shard_id,
+                              expert_id)
+
+        return moe_wna16_weight_loader