[Rocm] [quantization] Fix quark ptpc moe and add test case

tests/quantization/test_quark.py

@@ -77,6 +77,31 @@ def check_model(model):
         assert output
 
 
+@pytest.mark.parametrize('tp', [1])
+def test_quark_fp8_w_per_channel_a_per_token(vllm_runner, tp):
+    model_path = "amd/Qwen2.5-1.5B-Instruct-ptpc-Quark-ts"
+    with vllm_runner(model_path, tensor_parallel_size=tp) as llm:
+
+        def check_model(model):
+            layer = model.model.layers[0]
+
+            qkv_proj = layer.self_attn.qkv_proj
+
+            assert isinstance(qkv_proj.quant_method, QuarkLinearMethod)
+            assert isinstance(qkv_proj.scheme, QuarkW8A8Fp8)
+
+            if 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
+
+        llm.apply_model(check_model)
+
+        output = llm.generate_greedy("Hello my name is", max_tokens=20)
+        assert output
+
+
 @pytest.mark.parametrize('tp', [1])
 def test_quark_int8_w_per_tensor_a_per_tensor(vllm_runner, tp):
     model_path = "amd/Llama-3.1-8B-Instruct-w-int8-a-int8-sym-test"

vllm/model_executor/layers/quantization/quark/quark_moe.py

@@ -5,17 +5,25 @@
 
 import torch
 
+import vllm.envs as envs
 from vllm import _custom_ops as ops
 from vllm.logger import init_logger
 from vllm.model_executor.layers.fused_moe import (FusedMoE, FusedMoEConfig,
                                                   FusedMoEMethodBase,
                                                   FusedMoeWeightScaleSupported)
+from vllm.model_executor.layers.fused_moe.rocm_aiter_fused_moe import (
+    is_rocm_aiter_moe_enabled)
+from vllm.model_executor.layers.quantization.utils.marlin_utils_fp8 import (
+    prepare_moe_fp8_layer_for_marlin)
 from vllm.model_executor.layers.quantization.utils.mxfp4_utils import (
     OCP_MX_BLOCK_SIZE)
+from vllm.model_executor.layers.quantization.utils.quant_utils import (
+    GroupShape)
 from vllm.model_executor.layers.quantization.utils.w8a8_utils import (
     all_close_1d, normalize_e4m3fn_to_e4m3fnuz, per_tensor_dequantize)
 from vllm.model_executor.utils import set_weight_attrs
 from vllm.platforms import current_platform
+from vllm.scalar_type import scalar_types
 
 logger = init_logger(__name__)
 
@@ -67,21 +75,45 @@ def __init__(
         self.weight_quant = weight_config
         self.input_quant = input_config
 
-        weight_qscheme = self.weight_quant.get("qscheme")
-        input_qscheme = self.input_quant.get("qscheme")
-        if not (weight_qscheme == "per_tensor"
-                and input_qscheme == "per_tensor"):
+        self.weight_qscheme = self.weight_quant.get("qscheme")
+        self.input_qscheme = self.input_quant.get("qscheme")
+        per_tensor = (self.weight_qscheme == "per_tensor"
+                      and self.input_qscheme == "per_tensor")
+        per_channel = (self.weight_qscheme == "per_channel"
+                       and self.input_qscheme == "per_channel")
+        self.act_quant_group_shape = GroupShape.PER_TOKEN \
+            if per_channel else GroupShape.PER_TENSOR
+        if not (per_tensor or per_channel):
             raise ValueError(
-                "For FP8 Fused MoE layers, only per-tensor scales "
-                "for weights and activations are supported. Found "
-                f"{weight_qscheme}, {input_qscheme}")  # noqa E501
+                "For FP8 Fused MoE layers, only per-tensor and per-channel "
+                "scales for weights and activations are supported. Found "
+                f"{self.weight_qscheme}, {self.input_qscheme}")  # noqa E501
 
         self.static_input_scales = not self.input_quant.get("is_dynamic")
+        if self.static_input_scales and per_channel:
+            raise ValueError(
+                "For FP8 Fused MoE layer, we require either per tensor or "
+                "channelwise, dynamic per token quantization.")
+
+        # For GPUs that lack FP8 hardware support, we can leverage the Marlin
+        # kernel for fast weight-only FP8 quantization
+        self.use_marlin = (not current_platform.has_device_capability(89)
+                           or envs.VLLM_TEST_FORCE_FP8_MARLIN)
+        # Disable marlin for rocm
+        if current_platform.is_rocm():
+            self.use_marlin = False
+
+        self.rocm_aiter_moe_enabled = is_rocm_aiter_moe_enabled()
 
     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.intermediate_size_per_partition = intermediate_size_per_partition
+        layer.hidden_size = hidden_size
+        layer.num_experts = num_experts
+        layer.orig_dtype = params_dtype
+        layer.weight_block_size = None
         params_dtype = torch.float8_e4m3fn
 
         # WEIGHTS
@@ -104,24 +136,39 @@ def create_weights(self, layer: torch.nn.Module, num_experts: int,
         set_weight_attrs(w2_weight, extra_weight_attrs)
 
         # WEIGHT_SCALES
-        # Allocate 2 scales for w1 and w3 respectively.
-        # They will be combined to a single scale after weight loading.
-        w13_weight_scale = torch.nn.Parameter(torch.ones(num_experts,
-                                                         2,
-                                                         dtype=torch.float32),
-                                              requires_grad=False)
-        layer.register_parameter("w13_weight_scale", w13_weight_scale)
-
-        w2_weight_scale = torch.nn.Parameter(torch.ones(num_experts,
-                                                        dtype=torch.float32),
-                                             requires_grad=False)
-        layer.register_parameter("w2_weight_scale", w2_weight_scale)
-        # Add the quantization method used (per tensor/grouped/channel)
-        # to ensure the weight scales are loaded in properly
-        extra_weight_attrs.update(
-            {"quant_method": FusedMoeWeightScaleSupported.TENSOR.value})
-        set_weight_attrs(w13_weight_scale, extra_weight_attrs)
-        set_weight_attrs(w2_weight_scale, extra_weight_attrs)
+        if self.weight_qscheme == "per_tensor":
+            # Allocate 2 scales for w1 and w3 respectively.
+            # They are combined to a single scale after weight loading.
+            w13_weight_scale = torch.nn.Parameter(torch.ones(
+                num_experts, 2, dtype=torch.float32),
+                                                  requires_grad=False)
+            layer.register_parameter("w13_weight_scale", w13_weight_scale)
+            w2_weight_scale = torch.nn.Parameter(torch.ones(
+                num_experts, dtype=torch.float32),
+                                                 requires_grad=False)
+            layer.register_parameter("w2_weight_scale", w2_weight_scale)
+            # Add PER-TENSOR quantization for FusedMoE.weight_loader.
+            extra_weight_attrs.update(
+                {"quant_method": FusedMoeWeightScaleSupported.TENSOR.value})
+            set_weight_attrs(w13_weight_scale, extra_weight_attrs)
+            set_weight_attrs(w2_weight_scale, extra_weight_attrs)
+        elif self.weight_qscheme == "per_channel":
+            # quark's scale is 1 dim.
+            w13_weight_scale = torch.nn.Parameter(torch.ones(
+                num_experts,
+                2 * intermediate_size_per_partition,
+                dtype=torch.float32),
+                                                  requires_grad=False)
+            layer.register_parameter("w13_weight_scale", w13_weight_scale)
+            w2_weight_scale = torch.nn.Parameter(torch.ones(
+                num_experts, hidden_size, dtype=torch.float32),
+                                                 requires_grad=False)
+            layer.register_parameter("w2_weight_scale", w2_weight_scale)
+            # Add PER-CHANNEL quantization for FusedMoE.weight_loader.
+            extra_weight_attrs.update(
+                {"quant_method": FusedMoeWeightScaleSupported.CHANNEL.value})
+            set_weight_attrs(w13_weight_scale, extra_weight_attrs)
+            set_weight_attrs(w2_weight_scale, extra_weight_attrs)
 
         # INPUT_SCALES
         if self.static_input_scales:
@@ -185,24 +232,60 @@ def process_weights_after_loading(self, layer: torch.nn.Module) -> None:
                 layer.w2_input_scale = torch.nn.Parameter(w2_input_scale,
                                                           requires_grad=False)
 
-        # Fp8 moe kernel needs single weight scale for w13 per expert.
-        # We take the max then dequant and requant each expert.
-        assert layer.w13_weight_scale is not None
-        shard_size = layer.intermediate_size_per_partition
-        max_w13_scales = layer.w13_weight_scale.max(dim=1).values
-        for expert_id in range(layer.local_num_experts):
-            start = 0
-            for shard_id in range(2):
-                dq_weight = per_tensor_dequantize(
-                    layer.w13_weight[expert_id][start:start + shard_size, :],
-                    layer.w13_weight_scale[expert_id][shard_id])
-                layer.w13_weight[expert_id][
-                    start:start + shard_size, :], _ = ops.scaled_fp8_quant(
-                        dq_weight, max_w13_scales[expert_id])
-                start += shard_size
-
-        layer.w13_weight_scale = torch.nn.Parameter(max_w13_scales,
-                                                    requires_grad=False)
+        # For per-tensor case, Fp8 moe kernel needs single weight scale
+        # for w13 per expert. Use max then dequant and requant each expert.
+        if self.weight_qscheme == "per_tensor":
+            assert layer.w13_weight_scale is not None
+            shard_size = layer.intermediate_size_per_partition
+            max_w13_scales = layer.w13_weight_scale.max(dim=1).values
+            for expert_id in range(layer.local_num_experts):
+                start = 0
+                for shard_id in range(2):
+                    dq_weight = per_tensor_dequantize(
+                        layer.w13_weight[expert_id][start:start +
+                                                    shard_size, :],
+                        layer.w13_weight_scale[expert_id][shard_id])
+                    layer.w13_weight[expert_id][
+                        start:start + shard_size, :], _ = ops.scaled_fp8_quant(
+                            dq_weight, max_w13_scales[expert_id])
+                    start += shard_size
+
+            layer.w13_weight_scale = torch.nn.Parameter(max_w13_scales,
+                                                        requires_grad=False)
+        # quark's scale is 1 dim.
+        elif self.weight_qscheme == "per_channel":
+            if self.act_quant_group_shape == GroupShape.PER_TOKEN:
+                w13_weight_scale = layer.w13_weight_scale.unsqueeze(-1)
+                layer.w13_weight_scale = torch.nn.Parameter(
+                    w13_weight_scale, requires_grad=False)
+                w2_weight_scale = layer.w2_weight_scale.unsqueeze(-1)
+                layer.w2_weight_scale = torch.nn.Parameter(w2_weight_scale,
+                                                           requires_grad=False)
+        # Property to determine if AITER is used
+        if self.rocm_aiter_moe_enabled:
+            from vllm.model_executor.layers.fused_moe.rocm_aiter_fused_moe import (  # noqa E501
+                rocm_aiter_fused_experts, shuffle_weights)
+
+            # reshaping weights is required for aiter moe kernel.
+            shuffled_w13, shuffled_w2 = shuffle_weights(
+                layer.w13_weight.data, layer.w2_weight.data)
+
+            layer.w13_weight = torch.nn.Parameter(shuffled_w13,
+                                                  requires_grad=False)
+            layer.w2_weight = torch.nn.Parameter(shuffled_w2,
+                                                 requires_grad=False)
+
+            self.rocm_aiter_fused_experts_func = rocm_aiter_fused_experts
+        elif self.use_marlin:
+
+            prepare_moe_fp8_layer_for_marlin(layer, False)
+            # Activations not quantized for marlin.
+            del layer.w13_input_scale
+            del layer.w2_input_scale
+            self.fused_experts_func = None
+        else:
+            from vllm.model_executor.layers.fused_moe import fused_experts
+            self.fused_experts_func = fused_experts
 
     def apply(
         self,
@@ -233,8 +316,6 @@ def apply(
             raise NotImplementedError(
                 "EPLB not supported for `QuarkW8A8Fp8MoEMethod` yet.")
 
-        from vllm.model_executor.layers.fused_moe import fused_experts
-
         topk_weights, topk_ids = FusedMoE.select_experts(
             hidden_states=x,
             router_logits=router_logits,
@@ -249,22 +330,60 @@ def apply(
             e_score_correction_bias=e_score_correction_bias,
             indices_type=self.topk_indices_dtype)
 
-        return fused_experts(
-            x,
-            layer.w13_weight,
-            layer.w2_weight,
+        if self.rocm_aiter_moe_enabled:
+            return self.rocm_aiter_fused_experts_func(
+                hidden_states=x,
+                w1=layer.w13_weight,
+                w2=layer.w2_weight,
+                topk_weights=topk_weights,
+                topk_ids=topk_ids,
+                activation=activation,
+                apply_router_weight_on_input=apply_router_weight_on_input,
+                use_fp8_w8a8=True,
+                per_channel_quant=self.weight_qscheme == "per_channel",
+                w1_scale=layer.w13_weight_scale,
+                w2_scale=layer.w2_weight_scale,
+                a1_scale=layer.w13_input_scale,
+                a2_scale=layer.w2_input_scale,
+                expert_map=expert_map)
+        if self.use_marlin:
+            assert activation == "silu", (
+                f"{activation} not supported for Marlin MoE.")
+            return torch.ops.vllm.fused_marlin_moe(
+                x,
+                layer.w13_weight,
+                layer.w2_weight,
+                None,
+                None,
+                layer.w13_weight_scale,
+                layer.w2_weight_scale,
+                router_logits,
+                topk_weights,
+                topk_ids,
+                quant_type_id=scalar_types.float8_e4m3fn.id,
+                apply_router_weight_on_input=apply_router_weight_on_input,
+                global_num_experts=global_num_experts,
+                expert_map=expert_map)
+
+        assert self.fused_experts_func is not None
+
+        return self.fused_experts_func(
+            hidden_states=x,
+            w1=layer.w13_weight,
+            w2=layer.w2_weight,
             topk_weights=topk_weights,
             topk_ids=topk_ids,
             inplace=True,
+            activation=activation,
+            apply_router_weight_on_input=apply_router_weight_on_input,
             use_fp8_w8a8=True,
+            per_channel_quant=self.weight_qscheme == "per_channel",
             global_num_experts=global_num_experts,
-            apply_router_weight_on_input=apply_router_weight_on_input,
             expert_map=expert_map,
             w1_scale=layer.w13_weight_scale,
             w2_scale=layer.w2_weight_scale,
             a1_scale=layer.w13_input_scale,
-            a2_scale=layer.w2_input_scale,
-            activation=activation)
+            a2_scale=layer.w2_input_scale)
 
 
 class QuarkW4A4MXFp4MoEMethod(QuarkMoEMethod):