Enable PTPC FP8 for CompressedTensorsW8A8Fp8MoEMethod (triton fused_moe) (vllm-project#16537)

Signed-off-by: mgoin <mgoin64@gmail.com>
Signed-off-by: Yang Wang <elainewy@meta.com>

Author: mgoin

vllm/model_executor/layers/quantization/compressed_tensors/compressed_tensors_moe.py

@@ -88,14 +88,23 @@ def __init__(
         self.input_quant = self.quant_config.target_scheme_map["Linear"].get(
             "input_activations")
 
-        if not (self.weight_quant.strategy == QuantizationStrategy.TENSOR
-                and self.input_quant.strategy == QuantizationStrategy.TENSOR):
+        per_tensor = (self.weight_quant.strategy == QuantizationStrategy.TENSOR
+                      and self.input_quant.strategy
+                      == QuantizationStrategy.TENSOR)
+        per_channel = (
+            self.weight_quant.strategy == QuantizationStrategy.CHANNEL
+            and self.input_quant.strategy == QuantizationStrategy.TOKEN)
+        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 "
+                "For FP8 Fused MoE layers, we require per tensor "
+                "or channelwise, dynamic per token quantization. Found "
                 f"{self.weight_quant}, {self.input_quant}")
 
         self.static_input_scales = not self.input_quant.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.")
 
     def create_weights(self, layer: torch.nn.Module, num_experts: int,
                        hidden_size: int, intermediate_size_per_partition: int,
@@ -123,24 +132,40 @@ 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)
+        if self.weight_quant.strategy == QuantizationStrategy.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)
 
-        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)
+        elif self.weight_quant.strategy == QuantizationStrategy.CHANNEL:
+            w13_weight_scale = torch.nn.Parameter(torch.ones(
+                num_experts,
+                2 * intermediate_size_per_partition,
+                1,
+                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, 1, 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:
@@ -163,6 +188,7 @@ def process_weights_after_loading(self, layer: torch.nn.Module) -> None:
         # Fp8 moe kernels require a single activation scale.
         # We take the max of all the scales in case they differ.
         if self.static_input_scales:
+            assert self.input_quant.strategy == QuantizationStrategy.TENSOR
             if (layer.w13_input_scale is None or layer.w2_input_scale is None):
                 raise ValueError(
                     "QuantConfig has static quantization, but found "
@@ -204,24 +230,25 @@ 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_quant.strategy == QuantizationStrategy.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)
 
     def apply(
         self,
@@ -265,6 +292,8 @@ def apply(
             activation=activation,
             apply_router_weight_on_input=apply_router_weight_on_input,
             use_fp8_w8a8=True,
+            per_channel_quant=self.weight_quant.strategy ==
+            QuantizationStrategy.CHANNEL,
             global_num_experts=global_num_experts,
             expert_map=expert_map,
             w1_scale=layer.w13_weight_scale,