[ Misc ] Refactor MoE to isolate Fp8 From Mixtral (#5970)

Co-authored-by: Robert Shaw <rshaw@neuralmagic>
Co-authored-by: Michael Goin <michael@neuralmagic.com>

vllm/model_executor/layers/fused_moe/layer.py

@@ -0,0 +1,197 @@
+from abc import abstractmethod
+from typing import Optional
+
+import torch
+
+from vllm.distributed import (get_tensor_model_parallel_rank,
+                              get_tensor_model_parallel_world_size,
+                              tensor_model_parallel_all_reduce)
+from vllm.logger import init_logger
+from vllm.model_executor.layers.fused_moe.fused_moe import fused_moe
+from vllm.model_executor.layers.quantization.base_config import (
+    QuantizationConfig, QuantizeMethodBase)
+from vllm.model_executor.utils import set_weight_attrs
+
+logger = init_logger(__name__)
+
+
+class FusedMoEMethodBase(QuantizeMethodBase):
+
+    @abstractmethod
+    def create_weights(self, layer: torch.nn.Module, num_experts: int,
+                       hidden_size: int, intermediate_size: int,
+                       params_dtype: torch.dtype, **extra_weight_attrs):
+        raise NotImplementedError
+
+    @abstractmethod
+    def apply(self,
+              layer: torch.nn.Module,
+              x: torch.Tensor,
+              router_logits: torch.Tensor,
+              top_k: int,
+              renormalize: bool = True) -> torch.Tensor:
+        raise NotImplementedError
+
+
+class UnquantizedFusedMoEMethod(FusedMoEMethodBase):
+    """MoE method without quantization."""
+
+    def create_weights(self, layer: torch.nn.Module, num_experts: int,
+                       hidden_size: int, intermediate_size: int,
+                       params_dtype: torch.dtype, **extra_weight_attrs):
+
+        # Fused gate_up_proj (column parallel)
+        w13_weight = torch.nn.Parameter(torch.empty(num_experts,
+                                                    2 * intermediate_size,
+                                                    hidden_size,
+                                                    dtype=params_dtype),
+                                        requires_grad=False)
+        layer.register_parameter("w13_weight", w13_weight)
+        set_weight_attrs(w13_weight, extra_weight_attrs)
+
+        # down_proj (row parallel)
+        w2_weight = torch.nn.Parameter(torch.empty(num_experts,
+                                                   hidden_size,
+                                                   intermediate_size,
+                                                   dtype=params_dtype),
+                                       requires_grad=False)
+        layer.register_parameter("w2_weight", w2_weight)
+        set_weight_attrs(w2_weight, extra_weight_attrs)
+
+    def apply(self,
+              layer: torch.nn.Module,
+              x: torch.Tensor,
+              router_logits: torch.Tensor,
+              top_k: int,
+              renormalize: bool = True) -> torch.Tensor:
+
+        return fused_moe(x,
+                         layer.w13_weight,
+                         layer.w2_weight,
+                         router_logits,
+                         top_k,
+                         renormalize=renormalize,
+                         inplace=True)
+
+
+class FusedMoE(torch.nn.Module):
+    """FusedMoE layer for MoE models.
+
+    This layer contains both MergedColumnParallel weights (gate_up_proj / 
+    w13) and RowParallelLinear weights (down_proj/ w2).
+
+    Note: Mixtral uses w1, w2, and w3 for gate, up, and down_proj. We
+    copy that naming convention here and handle any remapping in the
+    load_weights function in each model implementation.
+
+    Args:
+        num_experts: Number of experts in the model
+        top_k: Number of experts selected for each token
+        hidden_size: Input hidden state size of the transformer
+        intermediate_size: Intermediate size of the experts
+        params_dtype: Data type for the parameters.
+        reduce_results: Whether to all all_reduce on the output of the layer
+        renomalize: Whether to renormalize the logits in the fused_moe kernel
+        quant_config: Quantization configure.
+    """
+
+    def __init__(
+        self,
+        num_experts: int,
+        top_k: int,
+        hidden_size: int,
+        intermediate_size: int,
+        params_dtype: Optional[torch.dtype] = None,
+        reduce_results: bool = False,
+        renormalize: bool = True,
+        quant_config: Optional[QuantizationConfig] = None,
+        tp_size: Optional[int] = None,
+    ):
+        super().__init__()
+
+        if params_dtype is None:
+            params_dtype = torch.get_default_dtype()
+
+        self.tp_size = (tp_size if tp_size is not None else
+                        get_tensor_model_parallel_world_size())
+        self.top_k = top_k
+        self.num_experts = num_experts
+        self.intermediate_size_per_partition = intermediate_size // self.tp_size
+        self.reduce_results = reduce_results
+        self.renormalize = renormalize
+
+        if quant_config is None:
+            self.quant_method: Optional[QuantizeMethodBase] = (
+                UnquantizedFusedMoEMethod())
+        else:
+            self.quant_method = quant_config.get_quant_method(self)
+        assert self.quant_method is not None
+
+        self.quant_method.create_weights(
+            layer=self,
+            num_experts=num_experts,
+            hidden_size=hidden_size,
+            intermediate_size=self.intermediate_size_per_partition,
+            params_dtype=params_dtype,
+            weight_loader=self.weight_loader)
+
+    def weight_loader(self, param: torch.nn.Parameter,
+                      loaded_weight: torch.Tensor, weight_name: str,
+                      shard_id: int, expert_id: int):
+        param_data = param.data
+
+        # FIXME(robertgshaw2-neuralmagic): Overfit to Mixtral.
+        # Follow up PR to enable fp8 for other MoE models.
+        if "input_scale" in weight_name or "w2.weight_scale" in weight_name:
+            if param_data[expert_id] != 1 and (param_data[expert_id] -
+                                               loaded_weight).abs() > 1e-5:
+                raise ValueError(
+                    "input_scales of w1 and w3 of a layer "
+                    f"must be equal. But got {param_data[expert_id]} "
+                    f"vs. {loaded_weight}")
+            param_data[expert_id] = loaded_weight
+        # FIXME(robertgshaw2-neuralmagic): Overfit to Mixtral.
+        # Follow up PR to enable fp8 for other MoE models.
+        elif "weight_scale" in weight_name:
+            # We have to keep the weight scales of w1 and w3 because
+            # we need to re-quantize w1/w3 weights after weight loading.
+            assert "w1" in weight_name or "w3" in weight_name
+            shard_id = 0 if "w1" in weight_name else 1
+            param_data[expert_id][shard_id] = loaded_weight
+        else:
+            tp_rank = get_tensor_model_parallel_rank()
+            shard_size = self.intermediate_size_per_partition
+            shard = slice(tp_rank * shard_size, (tp_rank + 1) * shard_size)
+
+            # w1, gate_proj case: Load into first shard of w13.
+            if shard_id == 0:
+                param_data[expert_id,
+                           0:shard_size, :] = loaded_weight[shard, :]
+            # w3, up_proj case: Load into second shard of w13.
+            elif shard_id == 2:
+                param_data[expert_id, shard_size:2 *
+                           shard_size, :] = loaded_weight[shard, :]
+            # w2, down_proj case: Load into only shard of w2.
+            elif shard_id == 1:
+                param_data[expert_id, :, :] = loaded_weight[:, shard]
+            else:
+                raise ValueError(
+                    f"Shard id must be in [0,1,2] but got {shard_id}")
+
+    def forward(self, hidden_states: torch.Tensor,
+                router_logits: torch.Tensor):
+        assert self.quant_method is not None
+
+        # Matrix multiply.
+        final_hidden_states = self.quant_method.apply(
+            self,
+            x=hidden_states,
+            router_logits=router_logits,
+            top_k=self.top_k,
+            renormalize=self.renormalize)
+
+        if self.reduce_results and self.tp_size > 1:
+            final_hidden_states = tensor_model_parallel_all_reduce(
+                final_hidden_states)
+
+        return final_hidden_states