[Hardware][Gaudi][Feature] Enable Dynamic MoE for Mixtral (#12303)

Signed-off-by: zhenwei <zhenweiliu@habana.ai>

vllm/model_executor/layers/fused_moe/layer.py

@@ -213,6 +213,34 @@ class UnquantizedFusedMoEMethod(FusedMoEMethodBase, CustomOp):
             e_score_correction_bias,
         )
 
+    def forward_hpu(
+        self,
+        layer: torch.nn.Module,
+        x: torch.Tensor,
+        use_grouped_topk: bool,
+        top_k: int,
+        router_logits: torch.Tensor,
+        renormalize: bool,
+        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:
+        assert not use_grouped_topk
+        assert num_expert_group is None
+        assert topk_group is None
+        assert custom_routing_function is None
+        assert layer is not None
+        if scoring_func != "softmax":
+            raise NotImplementedError(
+                "Only softmax scoring function is supported for HPU.")
+        if e_score_correction_bias is not None:
+            raise NotImplementedError(
+                "Expert score correction bias is not supported for HPU.")
+        return layer.hpu_fused_moe(x, layer.w13_weight, layer.w2_weight,
+                                   router_logits, top_k)
+
     def forward_tpu(
         self,
         layer: torch.nn.Module,
@@ -411,6 +439,9 @@ class FusedMoE(torch.nn.Module):
         if self.scoring_func != "softmax" and not self.use_grouped_topk:
             raise ValueError("Only softmax scoring function is supported for "
                              "non-grouped topk.")
+        if current_platform.is_hpu():
+            from vllm_hpu_extension.ops import DynamicFusedMOE
+            self.hpu_fused_moe = DynamicFusedMOE(self.num_experts)
 
         # Note: get_quant_method will look at the layer's local_num_experts
         # for heuristic purposes, so it must be initialized first.

vllm/model_executor/model_loader/loader.py

@@ -387,6 +387,16 @@ class DefaultModelLoader(BaseModelLoader):
 
             weights_iterator = _xla_weights_iterator(weights_iterator)
 
+        elif current_platform.is_hpu():
+            import habana_frameworks.torch.core as htcore
+
+            def _hpu_weights_iterator(iterator: Generator):
+                for weights in iterator:
+                    yield weights
+                    htcore.mark_step()
+
+            weights_iterator = _hpu_weights_iterator(weights_iterator)
+
         if self.counter_before_loading_weights == 0.0:
             self.counter_before_loading_weights = time.perf_counter()
         # Apply the prefix.

vllm/worker/hpu_model_runner.py

@@ -376,8 +376,22 @@ class HpuModelAdapter:
         mask = mask >= metadata.block_usage.unsqueeze(-1)
         attn_bias = (torch.zeros_like(mask, dtype=dtype).masked_fill_(
             mask, -math.inf))
-        block_mapping = torch.nn.functional.one_hot(metadata.block_groups,
-                                                    num_classes=batch_size)
+        if os.environ.get('VLLM_USE_FAKE_HPU',
+                          '0') == '0' and htorch.utils.internal.is_lazy():
+            block_mapping = torch.nn.functional.one_hot(metadata.block_groups,
+                                                        num_classes=batch_size)
+        else:
+            # Unfortunately one_hot on CPU/torch.compile mode/eager mode
+            # doesn't handle out of bounds classes so we need to convert
+            # all negative values to 0 (block_mapping) or bs (block_groups)
+            block_groups = metadata.block_groups.to(torch.long)
+            block_mapping = torch.nn.functional.relu(block_groups)
+            block_mapping = torch.nn.functional.one_hot(block_mapping,
+                                                        num_classes=batch_size)
+            oob_values = block_groups.lt(0)
+            block_mapping.masked_fill_(oob_values.unsqueeze(-1), 0)
+            block_groups.masked_fill_(oob_values, batch_size)
+            metadata = metadata._replace(block_groups=block_groups)
         block_mapping = block_mapping.to(dtype)
         metadata = metadata._replace(block_mapping=block_mapping,
                                      attn_bias=attn_bias)