Initial commit for V1 successfull compilation

Author: maleksan85

vllm/model_executor/layers/activation.py

@@ -70,7 +70,9 @@ def __init__(self):
             from vllm._ipex_ops import ipex_ops
             self.op = ipex_ops.silu_and_mul
 
-    def forward_native(self, x: torch.Tensor) -> torch.Tensor:
+    def forward_native(self,
+                       x: torch.Tensor,
+                       scale: Optional[torch.Tensor] = None) -> torch.Tensor:
         """PyTorch-native implementation equivalent to forward()."""
         d = x.shape[-1] // 2
         return F.silu(x[..., :d]) * x[..., d:]

vllm/model_executor/layers/layernorm.py

@@ -39,6 +39,7 @@ def forward_native(
         self,
         x: torch.Tensor,
         residual: Optional[torch.Tensor] = None,
+        scale: Optional[torch.Tensor] = None,
     ) -> Union[torch.Tensor, Tuple[torch.Tensor, torch.Tensor]]:
         """PyTorch-native implementation equivalent to forward()."""
         orig_dtype = x.dtype

vllm/model_executor/layers/linear.py

@@ -15,7 +15,6 @@
 from vllm.logger import init_logger
 from vllm.model_executor.layers.quantization.base_config import (
     QuantizationConfig, QuantizeMethodBase)
-from vllm.model_executor.layers.tuned_gemm import tgemm
 # yapf: disable
 from vllm.model_executor.parameter import (BasevLLMParameter,
                                            BlockQuantScaleParameter,
@@ -138,7 +137,7 @@ def apply(self,
               layer: torch.nn.Module,
               x: torch.Tensor,
               bias: Optional[torch.Tensor] = None) -> torch.Tensor:
-        return tgemm.mm(x, layer.weight, bias)
+        return torch.mm(x, torch.transpose(layer.weight, 0, 1))
 
 
 class LinearBase(torch.nn.Module):

vllm/v1/attention/backends/rocm_attn.py

@@ -109,6 +109,7 @@ def forward(
         value: torch.Tensor,
         kv_cache: torch.Tensor,
         attn_metadata: FlashAttentionMetadata,
+        fp8_out_scale: Optional[torch.Tensor],
         output: Optional[torch.Tensor] = None,
     ) -> torch.Tensor:
         """Forward pass with FlashAttention.