Enable GitHub Actions static checks for habana_main

.github/workflows/clang-format.yml

@@ -2,13 +2,13 @@ name: clang-format
 
 on:
   # Trigger the workflow on push or pull request,
-  # but only for the main branch
+  # but only for the habana_main branch
   push:
     branches:
-      - main
+      - habana_main
   pull_request:
     branches:
-      - main
+      - habana_main
 
 jobs:
   clang-format:

.github/workflows/mypy.yaml

@@ -2,13 +2,13 @@ name: mypy
 
 on:
   # Trigger the workflow on push or pull request,
-  # but only for the main branch
+  # but only for the habana_main branch
   push:
     branches:
-      - main
+      - habana_main
   pull_request:
     branches:
-      - main
+      - habana_main
 
 jobs:
   ruff:
@@ -50,4 +50,6 @@ jobs:
         mypy vllm/transformers_utils --config-file pyproject.toml
         mypy vllm/usage --config-file pyproject.toml
         mypy vllm/worker --config-file pyproject.toml
+        mypy vllm/hpu --config-file pyproject.toml
+
 

.github/workflows/ruff.yml

@@ -2,13 +2,13 @@ name: ruff
 
 on:
   # Trigger the workflow on push or pull request,
-  # but only for the main branch
+  # but only for the habana_main branch
   push:
     branches:
-      - main
+      - habana_main
   pull_request:
     branches:
-      - main
+      - habana_main
 
 jobs:
   ruff:

.github/workflows/yapf.yml

@@ -2,13 +2,13 @@ name: yapf
 
 on:
   # Trigger the workflow on push or pull request,
-  # but only for the main branch
+  # but only for the habana_main branch
   push:
     branches:
-      - main
+      - habana_main
   pull_request:
     branches:
-      - main
+      - habana_main
 jobs:
   yapf:
     runs-on: ubuntu-latest

format.sh

@@ -113,6 +113,7 @@ mypy vllm/spec_decode --config-file pyproject.toml
 mypy vllm/transformers_utils --config-file pyproject.toml
 mypy vllm/usage --config-file pyproject.toml
 mypy vllm/worker --config-file pyproject.toml
+mypy vllm/hpu --config-file pyproject.toml
 
 
 # If git diff returns a file that is in the skip list, the file may be checked anyway:

vllm/hpu/ops.py

@@ -12,6 +12,16 @@
 import torch.nn.functional as F
 
 import vllm.hpu.utils as hpu_utils
+from vllm.logger import init_logger
+
+logger = init_logger()
+HPUFusedRMSNorm = None
+try:
+    from habana_frameworks.torch.hpex.normalization import FusedRMSNorm
+    HPUFusedRMSNorm = FusedRMSNorm
+except ImportError:
+    logger.warning("Could not import HPU FusedRMSNorm kernel. "
+                   "vLLM will use forward_native implementation of RMSNorm.")
 
 PA_SPLIT_VALUE = (os.environ.get('PA_SPLIT_VALUE', '1') == '1')
 
@@ -52,8 +62,7 @@ def paged_attention_v1(query,
         keys = [k.unflatten(1, (kv_heads, 1)) for k in keys]
         mask = mask.unsqueeze(2)
 
-    attn_weights = [torch.matmul(query, k) for k in keys]
-    attn_weights = torch.cat(attn_weights, dim=-1)
+    attn_weights = torch.cat([torch.matmul(query, k) for k in keys], dim=-1)
     if alibi_slopes is not None:
         attn_weights.add_(alibi_slopes[:, :, -attn_weights.size(2):,
                                        -attn_weights.size(3):])
@@ -128,7 +137,8 @@ def prompt_attention(
         query = query.unflatten(1, (kv_heads, -1))
         key = key.unflatten(1, (kv_heads, 1))
         value = value.unflatten(1, (kv_heads, 1))
-        attn_bias = attn_bias.unsqueeze(2)
+        if attn_bias is not None:
+            attn_bias = attn_bias.unsqueeze(2)
     attn_weights = torch.matmul(query * scale, key.transpose(-1, -2))
     if attn_bias is not None:
         attn_weights.add_(attn_bias)

vllm/model_executor/layers/layernorm.py

@@ -6,19 +6,8 @@
 
 from vllm.logger import init_logger
 from vllm.model_executor.custom_op import CustomOp
-from vllm.utils import is_hpu
 
 logger = init_logger(__name__)
-if is_hpu():
-    try:
-        from habana_frameworks.torch.hpex.normalization import (FusedRMSNorm as
-                                                                HPUFusedRMSNorm
-                                                                )
-    except ImportError:
-        logger.warning(
-            "Could not import HPU FusedRMSNorm kernel. "
-            "vLLM will use forward_native implementation of RMSNorm.")
-        HPUFusedRMSNorm = None
 
 
 class RMSNorm(CustomOp):
@@ -86,6 +75,7 @@ def forward_hpu(
         x: torch.Tensor,
         residual: Optional[torch.Tensor] = None,
     ) -> Union[torch.Tensor, Tuple[torch.Tensor, torch.Tensor]]:
+        from vllm.hpu.ops import HPUFusedRMSNorm
         if HPUFusedRMSNorm is None:
             return self.forward_native(x, residual)
         if residual is not None: