PR comments

Signed-off-by: luka <[email protected]>

tests/kernels/test_fused_quant_layernorm.py

@@ -9,8 +9,8 @@
 DTYPES = [torch.bfloat16, torch.float]
 QUANT_DTYPES = [torch.int8, torch.float8_e4m3fn]
 NUM_TOKENS = [1, 7, 83, 2048, 4096]  # Arbitrary values for testing
-HIDDEN_SIZES = [1, 2, 3, 4, 16, 64, 67, 768, 2048, 5120, 5137, 8192,
-                8193]  # Arbitrary values for testing
+HIDDEN_SIZES = [1, 3, 4, 16, 64, 2048, 5120,
+                5137]  # Arbitrary values for testing
 HIDDEN_SIZES += list(range(1024, 1033))  # vectorized conversion edge cases
 ADD_RESIDUAL = [False, True]
 SCALE_UBS = [True, False]

vllm/_custom_ops.py

@@ -22,7 +22,6 @@
 supports_moe_ops = False
 with contextlib.suppress(ImportError):
     import vllm._moe_C  # noqa: F401
-
     supports_moe_ops = True
 
 # neuron has torch version that doesn't even have impl_abstract
@@ -242,6 +241,7 @@ def advance_step_flashinfer(num_seqs: int, num_queries: int, block_size: int,
                             paged_kv_indptr: torch.Tensor,
                             paged_kv_last_page_len: torch.Tensor,
                             block_table_bound: torch.Tensor) -> None:
+
     return torch.ops._C.advance_step_flashinfer(
         num_seqs, num_queries, block_size, input_tokens, sampled_token_ids,
         input_positions, seq_lens, slot_mapping, block_tables,
@@ -737,7 +737,7 @@ def scaled_fp8_quant(
     shape: Union[Tuple[int, int], torch.Size] = input.shape
     # For rocm, the output fp8 dtype is torch.float_e3m3fnuz
     out_dtype: torch.dtype = torch.float8_e4m3fnuz \
-        if current_platform.is_rocm() else torch.float8_e4m3fn
+            if current_platform.is_rocm() else torch.float8_e4m3fn
     if num_token_padding:
         shape = (max(num_token_padding, input.shape[0]), shape[1])
     output = torch.empty(shape, device=input.device, dtype=out_dtype)
@@ -1020,9 +1020,9 @@ def register_graph_buffers(fa: int, handles: List[List[int]],
     # the case when users use `import __annotations__` to turn type
     # hints into strings.
     if isinstance(v, fn_type) \
-            and v.__code__.co_filename == __file__ \
-            and any(arg is torch.Tensor or arg == "torch.Tensor"
-                    for arg in v.__annotations__.values()):
+        and v.__code__.co_filename == __file__ \
+        and any(arg is torch.Tensor or arg == "torch.Tensor"
+                for arg in v.__annotations__.values()):
         names_and_values_to_update[k] = hint_on_error(v)
 
 names_and_values.update(names_and_values_to_update)