Allow n_heads specification in fp8 attention bench

Summary:
Currently fp8 attention benchmark only allows specifying the `embedding_dim` and the `h_dimension`, which leads to inconsistencies with the bf16 benchmark.

- Adding support to provide `n_heads` as input and raise `ValueError` incase mismatch between providing inputs.
- Dropping default value for `embedding_dim`

Differential Revision: D68531784

tritonbench/operators/fp8_attention/operator.py

@@ -36,9 +36,9 @@ def parse_op_args(args: List[str]):
     parser.add_argument(
         "--embedding-dim",
         type=int,
-        default=2048,
         help="specify embedding dim, embedding dim = n_heads * head_dim",
     )
+    parser.add_argument("--n-heads", type=int, default=48, help="Number of heads")
     parser.add_argument("--d-head", type=int, default=64, help="specify head dimension")
     parser.add_argument("--causal", action="store_true", help="enable causal")
     return parser.parse_args(args)
@@ -56,6 +56,7 @@ def __init__(
         self.BATCH = args.batch
         self.SEQ_LEN = args.seq_len
         self.embedding_dim = args.embedding_dim
+        self.H = args.n_heads
         self.D_HEAD = args.d_head
         self.causal = args.causal
         # We always turn on causal for backward
@@ -65,6 +66,11 @@ def __init__(
         self.requires_grad = not self.tb_args.mode == "fwd_no_grad"
         self.sm_scale = 1.3
 
+        if self.embedding_dim and self.H != self.embedding_dim // self.D_HEAD:
+            raise ValueError(
+                f"embedding_dim {self.embedding_dim} is inconsistent with n_heads {self.H}. embedding_dim = n_heads * d_head "
+            )
+
     def colfax_preprocess(self, q, k, v):
         # colfax expects q,k: BATCH, N_CTX, H, D_HEAD and v: BATCH, D_HEAD, H, N_CTX
         # passed-in: BATCH, H, N_CTX, D_HEAD
@@ -133,20 +139,16 @@ def triton_flash_v2_tma(
             triton_q, triton_k, triton_v, self.causal, self.sm_scale, "tma"
         )
 
-    def get_x_val(self, _example_inputs) -> Tuple[int, int, int, int]:
-        H = self.embedding_dim // self.D_HEAD
-        return (self.BATCH, self.N_CTX, H, self.D_HEAD)
-
     def get_input_iter(self) -> Generator:
         # The non-fp8 FA varies N_CTX and fixes other variables. Let's do the same for fp8.
         # The autotune config only depends on N_CTX in OSS Triton tutorial.
 
         BATCH = self.BATCH
         D_HEAD = self.D_HEAD
         SEQ_LEN_LOG2 = 7
+        H = self.H
 
         def get_ctx_vals():
-            H = self.embedding_dim // D_HEAD
             if self.SEQ_LEN:
                 yield (BATCH, H, self.SEQ_LEN, self.D_HEAD)
                 return