fix logits tests

Signed-off-by: Austin Liu <[email protected]>

Author: austin362667

src/liger_kernel/transformers/model/llama.py

@@ -8,7 +8,6 @@
 import torch.nn.functional as F
 
 from torch.nn import CrossEntropyLoss
-from torch.nn.attention.flex_attention import create_block_mask
 from torch.nn.attention.flex_attention import flex_attention
 from transformers.modeling_outputs import CausalLMOutputWithPast
 from transformers.models.llama.modeling_llama import _CONFIG_FOR_DOC
@@ -256,8 +255,6 @@ def lce_forward(
 
 
 # Adapted from https://github.com/huggingface/transformers/blob/main/src/transformers/integrations/flex_attention.py#L12
-
-
 def flex_attention_forward(
     module: torch.nn.Module,
     query: torch.Tensor,
@@ -279,26 +276,24 @@ def causal_mod(score, b, h, q_idx, kv_idx):
             score = score + causal_mask[b][0][q_idx][kv_idx]
         return score
 
-    # We only got `attention_mask` tensors, so we recreate `causal_mask` function as specific llama causal attention
-    # TODO: Consider other customized `attention_mask` in the future, e.g., shared prefix
-    def causal_mask_fn(b, h, q_idx, kv_idx):
-        return q_idx >= kv_idx
+    # def causal_mask_fn(b, h, q_idx, kv_idx):
+    #     return q_idx >= kv_idx
 
-    #  To construct block attention mask that leverages sparsity.
-    sparse_causal_mask = create_block_mask(causal_mask_fn, None, None, query.shape[-2], query.shape[-2], device="cuda")
+    # TODO: Construct block attention mask that leverages sparsity
+    # sparse_causal_mask = create_block_mask(
+    #     causal_mask_fn, B=None, H=None, Q_LEN=query.shape[-2], KV_LEN=key.shape[-2], device=query.device, BLOCK_SIZE=1
+    # )
 
     attn_output, attention_weights = flex_attention(
         query,
         key,
         value,
         score_mod=causal_mod,
-        block_mask=sparse_causal_mask,
+        # block_mask=sparse_causal_mask,
         enable_gqa=True,
         scale=scaling,
-        # Last time checked on PyTorch == 2.5.1: Flex Attention always computes the lse regardless.
-        # For simplification, we thus always return it as no additional computations are introduced.
         return_lse=True,
-        kernel_options={  # different harware might need different configs
+        kernel_options={
             "BLOCK_M": 32,
             "BLOCK_N": 32,
             "BLOCK_M1": 16,
@@ -307,7 +302,7 @@ def causal_mask_fn(b, h, q_idx, kv_idx):
             "BLOCK_N2": 16,
         },
     )
-    # lse is returned in float32
+
     attention_weights = attention_weights.to(value.dtype)
     attn_output = attn_output.transpose(1, 2).contiguous()