[`core`] Add `is_hf_transformers` flag (#195)

awq/modules/fused/attn.py

@@ -100,6 +100,7 @@ class QuantAttentionFused(nn.Module):
         self.use_alibi = use_alibi
         self.cache_batch_size = int(os.getenv("AWQ_BATCH_SIZE", "1"))
         self.max_seq_len = max_seq_len
+        self.is_hf_transformers = False
 
         # attention shapes for self attention
         self.attention_shapes = get_attention_shapes(
@@ -138,7 +139,8 @@ class QuantAttentionFused(nn.Module):
         # In case we re-generate, we need to refresh the starting position 
         # to 0. We detect it by checking if `past_key_values` is set to None, 
         # which indicates that we are on the first step of `generate()`.
-        if"past_key_value" in kwargs and kwargs["past_key_value"] is None:
+        # This is only applicable for `transformers` integration
+        if self.is_hf_transformers and "past_key_value" in kwargs and kwargs["past_key_value"] is None:
             self.start_pos = 0
 
         xqkv = self.qkv_proj(hidden_states)