Remove flash attention backend

Signed-off-by: char-1ee <xingjianli59@gmail.com>

colossalai/inference/modeling/backends/attention_backend.py

@@ -38,82 +38,51 @@ class AttentionBackend(ABC):
         raise NotImplementedError
 
 
-class FlashAttentionBackend(AttentionBackend):
-    """
-    Attention backend when use_cuda_kernel is True and flash-attn is installed. It uses
-    `flash_attn_varlen_func` for prefilling and our cuda op `flash_decoding_attention` for decoding.
-    """
-
-    def __init__(self):
-        super().__init__()
-        self.inference_ops = InferenceOpsLoader().load()
-
-    def prefill(self, attn_metadata: AttentionMetaData, **kwargs):
-        token_nums = kwargs.get("token_nums", -1)
-
-        attn_output = flash_attn_varlen_func(
-            attn_metadata.query_states,
-            attn_metadata.key_states,
-            attn_metadata.value_states,
-            cu_seqlens_q=attn_metadata.cu_seqlens,
-            cu_seqlens_k=attn_metadata.cu_seqlens,
-            max_seqlen_q=attn_metadata.kv_seq_len,
-            max_seqlen_k=attn_metadata.kv_seq_len,
-            dropout_p=0.0,
-            softmax_scale=attn_metadata.sm_scale,
-            causal=True,
-            alibi_slopes=attn_metadata.alibi_slopes,
-        )
-        attn_output = attn_output.view(token_nums, -1)
-        return attn_output
-
-    def decode(self, attn_metadata: AttentionMetaData, **kwargs):
-        fd_inter_tensor = kwargs.get("fd_inter_tensor", None)
-        output_tensor = attn_metadata.output_tensor
-        self.inference_ops.flash_decoding_attention(
-            output_tensor,
-            attn_metadata.query_states,
-            attn_metadata.k_cache,
-            attn_metadata.v_cache,
-            attn_metadata.sequence_lengths,
-            attn_metadata.block_tables,
-            attn_metadata.block_size,
-            attn_metadata.kv_seq_len,
-            fd_inter_tensor.mid_output,
-            fd_inter_tensor.exp_sums,
-            fd_inter_tensor.max_logits,
-            attn_metadata.alibi_slopes,
-            attn_metadata.sm_scale,
-        )
-        return output_tensor
-
-
 class CudaAttentionBackend(AttentionBackend):
     """
     Attention backend when use_cuda_kernel is True but flash-attn not found. If flash-attn is not found,
     it uses Triton op `context_attention_unpadded` for prefilling and our cuda op `flash_decoding_attention` for decoding.
     """
 
-    def __init__(self):
+    def __init__(self, use_flash_attn: bool):
         super().__init__()
         self.inference_ops = InferenceOpsLoader().load()
+        self.use_flash_attn = use_flash_attn
 
     def prefill(self, attn_metadata: AttentionMetaData, **kwargs):
-        return context_attention_unpadded(
-            q=attn_metadata.query_states,
-            k=attn_metadata.key_states,
-            v=attn_metadata.value_states,
-            k_cache=attn_metadata.k_cache,
-            v_cache=attn_metadata.v_cache,
-            context_lengths=attn_metadata.sequence_lengths,
-            block_tables=attn_metadata.block_tables,
-            block_size=attn_metadata.block_size,
-            output=attn_metadata.output_tensor,
-            alibi_slopes=attn_metadata.alibi_slopes,
-            max_seq_len=attn_metadata.kv_seq_len,
-            sm_scale=attn_metadata.sm_scale,
-            use_new_kcache_layout=True,  # use new k cache layout for cuda kernels in this triton op
-        )
+        if self.use_flash_attn:
+            token_nums = kwargs.get("token_nums", -1)
+            attn_output = flash_attn_varlen_func(
+                attn_metadata.query_states,
+                attn_metadata.key_states,
+                attn_metadata.value_states,
+                cu_seqlens_q=attn_metadata.cu_seqlens,
+                cu_seqlens_k=attn_metadata.cu_seqlens,
+                max_seqlen_q=attn_metadata.kv_seq_len,
+                max_seqlen_k=attn_metadata.kv_seq_len,
+                dropout_p=0.0,
+                softmax_scale=attn_metadata.sm_scale,
+                causal=True,
+                alibi_slopes=attn_metadata.alibi_slopes,
+            )
+            attn_output = attn_output.view(token_nums, -1)
+        else:
+            attn_output = context_attention_unpadded(
+                q=attn_metadata.query_states,
+                k=attn_metadata.key_states,
+                v=attn_metadata.value_states,
+                k_cache=attn_metadata.k_cache,
+                v_cache=attn_metadata.v_cache,
+                context_lengths=attn_metadata.sequence_lengths,
+                block_tables=attn_metadata.block_tables,
+                block_size=attn_metadata.block_size,
+                output=attn_metadata.output_tensor,
+                alibi_slopes=attn_metadata.alibi_slopes,
+                max_seq_len=attn_metadata.kv_seq_len,
+                sm_scale=attn_metadata.sm_scale,
+                use_new_kcache_layout=True,  # use new k-cache layout
+            )
+        return attn_output
 
     def decode(self, attn_metadata: AttentionMetaData, **kwargs):
         fd_inter_tensor = kwargs.get("fd_inter_tensor", None)
@@ -155,7 +124,6 @@ class TritonAttentionBackend(AttentionBackend):
             alibi_slopes=attn_metadata.alibi_slopes,
             max_seq_len=attn_metadata.kv_seq_len,
             sm_scale=attn_metadata.sm_scale,
-            use_new_kcache_layout=False,
         )
 
     def decode(self, attn_metadata: AttentionMetaData, **kwargs):
@@ -195,8 +163,6 @@ def get_attention_backend(
         return TritonAttentionBackend()
 
     if model_shard_infer_config.use_cuda_kernel:
-        if model_shard_infer_config.use_flash_attn:
-            return FlashAttentionBackend()
-        return CudaAttentionBackend()
+        return CudaAttentionBackend(model_shard_infer_config.use_flash_attn)
 
     return TritonAttentionBackend()

colossalai/inference/modeling/backends/pre_attention_backend.py

@@ -16,71 +16,37 @@ class PreAttentionBackend(ABC):
         raise NotImplementedError
 
 
-class FlashPreAttentionBackend(PreAttentionBackend):
-    """
-    FlashPreAttentionBackend handles KV cache initialization and positional encoding for FlashAttentionBackend.
-    """
-
-    def __init__(self):
-        super().__init__()
-        self.inference_ops = InferenceOpsLoader().load()
-
-    def prefill(self, attn_metadata: AttentionMetaData, **kwargs):
-        if not attn_metadata.use_alibi_attn:
-            self.inference_ops.rotary_embedding(
-                attn_metadata.query_states,
-                attn_metadata.key_states,
-                kwargs.get("cos", None),
-                kwargs.get("sin", None),
-                kwargs.get("high_precision", False),
-            )
-        self.inference_ops.context_kv_cache_memcpy(
-            attn_metadata.key_states,
-            attn_metadata.value_states,
-            attn_metadata.k_cache,
-            attn_metadata.v_cache,
-            attn_metadata.sequence_lengths,
-            attn_metadata.cu_seqlens,
-            attn_metadata.block_tables,
-            attn_metadata.kv_seq_len,
-        )
-
-    def decode(self, attn_metadata: AttentionMetaData, **kwargs):
-        if not attn_metadata.use_alibi_attn:
-            self.inference_ops.rotary_embedding_and_cache_copy(
-                attn_metadata.query_states,
-                attn_metadata.key_states,
-                attn_metadata.value_states,
-                kwargs.get("cos", None),
-                kwargs.get("sin", None),
-                attn_metadata.k_cache,
-                attn_metadata.v_cache,
-                attn_metadata.sequence_lengths,
-                attn_metadata.block_tables,
-                kwargs.get("high_precision", None),
-            )
-        else:
-            self.inference_ops.decode_kv_cache_memcpy(
-                attn_metadata.key_states,
-                attn_metadata.value_states,
-                attn_metadata.k_cache,
-                attn_metadata.v_cache,
-                attn_metadata.sequence_lengths,
-                attn_metadata.block_tables,
-            )
-
-
 class CudaPreAttentionBackend(PreAttentionBackend):
     """
     CudaPreAttentionBackend handles KV cache initialization and positional encoding for CudaAttentionBackend.
     """
 
-    def __init__(self):
+    def __init__(self, use_flash_attn: bool):
         super().__init__()
         self.inference_ops = InferenceOpsLoader().load()
+        self.use_flash_attn = use_flash_attn
 
     def prefill(self, attn_metadata: AttentionMetaData, **kwargs):
-        if not attn_metadata.use_alibi_attn:
+        if self.use_flash_attn:
+            if not attn_metadata.use_alibi_attn:
+                self.inference_ops.rotary_embedding(
+                    attn_metadata.query_states,
+                    attn_metadata.key_states,
+                    kwargs.get("cos", None),
+                    kwargs.get("sin", None),
+                    kwargs.get("high_precision", False),
+                )
+            self.inference_ops.context_kv_cache_memcpy(
+                attn_metadata.key_states,
+                attn_metadata.value_states,
+                attn_metadata.k_cache,
+                attn_metadata.v_cache,
+                attn_metadata.sequence_lengths,
+                attn_metadata.cu_seqlens,
+                attn_metadata.block_tables,
+                attn_metadata.kv_seq_len,
+            )
+        elif not attn_metadata.use_alibi_attn:
             rotary_embedding(
                 attn_metadata.query_states,
                 attn_metadata.key_states,
@@ -175,8 +141,6 @@ def get_pre_attention_backend(
         return TritonPreAttentionBackend()
 
     if model_shard_infer_config.use_cuda_kernel:
-        if model_shard_infer_config.use_flash_attn:
-            return FlashPreAttentionBackend()
-        return CudaPreAttentionBackend()
+        return CudaPreAttentionBackend(model_shard_infer_config.use_flash_attn)
 
     return TritonPreAttentionBackend()