Disable attention mask when it is not needed (#813)

* disable attention mask when not needed

* fix for sm<80 and float data type

src/turbomind/models/llama/LlamaBatch.cc

@@ -894,6 +894,8 @@ LlamaBatch<T>::LlamaBatch(const EngineParams& params, int cache_block_seq_len, i
         session_len_ = max_session_len;
     }
 
+    FT_CHECK(max_context_token_num_ >= session_len_);
+
     for (auto& s : states_) {
         s.requests.resize(max_batch_size_);
         s.sequences.resize(max_batch_size_);

src/turbomind/models/llama/unified_decoder.cc

@@ -15,8 +15,14 @@ void UnifiedDecoder<T>::allocateBuffer(size_t num_token, size_t pf_batch_size, s
     TM_LOG_DEBUG(__PRETTY_FUNCTION__);
 
     if (pf_batch_size) {
-        attention_mask_ =
-            (T*)allocator_->reMalloc(attention_mask_, sizeof(T) * pf_batch_size * pf_max_q_len * pf_max_k_len, false);
+        if (need_causal_mask_) {
+            attention_mask_ = (T*)allocator_->reMalloc(
+                attention_mask_, sizeof(T) * pf_batch_size * pf_max_q_len * pf_max_k_len, false);
+        }
+        else {
+            // just to avoid nullptr
+            attention_mask_ = (T*)allocator_->reMalloc(attention_mask_, sizeof(T), false);
+        }
         padding_offset_ =
             (int*)allocator_->reMalloc(padding_offset_, sizeof(int) * pf_batch_size * pf_max_q_len, false);
         cu_seqlens_ = (int*)allocator_->reMalloc(cu_seqlens_, sizeof(int) * (pf_batch_size + 1), false);
@@ -162,14 +168,16 @@ void UnifiedDecoder<T>::forward(TensorMap* outputs, const TensorMap* inputs, con
 
         FT_CHECK(tmp_token_num == token_num - dc_batch_size);
 
-        invokeCreateCausalMasks(attention_mask_,
-                                input_length + pf_offset,
-                                context_length + pf_offset,
-                                pf_max_q_len,
-                                pf_max_k_len,
-                                pf_batch_size,
-                                stream_);
-        sync_check_cuda_error();
+        if (need_causal_mask_) {
+            invokeCreateCausalMasks(attention_mask_,
+                                    input_length + pf_offset,
+                                    context_length + pf_offset,
+                                    pf_max_q_len,
+                                    pf_max_k_len,
+                                    pf_batch_size,
+                                    stream_);
+            sync_check_cuda_error();
+        }
     }
 
     /////////////////////////////////////////////

src/turbomind/models/llama/unified_decoder.h

@@ -5,6 +5,7 @@
 #include "src/turbomind/models/llama/llama_params.h"
 #include "src/turbomind/models/llama/unified_attention_layer.h"
 #include "src/turbomind/utils/cublasMMWrapper.h"
+#include "src/turbomind/utils/cuda_utils.h"
 #include "src/turbomind/utils/nccl_utils.h"
 
 namespace turbomind {
@@ -46,6 +47,8 @@ protected:
 
     const DataType dtype_;
 
+    bool need_causal_mask_{false};
+
     using WeightType = LlamaDecoderLayerWeight<T>;
 
     void forwardSelfAttn(T*                             attn_io,
@@ -88,6 +91,14 @@ public:
         tensor_para_(tensor_para),
         dtype_(getTensorType<T>())
     {
+#ifdef _MSC_VER
+        // Both unfused MHA and flash attention 1 need causal mask
+        need_causal_mask_ = true;
+#endif
+        // attention mask is not used for FA-1 (which requires sm80+ and half/bf16 data type)
+        if (!use_fmha || (getSMVersion() < 80 || sizeof(T) != 2)) {
+            need_causal_mask_ = true;
+        }
         initialize(attn_params, kv_head_num, use_fmha, cache_block_seq_len, quant_policy);
     }