update triton kernel to optimize torch cat for ds prefill

vllm/v1/attention/backends/mla/concatv3Tritonfinal.py

@@ -48,6 +48,21 @@ import math
     (((896, 32, 512), (896, 32, 64)), 2),
     (((1024, 32, 512), (1024, 32, 64)), 2),
 
+
+
+    (((4, 32, 128), (4, 32, 64)), 2),
+    (((8, 32, 128), (8, 32, 64)), 2),
+    (((16, 32, 128), (16, 32, 64)), 2),
+    (((32, 32, 128), (32, 32, 64)), 2),
+    (((64, 32, 128), (64, 32, 64)), 2),
+    (((128, 32, 128), (128, 32, 64)), 2),
+    (((256, 32, 128), (256, 32, 64)), 2),
+    (((512, 32, 128), (512, 32, 64)), 2),
+    (((672, 32, 128), (672, 32, 64)), 2),    
+    (((768, 32, 128), (768, 32, 64)), 2),
+    (((896, 32, 128), (896, 32, 64)), 2),
+    (((1024, 32, 128), (1024, 32, 64)), 2),
+    
 ])
 def test_concat_Acc(shape_pair, dim):
 
@@ -60,6 +75,47 @@ def test_concat_Acc(shape_pair, dim):
     assert torch.allclose(result, expected, rtol=1e-5, atol=1e-5), "Mismatch"
 
 
+
+
+
+@triton.jit
+def concat_kernel_prefill(
+    A_ptr, B_ptr, C_ptr,
+    A_section_numel, B_section_numel, C_section_numel,
+    Per_block, 
+    section_num, 
+    BLOCK_SIZE: tl.constexpr
+):
+    block_idx = tl.program_id(0)# 获取当前block的索引
+    
+    for sub_section_index in range(Per_block//2):
+        sub_section_offset = block_idx * Per_block + sub_section_index * 2  
+        if sub_section_offset <= section_num-1:
+            
+            C_section_start = C_ptr + sub_section_offset * C_section_numel
+            A_section_start = A_ptr + sub_section_offset * A_section_numel
+            B_section_start = B_ptr + sub_section_offset * B_section_numel
+
+            Arrange_doubleA = tl.arange(0, 256)                
+            mask = Arrange_doubleA < (256)
+            Arrange2 = (tl.arange(0, 128)[None,:] +  tl.arange(0, 2)[:,None]).reshape(256)
+            val_from_A = tl.load(A_section_start + Arrange_doubleA)
+            tensorAsn = tl.full((256,), 0, tl.int32)
+            tensorAsn2 = tl.full((256,), (C_section_numel-1), tl.int32)
+            tensor_offsets = tl.where(Arrange_doubleA < A_section_numel,tensorAsn , tensorAsn2)
+            off = Arrange2 + tensor_offsets
+            tl.store(C_section_start + off,val_from_A,mask=mask)
+
+            Arrange_doubleB = tl.arange(0, 128)
+            mask = Arrange_doubleB < (B_section_numel*2)
+            val_from_B = tl.load(B_section_start + Arrange_doubleB,mask=mask)
+
+            Arrange3 = (tl.arange(0, 64)[None,:] +  tl.arange(0, 2)[:,None]).reshape(128)
+            tensorAsn = tl.full((128,), A_section_numel, tl.int32)
+            tensorAsn2 = tl.full((128,), (C_section_numel + A_section_numel-1), tl.int32)
+            tensor_offsets = tl.where(Arrange_doubleB < B_section_numel,tensorAsn , tensorAsn2)
+            tl.store(C_section_start+ Arrange3 + tensor_offsets , val_from_B)
+
 @triton.jit
 def concat_kernel(
     A_ptr, B_ptr, C_ptr,
@@ -94,11 +150,25 @@ def concat_helper(A:torch.Tensor, B:torch.Tensor, dim:int):
     output_shape = list(A.shape)
     output_shape[dim] = A.shape[dim] + B.shape[dim]
     C = torch.empty(output_shape, device=A.device, dtype=A.dtype)
+    
     if dim!=0 :
         block_num = reduce(lambda x, y: x * y, output_shape[:dim])
-        unit_offset_A, unit_offset_B, unit_offset_C = A.stride(dim-1),B.stride(dim-1),C.stride(dim-1) 
         Per_block = 1 
-        if (A.shape[1]==8 and A.shape[0] > 128) or ( A.shape[1]==16 and A.shape[0] > 96) or ( A.shape[1]==32 and A.shape[0] > 64):
+        unit_offset_A, unit_offset_B, unit_offset_C = A.stride(dim-1),B.stride(dim-1),C.stride(dim-1)
+        #case prefill
+        if (A.shape[2] == 128 and  B.shape[2] == 64 and A.shape[0] > 16):
+            Per_block = 8
+            num_blocks = math.ceil(block_num/Per_block)
+            concat_kernel_prefill[(num_blocks,)](
+                A, B, C,
+                unit_offset_A, unit_offset_B, unit_offset_C,
+                Per_block, 
+                block_num, 
+                BLOCK_SIZE=1024)
+            return C
+            
+        else:
+            if (A.shape[1]==8 and A.shape[0] > 128) or ( A.shape[1]==16 and A.shape[0] > 96) or ( A.shape[1]==32 and A.shape[2] == 512 and A.shape[0] > 64):
                 Per_block = 2
             num_blocks = math.ceil(block_num/Per_block)
             concat_kernel[(num_blocks,)](
@@ -160,7 +230,19 @@ def benchmark_32(size, provider, dim):
         ms, min_ms, max_ms = triton.testing.do_bench(lambda: concat_helper(x, y,dim=dim), quantiles=quantiles)
     return (ms*1000), (max_ms*1000), (min_ms*1000)
 
+@triton.testing.perf_report(configs)
+def benchmark_prefill(size, provider, dim):
+    x = torch.rand([size,32,128], device='cuda', dtype=torch.bfloat16)
+    y = torch.rand([size,32,64], device='cuda', dtype=torch.bfloat16)
+    quantiles = [0.5, 0.2, 0.8]
+    if provider == 'torch':
+        ms, min_ms, max_ms = triton.testing.do_bench(lambda: torch.cat([x,y],dim=dim), quantiles=quantiles)
+    if provider == 'triton':
+        ms, min_ms, max_ms = triton.testing.do_bench(lambda: concat_helper(x, y,dim=dim), quantiles=quantiles)
+    return (ms*1000), (max_ms*1000), (min_ms*1000)
+
 if __name__ == '__main__':
-    benchmark.run(save_path="./triton_test_8",print_data=True)
-    benchmark_16.run(save_path="./triton_test_16",print_data=True)
-    benchmark_32.run(save_path="./triton_test_32",print_data=True)
\ No newline at end of file
+    # benchmark.run(save_path="./triton_test_8",print_data=True)
+    # benchmark_16.run(save_path="./triton_test_16",print_data=True)
+    # benchmark_32.run(save_path="./triton_test_32",print_data=True)
+    benchmark_prefill.run(save_path="./triton_test_prefill",print_data=True)
\ No newline at end of file