update triton cat scheduling

update the default values of VLLM_USE_TRITON_CAT and VLLM_USE_LIGHT_OP to True

vllm/envs.py

@@ -1093,11 +1093,11 @@ environment_variables: dict[str, Callable[[], Any]] = {
                  ("true", "1")),
     # vLLM will use global cache for moe
     "VLLM_USE_LIGHT_OP":
-        lambda: (os.environ.get("VLLM_USE_LIGHT_OP", "False").lower() in
+        lambda: (os.environ.get("VLLM_USE_LIGHT_OP", "True").lower() in
                  ("true", "1")),
     # vLLM will use global cache for moe
     "VLLM_USE_TRITON_CAT":
-        lambda: (os.environ.get("VLLM_USE_TRITON_CAT", "False").lower() in
+        lambda: (os.environ.get("VLLM_USE_TRITON_CAT", "True").lower() in
                  ("true", "1")),  
 }
 

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

@@ -216,7 +216,7 @@ from vllm.v1.attention.backends.utils import (AttentionMetadataBuilder,
                                               CommonAttentionMetadata)
 from vllm.v1.kv_cache_interface import AttentionSpec
 from vllm.v1.worker.block_table import BlockTable
-from vllm.v1.attention.backends.mla.concatv3Tritonfinal import concat_helper
+from vllm.v1.attention.backends.mla.concatv3Tritonfinalv2 import concat_helper
 
 try:
     from vllm.vllm_flash_attn import flash_attn_varlen_func

vllm/v1/attention/backends/mla/concatv3Tritonfinalv2.py

+import triton
+import triton.language as tl
+import torch
+from functools import reduce
+import pytest
+import torch
+import math
+
+@pytest.mark.parametrize("shape_pair,dim", [
+
+    (((4, 8, 512), (4, 8, 64)), 2),
+    (((8, 8, 512), (8, 8, 64)), 2),
+    (((16, 8, 512), (16, 8, 64)), 2),
+    (((32, 8, 512), (32, 8, 64)), 2),
+    (((64, 8, 512), (64, 8, 64)), 2),
+    (((128, 8, 512), (128, 8, 64)), 2),
+    (((256, 8, 512), (256, 8, 64)), 2),
+    (((512, 8, 512), (512, 8, 64)), 2),
+    (((672, 8, 512), (672, 8, 64)), 2),    
+    (((768, 8, 512), (768, 8, 64)), 2),
+    (((896, 8, 512), (896, 8, 64)), 2),
+    (((1024, 8, 512), (1024, 8, 64)), 2),
+
+    (((4, 16, 512), (4, 16, 64)), 2),
+    (((8, 16, 512), (8, 16, 64)), 2),
+    (((16, 16, 512), (16, 16, 64)), 2),
+    (((32, 16, 512), (32, 16, 64)), 2),
+    (((64, 16, 512), (64, 16, 64)), 2),
+    (((128, 16, 512), (128, 16, 64)), 2),
+    (((256, 16, 512), (256, 16, 64)), 2),
+    (((512, 16, 512), (512, 16, 64)), 2),
+    (((672, 16, 512), (672, 16, 64)), 2),    
+    (((768, 16, 512), (768, 16, 64)), 2),
+    (((896, 16, 512), (896, 16, 64)), 2),
+    (((1024, 16, 512), (1024, 16, 64)), 2),
+
+
+    (((4, 32, 512), (4, 32, 64)), 2),
+    (((8, 32, 512), (8, 32, 64)), 2),
+    (((16, 32, 512), (16, 32, 64)), 2),
+    (((32, 32, 512), (32, 32, 64)), 2),
+    (((64, 32, 512), (64, 32, 64)), 2),
+    (((128, 32, 512), (128, 32, 64)), 2),
+    (((256, 32, 512), (256, 32, 64)), 2),
+    (((512, 32, 512), (512, 32, 64)), 2),
+    (((672, 32, 512), (672, 32, 64)), 2),    
+    (((768, 32, 512), (768, 32, 64)), 2),
+    (((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):
+
+    torch.manual_seed(1)
+    shape1, shape2 = shape_pair
+    x = torch.randn(*shape1, device='cuda', dtype=torch.bfloat16)
+    y = torch.randn(*shape2, device='cuda', dtype=torch.bfloat16)   
+    expected = torch.cat([x,y], dim=dim)
+    result = concat_helper(x, y, dim=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,
+    A_section_numel, B_section_numel, C_section_numel,
+    Per_block, 
+    section_num, 
+    BLOCK_SIZE: tl.constexpr
+):
+    block_idx = tl.program_id(0)
+    for sub_section_index in range(Per_block):
+        sub_offset = block_idx * Per_block + sub_section_index  
+        if sub_offset <= section_num-1:
+            C_ptr_block_start = C_ptr + sub_offset * C_section_numel
+            A_ptr_block_start = A_ptr + sub_offset * A_section_numel
+            B_ptr_block_start = B_ptr + sub_offset * B_section_numel
+            
+            for offset in range(0, A_section_numel, BLOCK_SIZE):
+                offset_idx = offset + tl.arange(0, BLOCK_SIZE)
+                mask = offset_idx < A_section_numel
+                val_from_A = tl.load(A_ptr_block_start + offset_idx, mask=mask)
+                tl.store(C_ptr_block_start + offset_idx, val_from_A, mask=mask)
+
+            for offset in range(0, B_section_numel, BLOCK_SIZE):
+                offset_idx = offset + tl.arange(0, BLOCK_SIZE)
+                mask = offset_idx < B_section_numel
+                val_from_B = tl.load(B_ptr_block_start + offset_idx, mask=mask)
+                tl.store(C_ptr_block_start + A_section_numel + offset_idx, val_from_B, mask=mask)                
+    
+def concat_helper(A:torch.Tensor, B:torch.Tensor, dim:int):
+    A = A.contiguous()
+    B = B.contiguous()
+    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])
+        Per_block = 1 
+        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,)](
+                A, B, C,
+                unit_offset_A, unit_offset_B, unit_offset_C,
+                Per_block, 
+                block_num, 
+                BLOCK_SIZE=1024)
+            return C
+    assert False, "not support"
+
+
+configs = []
+configs.append(
+    triton.testing.Benchmark(
+        x_names=['size'],
+        x_vals=[4,8,16,32,64,96,128,256,512,768,1024],
+        x_log=True,
+        line_arg='provider',
+        line_vals=['triton', 'torch'],
+        line_names=['Triton', 'Torch'],
+        styles=[('blue', '-'), ('green', '-')],
+        ylabel='s',
+        plot_name='concat-dim2',
+        args={"dim":2},
+    ),
+)
+
+@triton.testing.perf_report(configs)
+def benchmark(size, provider, dim):
+    x = torch.rand([size,8,512], device='cuda', dtype=torch.bfloat16)
+    y = torch.rand([size,8,64], 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)
+
+@triton.testing.perf_report(configs)
+def benchmark_16(size, provider, dim):
+    x = torch.rand([size,16,512], device='cuda', dtype=torch.bfloat16)
+    y = torch.rand([size,16,64], 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)
+
+@triton.testing.perf_report(configs)
+def benchmark_32(size, provider, dim):
+    x = torch.rand([size,32,512], 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)
+
+@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)
+    benchmark_prefill.run(save_path="./triton_test_prefill",print_data=True)
+
+   
\ No newline at end of file

vllm/v1/attention/backends/mla/concatv3Tritonfinal.py → vllm/v1/attention/backends/mla/concatv4_decode_only.py

@@ -243,4 +243,6 @@ if __name__ == '__main__':
     benchmark.run(save_path="./triton_test",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)
+    # benchmark_prefill.run(save_path="./triton_test_prefill",print_data=True)
\ No newline at end of file
+
+   
\ No newline at end of file

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

@@ -20,7 +20,7 @@ from vllm.v1.attention.backends.mla.common import (MLACommonBackend,
 from vllm.v1.kv_cache_interface import AttentionSpec
 from vllm.v1.worker.block_table import BlockTable
 from vllm import envs
-from vllm.v1.attention.backends.mla.concatv3Tritonfinal import concat_helper
+from vllm.v1.attention.backends.mla.concatv4_decode_only import concat_helper
 
 logger = init_logger(__name__)
 
@@ -167,8 +167,12 @@ class FlashMLAImpl(MLACommonImpl[FlashMLAMetadata]):
         assert attn_metadata.decode is not None
 
         if envs.VLLM_USE_TRITON_CAT:
-            q = concat_helper(q_nope, q_pe, dim=-1)\
-                .unsqueeze(1)
+            if q_nope.shape[0] <= 1024:
+                q = concat_helper(q_nope, q_pe, dim=-1)\
+                    .unsqueeze(1)
+            else:
+                q = torch.cat([q_nope, q_pe], dim=-1)\
+                .unsqueeze(1) # Add seqlen dim of 1 (decode)
         else:
             q = torch.cat([q_nope, q_pe], dim=-1)\
                 .unsqueeze(1) # Add seqlen dim of 1 (decode)