Merge branch 'v0.9.2-dev-fth' into 'v0.9.2-dev'

接入新的concat算子，包含decode和prefill，并根据size的不同进行选择

See merge request dcutoolkit/deeplearing/vllm!207

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.concatv3Tritonfinalv2 import concat_helper
+from vllm.v1.attention.backends.mla.test_concat import lightop_concat_prefill_helper
 
 try:
     from vllm.vllm_flash_attn import flash_attn_varlen_func
@@ -926,7 +926,11 @@ class MLACommonImpl(MLAAttentionImpl[M], Generic[M]):
                 .split([self.qk_nope_head_dim, self.v_head_dim], dim=-1)
 
             if envs.VLLM_USE_TRITON_CAT:
-                k = concat_helper(k_nope, k_pe.expand((*k_nope.shape[:-1], -1)),
+                if k_nope.shape[0] > 1024:
+                    k = lightop_concat_prefill_helper(k_nope, k_pe.expand((*k_nope.shape[:-1], -1)),
+                                                      dim=2)
+                else:
+                    k = torch.cat((k_nope, k_pe.expand((*k_nope.shape[:-1], -1))),
                                   dim=-1)
             else:
                 k = torch.cat((k_nope, k_pe.expand((*k_nope.shape[:-1], -1))),
@@ -986,7 +990,12 @@ class MLACommonImpl(MLAAttentionImpl[M], Generic[M]):
             .split([self.qk_nope_head_dim, self.v_head_dim], dim=-1)
 
         if envs.VLLM_USE_TRITON_CAT:
-            k = concat_helper(k_nope, k_pe.expand((*k_nope.shape[:-1], -1)), dim=-1)
+            if k_nope.shape[0] > 1024:
+                k = lightop_concat_prefill_helper(k_nope, k_pe.expand((*k_nope.shape[:-1], -1)),
+                                                  dim=2)
+            else:
+                k = torch.cat((k_nope, k_pe.expand((*k_nope.shape[:-1], -1))),
+                              dim=-1)
         else:
             k = torch.cat((k_nope, k_pe.expand((*k_nope.shape[:-1], -1))), dim=-1)
 

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.concatv4_decode_only import concat_helper
+from vllm.v1.attention.backends.mla.test_concat import concat_helper_decode
 
 logger = init_logger(__name__)
 
@@ -168,7 +168,7 @@ class FlashMLAImpl(MLACommonImpl[FlashMLAMetadata]):
 
         if envs.VLLM_USE_TRITON_CAT:
             if q_nope.shape[0] <= 1024:
-                q = concat_helper(q_nope, q_pe, dim=-1)\
+                q = concat_helper_decode(q_nope, q_pe, dim=2)\
                     .unsqueeze(1)
             else:
                 q = torch.cat([q_nope, q_pe], dim=-1)\

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

+import triton
+import triton.language as tl
+import torch
+from functools import reduce
+import pytest
+import torch
+import math
+from lightop import ds_cat
+
+def test_concat_Acc_prefill(shape_pair, dim):
+
+    torch.manual_seed(1)
+    shape1, shape2 = shape_pair
+    M = shape1[0]
+    N = shape1[1]
+    x_sizes = [M, N, 128]
+    x_strides = [N//8 * 2048, 256, 1]
+    x_max_index = N//8 * 2048 * M
+    x_required_length = x_max_index
+    x_data = torch.arange(x_required_length,device='cuda').bfloat16()  
+    x = torch.as_strided(x_data, size=x_sizes, stride=x_strides)
+
+    
+    y_sizes = [M, N, 64]
+    y_strides = [576, 0, 1]
+    y_max_index = 576 * M
+    y_required_length = y_max_index
+    y_data = torch.arange(y_required_length,device='cuda').bfloat16()  
+    y = torch.as_strided(y_data, size=y_sizes, stride=y_strides)
+    
+    expected = torch.cat([x,y], dim=dim)
+    result = concat_prefill_helper_Triton(x, y, dim=dim)
+    result_lightop = lightop_concat_prefill_helper(x, y, dim=dim)
+    assert torch.allclose(result, expected, rtol=1e-5, atol=1e-5), "Mismatch"
+    # print("精度验证通过")
+    # print("expected",expected)
+    # print("result_lightop",result_lightop)
+    assert torch.allclose(result, result_lightop, rtol=1e-5, atol=1e-5), "result_lightop Mismatch Triton error"
+    assert torch.allclose(expected, result_lightop, rtol=1e-5, atol=1e-5), "result_lightop Mismatch torch error"
+    print("prefill 精度验证通过")
+
+def test_concat_Acc_decode(shape_pair, dim):
+
+    torch.manual_seed(1)
+    shape1, shape2 = shape_pair
+    M = shape1[0]
+    N = shape1[1]
+    x_sizes = [M, N, 512]
+    x_strides = [512, 512*M, 1]
+    x_max_index = M * N * 512
+    x_required_length = x_max_index
+    x_data = torch.arange(x_required_length,device='cuda').bfloat16()  
+    x = torch.as_strided(x_data, size=x_sizes, stride=x_strides)
+    # print("形状:", x.shape)      
+    # print("步幅:", x.stride())  
+    y_sizes = [M, N, 64]
+    y_strides = [1536*(N//8), 192, 1]
+    y_max_index = 1536*(N//8) * M
+    y_required_length = y_max_index
+    y_data = torch.arange(y_required_length,device='cuda').bfloat16()  
+    y = torch.as_strided(y_data, size=y_sizes, stride=y_strides)
+    # print("形状:", y.shape)     
+    # print("步幅:", y.stride())   
+    
+    
+    expected = torch.cat([x,y], dim=dim)
+    result = concat_helper_decode(x, y, dim=dim)
+    assert torch.allclose(result, expected, rtol=1e-5, atol=1e-5), "Mismatch"
+
+    print("decode 精度正常")
+
+
+
+@triton.jit
+def concat_kernel(
+    A_ptr, B_ptr, C_ptr,
+    A_section_numel, B_section_numel, C_section_numel,
+    Per_block_A,
+    Per_block_B, 
+    section_numA,
+    section_numB,
+    M,
+    N,
+    Astride_0,
+    Astride_1,
+    Astride_2,
+    Bstride_0,
+    Bstride_1,
+    Bstride_2,
+    BLOCK_SIZE: tl.constexpr
+):
+    block_idx = tl.program_id(0)
+    numA = section_numA // Per_block_A
+    if (block_idx < numA):
+        #处理A的block
+        for sub_section_index in range(Per_block_A):
+            sub_offset = block_idx * Per_block_A + sub_section_index
+            if sub_offset <= section_numA-1:
+                M_idx = sub_offset // N
+                N_idx = sub_offset % N
+                C_ptr_block_start = C_ptr + sub_offset * C_section_numel
+                A_ptr_block_start = A_ptr + M_idx * Astride_0 + N_idx * Astride_1 
+                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)
+    else:
+        #处理B的block
+        #shape是1024*8*64，实际上只有1024 * 64 块数据，开了1024/4=256个线程块来处理。每个线程块处理1块连续的数据 
+        #需要注意C的分块也是有M * N 大小的，而这里只有M大小个线程块，每个线程块需要写入N次数据到C中。
+        for sub_section_index in range(Per_block_B):
+            sub_offset = (block_idx - numA) * Per_block_B + sub_section_index 
+            if sub_offset <= section_numB-1:
+                C_ptr_block_start = C_ptr + sub_offset * N * C_section_numel
+                B_ptr_block_start = B_ptr + sub_offset * Bstride_0
+                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)
+                    for idx in range(0,N,1):
+                        tl.store(C_ptr_block_start + idx * C_section_numel + A_section_numel + offset_idx, val_from_B, mask=mask)                
+    
+def concat_prefill_helper_Triton(A:torch.Tensor, B:torch.Tensor, dim:int):
+
+    output_shape = list(A.shape)
+    output_shape[dim] = A.shape[dim] + B.shape[dim]  #128+64=192
+    C = torch.empty(output_shape, device=A.device, dtype=A.dtype)
+    
+    if dim!=0 :
+        #分开计算block块A需要
+        Per_block_A = 64
+        Per_block_B = 1
+        #128 \64 \192
+        unit_offset_A, unit_offset_B, unit_offset_C = A.shape[dim],B.shape[dim],C.shape[dim]
+        #A的分块数是：M * N 这里的demo是1024 * 8
+        block_numA = reduce(lambda x, y: x * y, output_shape[:dim])
+        #B的分块数是：M 这里的demo是1024
+        block_numB = output_shape[0]
+        #A的每个分块可以处理多份数据的读取和写入，这是因为单次的任务量太小。假设这里Per_block = 8 那么A就开启了1024个线程块，每个线程块处理8份数据的读取和写入
+        #B的每个分块处理1次B的读取和8次C的写入，L2 cache复用率高
+        block_num = block_numA // Per_block_A + block_numB // Per_block_B
+
+        num_blocks = math.ceil(block_num)
+        concat_kernel[(num_blocks,)](
+                A, B, C,
+                unit_offset_A, unit_offset_B, unit_offset_C,
+                Per_block_A,
+                Per_block_B, 
+                block_numA,
+                block_numB,
+                output_shape[0],
+                output_shape[1],
+                A.stride(0),
+                A.stride(1),
+                A.stride(2),
+                B.stride(0),
+                B.stride(1),
+                B.stride(2),                 
+                BLOCK_SIZE=1024)
+        return C
+
+    assert False, "not support"
+
+
+def concat_helper_decode(A:torch.Tensor, B:torch.Tensor, dim:int):
+    assert dim==2 , "tensor dim must be 3 and concat dim must be 2"
+    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)
+    mode = 0
+    if dim!=0 :
+        ds_cat( A, B, C, mode)
+        return C
+    assert False, "not support"
+
+def lightop_concat_prefill_helper(A:torch.Tensor, B:torch.Tensor, dim:int):
+    assert dim==2 , "tensor dim must be 3 and concat dim must be 2"
+    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)
+    mode = 6
+    if dim!=0 :
+        ds_cat( A, B, C, mode)
+        return C
+    assert False, "not support"
+
+
+configs = []
+configs.append(
+    triton.testing.Benchmark(
+        x_names=['M','N'],
+        x_vals=[(1024,8),(2048,8),(3072,8),(4096,8),(6144,8),(8192,8),\
+                (1024,16),(2048,16),(3072,16),(4096,16),(6144,16),(8192,16),\
+                    (1024,32),(2048,32),(3072,32),(4096,32),(6144,32),(8192,32)
+                    ],
+        x_log=True,
+        line_arg='provider',
+        line_vals=['triton', 'torch', 'lightop'],
+        line_names=['Triton', 'Torch','Lightop'],
+        styles=[('blue', '-'), ('green', '-'), ('yellow', '-')],
+        ylabel='s',
+        plot_name='concat-dim2',
+        args={"dim":2},
+    ),
+)
+
+
+configs_decode = []
+configs_decode.append(
+    triton.testing.Benchmark(
+        x_names=['M','N'],
+        x_vals=[(4,8),(8,8),(16,8),(32,8),(64,8),(96,8),(128,8),(256,8),(512,8),(768,8),(767,8),(765,8),(766,8), \
+                (4,16),(8,16),(16,16),(32,16),(64,16),(96,16),(128,16),(256,16),(512,16),(768,16),(767,16),(765,16),(766,16), \
+                (4,32),(8,32),(16,32),(32,32),(64,32),(96,32),(128,32),(256,32),(512,32),(768,32),(767,32),(765,32),(766,32)],
+        x_log=True,
+        line_arg='provider',
+        line_vals=['lightop', 'torch'],
+        line_names=['Lightop', 'Torch'],
+        styles=[('blue', '-'), ('green', '-')],
+        ylabel='s',
+        plot_name='concat-dim2',
+        args={"dim":2},
+    ),
+)
+
+@triton.testing.perf_report(configs)
+def benchmark_prefill(M, N, provider, dim):
+
+    x_sizes = [M, N, 128]
+    x_strides = [N//8 * 2048, 256, 1]
+    x_max_index = N//8 * 2048 * M
+    x_required_length = x_max_index
+    x_data = torch.arange(x_required_length,device='cuda').bfloat16()  
+    x = torch.as_strided(x_data, size=x_sizes, stride=x_strides)
+    
+    y_sizes = [M, N, 64]
+    y_strides = [576, 0, 1]
+    y_max_index = 576 * M
+    y_required_length = y_max_index
+    y_data = torch.arange(y_required_length,device='cuda').bfloat16()  
+    y = torch.as_strided(y_data, size=y_sizes, stride=y_strides)
+  
+    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_prefill_helper_Triton(x, y,dim=dim), quantiles=quantiles)
+    if provider == 'lightop':
+        ms, min_ms, max_ms = triton.testing.do_bench(lambda: lightop_concat_prefill_helper(x, y,dim=dim), quantiles=quantiles)
+    return (ms*1000), (max_ms*1000), (min_ms*1000)
+
+@triton.testing.perf_report(configs_decode)
+def benchmark_decode(M, N, provider, dim):
+
+    x_sizes = [M, N, 512]
+    x_strides = [512, 512*M, 1]
+    x_max_index = M * N * 512
+    x_required_length = x_max_index
+    x_data = torch.arange(x_required_length,device='cuda').bfloat16()  
+    x = torch.as_strided(x_data, size=x_sizes, stride=x_strides)
+
+    # print("形状:", x.shape)      
+    # print("步幅:", x.stride())  
+    
+    y_sizes = [M, N, 64]
+    y_strides = [1536*(N//8), 192, 1]
+    y_max_index = 1536*(N//8) * M
+    y_required_length = y_max_index 
+    y_data = torch.arange(y_required_length,device='cuda').bfloat16()  
+    y = torch.as_strided(y_data, size=y_sizes, stride=y_strides)
+
+    # print("形状:", y.shape)     
+    # print("步幅:", y.stride())   
+
+    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 == 'lightop':
+        ms, min_ms, max_ms = triton.testing.do_bench(lambda: concat_helper_decode(x, y,dim=dim), quantiles=quantiles)
+    return (ms*1000), (max_ms*1000), (min_ms*1000)
+
+
+if __name__ == '__main__':
+    benchmark_prefill.run(save_path="./triton_test",print_data=True)
+    test_concat_Acc_prefill(((1024, 8, 128), (1024, 8, 64)),  2)  
+    test_concat_Acc_prefill(((2048, 8, 128), (2048, 8, 64)),  2)  
+    test_concat_Acc_prefill(((4096, 8, 128), (4096, 8, 64)),  2)  
+    test_concat_Acc_prefill(((8192, 8, 128), (8192, 8, 64)),  2)
+    test_concat_Acc_prefill(((1024, 16, 128), (1024, 16, 64)),  2)  
+    test_concat_Acc_prefill(((2048, 16, 128), (2048, 16, 64)),  2)  
+    test_concat_Acc_prefill(((4096, 16, 128), (4096, 16, 64)),  2)  
+    test_concat_Acc_prefill(((8192, 16, 128), (8192, 16, 64)),  2)  
+    test_concat_Acc_prefill(((1024, 32, 128), (1024, 32, 64)),  2)  
+    test_concat_Acc_prefill(((2048, 32, 128), (2048, 32, 64)),  2)  
+    test_concat_Acc_prefill(((4096, 32, 128), (4096, 32, 64)),  2)  
+    test_concat_Acc_prefill(((8192, 32, 128), (8192, 32, 64)),  2)
+
+    benchmark_decode.run(save_path="./cat_triton_test",print_data=True)
+    test_concat_Acc_decode(((16, 8, 512), (16, 8, 64)),  2)
+    test_concat_Acc_decode(((32, 8, 512), (32, 8, 64)),  2)
+    test_concat_Acc_decode(((128, 8, 512), (128, 8, 64)),  2)
+    test_concat_Acc_decode(((768, 8, 512), (768, 8, 64)),  2)    
+    test_concat_Acc_decode(((32, 16, 512), (32, 16, 64)),  2)
+    test_concat_Acc_decode(((32, 32, 512), (32, 32, 64)),  2)
+    test_concat_Acc_decode(((768, 32, 512), (768, 32, 64)),  2)
+    test_concat_Acc_decode(((128, 32, 512), (128, 32, 64)),  2)
+    test_concat_Acc_decode(((512, 32, 512), (512, 32, 64)),  2)
+
+    test_concat_Acc_decode(((765, 8, 512), (765, 8, 64)),  2)
+    test_concat_Acc_decode(((766, 8, 512), (766, 8, 64)),  2)
+    test_concat_Acc_decode(((767, 8, 512), (767, 8, 64)),  2)
+    test_concat_Acc_decode(((765, 16, 512), (765, 16, 64)),  2)
+    test_concat_Acc_decode(((766, 16, 512), (766, 16, 64)),  2)
+    test_concat_Acc_decode(((765, 32, 512), (765, 32, 64)),  2)
+    test_concat_Acc_decode(((767, 32, 512), (767, 32, 64)),  2)    
+    
\ No newline at end of file