update triton cat scheduling

update the default values of VLLM_USE_TRITON_CAT, VLLM_USE_LIGHT_OP and VLLM_HAS_CONTEXT_DEFAULT to True

vllm/envs.py

@@ -1336,7 +1336,7 @@ environment_variables: dict[str, Callable[[], Any]] = {
     # If there are any problems during use, use environment variables 
     # to restore the default usage.
     "VLLM_HAS_CONTEXT_DEFAULT":
-    lambda: bool(int(os.getenv("VLLM_HAS_CONTEXT_DEFAULT", "0"))),
+    lambda: bool(int(os.getenv("VLLM_HAS_CONTEXT_DEFAULT", "1"))),
     
     # If set, vLLM will transpose weight to use nn layout
     "VLLM_USE_NN":
@@ -1384,12 +1384,12 @@ environment_variables: dict[str, Callable[[], Any]] = {
         
     # vLLM will use lightop for moe_fused_gate and moe_align_block_size
     "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

@@ -225,7 +225,7 @@ from vllm.v1.attention.backends.utils import (AttentionMetadataBuilder,
                                               split_decodes_and_prefills)
 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/concatv4_decode_only.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),
+
+
+
+
+    
+])
+def test_concat_Acc(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)      # [4, 8, 512]
+    # print("步幅:", x.stride())   # (1536, 192, 1)
+    
+    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)
+ 
+    
+    
+    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(
+    A_ptr, B_ptr, C_ptr,
+    A_section_numel, B_section_numel, C_section_numel,
+    Per_block, 
+    section_num,
+    M,
+    N,
+    Astride_0,
+    Astride_1,
+    Astride_2,
+    Bstride_0,
+    Bstride_1,
+    Bstride_2,
+    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
+        M_idx = sub_offset // N
+        N_idx = sub_offset % N
+        if sub_offset <= section_num-1:
+            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 
+            B_ptr_block_start = B_ptr + M_idx * Bstride_0 + N_idx * Bstride_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)
+
+            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):
+
+    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.shape[dim],B.shape[dim],C.shape[dim]
+        if (A.shape[1]==8 and A.shape[0] > 512) or ( A.shape[1]==16 and A.shape[0] > 256):
+            Per_block = 2
+        if ( A.shape[1]==32 and A.shape[2] == 512 and A.shape[0] > 256):
+            Per_block = 8
+        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,
+                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"
+
+
+configs = []
+configs.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),(1024,8), \
+                (4,16),(8,16),(16,16),(32,16),(64,16),(96,16),(128,16),(256,16),(512,16),(768,16),(1024,16), \
+                (4,32),(8,32),(16,32),(32,32),(64,32),(96,32),(128,32),(256,32),(512,32),(768,32),(1024,32)],
+        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(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)      # [M, 8, 512]
+    # print("步幅:", x.stride())   # (512, 512*M, 1)
+    
+    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)      # [M, 8, 64]
+    # print("步幅:", y.stride())   # (1536, 192, 1)
+  
+    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",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/flashmla.py

@@ -21,7 +21,7 @@ from vllm.v1.attention.backends.mla.common import (MLACommonBackend,
 from vllm.v1.attention.backends.utils import AttentionCGSupport
 from vllm.v1.kv_cache_interface import AttentionSpec
 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__)
 
@@ -196,6 +196,12 @@ class FlashMLAImpl(MLACommonImpl[FlashMLAMetadata]):
         # 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)