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[Attention] Blackwell FP8 MLA support with CUTLASS_MLA backend #23289
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LucasWilkinson
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vllm-project:main
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MatthewBonanni:feature/fp8_mla_cutlass_blackwell
Sep 3, 2025
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -1,96 +1,180 @@ | ||
| # SPDX-License-Identifier: Apache-2.0 | ||
| # SPDX-FileCopyrightText: Copyright contributors to the vLLM project | ||
| import math | ||
| import random | ||
|
|
||
| import pytest | ||
| import torch | ||
| import torch.nn.functional as F | ||
| from torch import Tensor | ||
|
|
||
| import vllm._custom_ops as ops | ||
| from vllm.platforms import current_platform | ||
|
|
||
| if not current_platform.has_device_capability(100): | ||
| pytest.skip( | ||
| reason="Cutlass MLA Requires compute capability of 10 or above.", | ||
| allow_module_level=True) | ||
|
|
||
|
|
||
| def ref_mla( | ||
| out: Tensor, # (bs, num_heads, v_head_dim) | ||
| query: Tensor, # (bs, num_heads, head_dim) | ||
| kv_cache: Tensor, # (num_blocks, block_size, head_dim) | ||
| scale: float, | ||
| block_tables: Tensor, # (bs, max_num_blocks) | ||
| seq_lens: Tensor, # (bs,) | ||
| ): | ||
| bs, num_heads, v_head_dim = out.shape | ||
| head_dim = query.shape[2] | ||
|
|
||
| for i in range(bs): | ||
| # gather and flatten KV-cache | ||
| kv = kv_cache[ | ||
| block_tables[i]] # (max_num_blocks, block_size, head_dim) | ||
| kv = kv.view(1, -1, | ||
| head_dim)[:, :seq_lens[i]] # (1, seq_len, head_dim) | ||
| v = kv[:, :, :v_head_dim] | ||
|
|
||
| q = query[i].view(num_heads, 1, head_dim) | ||
| o = F.scaled_dot_product_attention(q, | ||
| kv, | ||
| v, | ||
| scale=scale, | ||
| enable_gqa=True) | ||
| out[i] = o.view(num_heads, v_head_dim) | ||
|
|
||
| return out | ||
|
|
||
|
|
||
| @pytest.mark.parametrize("dtype", [torch.bfloat16, torch.float16]) | ||
| @pytest.mark.parametrize("mean_seq_len", [128, 1024, 4096]) | ||
| @pytest.mark.parametrize("bs", [1, 2, 4]) | ||
| from vllm.triton_utils import triton | ||
|
|
||
|
|
||
| def cal_diff(x: torch.Tensor, | ||
| y: torch.Tensor, | ||
| name: str, | ||
| use_fp8: bool = False) -> None: | ||
| x, y = x.double(), y.double() | ||
| cos_diff = 1 - 2 * (x * y).sum().item() / max( | ||
| (x * x + y * y).sum().item(), 1e-12) | ||
| if (use_fp8): | ||
| assert cos_diff < 1e-4 | ||
| else: | ||
| assert cos_diff < 1e-5 | ||
|
|
||
|
|
||
| CUTLASS_MLA_UNSUPPORTED_REASON = \ | ||
| "Cutlass MLA Requires compute capability of 10 or above." \ | ||
| if not current_platform.is_device_capability(100) \ | ||
| else "Cutlass MLA is supported" | ||
|
|
||
|
|
||
| @pytest.mark.skipif(not current_platform.has_device_capability(100), | ||
| reason=CUTLASS_MLA_UNSUPPORTED_REASON) | ||
| @pytest.mark.parametrize("b", [128]) | ||
| @pytest.mark.parametrize("s_q", [1]) | ||
| @pytest.mark.parametrize("mean_sk", [4096, 8192, 16384]) | ||
| @pytest.mark.parametrize("h_q", [16, 32, 64, 128]) | ||
| @pytest.mark.parametrize("h_kv", [1]) | ||
| @pytest.mark.parametrize("d", [576]) | ||
| @pytest.mark.parametrize("dv", [512]) | ||
| @pytest.mark.parametrize("block_size", [64]) | ||
| @pytest.mark.parametrize("causal", [True]) | ||
| @pytest.mark.parametrize("varlen", [False, True]) | ||
| @pytest.mark.parametrize("block_size", [16, 64, 128]) | ||
| def test_cutlass_mla_decode(dtype: torch.dtype, mean_seq_len: int, bs: int, | ||
| varlen: bool, block_size: int): | ||
| torch.set_default_dtype(dtype) | ||
| torch.set_default_device('cuda') | ||
| @pytest.mark.parametrize("torch_dtype", [torch.bfloat16, torch.float8_e4m3fn]) | ||
| @torch.inference_mode() | ||
| def test_cutlass_mla_decode(b, s_q, mean_sk, h_q, h_kv, d, dv, block_size, | ||
| causal, varlen, torch_dtype): | ||
| device = torch.device("cuda:0") | ||
| if torch_dtype == torch.float8_e4m3fn: | ||
| init_dtype = torch.bfloat16 | ||
| else: | ||
| init_dtype = torch_dtype | ||
| torch.set_default_dtype(init_dtype) | ||
| torch.set_default_device(device) | ||
| torch.cuda.set_device(device) | ||
| torch.manual_seed(42) | ||
| random.seed(42) | ||
|
|
||
| d = 576 | ||
| h_q = 128 | ||
| dv = 512 | ||
| print(f"{b=}, {s_q=}, {mean_sk=}, {h_q=}, {h_kv=}, " | ||
| f"{d=}, {dv=}, {causal=}, {varlen=}, {torch_dtype=}") | ||
|
|
||
| q_nope_dim = 128 | ||
| q_pe_dim = 64 | ||
| scale = (q_nope_dim + q_pe_dim)**(-0.5) | ||
| use_fp8 = torch_dtype == torch.float8_e4m3fn | ||
| scale = math.sqrt(d)**(-1) | ||
| cache_seqlens = torch.full((b, ), mean_sk, dtype=torch.int32) | ||
| if varlen: | ||
| seq_lens = torch.empty(bs).normal_(mean_seq_len, mean_seq_len / 2) | ||
| seq_lens = seq_lens.clip(2).to(torch.int32) | ||
| for i in range(b): | ||
| cache_seqlens[i] = max(random.normalvariate(mean_sk, mean_sk / 2), | ||
| s_q) | ||
| total_seqlens = cache_seqlens.sum().item() | ||
| max_seqlen = cache_seqlens.max().item() | ||
| max_seqlen_pad = triton.cdiv(max_seqlen, 256) * 256 | ||
|
|
||
| q = torch.randn(b, s_q, h_q, d) | ||
| block_table = torch.arange(b * max_seqlen_pad // block_size, | ||
| dtype=torch.int32).view( | ||
| b, max_seqlen_pad // block_size) | ||
| blocked_k = torch.randn(block_table.numel(), block_size, h_kv, d) | ||
| blocked_v = blocked_k[..., :dv] | ||
|
|
||
| init_dtype = q.dtype | ||
| if use_fp8: | ||
| fp8_dtype = torch.float8_e4m3fn | ||
| descale_q = torch.ones((1), dtype=torch.float32) | ||
| descale_k = torch.ones((1), dtype=torch.float32) | ||
|
|
||
| q = q.to(fp8_dtype) | ||
| blocked_k = blocked_k.to(fp8_dtype) | ||
| blocked_v = blocked_v.to(fp8_dtype) | ||
| else: | ||
| seq_lens = torch.full((bs, ), mean_seq_len, dtype=torch.int32) | ||
| max_seq_len = seq_lens.max().item() | ||
| block_num = (max_seq_len + block_size - 1) // block_size | ||
|
|
||
| # Pad block_num so that small blocks can be packed into full 128-sized | ||
| # CUTLASS tiles. One 128-wide tile can hold (128 // block_size) small | ||
| # blocks. | ||
| pack_factor = 128 // block_size | ||
| block_num = ((block_num + pack_factor - 1) // pack_factor) * pack_factor | ||
|
|
||
| # Amplify input values to ensure test coverage of edge cases where CUTLASS | ||
| # kernel errors occur with split_k settings. | ||
| q = torch.randn(bs, h_q, d) * 100 | ||
| block_table = torch.randint(0, | ||
| bs * block_num, (bs, block_num), | ||
| dtype=torch.int32) | ||
|
|
||
| kv_cache = torch.randn(block_table.numel(), block_size, d) | ||
|
|
||
| out_ref = q.new_zeros(bs, h_q, dv) | ||
| ref_mla(out_ref, q, kv_cache, scale, block_table, seq_lens) | ||
| out_ans = torch.zeros_like(out_ref) | ||
| q_nope = q[:, :, :dv].clone() | ||
| q_pe = q[:, :, dv:].clone() | ||
| ops.cutlass_mla_decode(out_ans, q_nope, q_pe, kv_cache, seq_lens, | ||
| block_table, scale) | ||
|
|
||
| torch.testing.assert_close(out_ans, out_ref, atol=1e-2, rtol=1e-2) | ||
| descale_q = None | ||
| descale_k = None | ||
|
|
||
| def cutlass_mla(): | ||
| MAX_HEADS = 128 | ||
|
|
||
| q_reshaped = q.squeeze(1) | ||
| q_nope = q_reshaped[:, :, :dv].clone() | ||
| q_pe = q_reshaped[:, :, dv:].clone() | ||
|
|
||
| if h_q < MAX_HEADS: | ||
| q_nope_padded = q_nope.new_empty((b, MAX_HEADS, dv)) | ||
| q_nope_padded[:, :h_q] = q_nope | ||
| q_nope = q_nope_padded | ||
|
|
||
| q_pe_padded = q_pe.new_empty((b, MAX_HEADS, d - dv)) | ||
| q_pe_padded[:, :h_q] = q_pe | ||
| q_pe = q_pe_padded | ||
|
|
||
| kv_cache_flat = blocked_k.squeeze(2) | ||
| device_properties = torch.cuda.get_device_properties( | ||
| torch.device("cuda:0")) | ||
| sm_count = device_properties.multi_processor_count | ||
| workspace_size = ops.sm100_cutlass_mla_get_workspace_size( | ||
| max_seqlen * block_size, b, sm_count, num_kv_splits=1) | ||
| workspace = torch.empty(workspace_size, | ||
| device="cuda", | ||
| dtype=torch.uint8) | ||
|
|
||
| out_ans = torch.empty(b, MAX_HEADS, dv, dtype=init_dtype) | ||
|
|
||
| ops.sm100_cutlass_mla_decode(out_ans, q_nope, q_pe, kv_cache_flat, | ||
| cache_seqlens, block_table, workspace, | ||
| scale, 1) | ||
| return out_ans[:, :h_q].contiguous() | ||
|
|
||
| def scaled_dot_product_attention(query, key, value, is_causal=False): | ||
| query = query.float() | ||
| key = key.float() | ||
| value = value.float() | ||
| key = key.repeat_interleave(h_q // h_kv, dim=0) | ||
| value = value.repeat_interleave(h_q // h_kv, dim=0) | ||
| attn_weight = query @ key.transpose(-2, -1) / math.sqrt(query.size(-1)) | ||
| if is_causal: | ||
| s_q = query.shape[-2] | ||
| s_k = key.shape[-2] | ||
| attn_bias = torch.zeros(s_q, s_k, dtype=query.dtype) | ||
| temp_mask = torch.ones(s_q, s_k, | ||
| dtype=torch.bool).tril(diagonal=s_k - s_q) | ||
| attn_bias.masked_fill_(temp_mask.logical_not(), float("-inf")) | ||
| attn_bias.to(query.dtype) | ||
| attn_weight += attn_bias | ||
| lse = attn_weight.logsumexp(dim=-1) | ||
| attn_weight = torch.softmax(attn_weight, dim=-1, dtype=torch.float32) | ||
| return attn_weight @ value, lse | ||
|
|
||
| def ref_mla(): | ||
| q_ = (q.to(torch.float) * descale_q).to(init_dtype) if use_fp8 else q | ||
| blocked_k_ = (blocked_k.to(torch.float) * | ||
| descale_k).to(init_dtype) if use_fp8 else blocked_k | ||
| blocked_v_ = (blocked_v.to(torch.float) * | ||
| descale_k).to(init_dtype) if use_fp8 else blocked_v | ||
| out = torch.empty(b, s_q, h_q, dv, dtype=torch.float32) | ||
| lse = torch.empty(b, h_q, s_q, dtype=torch.float32) | ||
| for i in range(b): | ||
| begin = i * max_seqlen_pad | ||
| end = begin + cache_seqlens[i] | ||
| out_i, lse_i = scaled_dot_product_attention( | ||
| q_[i].transpose(0, 1), | ||
| blocked_k_.view(-1, h_kv, d)[begin:end].transpose(0, 1), | ||
| blocked_v_.view(-1, h_kv, dv)[begin:end].transpose(0, 1), | ||
| is_causal=causal, | ||
| ) | ||
| out[i] = out_i.transpose(0, 1) | ||
| lse[i] = lse_i | ||
| return out, lse | ||
|
|
||
| out_cutlass = cutlass_mla() | ||
| out_torch, lse_torch = ref_mla() | ||
| # Extract the single token (s_q=1) slice to match cutlass output shape | ||
| out_torch_slice = out_torch[:, 0, :, :] # [b, h_q, dv] | ||
| cal_diff(out_cutlass, out_torch_slice, "out", use_fp8) | ||
|
|
||
| t = triton.testing.do_bench(cutlass_mla) | ||
| FLOPS = s_q * total_seqlens * h_q * (d + dv) * 2 | ||
| bytes = (total_seqlens * h_kv * d + | ||
| b * s_q * h_q * d) * (torch.finfo(torch_dtype).bits // 8) + ( | ||
| b * s_q * h_q * dv) * (torch.finfo(init_dtype).bits // 8) | ||
| print(f"{t:.3f} ms, {FLOPS / 10 ** 9 / t:.0f} TFLOPS,", | ||
| f"{bytes / 10 ** 6 / t:.0f} GB/s") |
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