diff --git a/.gitignore b/.gitignore index bfe80b5..5f9e980 100644 --- a/.gitignore +++ b/.gitignore @@ -3,3 +3,5 @@ build *.egg-info/ __pycache__/ dist/ +*perf.csv +*.png diff --git a/README.md b/README.md index bb55395..b79757c 100644 --- a/README.md +++ b/README.md @@ -3,7 +3,7 @@ FlashMLA is an efficient MLA decoding kernel for Hopper GPUs, optimized for variable-length sequences serving. Currently released: -- BF16 +- BF16, FP16 - Paged kvcache with block size of 64 ## Quick start @@ -20,7 +20,7 @@ python setup.py install python tests/test_flash_mla.py ``` -Achieving up to 3000 GB/s in memory-bound configuration and 580 TFLOPS in computation-bound configuration on H800 SXM5, using CUDA 12.6. +Achieving up to 3000 GB/s in memory-bound configuration and 580 TFLOPS in computation-bound configuration on H800 SXM5, using CUDA 12.8. ### Usage @@ -42,17 +42,49 @@ for i in range(num_layers): - Hopper GPUs - CUDA 12.3 and above + - **But we highly recommend 12.8 or above for the best performance** - PyTorch 2.0 and above ## Acknowledgement FlashMLA is inspired by [FlashAttention 2&3](https://github.com/dao-AILab/flash-attention/) and [cutlass](https://github.com/nvidia/cutlass) projects. +## Community Support + +### MetaX +For MetaX GPUs, visit the official website: [MetaX](https://www.metax-tech.com). + +The corresponding FlashMLA version can be found at: [MetaX-MACA/FlashMLA](https://github.com/MetaX-MACA/FlashMLA) + + +### Moore Threads +For the Moore Threads GPU, visit the official website: [Moore Threads](https://www.mthreads.com/). + +The corresponding FlashMLA version is available on GitHub: [MooreThreads/MT-flashMLA](https://github.com/MooreThreads/MT-flashMLA). + + +### Hygon DCU +For the Hygon DCU, visit the official website: [Hygon Developer](https://developer.sourcefind.cn/). + +The corresponding FlashMLA version is available here: [OpenDAS/MLAttention](https://developer.sourcefind.cn/codes/OpenDAS/MLAttention). + + +### Intellifusion +For the Intellifusion NNP, visit the official website: [Intellifusion](https://www.intellif.com). + +The corresponding FlashMLA version is available on Gitee: [Intellifusion/tyllm](https://gitee.com/Intellifusion_2025/tyllm/blob/master/python/tylang/flash_mla.py). + + +### Iluvatar Corex +For Iluvatar Corex GPUs, visit the official website: [Iluvatar Corex](https://www.iluvatar.com). + +The corresponding FlashMLA version is available on GitHub: [Deep-Spark/FlashMLA](https://github.com/Deep-Spark/FlashMLA/tree/iluvatar_flashmla) + ## Citation ```bibtex @misc{flashmla2025, - title={FlashMLA: Efficient MLA decoding kernel}, + title={FlashMLA: Efficient MLA decoding kernels}, author={Jiashi Li}, year={2025}, publisher = {GitHub}, diff --git a/benchmark/bench_flash_mla.py b/benchmark/bench_flash_mla.py new file mode 100644 index 0000000..14e1352 --- /dev/null +++ b/benchmark/bench_flash_mla.py @@ -0,0 +1,520 @@ +# MLA Triton kernel is from: https://github.com/monellz/vllm/commit/feebaa7c063be6bfb590a876741aeef1c5f58cf8#diff-7b2e1c9032522f7266051b9887246a65753871dfb3625a258fee40109fe6e87a +import argparse +import math +import random + +import flashinfer +import torch +import triton +import triton.language as tl + +# pip install flashinfer-python +from flash_mla import flash_mla_with_kvcache, get_mla_metadata + + +def scaled_dot_product_attention(query, key, value, h_q, h_kv, 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 + + +@torch.inference_mode() +def run_torch_mla(q, block_table, blocked_k, max_seqlen_pad, block_size, b, s_q, cache_seqlens, h_q, h_kv, d, dv, causal, dtype): + for i in range(b): + blocked_k.view(b, max_seqlen_pad, h_kv, d)[i, cache_seqlens[i].item():] = float("nan") + blocked_v = blocked_k[..., :dv] + + def ref_mla(): + 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] + O, LSE = 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), + h_q, h_kv, + is_causal=causal, + ) + out[i] = O.transpose(0, 1) + lse[i] = LSE + return out, lse + + out_torch, lse_torch = ref_mla() + t = triton.testing.do_bench(ref_mla) + return out_torch, lse_torch, t + +@torch.inference_mode() +def run_flash_mla(q, block_table, blocked_k, max_seqlen_pad, block_size, b, s_q, cache_seqlens, h_q, h_kv, d, dv, causal, dtype): + for i in range(b): + blocked_k.view(b, max_seqlen_pad, h_kv, d)[i, cache_seqlens[i].item():] = float("nan") + blocked_v = blocked_k[..., :dv] + + tile_scheduler_metadata, num_splits = get_mla_metadata(cache_seqlens, s_q * h_q // h_kv, h_kv) + + def flash_mla(): + return flash_mla_with_kvcache( + q, blocked_k, block_table, cache_seqlens, dv, + tile_scheduler_metadata, num_splits, causal=causal, + ) + + out_flash, lse_flash = flash_mla() + t = triton.testing.do_bench(flash_mla) + return out_flash, lse_flash, t + + +@torch.inference_mode() +def run_flash_infer(q, block_table, blocked_k, max_seqlen_pad, block_size, b, s_q, cache_seqlens, h_q, h_kv, d, dv, causal, dtype): + + for i in range(b): + blocked_k.view(b, max_seqlen_pad, h_kv, d)[i, cache_seqlens[i].item():] = float("nan") + + assert d > dv, "mla with rope dim should be larger than no rope dim" + q_nope, q_pe = q[..., :dv].contiguous(), q[..., dv:].contiguous() + blocked_k_nope, blocked_k_pe = blocked_k[..., :dv].contiguous(), blocked_k[..., dv:].contiguous() + + + kv_indptr = [0] + kv_indices = [] + for i in range(b): + seq_len = cache_seqlens[i] + assert seq_len > 0 + num_blocks = (seq_len + block_size - 1) // block_size + kv_indices.extend(block_table[i, :num_blocks]) + kv_indptr.append(kv_indptr[-1] + num_blocks) + for seq_len in cache_seqlens[1:]: + kv_indptr.append((seq_len + block_size - 1) // block_size + kv_indptr[-1]) + + q_indptr = torch.arange(0, b + 1).int() * s_q + kv_indptr = torch.tensor(kv_indptr, dtype=torch.int32) + kv_indices = torch.tensor(kv_indices, dtype=torch.int32) + + mla_wrapper = flashinfer.mla.BatchMLAPagedAttentionWrapper( + torch.empty(128 * 1024 * 1024, dtype=torch.int8), + backend="fa3" + ) + mla_wrapper.plan( + q_indptr, + kv_indptr, + kv_indices, + cache_seqlens, + h_q, + dv, + d-dv, + block_size, + causal, + 1 / math.sqrt(d), + q.dtype, + blocked_k.dtype, + ) + + def flash_infer(): + output, lse = mla_wrapper.run(q_nope.view(-1, h_q, dv), q_pe.view(-1, h_q, d-dv), blocked_k_nope, blocked_k_pe, return_lse=True) + return output.view(b, -1, h_q, dv), lse.view(b, h_q, 1) + + out_flash, lse_flash = flash_infer() + t = triton.testing.do_bench(flash_infer) + return out_flash, lse_flash, t + + +@triton.jit +def _mla_attn_kernel( + Q_nope, + Q_pe, + Kv_c_cache, + K_pe_cache, + Req_to_tokens, + B_seq_len, + O, + sm_scale, + stride_q_nope_bs, + stride_q_nope_h, + stride_q_pe_bs, + stride_q_pe_h, + stride_kv_c_bs, + stride_k_pe_bs, + stride_req_to_tokens_bs, + stride_o_b, + stride_o_h, + stride_o_s, + BLOCK_H: tl.constexpr, + BLOCK_N: tl.constexpr, + NUM_KV_SPLITS: tl.constexpr, + PAGE_SIZE: tl.constexpr, + HEAD_DIM_CKV: tl.constexpr, + HEAD_DIM_KPE: tl.constexpr, +): + cur_batch = tl.program_id(1) + cur_head_id = tl.program_id(0) + split_kv_id = tl.program_id(2) + + cur_batch_seq_len = tl.load(B_seq_len + cur_batch) + + offs_d_ckv = tl.arange(0, HEAD_DIM_CKV) + cur_head = cur_head_id * BLOCK_H + tl.arange(0, BLOCK_H) + offs_q_nope = cur_batch * stride_q_nope_bs + cur_head[:, None] * stride_q_nope_h + offs_d_ckv[None, :] + q_nope = tl.load(Q_nope + offs_q_nope) + + offs_d_kpe = tl.arange(0, HEAD_DIM_KPE) + offs_q_pe = cur_batch * stride_q_pe_bs + cur_head[:, None] * stride_q_pe_h + offs_d_kpe[None, :] + q_pe = tl.load(Q_pe + offs_q_pe) + + e_max = tl.zeros([BLOCK_H], dtype=tl.float32) - float("inf") + e_sum = tl.zeros([BLOCK_H], dtype=tl.float32) + acc = tl.zeros([BLOCK_H, HEAD_DIM_CKV], dtype=tl.float32) + + kv_len_per_split = tl.cdiv(cur_batch_seq_len, NUM_KV_SPLITS) + split_kv_start = kv_len_per_split * split_kv_id + split_kv_end = tl.minimum(split_kv_start + kv_len_per_split, cur_batch_seq_len) + + for start_n in range(split_kv_start, split_kv_end, BLOCK_N): + offs_n = start_n + tl.arange(0, BLOCK_N) + kv_page_number = tl.load( + Req_to_tokens + stride_req_to_tokens_bs * cur_batch + offs_n // PAGE_SIZE, + mask=offs_n < split_kv_end, + other=0, + ) + kv_loc = kv_page_number * PAGE_SIZE + offs_n % PAGE_SIZE + offs_k_c = kv_loc[None, :] * stride_kv_c_bs + offs_d_ckv[:, None] + k_c = tl.load(Kv_c_cache + offs_k_c, mask=offs_n[None, :] < split_kv_end, other=0.0) + + qk = tl.dot(q_nope, k_c.to(q_nope.dtype)) + + offs_k_pe = kv_loc[None, :] * stride_k_pe_bs + offs_d_kpe[:, None] + k_pe = tl.load(K_pe_cache + offs_k_pe, mask=offs_n[None, :] < split_kv_end, other=0.0) + + qk += tl.dot(q_pe, k_pe.to(q_pe.dtype)) + qk *= sm_scale + + qk = tl.where(offs_n[None, :] < split_kv_end, qk, float("-inf")) + + v_c = tl.trans(k_c) + + n_e_max = tl.maximum(tl.max(qk, 1), e_max) + re_scale = tl.exp(e_max - n_e_max) + p = tl.exp(qk - n_e_max[:, None]) + acc *= re_scale[:, None] + acc += tl.dot(p.to(v_c.dtype), v_c) + + e_sum = e_sum * re_scale + tl.sum(p, 1) + e_max = n_e_max + offs_o = cur_batch * stride_o_b + cur_head[:, None] * stride_o_h + split_kv_id * stride_o_s + offs_d_ckv[None, :] + tl.store(O + offs_o, acc / e_sum[:, None]) + offs_o_1 = cur_batch * stride_o_b + cur_head * stride_o_h + split_kv_id * stride_o_s + HEAD_DIM_CKV + tl.store(O + offs_o_1, e_max + tl.log(e_sum)) + + +def _mla_attn( + q_nope, + q_pe, + kv_c_cache, + k_pe_cache, + attn_logits, + req_to_tokens, + b_seq_len, + num_kv_splits, + sm_scale, + page_size, +): + batch_size, head_num = q_nope.shape[0], q_nope.shape[1] + head_dim_ckv = q_nope.shape[-1] + head_dim_kpe = q_pe.shape[-1] + + BLOCK_H = 16 + BLOCK_N = 64 + grid = ( + triton.cdiv(head_num, BLOCK_H), + batch_size, + num_kv_splits, + ) + _mla_attn_kernel[grid]( + q_nope, + q_pe, + kv_c_cache, + k_pe_cache, + req_to_tokens, + b_seq_len, + attn_logits, + sm_scale, + # stride + q_nope.stride(0), + q_nope.stride(1), + q_pe.stride(0), + q_pe.stride(1), + kv_c_cache.stride(-2), + k_pe_cache.stride(-2), + req_to_tokens.stride(0), + attn_logits.stride(0), + attn_logits.stride(1), + attn_logits.stride(2), + BLOCK_H=BLOCK_H, + BLOCK_N=BLOCK_N, + NUM_KV_SPLITS=num_kv_splits, + PAGE_SIZE=page_size, + HEAD_DIM_CKV=head_dim_ckv, + HEAD_DIM_KPE=head_dim_kpe, + ) + +@triton.jit +def _mla_softmax_reducev_kernel( + Logits, + B_seq_len, + O, + stride_l_b, + stride_l_h, + stride_l_s, + stride_o_b, + stride_o_h, + NUM_KV_SPLITS: tl.constexpr, + HEAD_DIM_CKV: tl.constexpr, +): + cur_batch = tl.program_id(0) + cur_head = tl.program_id(1) + cur_batch_seq_len = tl.load(B_seq_len + cur_batch) + + offs_d_ckv = tl.arange(0, HEAD_DIM_CKV) + + e_sum = 0.0 + e_max = -float("inf") + acc = tl.zeros([HEAD_DIM_CKV], dtype=tl.float32) + + offs_l = cur_batch * stride_l_b + cur_head * stride_l_h + offs_d_ckv + offs_l_1 = cur_batch * stride_l_b + cur_head * stride_l_h + HEAD_DIM_CKV + + for split_kv_id in range(0, NUM_KV_SPLITS): + kv_len_per_split = tl.cdiv(cur_batch_seq_len, NUM_KV_SPLITS) + split_kv_start = kv_len_per_split * split_kv_id + split_kv_end = tl.minimum(split_kv_start + kv_len_per_split, cur_batch_seq_len) + + if split_kv_end > split_kv_start: + logits = tl.load(Logits + offs_l + split_kv_id * stride_l_s) + logits_1 = tl.load(Logits + offs_l_1 + split_kv_id * stride_l_s) + + n_e_max = tl.maximum(logits_1, e_max) + old_scale = tl.exp(e_max - n_e_max) + acc *= old_scale + exp_logic = tl.exp(logits_1 - n_e_max) + acc += exp_logic * logits + + e_sum = e_sum * old_scale + exp_logic + e_max = n_e_max + + tl.store( + O + cur_batch * stride_o_b + cur_head * stride_o_h + offs_d_ckv, + acc / e_sum, + ) + + +def _mla_softmax_reducev( + logits, + o, + b_seq_len, + num_kv_splits, +): + batch_size, head_num, head_dim_ckv = o.shape[0], o.shape[1], o.shape[2] + grid = (batch_size, head_num) + _mla_softmax_reducev_kernel[grid]( + logits, + b_seq_len, + o, + logits.stride(0), + logits.stride(1), + logits.stride(2), + o.stride(0), + o.stride(1), + NUM_KV_SPLITS=num_kv_splits, + HEAD_DIM_CKV=head_dim_ckv, + num_warps=4, + num_stages=2, + ) + +def mla_decode_triton( + q_nope, + q_pe, + kv_c_cache, + k_pe_cache, + o, + req_to_tokens, + b_seq_len, + attn_logits, + num_kv_splits, + sm_scale, + page_size, +): + assert num_kv_splits == attn_logits.shape[2] + _mla_attn( + q_nope, + q_pe, + kv_c_cache, + k_pe_cache, + attn_logits, + req_to_tokens, + b_seq_len, + num_kv_splits, + sm_scale, + page_size, + ) + _mla_softmax_reducev( + attn_logits, + o, + b_seq_len, + num_kv_splits, + ) + + +@torch.inference_mode() +def run_flash_mla_triton(q, block_table, blocked_k, max_seqlen_pad, block_size, b, s_q, cache_seqlens, h_q, h_kv, d, dv, causal, dtype): + + for i in range(b): + blocked_k.view(b, max_seqlen_pad, h_kv, d)[i, cache_seqlens[i].item():] = float("nan") + blocked_v = blocked_k[..., :dv] + + assert d > dv, "mla with rope dim should be larger than no rope dim" + q_nope, q_pe = q[..., :dv].contiguous(), q[..., dv:].contiguous() + blocked_k_nope, blocked_k_pe = blocked_k[..., :dv].contiguous(), blocked_k[..., dv:].contiguous() + + def flash_mla_triton(): + num_kv_splits = 32 + o = torch.empty([b * s_q, h_q, dv]) + attn_logits = torch.empty([b * s_q, h_q, num_kv_splits, dv + 1]) + mla_decode_triton(q_nope.view(-1, h_q, dv), q_pe.view(-1, h_q, d-dv), blocked_k_nope.view(-1, dv), blocked_k_pe.view(-1, d-dv), o, block_table, cache_seqlens, attn_logits, num_kv_splits, 1 / math.sqrt(d), block_size) + return o.view([b, s_q, h_q, dv]) + + out_flash = flash_mla_triton() + t = triton.testing.do_bench(flash_mla_triton) + return out_flash, None, t + + +FUNC_TABLE = { + "torch": run_torch_mla, + "flash_mla": run_flash_mla, + "flash_infer": run_flash_infer, + "flash_mla_triton": run_flash_mla_triton, +} + +def compare_ab(baseline, target, b, s_q, cache_seqlens, h_q, h_kv, d, dv, causal, dtype): + print(f"comparing {baseline} vs {target}: {b=}, {s_q=}, mean_seqlens={cache_seqlens.float().mean()}, {h_q=}, {h_kv=}, {d=}, {dv=}, {causal=}, {dtype=}") + device = torch.device("cuda:0") + torch.set_default_dtype(dtype) + torch.set_default_device(device) + torch.cuda.set_device(device) + torch.manual_seed(0) + random.seed(0) + assert baseline in FUNC_TABLE + assert target in FUNC_TABLE + baseline_func = FUNC_TABLE[baseline] + target_func = FUNC_TABLE[target] + + total_seqlens = cache_seqlens.sum().item() + mean_seqlens = cache_seqlens.float().mean().int().item() + max_seqlen = cache_seqlens.max().item() + max_seqlen_pad = triton.cdiv(max_seqlen, 256) * 256 + # print(f"{total_seqlens=}, {mean_seqlens=}, {max_seqlen=}") + + q = torch.randn(b, s_q, h_q, d) + block_size = 64 + 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) + + out_a, lse_a, perf_a = baseline_func(q, block_table, blocked_k, max_seqlen_pad, block_size, b, s_q, cache_seqlens, h_q, h_kv, d, dv, causal, dtype) + out_b, lse_b, perf_b = target_func(q, block_table, blocked_k, max_seqlen_pad, block_size, b, s_q, cache_seqlens, h_q, h_kv, d, dv, causal, dtype) + + torch.testing.assert_close(out_b.float(), out_a.float(), atol=1e-2, rtol=1e-2), "out" + if target not in ["flash_infer", "flash_mla_triton"]: + # flash_infer has a different lse return value + # flash_mla_triton doesn't return lse + torch.testing.assert_close(lse_b.float(), lse_a.float(), atol=1e-2, rtol=1e-2), "lse" + + FLOPS = s_q * total_seqlens * h_q * (d + dv) * 2 + bytes = (total_seqlens * h_kv * d + b * s_q * h_q * d + b * s_q * h_q * dv) * (torch.finfo(dtype).bits // 8) + print(f"perf {baseline}: {perf_a:.3f} ms, {FLOPS / 10 ** 9 / perf_a:.0f} TFLOPS, {bytes / 10 ** 6 / perf_a:.0f} GB/s") + print(f"perf {target}: {perf_b:.3f} ms, {FLOPS / 10 ** 9 / perf_b:.0f} TFLOPS, {bytes / 10 ** 6 / perf_b:.0f} GB/s") + return bytes / 10 ** 6 / perf_a, bytes / 10 ** 6 / perf_b + + +def compare_a(target, b, s_q, cache_seqlens, h_q, h_kv, d, dv, causal, dtype): + print(f"{target}: {b=}, {s_q=}, mean_seqlens={cache_seqlens.float().mean()}, {h_q=}, {h_kv=}, {d=}, {dv=}, {causal=}, {dtype=}") + torch.set_default_dtype(dtype) + device = torch.device("cuda:0") + torch.set_default_device(device) + torch.cuda.set_device(device) + torch.manual_seed(0) + random.seed(0) + assert target in FUNC_TABLE + target_func = FUNC_TABLE[target] + + total_seqlens = cache_seqlens.sum().item() + mean_seqlens = cache_seqlens.float().mean().int().item() + max_seqlen = cache_seqlens.max().item() + max_seqlen_pad = triton.cdiv(max_seqlen, 256) * 256 + # print(f"{total_seqlens=}, {mean_seqlens=}, {max_seqlen=}") + + q = torch.randn(b, s_q, h_q, d) + block_size = 64 + 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) + + out_b, lse_b, perf_b = target_func(q, block_table, blocked_k, max_seqlen_pad, block_size, b, s_q, cache_seqlens, h_q, h_kv, d, dv, causal, dtype) + + FLOPS = s_q * total_seqlens * h_q * (d + dv) * 2 + bytes = (total_seqlens * h_kv * d + b * s_q * h_q * d + b * s_q * h_q * dv) * (torch.finfo(dtype).bits // 8) + print(f"perf {target}: {perf_b:.3f} ms, {FLOPS / 10 ** 9 / perf_b:.0f} TFLOPS, {bytes / 10 ** 6 / perf_b:.0f} GB/s") + return bytes / 10 ** 6 / perf_b + + +available_targets = [ + "torch", + "flash_mla", + "flash_infer", + "flash_mla_triton", +] + +shape_configs = [ + {"b": batch, "s_q": 1, "cache_seqlens": torch.tensor([seqlen + 2 * i for i in range(batch)], dtype=torch.int32, device="cuda"), "h_q": head, "h_kv": 1, "d": 512+64, "dv": 512, "causal": True, "dtype": torch.bfloat16} + for batch in [128] for seqlen in [1024, 2048, 4096, 8192, 8192*2, 8192*4] for head in [128] +] + + +def get_args(): + parser = argparse.ArgumentParser() + parser.add_argument("--baseline", type=str, default="torch") + parser.add_argument("--target", type=str, default="flash_mla") + parser.add_argument("--all", action="store_true") + parser.add_argument("--one", action="store_true") + parser.add_argument("--compare", action="store_true") + args = parser.parse_args() + return args + + +if __name__ == "__main__": + args = get_args() + benchmark_type = "all" if args.all else f"{args.baseline}_vs_{args.target}" if args.compare else args.target + with open(f"{benchmark_type}_perf.csv", "w") as fout: + fout.write("name,batch,seqlen,head,bw\n") + for shape in shape_configs: + if args.all: + for target in available_targets: + perf = compare_a(target, shape["b"], shape["s_q"], shape["cache_seqlens"], shape["h_q"], shape["h_kv"], shape["d"], shape["dv"], shape["causal"], shape["dtype"]) + fout.write(f'{target},{shape["b"]},{shape["cache_seqlens"].float().mean().cpu().item():.0f},{shape["h_q"]},{perf:.0f}\n') + elif args.compare: + perfa, prefb = compare_ab(args.baseline, args.target, shape["b"], shape["s_q"], shape["cache_seqlens"], shape["h_q"], shape["h_kv"], shape["d"], shape["dv"], shape["causal"], shape["dtype"]) + fout.write(f'{args.baseline},{shape["b"]},{shape["cache_seqlens"].float().mean().cpu().item():.0f},{shape["h_q"]},{perfa:.0f}\n') + fout.write(f'{args.target},{shape["b"]},{shape["cache_seqlens"].float().mean().cpu().item():.0f},{shape["h_q"]},{prefb:.0f}\n') + elif args.one: + perf = compare_a(args.target, shape["b"], shape["s_q"], shape["cache_seqlens"], shape["h_q"], shape["h_kv"], shape["d"], shape["dv"], shape["causal"], shape["dtype"]) + fout.write(f'{args.target},{shape["b"]},{shape["cache_seqlens"].float().mean().cpu().item():.0f},{shape["h_q"]},{perf:.0f}\n') \ No newline at end of file diff --git a/benchmark/visualize.py b/benchmark/visualize.py new file mode 100644 index 0000000..c1fb37e --- /dev/null +++ b/benchmark/visualize.py @@ -0,0 +1,29 @@ +import argparse + +import matplotlib.pyplot as plt +import pandas as pd + + +def parse_args(): + parser = argparse.ArgumentParser(description='Visualize benchmark results') + parser.add_argument('--file', type=str, default='all_perf.csv', + help='Path to the CSV file with benchmark results (default: all_perf.csv)') + return parser.parse_args() + +args = parse_args() +file_path = args.file + +df = pd.read_csv(file_path) + +names = df['name'].unique() + +for name in names: + subset = df[df['name'] == name] + plt.plot(subset['seqlen'], subset['bw'], label=name) + +plt.title('bandwidth') +plt.xlabel('seqlen') +plt.ylabel('bw (GB/s)') +plt.legend() + +plt.savefig(f'{file_path.split(".")[0].split("/")[-1]}_bandwidth_vs_seqlen.png') \ No newline at end of file diff --git a/csrc/flash_api.cpp b/csrc/flash_api.cpp index 9631b32..a865fc5 100644 --- a/csrc/flash_api.cpp +++ b/csrc/flash_api.cpp @@ -61,7 +61,7 @@ std::vector mha_fwd_kvcache_mla( at::Tensor &q, // batch_size x seqlen_q x num_heads x head_size const at::Tensor &kcache, // num_blocks x page_block_size x num_heads_k x head_size - c10::optional &vcache_, // num_blocks x page_block_size x num_heads_k x head_size_v + std::optional &vcache_, // num_blocks x page_block_size x num_heads_k x head_size_v const int head_size_v, const at::Tensor &seqlens_k, // batch_size const at::Tensor &block_table, // batch_size x max_num_blocks_per_seq @@ -79,9 +79,8 @@ mha_fwd_kvcache_mla( at::Tensor vcache = vcache_.has_value() ? vcache_.value() : kcache; auto q_dtype = q.scalar_type(); - TORCH_CHECK(q_dtype == torch::kBFloat16 || q_dtype == torch::kFloat8_e4m3fn); + TORCH_CHECK(q_dtype == torch::kBFloat16 || q_dtype == torch::kHalf || q_dtype == torch::kFloat8_e4m3fn); TORCH_CHECK(kcache.scalar_type() == q_dtype, "query and key must have the same dtype"); - bool is_fp8 = q_dtype == torch::kFloat8_e4m3fn; CHECK_DEVICE(q); CHECK_DEVICE(kcache); CHECK_DEVICE(vcache); @@ -108,7 +107,7 @@ mha_fwd_kvcache_mla( TORCH_CHECK(batch_size > 0, "batch size must be postive"); TORCH_CHECK(num_heads_ori % num_heads_k == 0, "Number of heads in key/value must divide number of heads in query"); - if (is_fp8) { + if (q_dtype == torch::kFloat8_e4m3fn) { TORCH_CHECK(descale_q_.has_value() && descale_k_.has_value(), "descale is required when input dtype is fp8"); auto descale_q = descale_q_.value(); auto descale_k = descale_k_.value(); @@ -145,7 +144,7 @@ mha_fwd_kvcache_mla( at::cuda::CUDAGuard device_guard{(char)q.get_device()}; auto opts = q.options(); - auto out_type = is_fp8 ? torch::kBFloat16 : q_dtype; + auto out_type = (q_dtype == torch::kFloat8_e4m3fn) ? torch::kBFloat16 : q_dtype; at::Tensor out = torch::empty({batch_size, seqlen_q, num_heads, head_size_v}, opts.dtype(out_type)); at::Tensor softmax_lse = torch::empty({batch_size, num_heads, seqlen_q}, opts.dtype(at::kFloat)); @@ -186,7 +185,7 @@ mha_fwd_kvcache_mla( params.block_table_batch_stride = block_table.stride(0); params.page_block_size = page_block_size; - if (is_fp8) { + if (q_dtype == torch::kFloat8_e4m3fn) { params.descale_q_ptr = reinterpret_cast(descale_q_.value().data_ptr()); params.descale_k_ptr = reinterpret_cast(descale_k_.value().data_ptr()); } @@ -210,10 +209,19 @@ mha_fwd_kvcache_mla( auto stream = at::cuda::getCurrentCUDAStream().stream(); TORCH_CHECK(head_size == 576); - if (is_fp8) { + + if (q_dtype == torch::kBFloat16) { + run_mha_fwd_splitkv_mla(params, stream); + } + #ifndef FLASH_MLA_DISABLE_FP16 + else if (q_dtype == torch::kHalf) { + run_mha_fwd_splitkv_mla(params, stream); + } + #endif + else if (q_dtype == torch::kFloat8_e4m3fn) { run_mha_fwd_splitkv_mla(params, stream); } else { - run_mha_fwd_splitkv_mla(params, stream); + TORCH_CHECK(false, "Unsupported tensor dtype for query"); } out = out.view({batch_size, seqlen_q_ori, ngroups, num_heads_k, head_size_v}).transpose(2, 3) diff --git a/csrc/flash_fwd_mla_fp16_sm90.cu b/csrc/flash_fwd_mla_fp16_sm90.cu new file mode 100644 index 0000000..a7f09b8 --- /dev/null +++ b/csrc/flash_fwd_mla_fp16_sm90.cu @@ -0,0 +1,3 @@ +#include "flash_fwd_mla_kernel.h" + +template void run_mha_fwd_splitkv_mla(Flash_fwd_mla_params ¶ms, cudaStream_t stream); diff --git a/csrc/flash_mla_utils.cu b/csrc/flash_fwd_mla_metadata.cu similarity index 95% rename from csrc/flash_mla_utils.cu rename to csrc/flash_fwd_mla_metadata.cu index 38c74e4..82f5b5a 100644 --- a/csrc/flash_mla_utils.cu +++ b/csrc/flash_fwd_mla_metadata.cu @@ -1,12 +1,4 @@ -#include -#include -#include - -using namespace cute; - -#include "flash_mla.h" -#include "static_switch.h" -#include "utils.h" +#include "flash_fwd_mla_kernel.h" static constexpr int MaxBatchSize = 4096; diff --git a/flash_mla/flash_mla_interface.py b/flash_mla/flash_mla_interface.py index f249315..736ac69 100644 --- a/flash_mla/flash_mla_interface.py +++ b/flash_mla/flash_mla_interface.py @@ -16,7 +16,7 @@ def get_mla_metadata( num_heads_per_head_k: Equals to seq_len_q * num_heads_q // num_heads_k. num_heads_k: num_heads_k. - Return: + Returns: tile_scheduler_metadata: (num_sm_parts, TileSchedulerMetaDataSize), dtype torch.int32. num_splits: (batch_size + 1), dtype torch.int32. """ @@ -42,10 +42,10 @@ def flash_mla_with_kvcache( k_cache: (num_blocks, page_block_size, num_heads_k, head_dim). block_table: (batch_size, max_num_blocks_per_seq), torch.int32. cache_seqlens: (batch_size), torch.int32. - head_dim_v: Head_dim of v. - tile_scheduler_metadata: (num_sm_parts, TileSchedulerMetaDataSize), torch.int32, return by get_mla_metadata. - num_splits: (batch_size + 1), torch.int32, return by get_mla_metadata. - softmax_scale: float. The scaling of QK^T before applying softmax. Default to 1 / sqrt(head_dim). + head_dim_v: Head dimension of v. + tile_scheduler_metadata: (num_sm_parts, TileSchedulerMetaDataSize), torch.int32, returned by get_mla_metadata. + num_splits: (batch_size + 1), torch.int32, returned by get_mla_metadata. + softmax_scale: float. The scale of QK^T before applying softmax. Default to 1 / sqrt(head_dim). causal: bool. Whether to apply causal attention mask. descale_q: (batch_size), torch.float. dequant scale for query descale_k: (batch_size), torch.float. dequant scale for key diff --git a/setup.py b/setup.py index c184953..ef1a8a7 100644 --- a/setup.py +++ b/setup.py @@ -11,12 +11,35 @@ from torch.utils.cpp_extension import ( IS_WINDOWS, ) +DISABLE_FP16 = os.getenv("FLASH_MLA_DISABLE_FP16", "FALSE") == "TRUE" + def append_nvcc_threads(nvcc_extra_args): nvcc_threads = os.getenv("NVCC_THREADS") or "32" return nvcc_extra_args + ["--threads", nvcc_threads] +def get_sources(): + sources = [ + "csrc/flash_api.cpp", + "csrc/flash_fwd_mla_bf16_sm90.cu", + "csrc/flash_fwd_mla_fp8_sm90.cu", + "csrc/flash_fwd_mla_metadata.cu", + ] + + if not DISABLE_FP16: + sources.append("csrc/flash_fwd_mla_fp16_sm90.cu") + + return sources + + +def get_features_args(): + features_args = [] + if DISABLE_FP16: + features_args.append("-DFLASH_MLA_DISABLE_FP16") + return features_args + + subprocess.run(["git", "submodule", "update", "--init", "csrc/cutlass"]) cc_flag = [] @@ -34,14 +57,9 @@ ext_modules = [] ext_modules.append( CUDAExtension( name="flash_mla_cuda", - sources=[ - "csrc/flash_api.cpp", - "csrc/flash_mla_utils.cu", - "csrc/flash_fwd_mla_bf16_sm90.cu", - "csrc/flash_fwd_mla_fp8_sm90.cu", - ], + sources=get_sources(), extra_compile_args={ - "cxx": cxx_args, + "cxx": cxx_args + get_features_args(), "nvcc": append_nvcc_threads( [ "-O3", @@ -60,7 +78,7 @@ ext_modules.append( "--ftemplate-backtrace-limit=0" ] + cc_flag - ), + ) + get_features_args(), }, include_dirs=[ Path(this_dir) / "csrc", diff --git a/tests/test_flash_mla.py b/tests/test_flash_mla.py index ff7cd27..9c1ddcf 100644 --- a/tests/test_flash_mla.py +++ b/tests/test_flash_mla.py @@ -1,10 +1,11 @@ +import argparse import math import random import torch import triton -from flash_mla import get_mla_metadata, flash_mla_with_kvcache +from flash_mla import flash_mla_with_kvcache, get_mla_metadata def scaled_dot_product_attention(query, key, value, h_q, h_kv, is_causal=False): @@ -42,7 +43,9 @@ def cal_diff(x: torch.Tensor, y: torch.Tensor, name: str, use_fp8: bool=False) - @torch.inference_mode() def test_flash_mla(b, s_q, mean_sk, h_q, h_kv, d, dv, causal, varlen, use_fp8 = False): - print(f"{b=}, {s_q=}, {mean_sk=}, {h_q=}, {h_kv=}, {d=}, {dv=}, {causal=}, {varlen=}") + print( + f"{b=}, {s_q=}, {mean_sk=}, {h_q=}, {h_kv=}, {d=}, {dv=}, {causal=}, {varlen=}, {use_fp8=}" + ) cache_seqlens = torch.full((b,), mean_sk, dtype=torch.int32) if varlen: @@ -56,15 +59,19 @@ def test_flash_mla(b, s_q, mean_sk, h_q, h_kv, d, dv, causal, varlen, use_fp8 = q = torch.randn(b, s_q, h_q, d) block_size = 64 - block_table = torch.arange(b * max_seqlen_pad // block_size, dtype=torch.int32).view(b, max_seqlen_pad // block_size) + 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) for i in range(b): - blocked_k.view(b, max_seqlen_pad, h_kv, d)[i, cache_seqlens[i].item():] = float("nan") + blocked_k.view(b, max_seqlen_pad, h_kv, d)[i, cache_seqlens[i].item():] = ( + float("nan") + ) blocked_v = blocked_k[..., :dv] - tile_scheduler_metadata, num_splits = get_mla_metadata(cache_seqlens, s_q * h_q // h_kv, h_kv) - - init_dtype = q.dtype + tile_scheduler_metadata, num_splits = get_mla_metadata( + cache_seqlens, s_q * h_q // h_kv, h_kv + ) def prepare_fp8_input(): q_fp8, blocked_k_fp8, descale_q, descale_k = None, None, None, None @@ -90,9 +97,16 @@ def test_flash_mla(b, s_q, mean_sk, h_q, h_kv, d, dv, causal, varlen, use_fp8 = def flash_mla(): return flash_mla_with_kvcache( - q, blocked_k, block_table, cache_seqlens, dv, - tile_scheduler_metadata, num_splits, causal=causal, - descale_q=descale_q, descale_k=descale_k, + q, + blocked_k, + block_table, + cache_seqlens, + dv, + tile_scheduler_metadata, + num_splits, + causal=causal, + descale_q=descale_q, + descale_k=descale_k, ) def ref_mla(): @@ -124,14 +138,18 @@ def test_flash_mla(b, s_q, mean_sk, h_q, h_kv, d, dv, causal, varlen, use_fp8 = t = triton.testing.do_bench(flash_mla) FLOPS = s_q * total_seqlens * h_q * (d + dv) * 2 - bytes = (total_seqlens * h_kv * d + b * s_q * h_q * d + b * s_q * h_q * dv) * (torch.finfo(dtype).bits // 8) - print(f"{t:.3f} ms, {FLOPS / 10 ** 9 / t:.0f} TFLOPS, {bytes / 10 ** 6 / t:.0f} GB/s") + bytes = (total_seqlens * h_kv * d + b * s_q * h_q * d + b * s_q * h_q * dv) * ( + torch.finfo(q.dtype).bits // 8 + ) + print( + f"{t:.3f} ms, {FLOPS / 10 ** 9 / t:.0f} TFLOPS, {bytes / 10 ** 6 / t:.0f} GB/s" + ) -if __name__ == "__main__": - dtype = torch.bfloat16 +def main(torch_dtype): device = torch.device("cuda:0") - torch.set_default_dtype(dtype) + init_dtype = torch.bfloat16 if torch_dtype == torch.float8_e4m3fn else torch_dtype + torch.set_default_dtype(init_dtype) torch.set_default_device(device) torch.cuda.set_device(device) torch.manual_seed(0) @@ -140,11 +158,32 @@ if __name__ == "__main__": h_kv = 1 d, dv = 576, 512 causal = False - use_fp8 = True + use_fp8 = torch_dtype == torch.float8_e4m3fn - for b in [16]: - for s in [4096]: - for h_q in [128]: # TP = 8, 4, 2, 1 - for s_q in [2]: # MTP = 1, 2 - for varlen in [False]: + for b in [128]: + for s in [4096, 8192]: + for h_q in [16, 32, 64, 128]: # TP = 8, 4, 2, 1 + for s_q in [1, 2]: # MTP = 1, 2 + for varlen in [False, True]: test_flash_mla(b, s_q, s, h_q, h_kv, d, dv, causal, varlen, use_fp8) + + +if __name__ == "__main__": + parser = argparse.ArgumentParser() + parser.add_argument( + "--dtype", + type=str, + choices=["bf16", "fp16", "e4m3"], + default="bf16", + help="Data type to use for testing (bf16/fp16/e4m3)", + ) + + args = parser.parse_args() + + torch_dtype = torch.bfloat16 + if args.dtype == "fp16": + torch_dtype = torch.float16 + elif args.dtype = "e4m3": + torch.dtype = torch.float8_e4m3fn + + main(torch_dtype)