diff --git a/hopper/epilogue_fwd.hpp b/hopper/epilogue_fwd.hpp index dac981a218e..9a1171c3e48 100644 --- a/hopper/epilogue_fwd.hpp +++ b/hopper/epilogue_fwd.hpp @@ -34,11 +34,11 @@ struct CollectiveEpilogueFwd { static constexpr bool PackGQA = PackGQA_; static constexpr bool Split = Split_; static constexpr bool Use_smem = !(Split && !Varlen); - static constexpr bool Use_TMA_O = ArchTag::kMinComputeCapability >= 90 && !Varlen && !Split && !PackGQA; + static constexpr bool Use_TMA_O = ArchTag::kMinComputeCapability >= 90 && !Varlen && !Split && !PackGQA && sizeof(Element) <= 2; static_assert(ArchTag::kMinComputeCapability >= 80); static_assert(ArchTag::kMinComputeCapability >= 90 || CUTE_STATIC_V(size(ClusterShape{})) == 1); - static_assert(sizeof(Element) <= 2); + static_assert(sizeof(Element) <= 4); static constexpr int kBlockM = get<0>(TileShape_MNK_PV{}); static constexpr int kHeadDimV = get<1>(TileShape_MNK_PV{}); @@ -48,7 +48,7 @@ struct CollectiveEpilogueFwd { using GmemTiledCopyOTMA = cute::SM90_TMA_STORE; // These are for storing the output tensor without TMA (e.g., for setting output to zero) - static constexpr int kGmemElemsPerStore = sizeof(cute::uint128_t) / sizeof(Element); + static constexpr int kGmemElemsPerStore = kBlockM >= 32 ? sizeof(cute::uint128_t) / sizeof(Element) : sizeof(cute::uint64_t) / sizeof(Element); static_assert(kHeadDimV % kGmemElemsPerStore == 0, "Headdim must be a multiple of kGmemElemsPerStore"); // We want each "row" to have 64 elements (128 bytes, i.e. 1 cache line). We want each thread to have 4 elements // in the M direction and 2 elements in the K direction. In the case of PackGQA, this reduces the number of times @@ -57,23 +57,28 @@ struct CollectiveEpilogueFwd { static constexpr int kBlockKGmem = (kBytePerRow % 128 == 0 ? 128 : (kBytePerRow % 64 == 0 ? 64 : 32)) / sizeof(Element); static constexpr int kGmemThreadsPerRow = kBlockKGmem / kGmemElemsPerStore; // If PackGQA, we split the work of compute O_ptr among threads in the same row, so we need this to within a warp - static_assert(cutlass::NumThreadsPerWarp % kGmemThreadsPerRow == 0); + static_assert(!PackGQA || cutlass::NumThreadsPerWarp % kGmemThreadsPerRow == 0); static_assert(NumEpilogueThreads % kGmemThreadsPerRow == 0, "NumEpilogueThreads must be a multiple of kGmemThreadsPerRow"); using GmemLayoutAtom = Layout, Int>, Stride, _1>>; static_assert(kBlockM % CUTE_STATIC_V(shape<0>(GmemLayoutAtom{})) == 0, "kBlockM must be a multiple of NumEpilogueThreads / kGmemThreadsPerRow"); + using GmemTileCopyAtomO = std::conditional_t< + kBlockM >= 32, + Copy_Atom, Element>, + Copy_Atom, Element>>; using GmemTiledCopyO = decltype( - make_tiled_copy(Copy_Atom, Element>{}, + make_tiled_copy(GmemTileCopyAtomO{}, GmemLayoutAtom{}, - Layout>>{})); // Val layout, 8 or 16 vals per store + Layout>>{})); using SmemLayoutAtomOTMA = decltype(cutlass::gemm::collective::detail::ss_smem_selector(TileShape_MNK_PV{})), decltype(cute::get<1>(TileShape_MNK_PV{}))>()); using SmemLayoutOTMA = decltype(tile_to_shape(SmemLayoutAtomOTMA{}, select<0, 1>(TileShape_MNK_PV{}))); - static constexpr int kSwizzle = kBlockKGmem == 128 ? 4 : (kBlockKGmem == 64 ? 3 : (kBlockKGmem == 32 ? 2 : 1)); - static constexpr int kSwizzleBase = sizeof(Element) == 4 ? 2 : (sizeof(Element) == 2 ? 3 : 4); + static constexpr int kSwizzle = sizeof(Element) == 4 ? 2 : (kBlockKGmem == 128 ? 4 : (kBlockKGmem == 64 ? 3 : (kBlockKGmem == 32 ? 2 : 1))); + static constexpr int kSwizzleBase = sizeof(Element) == 4 ? 3 : (sizeof(Element) == 2 ? 3 : 4); + static constexpr int kSwizzleShift = sizeof(Element) == 4 ? 2 : (sizeof(Element) == 2 ? 3 : 4); using SmemLayoutAtomO = decltype( - composition(Swizzle{}, + composition(Swizzle{}, Layout>, Stride, _1>>{})); using SmemLayoutOSTS = decltype(tile_to_shape(SmemLayoutAtomO{}, select<0, 1>(TileShape_MNK_PV{}))); @@ -238,8 +243,15 @@ struct CollectiveEpilogueFwd { // If we will possibly need tOrO in FP32, we'd want to permute tOrO before type conversion. // Otherwise we can permute after conversion. if constexpr (NeedFP8Permute && Split) { flash::permute_output_fp8_Vcolmajor(tOrO); } - Tensor tOrO_out = make_tensor_like(tOrO); - flash::convert_type_out(tOrO, tOrO_out); + auto tOrO_out = [&] { + if constexpr (cute::is_same_v) { + return tOrO; + } else { + Tensor out = make_tensor_like(tOrO); + flash::convert_type_out(tOrO, out); + return out; + } + }(); if constexpr (NeedFP8Permute && !Split) { flash::permute_output_fp8_Vcolmajor(tOrO_out); } // Make sure all WGs have finished reading V diff --git a/hopper/flash_api.cpp b/hopper/flash_api.cpp index 5b9c507574a..0769e207f12 100644 --- a/hopper/flash_api.cpp +++ b/hopper/flash_api.cpp @@ -705,7 +705,7 @@ mha_fwd(at::Tensor q, // (b, s_q, h, d) or (total_q, h, d) if there is cu_seql int64_t num_splits, std::optional pack_gqa_, int64_t sm_margin, - std::optional learnable_sink_ + std::optional learnable_sink_ ) { auto dprops = at::cuda::getCurrentDeviceProperties(); @@ -857,11 +857,19 @@ mha_fwd(at::Tensor q, // (b, s_q, h, d) or (total_q, h, d) if there is cu_seql TORCH_CHECK(head_size_v % alignment == 0, "head_size_v should be a multiple of " + std::to_string(alignment)); auto opts = q.options(); - auto out_type = q_type == at::ScalarType::Float8_e4m3fn ? at::ScalarType::BFloat16 : q_type; + auto default_out_type = q_type == at::ScalarType::Float8_e4m3fn ? at::ScalarType::BFloat16 : q_type; + auto out_type = out_.has_value() ? out_.value().scalar_type() : default_out_type; at::Tensor out; if (out_.has_value()) { out = out_.value(); - TORCH_CHECK(out.scalar_type() == out_type, "For FP16/BF16 input, output must have the same dtype as inputs. For FP8 input, output must have dtype BF16"); + if (q_type == at::ScalarType::BFloat16) { + TORCH_CHECK( + out_type == at::ScalarType::BFloat16 || out_type == at::ScalarType::Float, + "For BF16 input, output must have dtype BF16 or FP32" + ); + } else { + TORCH_CHECK(out_type == default_out_type, "Output tensor must match the selected output dtype"); + } CHECK_DEVICE(out); TORCH_CHECK(out.stride(-1) == 1, "Output tensor must have contiguous last dimension"); if (!is_varlen_q) { @@ -912,6 +920,7 @@ mha_fwd(at::Tensor q, // (b, s_q, h, d) or (total_q, h, d) if there is cu_seql attention_chunk, softcap, sm_margin); + params.is_fp32 = out_type == at::ScalarType::Float; params.total_q = total_q; params.total_k = total_k; params.b_k = batch_size_k; @@ -1115,7 +1124,7 @@ mha_fwd(at::Tensor q, // (b, s_q, h, d) or (total_q, h, d) if there is cu_seql out_accum = torch::empty({params.num_splits, num_heads, total_q, head_size_v}, opts.dtype(outaccum_type)); softmax_lse_accum = torch::empty({params.num_splits, num_heads, total_q}, opts.dtype(at::kFloat)); } - params.is_fp32 = false; + params.is_fp32 = out_type == at::ScalarType::Float; params.oaccum_ptr = out_accum.data_ptr(); params.softmax_lseaccum_ptr = softmax_lse_accum.data_ptr(); params.oaccum_split_stride = out_accum.stride(0); @@ -1181,10 +1190,8 @@ mha_fwd(at::Tensor q, // (b, s_q, h, d) or (total_q, h, d) if there is cu_seql auto stream = at::cuda::getCurrentCUDAStream().stream(); run_mha_fwd(params, stream); if (params.num_splits > 1) { - if (out_type == at::ScalarType::BFloat16) { - // Since we want output in BF16. Otherwise fwd_combine will output to FP16 - params.is_bf16 = true; - } + params.is_fp32 = out_type == at::ScalarType::Float; + params.is_bf16 = out_type == at::ScalarType::BFloat16; // Unless there's seqused_q, for the purpose of attn_combine, we can just treat it as batch=1 // and seqlen = total_q, and don't need to dispatch to Varlen there. // However, with dynamic split, each row needs to know which batch it belongs to @@ -1300,7 +1307,7 @@ std::tuple mha_bwd( double softcap, bool deterministic, int64_t sm_margin, - std::optional learnable_sink_, + std::optional learnable_sink_, std::optional dsink_ ) { @@ -1749,7 +1756,8 @@ TORCH_LIBRARY(flash_attn_3, m) { "Tensor? scheduler_metadata = None," "int num_splits = 0," "bool? pack_gqa = None," - "int sm_margin = 0) -> (Tensor(out!), Tensor, Tensor, Tensor)"); + "int sm_margin = 0," + "Tensor? learnable_sink = None) -> (Tensor(out!), Tensor, Tensor, Tensor)"); m.def("bwd(" "Tensor dout," "Tensor q," @@ -1772,7 +1780,9 @@ TORCH_LIBRARY(flash_attn_3, m) { "int window_size_right = -1," "float softcap = 0.0," "bool deterministic = False," - "int sm_margin = 0) -> (Tensor, Tensor, Tensor, Tensor, Tensor)"); + "int sm_margin = 0," + "Tensor? learnable_sink = None," + "Tensor? dsink = None) -> (Tensor, Tensor, Tensor, Tensor, Tensor)"); m.def("fwd_combine(" "Tensor out_partial," "Tensor lse_partial," diff --git a/hopper/flash_attn_3/__init__.py b/hopper/flash_attn_3/__init__.py new file mode 100644 index 00000000000..e69de29bb2d diff --git a/hopper/flash_attn_interface.py b/hopper/flash_attn_interface.py index a38da112e0b..e62936648c8 100755 --- a/hopper/flash_attn_interface.py +++ b/hopper/flash_attn_interface.py @@ -141,10 +141,10 @@ def _flash_attn_forward( ) if out_accum is None: - out_accum = torch.tensor([], device=out.device) + out_accum = out.new_empty((0,), dtype=torch.float32) if softmax_lse_accum is None: - softmax_lse_accum = torch.tensor([], device=out.device) + softmax_lse_accum = out.new_empty((0,), dtype=torch.float32) return out, softmax_lse, out_accum, softmax_lse_accum @@ -191,70 +191,226 @@ def _flash_attn_forward_fake( Symbolic fake implementation of flash attention forward. Returns tensors with the correct shapes and dtypes without actual computation. """ + if out_ is not None: + raise TypeError( + "Tracing (torch.compile/torch.export) with pre-allocated output tensor is not supported on " + "flash_attn_3::_flash_attn_forward. Use flash_attn_3::_flash_attn_forward_into instead." + ) + + return _flash_attn_forward_fake_outputs( + q=q, + v=v, + out_=None, + cu_seqlens_q=cu_seqlens_q, + max_seqlen_q=max_seqlen_q, + num_splits=num_splits, + ) + + +# Keep preallocated-output semantics in a separate mutating op so tracing can +# model "write into out" without returning an input alias from the functional op. +@torch.library.custom_op("flash_attn_3::_flash_attn_forward_into", mutates_args=("out",), device_types="cuda") +def _flash_attn_forward_into( + q: torch.Tensor, + k: torch.Tensor, + v: torch.Tensor, + out: torch.Tensor, + k_new: Optional[torch.Tensor] = None, + v_new: Optional[torch.Tensor] = None, + qv: Optional[torch.Tensor] = None, + cu_seqlens_q: Optional[torch.Tensor] = None, + cu_seqlens_k: Optional[torch.Tensor] = None, + cu_seqlens_k_new: Optional[torch.Tensor] = None, + seqused_q: Optional[torch.Tensor] = None, + seqused_k: Optional[torch.Tensor] = None, + max_seqlen_q: Optional[int] = None, + max_seqlen_k: Optional[int] = None, + page_table: Optional[torch.Tensor] = None, + kv_batch_idx: Optional[torch.Tensor] = None, + leftpad_k: Optional[torch.Tensor] = None, + rotary_cos: Optional[torch.Tensor] = None, + rotary_sin: Optional[torch.Tensor] = None, + seqlens_rotary: Optional[torch.Tensor] = None, + q_descale: Optional[torch.Tensor] = None, + k_descale: Optional[torch.Tensor] = None, + v_descale: Optional[torch.Tensor] = None, + softmax_scale: Optional[float] = None, + causal: bool = False, + window_size_left: int = -1, + window_size_right: int = -1, + attention_chunk: int = 0, + softcap: float = 0.0, + rotary_interleaved: bool = True, + scheduler_metadata: Optional[torch.Tensor] = None, + num_splits: int = 1, + pack_gqa: Optional[bool] = None, + sm_margin: int = 0, + learnable_sink: Optional[torch.Tensor] = None, +) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]: + q, k, k_new, v_new = [maybe_contiguous(x) for x in (q, k, k_new, v_new)] + v = v.contiguous() if v.stride(-1) != 1 and v.stride(-3) != 1 else v + cu_seqlens_q, cu_seqlens_k, cu_seqlens_k_new = [ + maybe_contiguous(x) for x in (cu_seqlens_q, cu_seqlens_k, cu_seqlens_k_new) + ] + seqused_q, seqused_k = [maybe_contiguous(x) for x in (seqused_q, seqused_k)] + page_table, kv_batch_idx, leftpad_k = [ + maybe_contiguous(x) for x in (page_table, kv_batch_idx, leftpad_k) + ] + rotary_cos, rotary_sin = [maybe_contiguous(x) for x in (rotary_cos, rotary_sin)] + seqlens_rotary = maybe_contiguous(seqlens_rotary) + _, softmax_lse, out_accum, softmax_lse_accum = flash_attn_3_gpu.fwd( + q, + k, + v, + k_new, + v_new, + qv, + out, + cu_seqlens_q, + cu_seqlens_k, + cu_seqlens_k_new, + seqused_q, + seqused_k, + max_seqlen_q, + max_seqlen_k, + page_table, + kv_batch_idx, + leftpad_k, + rotary_cos, + rotary_sin, + seqlens_rotary, + q_descale, + k_descale, + v_descale, + softmax_scale, + causal, + window_size_left, + window_size_right, + attention_chunk, + softcap, + rotary_interleaved, + scheduler_metadata, + num_splits, + pack_gqa, + sm_margin, + learnable_sink, + ) + + if out_accum is None: + out_accum = out.new_empty((0,), dtype=torch.float32) + + if softmax_lse_accum is None: + softmax_lse_accum = out.new_empty((0,), dtype=torch.float32) - # Determine if we're in varlen mode + return softmax_lse, out_accum, softmax_lse_accum + + +def _flash_attn_forward_fake_outputs( + q: torch.Tensor, + v: torch.Tensor, + out_: Optional[torch.Tensor], + cu_seqlens_q: Optional[torch.Tensor], + max_seqlen_q: Optional[int], + num_splits: int, +) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]: is_varlen_q = cu_seqlens_q is not None - # Get dimensions from query tensor if is_varlen_q: - # varlen mode: q is (total_q, num_heads, head_size) - total_q, num_heads, head_size = q.shape + total_q, num_heads, _ = q.shape batch_size = cu_seqlens_q.shape[0] - 1 - if max_seqlen_q is None: raise ValueError("max_seqlen_q must be provided if cu_seqlens_q is provided") seqlen_q = max_seqlen_q else: - # batch mode: q is (batch_size, seqlen_q, num_heads, head_size) - batch_size, seqlen_q, num_heads, head_size = q.shape + batch_size, seqlen_q, num_heads, _ = q.shape total_q = batch_size * q.shape[1] - # Get value head dimension - head_size_v = v.shape[-1] - - # Determine output dtype (FP8 inputs produce BF16 outputs) - q_type = q.dtype - if q_type == torch.float8_e4m3fn: - out_dtype = torch.bfloat16 - else: - out_dtype = q_type - # Create output tensor - if out_ is not None: - # If out_ is provided, _flash_attn_forward becomes non-functional - raise TypeError("Tracing (torch.compile/torch.export) with pre-allocated output tensor is not supported.") - - if is_varlen_q: - out = torch.empty((total_q, num_heads, head_size_v), dtype=out_dtype, device=q.device) + head_size_v = v.shape[-1] + if out_ is None: + out_dtype = torch.bfloat16 if q.dtype == torch.float8_e4m3fn else q.dtype + if is_varlen_q: + out = torch.empty((total_q, num_heads, head_size_v), dtype=out_dtype, device=q.device) + else: + out = torch.empty((batch_size, seqlen_q, num_heads, head_size_v), dtype=out_dtype, device=q.device) else: - out = torch.empty((batch_size, seqlen_q, num_heads, head_size_v), dtype=out_dtype, device=q.device) + out = out_ - # Create softmax_lse tensor if is_varlen_q: - softmax_lse = torch.empty((num_heads, total_q), dtype=torch.float32, device=q.device) + softmax_lse = torch.empty((num_heads, total_q), dtype=torch.float32, device=out.device) else: - softmax_lse = torch.empty((batch_size, num_heads, seqlen_q), dtype=torch.float32, device=q.device) + softmax_lse = torch.empty((batch_size, num_heads, seqlen_q), dtype=torch.float32, device=out.device) - # TODO(guilhermeleobas): Implement "get_num_splits" - # There's an heuristic to compute num_splits when "num_splits <= 0" - # assert that num_splits is > 0 for now if num_splits <= 0: raise ValueError(f"tracing (torch.compile/torch.export) with num_splits <= 0 not supported. Got {num_splits=}") if num_splits > 1: if is_varlen_q: - out_accum = torch.empty((num_splits, num_heads, total_q, head_size_v), dtype=torch.float32, device=q.device) - softmax_lse_accum = torch.empty((num_splits, num_heads, total_q), dtype=torch.float32, device=q.device) + out_accum = torch.empty((num_splits, num_heads, total_q, head_size_v), dtype=torch.float32, device=out.device) + softmax_lse_accum = torch.empty((num_splits, num_heads, total_q), dtype=torch.float32, device=out.device) else: - out_accum = torch.empty((num_splits, batch_size, num_heads, seqlen_q, head_size_v), dtype=torch.float32, device=q.device) - softmax_lse_accum = torch.empty((num_splits, batch_size, num_heads, seqlen_q), dtype=torch.float32, device=q.device) + out_accum = torch.empty( + (num_splits, batch_size, num_heads, seqlen_q, head_size_v), dtype=torch.float32, device=out.device + ) + softmax_lse_accum = torch.empty( + (num_splits, batch_size, num_heads, seqlen_q), dtype=torch.float32, device=out.device + ) else: - # Tensors are not set when num_splits < 1 - out_accum = torch.tensor([], device=out.device) - softmax_lse_accum = torch.tensor([], device=out.device) + out_accum = out.new_empty((0,), dtype=torch.float32) + softmax_lse_accum = out.new_empty((0,), dtype=torch.float32) return out, softmax_lse, out_accum, softmax_lse_accum + +@torch.library.register_fake("flash_attn_3::_flash_attn_forward_into") +def _flash_attn_forward_into_fake( + q: torch.Tensor, + k: torch.Tensor, + v: torch.Tensor, + out: torch.Tensor, + k_new: Optional[torch.Tensor] = None, + v_new: Optional[torch.Tensor] = None, + qv: Optional[torch.Tensor] = None, + cu_seqlens_q: Optional[torch.Tensor] = None, + cu_seqlens_k: Optional[torch.Tensor] = None, + cu_seqlens_k_new: Optional[torch.Tensor] = None, + seqused_q: Optional[torch.Tensor] = None, + seqused_k: Optional[torch.Tensor] = None, + max_seqlen_q: Optional[int] = None, + max_seqlen_k: Optional[int] = None, + page_table: Optional[torch.Tensor] = None, + kv_batch_idx: Optional[torch.Tensor] = None, + leftpad_k: Optional[torch.Tensor] = None, + rotary_cos: Optional[torch.Tensor] = None, + rotary_sin: Optional[torch.Tensor] = None, + seqlens_rotary: Optional[torch.Tensor] = None, + q_descale: Optional[torch.Tensor] = None, + k_descale: Optional[torch.Tensor] = None, + v_descale: Optional[torch.Tensor] = None, + softmax_scale: Optional[float] = None, + causal: bool = False, + window_size_left: int = -1, + window_size_right: int = -1, + attention_chunk: int = 0, + softcap: float = 0.0, + rotary_interleaved: bool = True, + scheduler_metadata: Optional[torch.Tensor] = None, + num_splits: int = 1, + pack_gqa: Optional[bool] = None, + sm_margin: int = 0, + learnable_sink: Optional[torch.Tensor] = None, +) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]: + _, softmax_lse, out_accum, softmax_lse_accum = _flash_attn_forward_fake_outputs( + q=q, + v=v, + out_=out, + cu_seqlens_q=cu_seqlens_q, + max_seqlen_q=max_seqlen_q, + num_splits=num_splits, + ) + return softmax_lse, out_accum, softmax_lse_accum + + @torch.library.custom_op("flash_attn_3::_flash_attn_backward", mutates_args=("dq", "dk", "dv"), device_types="cuda") def _flash_attn_backward( dout: torch.Tensor, diff --git a/hopper/flash_fwd_launch_template.h b/hopper/flash_fwd_launch_template.h index f367d3132b8..37a81f86f15 100644 --- a/hopper/flash_fwd_launch_template.h +++ b/hopper/flash_fwd_launch_template.h @@ -199,24 +199,20 @@ void run_flash_fwd(Flash_fwd_params ¶ms, cudaStream_t stream) { } } -template -void run_mha_fwd_(Flash_fwd_params ¶ms, cudaStream_t stream) { - static_assert(sizeof(T) == 2 || sizeof(T) == 1, "Only 16bit and 8bit are supported"); +template +void launch_mha_fwd_with_output_(Flash_fwd_params ¶ms, cudaStream_t stream) { static constexpr bool Is_FP8 = cute::is_same_v || cute::is_same_v; - using T_out = std::conditional_t; CAUSAL_LOCAL_SWITCH(params.is_causal, params.is_local, Is_causal, Is_local, [&] { VCOLMAJOR_SWITCH(params.v_dim_stride != 1, V_colmajor_, [&] { static constexpr bool V_colmajor = V_colmajor_ && sizeof(T) == 1; VARLEN_SWITCH(params.cu_seqlens_q || params.cu_seqlens_k || params.seqused_q || params.seqused_k || params.leftpad_k, Varlen, [&] { - // Only needed here to decide if we should use cluster - static constexpr int kBlockM = Arch >= 90 ? std::get<0>(tile_size_fwd_sm90(kHeadDim, kHeadDimV, Is_causal, Is_local, sizeof(T) /*element_size*/, V_colmajor, PagedKVNonTMA, Has_softcap)) : 128; + static constexpr int kBlockM = Arch >= 90 ? std::get<0>(tile_size_fwd_sm90(kHeadDim, kHeadDimV, Is_causal, Is_local, sizeof(T), V_colmajor, PagedKVNonTMA, Has_softcap)) : 128; static constexpr bool Enable_cluster = Arch == 90 && (sizeof(T) == 2 ? (kHeadDim >= 128) : (kHeadDim == 192)) && !Is_causal && !Is_local && !Split && !PagedKVNonTMA && !Varlen; BOOL_SWITCH(params.qv_ptr, HasQV_, [&] { static constexpr bool HasQv = HasQV_ && Arch == 90 && !Is_FP8 && kHeadDim == 64 && kHeadDimV >= 256; APPENDKV_SWITCH(params.knew_ptr, AppendKV, [&] { SINK_SWITCH(params.learnable_sink_ptr != nullptr, Has_sink, [&] { static constexpr bool PackGQA_Sink = PackGQA && !Has_sink; - // Only use Cluster if number of tiles along seqlen_q is even and not varlen CLUSTER_SWITCH(cutlass::ceil_div(params.seqlen_q * (!PackGQA_Sink ? 1 : params.h / params.h_k), kBlockM) % 2 == 0, Use_cluster, [&] { static constexpr int ClusterM = Enable_cluster && Use_cluster ? 2 : 1; run_flash_fwd(params, stream); @@ -228,3 +224,17 @@ void run_mha_fwd_(Flash_fwd_params ¶ms, cudaStream_t stream) { }); }); } + +template +void run_mha_fwd_(Flash_fwd_params ¶ms, cudaStream_t stream) { + static_assert(sizeof(T) == 2 || sizeof(T) == 1, "Only 16bit and 8bit are supported"); + static constexpr bool Is_FP8 = cute::is_same_v || cute::is_same_v; + static constexpr bool Is_BF16 = cute::is_same_v; + using DefaultTOut = std::conditional_t; + if constexpr (Is_BF16) { + if (params.is_fp32) { + return launch_mha_fwd_with_output_(params, stream); + } + } + return launch_mha_fwd_with_output_(params, stream); +} diff --git a/hopper/setup.py b/hopper/setup.py index fb98a16260b..4935ea50ac4 100755 --- a/hopper/setup.py +++ b/hopper/setup.py @@ -452,7 +452,7 @@ def nvcc_threads_args(): # We want to use the nvcc front end from 12.6 however, since if we use nvcc 12.8 # Cutlass 3.8 will expect the new data types in cuda.h from CTK 12.8, which we don't have. # For CUDA 13.0+, use system nvcc instead of downloading CUDA 12.x toolchain - if bare_metal_version >= Version("12.3") and bare_metal_version < Version("13.0") and bare_metal_version != Version("12.8"): + if False: download_and_copy( name="nvcc", src_func=lambda system, arch, version: f"cuda_nvcc-{system}-{arch}-{version}-archive/bin", @@ -579,7 +579,7 @@ def nvcc_threads_args(): # Choose between flash_api.cpp and flash_api_stable.cpp based on torch version torch_version = parse(torch.__version__) - target_version = parse("2.9.0.dev20250830") + target_version = parse("99.0.0") stable_args = [] if torch_version >= target_version: diff --git a/pixi.lock b/pixi.lock new file mode 100644 index 00000000000..58091f02a1d --- /dev/null +++ b/pixi.lock @@ -0,0 +1,3366 @@ +version: 6 +environments: + default: + channels: + - url: https://srgconda.bj.bcebos.com/ + - url: https://repo.prefix.dev/meta-forge/ + - url: https://conda.anaconda.org/conda-forge/ + options: + pypi-prerelease-mode: if-necessary-or-explicit + packages: + linux-64: + - conda: https://conda.anaconda.org/conda-forge/linux-64/_openmp_mutex-4.5-7_kmp_llvm.conda + - 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