软fp8 e5m2搭框架

csrc/api/dense_decode_kvfp8.h

+#pragma once
+
+#include <cutlass/half.h>
+#include <cutlass/fast_math.h>
+
+#include "common.h"
+#include "params.h"
+
+// #include "sm90/decode/dense/splitkv_mla.h"
+#include "smxx/decode/get_decoding_sched_meta/get_decoding_sched_meta.h"
+#include "smxx/decode/combine/combine.h"
+
+static std::tuple<at::Tensor, at::Tensor, std::optional<at::Tensor>, std::optional<at::Tensor>>
+dense_attn_decode_kvfp8_interface(
+    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 (when is_fp8 is False) or num_blocks x num_heads_k x (page_block_size*656) (when is_fp8 is True)
+    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
+    const float softmax_scale,
+    bool is_causal,
+    std::optional<at::Tensor> &tile_scheduler_metadata,   // num_sm_parts x (DecodingSchedMetaSize/4)
+    std::optional<at::Tensor> &num_splits                 // batch_size + 1
+) {
+    // Check arch
+    Arch arch = Arch();
+    if (!arch.is_sm90a()) {
+        TORCH_CHECK(false, "Dense decode MLA is only supported on SM90a architecture");
+    }
+
+    // Check data types
+    auto q_dtype = q.dtype();
+    TORCH_CHECK(q_dtype == torch::kBFloat16 || q_dtype == torch::kHalf);
+    
+    TORCH_CHECK(kcache.dtype() == torch::kFloat8_e5m2fn, "key must have torch::kFloat8_e5m2fn");
+    TORCH_CHECK(seqlens_k.dtype() == torch::kInt32, "seqlens_k must have dtype int32");
+    TORCH_CHECK(block_table.dtype() == torch::kInt32, "block_table must have dtype torch.int32");
+
+    // Check device
+    KU_CHECK_DEVICE(q);
+    KU_CHECK_DEVICE(kcache);
+    KU_CHECK_DEVICE(seqlens_k);
+    KU_CHECK_DEVICE(block_table);
+    KU_CHECK_DEVICE(tile_scheduler_metadata);
+    KU_CHECK_DEVICE(num_splits);
+
+    // Check layout
+    TORCH_CHECK(q.stride(-1) == 1, "q must have contiguous last dimension");
+    TORCH_CHECK(kcache.stride(-1) == 1, "kcache must have contiguous last dimension");
+    KU_CHECK_CONTIGUOUS(seqlens_k);
+    TORCH_CHECK(block_table.stride(-1) == 1, "block_table must have contiguous last dimension");
+    KU_CHECK_CONTIGUOUS(tile_scheduler_metadata);
+    KU_CHECK_CONTIGUOUS(num_splits);
+
+    const auto sizes = q.sizes();
+    const int batch_size = sizes[0];
+    const int seqlen_q_ori = sizes[1];
+    const int num_heads_q = sizes[2];
+    const int head_size_k = sizes[3];
+    TORCH_CHECK(head_size_k == 576, "Only head_size_k == 576 or 512 is supported");
+    TORCH_CHECK(head_size_v == 512, "Only head_size_v == 576 is supported");
+    
+    const int max_num_blocks_per_seq = block_table.size(1);
+    const int num_blocks = kcache.size(0);
+    const int page_block_size = kcache.size(1);
+    const int num_heads_k = kcache.size(2);
+    TORCH_CHECK(page_block_size == 64, "Currently page_block_size must be 64");
+    TORCH_CHECK(batch_size > 0, "batch size must be positive");
+    TORCH_CHECK(num_heads_q % num_heads_k == 0, "Number of heads in key/value must divide number of heads in query");
+    
+    if (seqlen_q_ori == 1) { is_causal = false; }
+    
+    const int num_q_heads_per_hk = num_heads_q / num_heads_k;
+    const int q_seq_per_hk = seqlen_q_ori * num_q_heads_per_hk;
+    const int num_heads = num_heads_k;
+    q = q.view({batch_size, seqlen_q_ori, num_heads_k, num_q_heads_per_hk, head_size_k}).transpose(2, 3)
+        .reshape({batch_size, q_seq_per_hk, num_heads, head_size_k});
+    int num_sm_parts = std::max(arch.num_sms / num_heads_k / cutlass::ceil_div(seqlen_q_ori*num_heads_q/num_heads_k, 16), 1);
+
+    KU_CHECK_SHAPE(q, batch_size, q_seq_per_hk, num_heads, head_size_k);
+    KU_CHECK_SHAPE(kcache, num_blocks, page_block_size, num_heads_k, head_size_k);
+    KU_CHECK_SHAPE(seqlens_k, batch_size);
+    KU_CHECK_SHAPE(block_table, batch_size, max_num_blocks_per_seq);
+    KU_CHECK_SHAPE(tile_scheduler_metadata, num_sm_parts, DecodingSchedMetaSize/sizeof(int));
+    KU_CHECK_SHAPE(num_splits, batch_size+1);
+
+    at::cuda::CUDAGuard device_guard{(char)q.get_device()};
+
+    auto opts = q.options();
+    at::Tensor out = torch::empty({batch_size, num_heads, q_seq_per_hk, head_size_v}, opts);
+    at::Tensor lse = torch::empty({batch_size, num_heads, q_seq_per_hk}, opts.dtype(at::kFloat));
+    KU_CHECK_CONTIGUOUS(out);
+    KU_CHECK_CONTIGUOUS(lse);
+
+    if (!tile_scheduler_metadata.has_value()) {
+        tile_scheduler_metadata = torch::empty({num_sm_parts, sizeof(DecodingSchedMeta)/4}, opts.dtype(torch::kInt32));
+        num_splits = torch::empty({batch_size+1}, opts.dtype(torch::kInt32));
+        KU_CHECK_CONTIGUOUS(tile_scheduler_metadata);
+        KU_CHECK_CONTIGUOUS(num_splits);
+    
+        GetDecodeSchedMetaParams get_sched_meta_params = {
+            batch_size, seqlen_q_ori,
+            64,
+            5,
+            -1, -1,
+            nullptr, nullptr,
+            seqlens_k.data_ptr<int>(),
+            (DecodingSchedMeta*)tile_scheduler_metadata->data_ptr(),
+            num_splits->data_ptr<int>(),
+            num_sm_parts,
+            at::cuda::getCurrentCUDAStream().stream()
+        };
+        smxx::decode::run_get_decoding_sched_meta_kernel(get_sched_meta_params);
+    } else {
+        KU_CHECK_DTYPE(tile_scheduler_metadata, torch::kInt32);
+        KU_CHECK_DTYPE(num_splits, torch::kInt32);
+        KU_CHECK_DEVICE(tile_scheduler_metadata);
+        KU_CHECK_DEVICE(num_splits);
+        KU_CHECK_CONTIGUOUS(tile_scheduler_metadata);
+        KU_CHECK_CONTIGUOUS(num_splits);
+        KU_CHECK_SHAPE(tile_scheduler_metadata, num_sm_parts, sizeof(DecodingSchedMeta)/sizeof(int));
+        KU_CHECK_SHAPE(num_splits, batch_size+1);
+    }
+
+    // Set the sizes
+    DenseAttnDecodeParams params;
+    params.b = batch_size;
+    params.s_q = seqlen_q_ori;
+    params.q_seq_per_hk = q_seq_per_hk;
+    params.seqlens_k_ptr = seqlens_k.data_ptr<int>();
+    params.h_q = num_heads_q;
+    params.h_k = num_heads_k;
+    params.num_blocks = num_blocks;
+    params.q_head_per_hk = num_q_heads_per_hk;
+    params.is_causal = is_causal;
+    params.d = head_size_k;
+    params.d_v = head_size_v;
+    params.scale_softmax = softmax_scale;
+    params.scale_softmax_log2 = float(softmax_scale * M_LOG2E);
+    // Set the pointers and strides.
+    params.q_ptr = q.data_ptr();
+    params.k_ptr = kcache.data_ptr();
+    params.o_ptr = out.data_ptr();
+    params.softmax_lse_ptr = lse.data_ptr<float>();
+    // All stride are in elements, not bytes.
+    params.q_batch_stride = q.stride(0);
+    params.k_batch_stride = kcache.stride(0);
+    params.o_batch_stride = out.stride(0);
+    params.q_row_stride = q.stride(1);
+    params.k_row_stride = kcache.stride(1);
+    params.o_row_stride = out.stride(2);
+    params.q_head_stride = q.stride(2);
+    params.k_head_stride = kcache.stride(2);
+    params.o_head_stride = out.stride(1);
+
+    params.block_table = block_table.data_ptr<int>();
+    params.block_table_batch_stride = block_table.stride(0);
+    params.page_block_size = page_block_size;
+    
+    params.tile_scheduler_metadata_ptr = (DecodingSchedMeta*)tile_scheduler_metadata->data_ptr();
+    params.num_sm_parts = num_sm_parts;
+    params.num_splits_ptr = num_splits->data_ptr<int>();
+
+    const int total_num_splits = batch_size + params.num_sm_parts;
+    at::Tensor lse_accum = torch::empty({total_num_splits, num_heads, q_seq_per_hk}, opts.dtype(at::kFloat));
+    at::Tensor out_accum = torch::empty({total_num_splits, num_heads, q_seq_per_hk, head_size_v}, opts.dtype(at::kFloat));
+    KU_CHECK_CONTIGUOUS(lse_accum);
+    KU_CHECK_CONTIGUOUS(out_accum);
+    params.total_num_splits = total_num_splits;
+    params.softmax_lseaccum_ptr = lse_accum.data_ptr<float>();
+    params.oaccum_ptr = out_accum.data_ptr<float>();
+
+    params.stream = at::cuda::getCurrentCUDAStream().stream();
+
+    if (q_dtype == torch::kBFloat16) {
+        sm90::run_flash_splitkv_mla_kernel<cutlass::bfloat16_t>(params);
+    } else {
+        TORCH_CHECK(false, "Unsupported dtype for dense MLA on SM90");
+    }
+
+    CombineParams combine_params = {
+        batch_size, seqlen_q_ori,
+        num_heads_q, head_size_v,
+
+        params.softmax_lse_ptr,
+        params.o_ptr,
+        num_heads*q_seq_per_hk, num_heads_q,
+        num_heads_q*seqlen_q_ori*head_size_v, num_heads_q*head_size_v, head_size_v,
+
+        params.softmax_lseaccum_ptr,
+        params.oaccum_ptr,
+        num_heads*q_seq_per_hk, num_heads_q,
+        num_heads_q*seqlen_q_ori*head_size_v, num_heads_q*head_size_v, head_size_v,
+
+        params.tile_scheduler_metadata_ptr,
+        params.num_splits_ptr,
+        params.num_sm_parts,
+
+        nullptr,
+        at::cuda::getCurrentCUDAStream().stream()
+    };
+
+    if (q_dtype == torch::kBFloat16) {
+        smxx::decode::run_flash_mla_combine_kernel<cutlass::bfloat16_t>(combine_params);
+    } else if (q_dtype == torch::kHalf) {
+#ifndef FLASH_MLA_DISABLE_FP16
+        smxx::decode::run_flash_mla_combine_kernel<cutlass::half_t>(combine_params);
+#endif
+    } else {
+        TORCH_CHECK(false, "Unsupported tensor dtype for query");
+    }
+
+    out = out.view({batch_size, num_heads_k, seqlen_q_ori, num_q_heads_per_hk, head_size_v}).transpose(1, 2)
+            .reshape({batch_size, seqlen_q_ori, num_heads_q, head_size_v});
+    lse = lse.view({batch_size, num_heads_k, seqlen_q_ori, num_q_heads_per_hk}).transpose(2, 3)
+            .reshape({batch_size, num_heads_q, seqlen_q_ori});
+
+    return {out, lse, tile_scheduler_metadata, num_splits};
+}