d8f104e97a
* Support ck in fmha * Add ck submodule * Do not return lse if return_softmax == false * Use receipt to speed up ck compile time * Integrate new version of ck_tile * Support dropout for mha_fwd() * Add dropout to mha_varlen_fwd() * Update ck to develop * Extract padding function for dropout randval * Extract randval transformation function * Sync the code structure and coding style with FA * Remove this line, c++ api will handle this. Sync with test_flash_attn.py * fix compile error * Add mha_bwd * Generate dropout seed and offset from user generator * update CK * Add mha_varlen_bwd * Use same python as build flash-attn to generate ck kernel * Fix bug of group mode fwd about returning softmax lse * larger the test tollerance * Add test_flash_attn_output() and test_flash_attn_varlen_output() * Always fill softmax_lse * Remove duplicate benchmark script, since we already implement mha_bwd * Refine get value from tuple * Use default parameter for stream_config * unblock all platform * Add comment * refine the test code * Refine naming * Add unpack to namespace * Do not hardcode the warp size 64 * Add more targets * Add README * Optimize mha_fwd if seqlen_q == 1 * Support get_wheel_url for rocm * Detect rocm environment by pytorch's IS_HIP_EXTENSION * update to lastest ck * Add necessary compile flag * Sync the api with upstream FA --------- Co-authored-by: carlushuang <carlus.huang@amd.com> Co-authored-by: Yichen Yan <wenji.yyc@alibaba-inc.com> Co-authored-by: Po Yen Chen <PoYen.Chen@amd.com> Co-authored-by: Yichen Yan <oraluben@outlook.com>
349 lines
15 KiB
C++
349 lines
15 KiB
C++
/******************************************************************************
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* Copyright (c) 2024, Tri Dao.
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******************************************************************************/
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#include "flash_common.hpp"
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#include "fmha_fwd.hpp"
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#include "mask.hpp"
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fmha_fwd_traits get_ck_fmha_fwd_traits(const mask_info &mask,
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std::string dtype,
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int head_size,
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bool has_dropout,
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bool has_lse,
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bool enable_alibi)
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{
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return fmha_fwd_traits{head_size,
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head_size,
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dtype,
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false, // is_group_mode
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true, // is_v_rowmajor
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mask.type,
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enable_alibi ? bias_enum::alibi : bias_enum::no_bias,
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has_lse,
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has_dropout,
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false}; // do_fp8_static_quant
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}
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fmha_fwd_args get_ck_fmha_fwd_args(bool has_lse,
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bool has_dropout_randval,
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const mask_info &mask,
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// sizes
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const int b,
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const int seqlen_q,
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const int seqlen_k,
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const int h,
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const int h_k,
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const int d,
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// device pointers
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const at::Tensor q,
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const at::Tensor k,
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const at::Tensor v,
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c10::optional<at::Tensor> &alibi_slopes_,
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at::Tensor out,
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at::Tensor softmax_lse,
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at::Tensor dropout_randval,
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float softmax_scale,
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float p_dropout,
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uint64_t drop_seed,
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uint64_t drop_offset)
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{
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// q: (batch_size, seqlen_q, nheads, d)
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// k: (batch_size, seqlen_k, nheads_k, d)
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// v: (batch_size, seqlen_k, nheads_k, d)
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// o: (batch_size, seqlen_q, nheads, d)
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// alibi_slopes:(batch_size, nheads) or (nhead)
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// lse: (batch_size, nheads, seqlen_q)
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// randval: (batch_size, nheads, seqlen_q, seqlen_k)
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ck_tile::index_t stride_q = q.stride(1);
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ck_tile::index_t stride_k = k.stride(1);
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ck_tile::index_t stride_v = v.stride(1);
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ck_tile::index_t stride_o = out.stride(1);
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ck_tile::index_t stride_randval = has_dropout_randval ? dropout_randval.stride(2) : 0;
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ck_tile::index_t nhead_stride_q = q.stride(2);
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ck_tile::index_t nhead_stride_k = k.stride(2);
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ck_tile::index_t nhead_stride_v = v.stride(2);
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ck_tile::index_t nhead_stride_o = out.stride(2);
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ck_tile::index_t nhead_stride_lse = has_lse ? softmax_lse.stride(1) : 0;
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ck_tile::index_t nhead_stride_randval = has_dropout_randval ? dropout_randval.stride(1) : 0;
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ck_tile::index_t batch_stride_q = q.stride(0);
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ck_tile::index_t batch_stride_k = k.stride(0);
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ck_tile::index_t batch_stride_v = v.stride(0);
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ck_tile::index_t batch_stride_o = out.stride(0);
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ck_tile::index_t batch_stride_lse = has_lse ? softmax_lse.stride(0) : 0;
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ck_tile::index_t batch_stride_randval = has_dropout_randval ? dropout_randval.stride(0) : 0;
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void *alibi_slopes_ptr = nullptr;
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ck_tile::index_t stride_alibi_slopes = 0;
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if (alibi_slopes_.has_value()) {
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auto alibi_slopes = alibi_slopes_.value();
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CHECK_DEVICE(alibi_slopes);
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TORCH_CHECK(alibi_slopes.stride(-1) == 1, "ALiBi slopes tensor must have contiguous last dimension");
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TORCH_CHECK(alibi_slopes.sizes() == torch::IntArrayRef({h}) || alibi_slopes.sizes() == torch::IntArrayRef({b, h}));
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alibi_slopes_ptr = alibi_slopes.data_ptr();
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stride_alibi_slopes = alibi_slopes.dim() == 2 ? alibi_slopes.stride(0) : 0;
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}
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return fmha_fwd_args{q.data_ptr(),
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k.data_ptr(),
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v.data_ptr(),
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alibi_slopes_ptr, // bias
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has_dropout_randval ? dropout_randval.data_ptr() : nullptr,
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nullptr, // lse_acc
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nullptr, // o_acc
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has_lse ? softmax_lse.data_ptr() : nullptr,
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out.data_ptr(),
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nullptr, // seqstart_q
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nullptr, // seqstart_k
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nullptr,
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seqlen_q,
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seqlen_k,
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b,
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seqlen_q, // max_seqlen_q
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d, // hdim_q
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d, // hdim_v
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h, // nhead
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h_k, // nhead_k
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1, // num_splits
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softmax_scale, // scale_s
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1, // scale_p
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1, // scale_o
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stride_q,
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stride_k,
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stride_v,
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stride_alibi_slopes,
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stride_randval,
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0, // stride_o_acc,
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stride_o,
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nhead_stride_q,
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nhead_stride_k,
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nhead_stride_v,
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0, // nhead_stride_bias, FA without bias
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nhead_stride_randval,
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nhead_stride_lse,
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0, // nhead_stride_lse_acc
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0, // nhead_stride_o_acc
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nhead_stride_o,
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batch_stride_q,
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batch_stride_k,
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batch_stride_v,
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0, // batch_stride_bias, FA without bias
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batch_stride_randval,
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batch_stride_lse,
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0, // batch_stride_lse_acc
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0, // batch_stride_o_acc
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batch_stride_o,
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0, // split_stride_lse_acc
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0, // split_stride_o_acc
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mask.left,
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mask.right,
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static_cast<ck_tile::index_t>(mask.type),
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p_dropout,
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has_dropout_randval,
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{drop_seed, drop_offset}};
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}
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std::vector<at::Tensor>
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mha_fwd(at::Tensor &q, // batch_size x seqlen_q x num_heads x head_size
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const at::Tensor &k, // batch_size x seqlen_k x num_heads_k x head_size
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const at::Tensor &v, // batch_size x seqlen_k x num_heads_k x head_size
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c10::optional<at::Tensor> &out_, // batch_size x seqlen_q x num_heads x head_size
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c10::optional<at::Tensor> &alibi_slopes_, // num_heads or batch_size x num_heads
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const float p_dropout,
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const float softmax_scale,
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bool is_causal,
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int window_size_left,
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int window_size_right,
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const float /*softcap*/,
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const bool return_dropout_randval,
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c10::optional<at::Generator> gen_)
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{
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auto q_dtype = q.dtype();
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TORCH_CHECK(q_dtype == torch::kFloat16 || q_dtype == torch::kBFloat16,
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"FlashAttention only support fp16 and bf16 data type");
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TORCH_CHECK(k.dtype() == q_dtype, "query and key must have the same dtype");
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TORCH_CHECK(v.dtype() == q_dtype, "query and value must have the same dtype");
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std::string q_dtype_str = q_dtype == torch::kFloat16 ? "fp16" : "bf16";
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CHECK_DEVICE(q); CHECK_DEVICE(k); CHECK_DEVICE(v);
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TORCH_CHECK(q.stride(-1) == 1, "Input tensor must have contiguous last dimension");
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TORCH_CHECK(k.stride(-1) == 1, "Input tensor must have contiguous last dimension");
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TORCH_CHECK(v.stride(-1) == 1, "Input tensor must have contiguous last dimension");
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const auto sizes = q.sizes();
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const int batch_size = sizes[0];
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int seqlen_q = sizes[1];
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int num_heads = sizes[2];
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const int head_size_og = sizes[3];
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const int seqlen_k = k.size(1);
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const int num_heads_k = k.size(2);
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TORCH_CHECK(batch_size > 0, "batch size must be positive");
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TORCH_CHECK(head_size_og <= 256, "CK only supports head dimension at most 256");
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TORCH_CHECK(num_heads % num_heads_k == 0, "Number of heads in key/value must divide number of heads in query");
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if (window_size_left >= seqlen_k) { window_size_left = -1; }
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if (window_size_right >= seqlen_k) { window_size_right = -1; }
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// causal=true is the same as causal=false in this case
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if (seqlen_q == 1 && !alibi_slopes_.has_value()) { is_causal = false; }
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mask_info mask;
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if (is_causal) {
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// Causal is the special case where window_size_right == 0 and window_size_left < 0.
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window_size_right = 0;
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std::string mask_identify = "b:" + std::to_string(window_size_left) + "," + "0";
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mask = mask_info::decode(mask_identify, seqlen_q, seqlen_k); // casual
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}
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else if (window_size_left == -1 && window_size_right == -1) {
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mask = mask_info::decode("0", seqlen_q, seqlen_k); // no mask
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}
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else {
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// Local is the more general case where window_size_right >= 0 or window_size_left >= 0.
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std::string mask_identify = "b:" + std::to_string(window_size_left) + "," + std::to_string(window_size_right);
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mask = mask_info::decode(mask_identify, seqlen_q, seqlen_k); // local
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}
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// Faster to transpose q from (b, 1, (nheads_kv ngroups), d) to (b, ngroups, nheads_kv, d) in this case
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// H/t Daniel Haziza
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const int seqlenq_ngroups_swapped = seqlen_q == 1 && num_heads > num_heads_k && window_size_left < 0 && window_size_right < 0 && p_dropout == 0.f && head_size_og % 8 == 0 && !alibi_slopes_.has_value();
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const int ngroups = num_heads / num_heads_k;
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if (seqlenq_ngroups_swapped) {
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q = q.reshape({batch_size, num_heads_k, ngroups, head_size_og}).transpose(1, 2);
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seqlen_q = ngroups;
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num_heads = num_heads_k;
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}
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CHECK_SHAPE(q, batch_size, seqlen_q, num_heads, head_size_og);
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CHECK_SHAPE(k, batch_size, seqlen_k, num_heads_k, head_size_og);
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CHECK_SHAPE(v, batch_size, seqlen_k, num_heads_k, head_size_og);
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at::Tensor q_padded, k_padded, v_padded;
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if (head_size_og % 8 != 0) {
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q_padded = torch::nn::functional::pad(q, torch::nn::functional::PadFuncOptions({0, 8 - head_size_og % 8}));
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k_padded = torch::nn::functional::pad(k, torch::nn::functional::PadFuncOptions({0, 8 - head_size_og % 8}));
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v_padded = torch::nn::functional::pad(v, torch::nn::functional::PadFuncOptions({0, 8 - head_size_og % 8}));
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}
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else {
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q_padded = q;
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k_padded = k;
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v_padded = v;
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}
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at::Tensor out;
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if (out_.has_value()) {
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out = out_.value();
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TORCH_CHECK(out.dtype() == q_dtype, "Output must have the same dtype as inputs");
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CHECK_DEVICE(out);
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TORCH_CHECK(out.stride(-1) == 1, "Output tensor must have contiguous last dimension");
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CHECK_SHAPE(out, batch_size, sizes[1], sizes[2], head_size_og);
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if (seqlenq_ngroups_swapped) {
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out = out.reshape({batch_size, num_heads_k, ngroups, head_size_og}).transpose(1, 2);
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}
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if (head_size_og % 8 != 0) { out = torch::empty_like(q_padded); }
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}
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else {
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out = torch::empty_like(q_padded);
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}
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auto round_multiple = [](int x, int m) { return (x + m - 1) / m * m; };
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const int head_size_8x = round_multiple(head_size_og, 8);
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// Otherwise the kernel will be launched from cuda:0 device
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// Cast to char to avoid compiler warning about narrowing
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at::cuda::CUDAGuard device_guard{(char)q.get_device()};
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auto opts = q.options();
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bool has_lse = true;
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bool has_dropout = p_dropout > 0.0f;
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at::Tensor softmax_lse;
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// TODO - check gradient, only training require lse
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softmax_lse = torch::empty({batch_size, num_heads, seqlen_q}, opts.dtype(torch::kFloat32));
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at::Tensor p;
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if (return_dropout_randval) {
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TORCH_CHECK(has_dropout, "return_dropout_randval require p_dropout > 0");
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p = torch::empty({batch_size, num_heads, seqlen_q, seqlen_k}, opts.dtype(torch::kUInt8));
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}
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uint64_t drop_seed = 1, drop_offset = 0;
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int64_t counter_offset = batch_size * num_heads * ck_tile::get_warp_size();
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auto options = torch::TensorOptions().dtype(torch::kFloat32).device(torch::kCUDA);
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auto rng_state = torch::empty({2}, options.dtype(torch::kInt64));
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if (p_dropout > 0.0) {
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auto gen = at::get_generator_or_default<at::CUDAGeneratorImpl>(
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gen_, at::cuda::detail::getDefaultCUDAGenerator());
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// See Note [Acquire lock when using random generators]
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std::lock_guard<std::mutex> lock(gen->mutex_);
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auto philox_args = gen->philox_cuda_state(counter_offset);
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std::tie(drop_seed, drop_offset) = flash::unpack(philox_args);
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}
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rng_state[0] = *(reinterpret_cast<int64_t*>(&drop_seed));
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rng_state[1] = *(reinterpret_cast<int64_t*>(&drop_offset));
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if (seqlen_k > 0) {
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auto stream = at::cuda::getCurrentHIPStream().stream();
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ck_tile::stream_config stream_config{stream};
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auto traits =
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get_ck_fmha_fwd_traits(mask, q_dtype_str, head_size_8x, has_dropout, has_lse, alibi_slopes_.has_value());
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auto args =
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get_ck_fmha_fwd_args(
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has_lse,
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return_dropout_randval,
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mask,
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batch_size,
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seqlen_q,
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seqlen_k,
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num_heads,
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num_heads_k,
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head_size_8x,
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q_padded,
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k_padded,
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v_padded,
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alibi_slopes_,
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out,
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softmax_lse,
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p,
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softmax_scale,
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p_dropout,
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drop_seed,
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drop_offset);
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fmha_fwd(traits, args, stream_config);
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}
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else {
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// If seqlen_k == 0, then we have an empty tensor. We need to set the output to 0.
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out.zero_();
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softmax_lse.fill_(std::numeric_limits<float>::infinity());
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}
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at::Tensor out_padded = out;
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if (head_size_og % 8 != 0) {
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out = out.index({"...", torch::indexing::Slice(torch::indexing::None, head_size_og)});
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if (out_.has_value()) { out_.value().copy_(out); }
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}
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if (seqlenq_ngroups_swapped) {
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out = out.transpose(1, 2).reshape({batch_size, 1, num_heads_k * seqlen_q, head_size_og});
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out_padded = out_padded.transpose(1, 2).reshape({batch_size, 1, num_heads_k * seqlen_q, head_size_og});
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q_padded = q_padded.transpose(1, 2).reshape({batch_size, 1, num_heads_k * seqlen_q, head_size_og});
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softmax_lse = softmax_lse.reshape({batch_size, num_heads_k * seqlen_q, 1});
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}
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return {out, q_padded, k_padded, v_padded, out_padded, softmax_lse, p, rng_state};
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}
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