fmha_api.cpp

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#include <torch/extension.h>
#include <ATen/cuda/CUDAContext.h>

#include "fmha.h"

void set_params(Fused_multihead_attention_fprop_params &params,
                // sizes
                const size_t b,
                const size_t s,
                const size_t h,
                const size_t d,
                // device pointers
                void *qkv_packed_d,
                void *cu_seqlens_d,
                void *o_packed_d,
                void *s_d,
                float p_dropout) {

    Data_type acc_type = DATA_TYPE_FP32;
    Data_type data_type = DATA_TYPE_FP16;

    // Reset the parameters
    memset(&params, 0, sizeof(params));

    // Set the pointers and strides.
    params.qkv_ptr = qkv_packed_d;
    params.qkv_stride_in_bytes = get_size_in_bytes(h * 3 * d, data_type);
    params.o_ptr = o_packed_d;
    params.o_stride_in_bytes = get_size_in_bytes(h * d, data_type);

    params.cu_seqlens = static_cast<int *>(cu_seqlens_d);

    // S = softmax(P)
    params.s_ptr = s_d;
    params.s_stride_in_bytes = get_size_in_bytes(b * h * s, data_type);

    // Set the dimensions.
    params.b = b;
    params.h = h;
    params.s = s;
    params.d = d;

    // Set the different scale values.
    const float scale_bmm1 = 1.f / sqrtf(d);
    constexpr float scale_softmax = 1.f;
    constexpr float scale_bmm2 = 1.f;

    set_alpha(params.scale_bmm1, scale_bmm1, acc_type);
    set_alpha(params.scale_softmax, scale_softmax, acc_type);
    set_alpha(params.scale_bmm2, scale_bmm2, data_type);

    // Set this to probability of keeping an element to simplify things.
    params.p_dropout = 1.f - p_dropout;
    params.rp_dropout = 1.f / params.p_dropout;
    TORCH_CHECK(p_dropout < 1.f);
    set_alpha(params.scale_dropout, params.rp_dropout, data_type);
}

std::vector<at::Tensor>
mha_fwd(const at::Tensor &qkv,  // total x num_heads x 3 x head_size, total := \sum_{i=0}^{b} s_i
        const at::Tensor &cu_seqlens,  // b+1
        const float p_dropout,
        const int max_seq_len,
        const bool is_training,
        const bool zero_tensors,
        c10::optional<at::Generator> gen_) {
    auto dprops = at::cuda::getCurrentDeviceProperties();
    TORCH_CHECK(dprops->major == 8 && dprops->minor == 0);
    int seq_len = 512;
    auto launch = &run_fmha_fp16_512_64_sm80;
    if( max_seq_len <= 128 ) {
        seq_len = 128;
        launch = &run_fmha_fp16_128_64_sm80;
    } else if( max_seq_len <= 256 ) {
        seq_len = 256;
        launch = &run_fmha_fp16_256_64_sm80;
    } else if( max_seq_len <= 384 ) {
        seq_len = 384;
        launch = &run_fmha_fp16_384_64_sm80;
    } else if( max_seq_len <= 512 ) {
        seq_len = 512;
        launch = &run_fmha_fp16_512_64_sm80;
    } else {
        TORCH_CHECK(false);
    }

    constexpr int warps_m = 1;
    constexpr int warps_n = 4;  // this leads to an upper bound
    const int mmas_m = seq_len / 16 / warps_m;
    const int mmas_n = seq_len / 16 / warps_n;
    
    const int elts_per_thread = 8 * mmas_m * mmas_n;

    auto stream = at::cuda::getCurrentCUDAStream().stream();

    TORCH_CHECK(qkv.dtype() == torch::kFloat16);
    TORCH_CHECK(cu_seqlens.dtype() == torch::kInt32);

    TORCH_CHECK(qkv.is_cuda())
    TORCH_CHECK(cu_seqlens.is_cuda())

    TORCH_CHECK(qkv.is_contiguous())
    TORCH_CHECK(cu_seqlens.is_contiguous())

    TORCH_CHECK(cu_seqlens.dim() == 1);
    TORCH_CHECK(qkv.dim() == 4);

    const auto sizes = qkv.sizes();

    TORCH_CHECK(sizes[THREE_DIM] == 3);

    const int batch_size = cu_seqlens.numel() - 1;
    const int total = sizes[TOTAL_DIM];
    const int num_heads = sizes[H_DIM];
    const int head_size = sizes[D_DIM];
    TORCH_CHECK(batch_size > 0);
    TORCH_CHECK(head_size == 64);
    auto opts = qkv.options();

    auto ctx = torch::empty({ total, num_heads, head_size }, opts);

    auto s = torch::empty({ batch_size, num_heads, seq_len, seq_len }, opts);

    if( zero_tensors ) {
        ctx.zero_();
        s.zero_();
    }

    auto gen = at::get_generator_or_default<at::CUDAGeneratorImpl>(
        gen_, at::cuda::detail::getDefaultCUDAGenerator());

    Fused_multihead_attention_fprop_params params;

    set_params(params,
               batch_size,
               seq_len,
               num_heads,
               head_size,
               qkv.data_ptr(),
               cu_seqlens.data_ptr(),
               ctx.data_ptr(),
               s.data_ptr(),
               p_dropout);

    // number of times random will be generated per thread, to offset philox counter in the random
    // state
    int64_t counter_offset = elts_per_thread;
    at::PhiloxCudaState rng_engine_inputs;

    if( is_training ) {
        // See Note [Acquire lock when using random generators]
        std::lock_guard<std::mutex> lock(gen->mutex_);
        params.philox_args = gen->philox_cuda_state(counter_offset);
    }

    launch(params, is_training, stream);

    return { ctx, s };
}

std::vector<at::Tensor>
mha_bwd(const at::Tensor &dout,  // total x num_heads, x head_size
        const at::Tensor &qkv,   // total x num_heads x 3 x head_size, total := \sum_{i=0}^{b} s_i
        at::Tensor &softmax,     // b x h x s x s softmax and dmask - will be overwritten with dP
        const at::Tensor &cu_seqlens,  // b+1
        const float p_dropout,         // probability to drop
        const int max_seq_len,          // max sequence length to choose the kernel
        const bool zero_tensors
) {
    auto dprops = at::cuda::getCurrentDeviceProperties();
    TORCH_CHECK(dprops->major == 8 && dprops->minor == 0);
    int seq_len = 512;
    auto launch = &run_fmha_dgrad_fp16_512_64_sm80;
    if( max_seq_len <= 128 ) {
        seq_len = 128;
        launch = &run_fmha_dgrad_fp16_128_64_sm80;
    } else if( max_seq_len <= 256 ) {
        seq_len = 256;
        launch = &run_fmha_dgrad_fp16_256_64_sm80;
    } else if( max_seq_len <= 384 ) {
        seq_len = 384;
        launch = &run_fmha_dgrad_fp16_384_64_sm80;
    } else if( max_seq_len <= 512 ) {
        seq_len = 512;
        launch = &run_fmha_dgrad_fp16_512_64_sm80;
    } else {
        TORCH_CHECK(false);
    }

    auto stream = at::cuda::getCurrentCUDAStream().stream();

    TORCH_CHECK(qkv.dtype() == torch::kFloat16);
    TORCH_CHECK(dout.dtype() == torch::kFloat16);
    TORCH_CHECK(softmax.dtype() == torch::kFloat16);
    TORCH_CHECK(cu_seqlens.dtype() == torch::kInt32);

    TORCH_CHECK(qkv.is_cuda());
    TORCH_CHECK(cu_seqlens.is_cuda());

    TORCH_CHECK(qkv.is_contiguous());
    TORCH_CHECK(cu_seqlens.is_contiguous());

    TORCH_CHECK(cu_seqlens.dim() == 1);
    TORCH_CHECK(qkv.dim() == 4);

    const auto sizes = qkv.sizes();

    TORCH_CHECK(sizes[THREE_DIM] == 3);

    const int batch_size = cu_seqlens.numel() - 1;
    const int num_heads = sizes[H_DIM];
    const int head_size = sizes[D_DIM];
    TORCH_CHECK(batch_size > 0);
    TORCH_CHECK(head_size == 64);

    auto dqkv = torch::empty_like(qkv);

    if( zero_tensors ) {
        dqkv.zero_();
    }

    Fused_multihead_attention_fprop_params params;

    set_params(params,
               batch_size,
               seq_len,
               num_heads,
               head_size,
               qkv.data_ptr(),
               cu_seqlens.data_ptr(),
               dout.data_ptr(),     // we set o_ptr to dout
               softmax.data_ptr(),  // softmax gets overwritten by dP!
               p_dropout);

    // we're re-using these scales
    Data_type acc_type = DATA_TYPE_FP32;
    set_alpha(params.scale_bmm1, 1.f, acc_type);
    set_alpha(params.scale_softmax, 1.f / sqrtf(head_size), acc_type);
    set_alpha(params.scale_bmm2, 1.f, DATA_TYPE_FP16);
    params.dqkv_ptr = dqkv.data_ptr();

    launch(params, stream);
    return { dqkv, softmax };
}

std::vector<at::Tensor> mha_fwd_nl(const at::Tensor &qkv,         // total x num_heads x 3 x head_size, total := \sum_{i=0}^{b} s_i
                                const at::Tensor &cu_seqlens,  // b+1
                                const float p_dropout,
                                const int max_seq_len,
                                const bool is_training,
                                const bool zero_tensors,
                                c10::optional<at::Generator> gen_) {
    int seq_len = 512;
    auto launch = &run_fmha_fp16_512_64_sm80_nl;
    TORCH_CHECK(max_seq_len == seq_len);

    constexpr int warps_m = 1;
    constexpr int warps_n = 4;  // this leads to an upper bound
    const int mmas_m = seq_len / 16 / warps_m;
    const int mmas_n = seq_len / 16 / warps_n;
    // static_assert( mmas_m == 32 );
    // static_assert( mmas_n == 4 );
    const int elts_per_thread = 8 * mmas_m * mmas_n;

    auto stream = at::cuda::getCurrentCUDAStream().stream();

    TORCH_CHECK(qkv.is_cuda())
    TORCH_CHECK(cu_seqlens.is_cuda())

    TORCH_CHECK(qkv.is_contiguous())
    TORCH_CHECK(cu_seqlens.is_contiguous())

    TORCH_CHECK(cu_seqlens.dim() == 1);
    TORCH_CHECK(qkv.dim() == 4);

    const auto sizes = qkv.sizes();

    TORCH_CHECK(sizes[THREE_DIM] == 3);

    const int batch_size = cu_seqlens.numel() - 1;
    const int total = sizes[TOTAL_DIM];
    const int num_heads = sizes[H_DIM];
    const int head_size = sizes[D_DIM];
    TORCH_CHECK(batch_size > 0);
    TORCH_CHECK(head_size == 64);
    auto opts = qkv.options();

    auto ctx = torch::empty({ total, num_heads, head_size }, opts);

    auto s = torch::empty({ batch_size, num_heads, seq_len, seq_len }, opts);

    if( zero_tensors ) {
        ctx.zero_();
        s.zero_();
    }

    auto gen = at::get_generator_or_default<at::CUDAGeneratorImpl>(gen_, at::cuda::detail::getDefaultCUDAGenerator());

    Fused_multihead_attention_fprop_params params;

    set_params(params,
               batch_size,
               seq_len,
               num_heads,
               head_size,
               qkv.data_ptr(),
               cu_seqlens.data_ptr(),
               ctx.data_ptr(),
               s.data_ptr(),
               p_dropout);

    // number of times random will be generated per thread, to offset philox counter in the random
    // state
    int64_t counter_offset = elts_per_thread;
    at::PhiloxCudaState rng_engine_inputs;

    if( is_training ) {
        // See Note [Acquire lock when using random generators]
        std::lock_guard<std::mutex> lock(gen->mutex_);
        params.philox_args = gen->philox_cuda_state(counter_offset);
    }
    int num_chunks = 3;
    if(batch_size == 3) {
        num_chunks = 2;
    }

    launch(params, is_training, num_chunks, stream);

    return { ctx, s };
}

std::vector<at::Tensor> mha_bwd_nl(const at::Tensor &dout,        // total x num_heads, x head_size
                                const at::Tensor &qkv,         // total x num_heads x 3 x head_size, total := \sum_{i=0}^{b} s_i
                                at::Tensor &softmax,           // b x h x s x s softmax and dmask - will be overwritten with dP
                                const at::Tensor &cu_seqlens,  // b+1
                                const float p_dropout,         // probability to drop
                                const int max_seq_len,          // max sequence length to choose the kernel
                                const bool zero_tensors
) {

    auto stream = at::cuda::getCurrentCUDAStream().stream();

    TORCH_CHECK(qkv.is_cuda())
    TORCH_CHECK(cu_seqlens.is_cuda())

    TORCH_CHECK(qkv.is_contiguous())
    TORCH_CHECK(cu_seqlens.is_contiguous())

    TORCH_CHECK(cu_seqlens.dim() == 1);

    TORCH_CHECK(qkv.dim() == 4);

    const auto sizes = qkv.sizes();

    TORCH_CHECK(sizes[THREE_DIM] == 3);

    const int batch_size = cu_seqlens.numel() - 1;
    
    const int total = sizes[TOTAL_DIM];
    const int num_heads = sizes[H_DIM];
    const int head_size = sizes[D_DIM];
    TORCH_CHECK(batch_size > 0);
    TORCH_CHECK(head_size == 64);

    int seq_len = 512;
    auto launch = &run_fmha_dgrad_fp16_512_64_sm80_nl;

    auto opts = qkv.options();

    auto dqkv = torch::empty_like(qkv);

    if( zero_tensors ) {
        dqkv.zero_();
    }
    
    int num_chunks = 2;
    if( batch_size == 1 ) {
        num_chunks = 4;
    }else if( batch_size == 2 ) {
        num_chunks = 3;
    }
    auto dkv = torch::empty({total, num_chunks, 2, num_heads, head_size}, opts);

    Fused_multihead_attention_fprop_params params;

    set_params(params,
               batch_size,
               seq_len,
               num_heads,
               head_size,
               qkv.data_ptr(),
               cu_seqlens.data_ptr(),
               dout.data_ptr(),     // o_ptr = dout
               softmax.data_ptr(),  // softmax gets overwritten by dP!
               p_dropout);

    params.dkv_ptr = dkv.data_ptr();

    Data_type acc_type = DATA_TYPE_FP32;
    set_alpha(params.scale_bmm1, 1.f, acc_type);
    set_alpha(params.scale_softmax, 1.f / sqrtf(head_size), acc_type);
    set_alpha(params.scale_bmm2, 1.f, DATA_TYPE_FP16);
    params.dqkv_ptr = dqkv.data_ptr();

    launch(params, num_chunks, stream);

    //SPLIT-K reduction of num_chunks dK, dV parts

    // The equivalent of the following Pytorch code:
    // using namespace torch::indexing;
    // at::Tensor view_out = dqkv.index({Slice(), Slice(1, None, None)});
    // torch::sum_out(view_out, dkv, 1);

    const int hidden_size = num_heads * head_size;
    fmha_run_noloop_reduce(
        dqkv.data_ptr(), dkv.data_ptr(), cu_seqlens.data_ptr<int>(), hidden_size, batch_size, total, num_chunks, stream);

    return { dqkv, softmax, dkv };
}

PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {
    m.doc() = "Fused Multi-head Self-attention for BERT";  
    m.def("fwd", &mha_fwd, "Forward pass");
    m.def("bwd", &mha_bwd, "Backward pass");
    m.def("fwd_nl", &mha_fwd_nl, "Forward pass (small-batch)");
    m.def("bwd_nl", &mha_bwd_nl, "Backward pass (small-batch)");
}