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https://github.com/ml-explore/mlx.git
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Add sdpa file
This commit is contained in:
Jagrit Digani
464
mlx/backend/cuda/scaled_dot_product_attention.cu
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464
mlx/backend/cuda/scaled_dot_product_attention.cu
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// Copyright © 2025 Apple Inc.
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#include "mlx/backend/cuda/device.h"
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#include "mlx/backend/cuda/device/config.h"
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#include "mlx/backend/cuda/kernel_utils.cuh"
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#include "mlx/backend/cuda/lru_cache.h"
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#include "mlx/backend/gpu/copy.h"
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#include "mlx/dtype_utils.h"
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#include "mlx/fast_primitives.h"
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// cudnn_frontend.h redefines this macro.
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#undef CHECK_CUDA_ERROR
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#include <cudnn_frontend.h>
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#include <fmt/format.h>
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#include <nvtx3/nvtx3.hpp>
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namespace fe = cudnn_frontend;
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namespace mlx::core {
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namespace cu {} // namespace cu
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namespace {
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struct SDPACacheKey {
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int device_id;
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fe::DataType_t cudnn_type;
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int B;
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int H;
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int D;
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int qL;
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int kL;
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int gqa_factor;
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float scale;
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int64_t Q_strides[3];
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int64_t K_strides[3];
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int64_t V_strides[3];
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int64_t O_strides[3];
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bool generate_stats;
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bool causal_mask;
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};
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auto& sdpa_cache() {
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static LRUBytesKeyCache<SDPACacheKey, std::shared_ptr<fe::graph::Graph>>
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cache(
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/* capacity */ 128);
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return cache;
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}
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#define Q_UID 1
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#define K_UID 2
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#define V_UID 3
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#define O_UID 4
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#define STATS_UID 5
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std::shared_ptr<fe::graph::Graph> get_sdpa_forward_graph(
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cu::CommandEncoder& encoder,
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const SDPACacheKey& cache_key) {
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// Check if graph has already been fully built
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if (auto it = sdpa_cache().find(cache_key); it != sdpa_cache().end()) {
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return it->second;
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}
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// Set up new graph
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auto graph = std::make_shared<fe::graph::Graph>();
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graph->set_io_data_type(cache_key.cudnn_type)
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.set_intermediate_data_type(fe::DataType_t::FLOAT)
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.set_compute_data_type(fe::DataType_t::FLOAT);
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auto Q = graph->tensor(
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fe::graph::Tensor_attributes()
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.set_name("Q")
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.set_uid(Q_UID)
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.set_dim({cache_key.B, cache_key.H, cache_key.qL, cache_key.D})
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.set_stride(
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{cache_key.Q_strides[0],
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cache_key.Q_strides[1],
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cache_key.Q_strides[2],
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1}));
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int h_kv = cache_key.H / cache_key.gqa_factor;
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auto K =
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graph->tensor(fe::graph::Tensor_attributes()
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.set_name("K")
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.set_uid(K_UID)
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.set_dim({cache_key.B, h_kv, cache_key.kL, cache_key.D})
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.set_stride(
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{cache_key.K_strides[0],
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cache_key.K_strides[1],
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cache_key.V_strides[2],
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1}));
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auto V =
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graph->tensor(fe::graph::Tensor_attributes()
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.set_name("V")
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.set_uid(V_UID)
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.set_dim({cache_key.B, h_kv, cache_key.kL, cache_key.D})
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.set_stride(
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{cache_key.V_strides[0],
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cache_key.V_strides[1],
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cache_key.V_strides[2],
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1}));
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auto sdpa_options = fe::graph::SDPA_attributes()
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.set_name("flash_attention")
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.set_is_inference(!cache_key.generate_stats)
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.set_attn_scale(cache_key.scale);
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if (cache_key.causal_mask && cache_key.qL > 1) {
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sdpa_options.set_diagonal_alignment(fe::DiagonalAlignment_t::TOP_LEFT)
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.set_diagonal_band_right_bound(0);
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}
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auto [O, Stats] = graph->sdpa(Q, K, V, sdpa_options);
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O->set_output(true)
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.set_uid(O_UID)
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.set_dim({cache_key.B, cache_key.H, cache_key.qL, cache_key.D})
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.set_stride(
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{cache_key.O_strides[0],
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cache_key.O_strides[1],
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cache_key.O_strides[2],
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1});
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if (cache_key.generate_stats) {
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Stats->set_output(true)
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.set_data_type(fe::DataType_t::FLOAT)
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.set_uid(STATS_UID);
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}
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// Build and Validate cudnn graph
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auto handle = encoder.device().cudnn_handle();
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// cuDNN only supports native CUDA graphs for sdpa in 9.6 or above.
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if (cudnnGetVersion() < 90600) {
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auto build_status = graph->build(handle, {fe::HeurMode_t::A});
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if (!build_status.is_good()) {
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throw std::runtime_error(
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"Unable to build cudnn graph for attention."
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" Failed with message: " +
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build_status.get_message());
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}
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} else {
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auto val_status = graph->validate();
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auto op_status = graph->build_operation_graph(handle);
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auto plan_stauts =
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graph->create_execution_plans({cudnn_frontend::HeurMode_t::A});
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if (!plan_stauts.is_good()) {
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throw std::runtime_error(
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"Unable to create exec plan for cudnn attention."
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" Failed with message: " +
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plan_stauts.get_message());
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}
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graph->select_behavior_notes(
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{cudnn_frontend::BehaviorNote_t::SUPPORTS_CUDA_GRAPH_NATIVE_API});
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auto support_status = graph->check_support(handle);
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if (!support_status.is_good()) {
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throw std::runtime_error(
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"No cuda graph support for cudnn attention."
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" Failed with message: " +
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support_status.get_message());
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}
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auto build_status = graph->build_plans(handle);
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if (!build_status.is_good()) {
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throw std::runtime_error(
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"Unable to build cudnn graph for attention."
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" Failed with message: " +
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build_status.get_message());
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}
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}
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auto [it, _] = sdpa_cache().emplace(cache_key, graph);
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return it->second;
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}
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inline fe::DataType_t dtype_to_cudnn_type(Dtype dtype) {
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switch (dtype) {
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case int8:
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return fe::DataType_t::INT8;
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case int32:
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return fe::DataType_t::INT32;
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case uint8:
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return fe::DataType_t::UINT8;
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case float16:
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return fe::DataType_t::HALF;
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case bfloat16:
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return fe::DataType_t::BFLOAT16;
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case float32:
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return fe::DataType_t::FLOAT;
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case float64:
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return fe::DataType_t::DOUBLE;
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default:
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throw std::runtime_error(fmt::format(
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"Unsupported dtype in SDPA: {}.", dtype_to_string(dtype)));
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}
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}
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void sdpa_cudnn(
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const Stream& s,
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cu::CommandEncoder& encoder,
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const array& q,
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const array& k,
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const array& v,
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const float scale,
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array& o,
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bool do_causal_ = false) {
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encoder.set_input_array(q);
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encoder.set_input_array(k);
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encoder.set_input_array(v);
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encoder.set_output_array(o);
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auto cudnn_type = dtype_to_cudnn_type(q.dtype());
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int B = q.shape(0);
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int H = q.shape(1);
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int D = q.shape(3);
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int gqa_factor = q.shape(1) / k.shape(1);
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int qL = q.shape(2);
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int kL = k.shape(2);
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SDPACacheKey cache_key{
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/* int device_id = */ encoder.device().cuda_device(),
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/* fe::DataType_t cudnn_type = */ cudnn_type,
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/* int B = */ B,
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/* int H = */ H,
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/* int D = */ D,
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/* int qL = */ qL,
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/* int kL = */ kL,
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/* int gqa_factor = */ gqa_factor,
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/* float scale = */ scale,
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/* int64_t Q_strides[3] = */ {q.strides(0), q.strides(1), q.strides(2)},
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/* int64_t K_strides[3] = */ {k.strides(0), k.strides(1), k.strides(2)},
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/* int64_t V_strides[3] = */ {v.strides(0), v.strides(1), v.strides(2)},
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/* int64_t O_strides[3] = */ {o.strides(0), o.strides(1), o.strides(2)},
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/* bool generate_stats = */ false,
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/* bool causal_mask = */ do_causal_};
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auto graph = get_sdpa_forward_graph(encoder, cache_key);
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int64_t workspace_size = 0;
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auto workspace_status = graph->get_workspace_size(workspace_size);
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if (!workspace_status.is_good()) {
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throw std::runtime_error("Unable to get workspace for cudnn attention.");
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}
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array workspace(
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allocator::malloc(workspace_size), {int(workspace_size)}, uint8);
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auto workspace_ptr = workspace.data<void>();
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std::unordered_map<int64_t, void*> variant_pack = {
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{Q_UID, const_cast<void*>(q.data<void>())},
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{K_UID, const_cast<void*>(k.data<void>())},
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{V_UID, const_cast<void*>(v.data<void>())},
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{O_UID, o.data<void>()}};
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auto handle = encoder.device().cudnn_handle();
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cudnnSetStream(handle, encoder.stream());
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// cuDNN only supports native CUDA graphs for sdpa in 9.6 or above.
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if (cudnnGetVersion() < 90600) {
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auto capture = encoder.capture_context();
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auto exec_status = graph->execute(handle, variant_pack, workspace_ptr);
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if (!exec_status.is_good()) {
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capture.discard = true;
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throw std::runtime_error(
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"Unable to execute cudnn attention."
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" Failed with message: " +
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exec_status.get_message());
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}
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} else {
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cudaGraph_t cu_graph;
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cudaGraphCreate(&cu_graph, 0);
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std::unique_ptr<cudaGraph_t, void (*)(cudaGraph_t*)> graph_freer(
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&cu_graph, [](cudaGraph_t* p) { cudaGraphDestroy(*p); });
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auto cu_graph_status = graph->populate_cuda_graph(
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handle, variant_pack, workspace_ptr, cu_graph);
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if (!cu_graph_status.is_good()) {
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throw std::runtime_error(
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"Unable to add cuda graph for cudnn attention."
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" Failed with message: " +
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cu_graph_status.get_message());
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}
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encoder.add_graph_node(cu_graph);
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}
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encoder.add_temporary(workspace);
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}
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} // namespace
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namespace fast {
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bool ScaledDotProductAttention::use_fallback(
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const array& q,
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const array& k,
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const array& v,
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bool has_mask,
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bool has_arr_mask,
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bool do_causal,
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Stream s) {
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if (s.device == Device::cpu) {
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return true;
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}
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auto& cu_device = cu::device(s.device);
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if (cu_device.compute_capability_major() < 8) {
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return true;
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}
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const int value_head_dim = v.shape(-1);
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const int query_head_dim = q.shape(-1);
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const int query_sequence_length = q.shape(2);
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const int key_sequence_length = k.shape(2);
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const bool sdpa_supported_head_dim = query_head_dim == value_head_dim &&
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(query_head_dim == 64 || query_head_dim == 96 || query_head_dim == 128);
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const bool supported_dtype = q.dtype() == float16 || q.dtype() == bfloat16;
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const bool supported_config = supported_dtype && sdpa_supported_head_dim;
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return has_arr_mask || !supported_config;
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}
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void ScaledDotProductAttention::eval_gpu(
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const std::vector<array>& inputs,
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array& out) {
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nvtx3::scoped_range r("ScaledDotProductAttention::eval_gpu");
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auto& s = stream();
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auto& encoder = cu::get_command_encoder(s);
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auto& q_pre = inputs[0];
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auto& k_pre = inputs[1];
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auto& v_pre = inputs[2];
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auto& o = out;
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std::vector<array> copies;
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// Define some copy functions to ensure the layout of the inputs is as
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// expected.
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copies.reserve(3);
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auto copy_unless = [&copies, &s](
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auto predicate, const array& arr) -> const array& {
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if (!predicate(arr)) {
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array arr_copy = contiguous_copy_gpu(arr, s);
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copies.push_back(std::move(arr_copy));
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return copies.back();
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} else {
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return arr;
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}
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};
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auto is_matrix_contiguous = [](const array& arr) {
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return arr.strides(-1) == 1;
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};
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// We are in vector mode ie single query
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if (q_pre.shape(2) <= 1) {
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auto q_copy_unless = [](const array& arr) {
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if (arr.flags().row_contiguous) {
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return true;
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}
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auto& strides = arr.strides();
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auto& shape = arr.shape();
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if (shape[0] == 1 || shape[1] == 1) {
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// If either the batch or head dimension is a singleton, the other can
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// be transposed with the sequence dimension
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auto bidx = shape[0] == 1 ? 1 : 0;
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return (strides[3] == 1) && (strides[2] == shape[3] * shape[bidx]) &&
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(strides[bidx] == shape[3]);
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}
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return false;
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};
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auto kv_copy_unless = [](const array& arr) {
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// keys and values should be copied if:
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// - the last dimension is not contiguous
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// - the batch and head dim are not contiguous
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auto& strides = arr.strides();
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auto& shape = arr.shape();
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if (strides.back() != 1) {
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return false;
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}
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if (shape[0] == 1 || shape[1] == 1) {
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return true;
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}
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return (strides[0] == strides[1] * shape[1]);
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};
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const auto& q = copy_unless(q_copy_unless, q_pre);
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const auto& k = copy_unless(kv_copy_unless, k_pre);
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const auto& v = copy_unless(kv_copy_unless, v_pre);
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for (const auto& cp : copies) {
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encoder.add_temporary(cp);
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}
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// Donate the query if possible
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if (q.is_donatable() && q.flags().row_contiguous && q.size() == o.size()) {
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o.copy_shared_buffer(q);
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} else {
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o.set_data(allocator::malloc(o.nbytes()));
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}
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return sdpa_cudnn(s, encoder, q, k, v, scale_, o, do_causal_);
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}
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// Full attention mode
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else {
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const auto& q = copy_unless(is_matrix_contiguous, q_pre);
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const auto& k = copy_unless(is_matrix_contiguous, k_pre);
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const auto& v = copy_unless(is_matrix_contiguous, v_pre);
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int64_t str_oD = 1;
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int64_t str_oH = o.shape(3);
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int64_t str_oL = o.shape(1) * str_oH;
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int64_t str_oB = o.shape(2) * str_oL;
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size_t data_size = o.shape(0) * str_oB;
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array::Flags flags{
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/* bool contiguous = */ 1,
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/* bool row_contiguous = */ 0,
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/* bool col_contiguous = */ 0,
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};
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o.set_data(
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allocator::malloc(o.nbytes()),
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data_size,
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{str_oB, str_oH, str_oL, str_oD},
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flags);
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return sdpa_cudnn(s, encoder, q, k, v, scale_, o, do_causal_);
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}
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}
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} // namespace fast
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} // namespace mlx::core
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