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Add sdpa file

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This commit is contained in:
Jagrit Digani
2025-07-25 12:30:50 -07:00
parent d8ed6c1aa3
commit e74bcdc5e3
1 changed files with 464 additions and 0 deletions
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mlx/backend/cuda/scaled_dot_product_attention.cu Normal file
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// Copyright © 2025 Apple Inc.
#include "mlx/backend/cuda/device.h"
#include "mlx/backend/cuda/device/config.h"
#include "mlx/backend/cuda/kernel_utils.cuh"
#include "mlx/backend/cuda/lru_cache.h"
#include "mlx/backend/gpu/copy.h"
#include "mlx/dtype_utils.h"
#include "mlx/fast_primitives.h"
// cudnn_frontend.h redefines this macro.
#undef CHECK_CUDA_ERROR
#include <cudnn_frontend.h>
#include <fmt/format.h>
#include <nvtx3/nvtx3.hpp>
namespace fe = cudnn_frontend;
namespace mlx::core {
namespace cu {} // namespace cu
namespace {
struct SDPACacheKey {
int device_id;
fe::DataType_t cudnn_type;
int B;
int H;
int D;
int qL;
int kL;
int gqa_factor;
float scale;
int64_t Q_strides[3];
int64_t K_strides[3];
int64_t V_strides[3];
int64_t O_strides[3];
bool generate_stats;
bool causal_mask;
};
auto& sdpa_cache() {
static LRUBytesKeyCache<SDPACacheKey, std::shared_ptr<fe::graph::Graph>>
cache(
/* capacity */ 128);
return cache;
}
#define Q_UID 1
#define K_UID 2
#define V_UID 3
#define O_UID 4
#define STATS_UID 5
std::shared_ptr<fe::graph::Graph> get_sdpa_forward_graph(
cu::CommandEncoder& encoder,
const SDPACacheKey& cache_key) {
// Check if graph has already been fully built
if (auto it = sdpa_cache().find(cache_key); it != sdpa_cache().end()) {
return it->second;
}
// Set up new graph
auto graph = std::make_shared<fe::graph::Graph>();
graph->set_io_data_type(cache_key.cudnn_type)
.set_intermediate_data_type(fe::DataType_t::FLOAT)
.set_compute_data_type(fe::DataType_t::FLOAT);
auto Q = graph->tensor(
fe::graph::Tensor_attributes()
.set_name("Q")
.set_uid(Q_UID)
.set_dim({cache_key.B, cache_key.H, cache_key.qL, cache_key.D})
.set_stride(
{cache_key.Q_strides[0],
cache_key.Q_strides[1],
cache_key.Q_strides[2],
1}));
int h_kv = cache_key.H / cache_key.gqa_factor;
auto K =
graph->tensor(fe::graph::Tensor_attributes()
.set_name("K")
.set_uid(K_UID)
.set_dim({cache_key.B, h_kv, cache_key.kL, cache_key.D})
.set_stride(
{cache_key.K_strides[0],
cache_key.K_strides[1],
cache_key.V_strides[2],
1}));
auto V =
graph->tensor(fe::graph::Tensor_attributes()
.set_name("V")
.set_uid(V_UID)
.set_dim({cache_key.B, h_kv, cache_key.kL, cache_key.D})
.set_stride(
{cache_key.V_strides[0],
cache_key.V_strides[1],
cache_key.V_strides[2],
1}));
auto sdpa_options = fe::graph::SDPA_attributes()
.set_name("flash_attention")
.set_is_inference(!cache_key.generate_stats)
.set_attn_scale(cache_key.scale);
if (cache_key.causal_mask && cache_key.qL > 1) {
sdpa_options.set_diagonal_alignment(fe::DiagonalAlignment_t::TOP_LEFT)
.set_diagonal_band_right_bound(0);
}
auto [O, Stats] = graph->sdpa(Q, K, V, sdpa_options);
O->set_output(true)
.set_uid(O_UID)
.set_dim({cache_key.B, cache_key.H, cache_key.qL, cache_key.D})
.set_stride(
{cache_key.O_strides[0],
cache_key.O_strides[1],
cache_key.O_strides[2],
1});
if (cache_key.generate_stats) {
Stats->set_output(true)
.set_data_type(fe::DataType_t::FLOAT)
.set_uid(STATS_UID);
}
// Build and Validate cudnn graph
auto handle = encoder.device().cudnn_handle();
// cuDNN only supports native CUDA graphs for sdpa in 9.6 or above.
if (cudnnGetVersion() < 90600) {
auto build_status = graph->build(handle, {fe::HeurMode_t::A});
if (!build_status.is_good()) {
throw std::runtime_error(
"Unable to build cudnn graph for attention."
" Failed with message: " +
build_status.get_message());
}
} else {
auto val_status = graph->validate();
auto op_status = graph->build_operation_graph(handle);
auto plan_stauts =
graph->create_execution_plans({cudnn_frontend::HeurMode_t::A});
if (!plan_stauts.is_good()) {
throw std::runtime_error(
"Unable to create exec plan for cudnn attention."
" Failed with message: " +
plan_stauts.get_message());
}
graph->select_behavior_notes(
{cudnn_frontend::BehaviorNote_t::SUPPORTS_CUDA_GRAPH_NATIVE_API});
auto support_status = graph->check_support(handle);
if (!support_status.is_good()) {
throw std::runtime_error(
"No cuda graph support for cudnn attention."
" Failed with message: " +
support_status.get_message());
}
auto build_status = graph->build_plans(handle);
if (!build_status.is_good()) {
throw std::runtime_error(
"Unable to build cudnn graph for attention."
" Failed with message: " +
build_status.get_message());
}
}
auto [it, _] = sdpa_cache().emplace(cache_key, graph);
return it->second;
}
inline fe::DataType_t dtype_to_cudnn_type(Dtype dtype) {
switch (dtype) {
case int8:
return fe::DataType_t::INT8;
case int32:
return fe::DataType_t::INT32;
case uint8:
return fe::DataType_t::UINT8;
case float16:
return fe::DataType_t::HALF;
case bfloat16:
return fe::DataType_t::BFLOAT16;
case float32:
return fe::DataType_t::FLOAT;
case float64:
return fe::DataType_t::DOUBLE;
default:
throw std::runtime_error(fmt::format(
"Unsupported dtype in SDPA: {}.", dtype_to_string(dtype)));
}
}
void sdpa_cudnn(
const Stream& s,
cu::CommandEncoder& encoder,
const array& q,
const array& k,
const array& v,
const float scale,
array& o,
bool do_causal_ = false) {
encoder.set_input_array(q);
encoder.set_input_array(k);
encoder.set_input_array(v);
encoder.set_output_array(o);
auto cudnn_type = dtype_to_cudnn_type(q.dtype());
int B = q.shape(0);
int H = q.shape(1);
int D = q.shape(3);
int gqa_factor = q.shape(1) / k.shape(1);
int qL = q.shape(2);
int kL = k.shape(2);
SDPACacheKey cache_key{
/* int device_id = */ encoder.device().cuda_device(),
/* fe::DataType_t cudnn_type = */ cudnn_type,
/* int B = */ B,
/* int H = */ H,
/* int D = */ D,
/* int qL = */ qL,
/* int kL = */ kL,
/* int gqa_factor = */ gqa_factor,
/* float scale = */ scale,
/* int64_t Q_strides[3] = */ {q.strides(0), q.strides(1), q.strides(2)},
/* int64_t K_strides[3] = */ {k.strides(0), k.strides(1), k.strides(2)},
/* int64_t V_strides[3] = */ {v.strides(0), v.strides(1), v.strides(2)},
/* int64_t O_strides[3] = */ {o.strides(0), o.strides(1), o.strides(2)},
/* bool generate_stats = */ false,
/* bool causal_mask = */ do_causal_};
auto graph = get_sdpa_forward_graph(encoder, cache_key);
int64_t workspace_size = 0;
auto workspace_status = graph->get_workspace_size(workspace_size);
if (!workspace_status.is_good()) {
throw std::runtime_error("Unable to get workspace for cudnn attention.");
}
array workspace(
allocator::malloc(workspace_size), {int(workspace_size)}, uint8);
auto workspace_ptr = workspace.data<void>();
std::unordered_map<int64_t, void*> variant_pack = {
{Q_UID, const_cast<void*>(q.data<void>())},
{K_UID, const_cast<void*>(k.data<void>())},
{V_UID, const_cast<void*>(v.data<void>())},
{O_UID, o.data<void>()}};
auto handle = encoder.device().cudnn_handle();
cudnnSetStream(handle, encoder.stream());
// cuDNN only supports native CUDA graphs for sdpa in 9.6 or above.
if (cudnnGetVersion() < 90600) {
auto capture = encoder.capture_context();
auto exec_status = graph->execute(handle, variant_pack, workspace_ptr);
if (!exec_status.is_good()) {
capture.discard = true;
throw std::runtime_error(
"Unable to execute cudnn attention."
" Failed with message: " +
exec_status.get_message());
}
} else {
cudaGraph_t cu_graph;
cudaGraphCreate(&cu_graph, 0);
std::unique_ptr<cudaGraph_t, void (*)(cudaGraph_t*)> graph_freer(
&cu_graph, [](cudaGraph_t* p) { cudaGraphDestroy(*p); });
auto cu_graph_status = graph->populate_cuda_graph(
handle, variant_pack, workspace_ptr, cu_graph);
if (!cu_graph_status.is_good()) {
throw std::runtime_error(
"Unable to add cuda graph for cudnn attention."
" Failed with message: " +
cu_graph_status.get_message());
}
encoder.add_graph_node(cu_graph);
}
encoder.add_temporary(workspace);
}
} // namespace
namespace fast {
bool ScaledDotProductAttention::use_fallback(
const array& q,
const array& k,
const array& v,
bool has_mask,
bool has_arr_mask,
bool do_causal,
Stream s) {
if (s.device == Device::cpu) {
return true;
}
auto& cu_device = cu::device(s.device);
if (cu_device.compute_capability_major() < 8) {
return true;
}
const int value_head_dim = v.shape(-1);
const int query_head_dim = q.shape(-1);
const int query_sequence_length = q.shape(2);
const int key_sequence_length = k.shape(2);
const bool sdpa_supported_head_dim = query_head_dim == value_head_dim &&
(query_head_dim == 64 || query_head_dim == 96 || query_head_dim == 128);
const bool supported_dtype = q.dtype() == float16 || q.dtype() == bfloat16;
const bool supported_config = supported_dtype && sdpa_supported_head_dim;
return has_arr_mask || !supported_config;
}
void ScaledDotProductAttention::eval_gpu(
const std::vector<array>& inputs,
array& out) {
nvtx3::scoped_range r("ScaledDotProductAttention::eval_gpu");
auto& s = stream();
auto& encoder = cu::get_command_encoder(s);
auto& q_pre = inputs[0];
auto& k_pre = inputs[1];
auto& v_pre = inputs[2];
auto& o = out;
std::vector<array> copies;
// Define some copy functions to ensure the layout of the inputs is as
// expected.
copies.reserve(3);
auto copy_unless = [&copies, &s](
auto predicate, const array& arr) -> const array& {
if (!predicate(arr)) {
array arr_copy = contiguous_copy_gpu(arr, s);
copies.push_back(std::move(arr_copy));
return copies.back();
} else {
return arr;
}
};
auto is_matrix_contiguous = [](const array& arr) {
return arr.strides(-1) == 1;
};
// We are in vector mode ie single query
if (q_pre.shape(2) <= 1) {
auto q_copy_unless = [](const array& arr) {
if (arr.flags().row_contiguous) {
return true;
}
auto& strides = arr.strides();
auto& shape = arr.shape();
if (shape[0] == 1 || shape[1] == 1) {
// If either the batch or head dimension is a singleton, the other can
// be transposed with the sequence dimension
auto bidx = shape[0] == 1 ? 1 : 0;
return (strides[3] == 1) && (strides[2] == shape[3] * shape[bidx]) &&
(strides[bidx] == shape[3]);
}
return false;
};
auto kv_copy_unless = [](const array& arr) {
// keys and values should be copied if:
// - the last dimension is not contiguous
// - the batch and head dim are not contiguous
auto& strides = arr.strides();
auto& shape = arr.shape();
if (strides.back() != 1) {
return false;
}
if (shape[0] == 1 || shape[1] == 1) {
return true;
}
return (strides[0] == strides[1] * shape[1]);
};
const auto& q = copy_unless(q_copy_unless, q_pre);
const auto& k = copy_unless(kv_copy_unless, k_pre);
const auto& v = copy_unless(kv_copy_unless, v_pre);
for (const auto& cp : copies) {
encoder.add_temporary(cp);
}
// Donate the query if possible
if (q.is_donatable() && q.flags().row_contiguous && q.size() == o.size()) {
o.copy_shared_buffer(q);
} else {
o.set_data(allocator::malloc(o.nbytes()));
}
return sdpa_cudnn(s, encoder, q, k, v, scale_, o, do_causal_);
}
// Full attention mode
else {
const auto& q = copy_unless(is_matrix_contiguous, q_pre);
const auto& k = copy_unless(is_matrix_contiguous, k_pre);
const auto& v = copy_unless(is_matrix_contiguous, v_pre);
int64_t str_oD = 1;
int64_t str_oH = o.shape(3);
int64_t str_oL = o.shape(1) * str_oH;
int64_t str_oB = o.shape(2) * str_oL;
size_t data_size = o.shape(0) * str_oB;
array::Flags flags{
/* bool contiguous = */ 1,
/* bool row_contiguous = */ 0,
/* bool col_contiguous = */ 0,
};
o.set_data(
allocator::malloc(o.nbytes()),
data_size,
{str_oB, str_oH, str_oL, str_oD},
flags);
return sdpa_cudnn(s, encoder, q, k, v, scale_, o, do_causal_);
}
}
} // namespace fast
} // namespace mlx::core