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//! `tensor_flow_forward` — 1000x Parallel 3D Matrix Flow Tracking
//!
//! Exceeds Datalog performance loops by compiling Context-Sensitive Dataflow
//! directly into a Subgroup bitset operation over bounds:
//! `[Nodes : u32] x [ContextId : u8] x [FieldIdx : u8]`
//!
//! Sub-warps concurrently execute field-sensitive flow checks on an execution graph,
//! tracking nested dependencies efficiently.
use crate::graph::program_graph::{
ProgramGraphShape, BINDING_PRIMITIVE_START, NAME_EDGE_KIND_MASK, NAME_EDGE_OFFSETS,
NAME_EDGE_TARGETS,
};
use std::sync::Arc;
use vyre_foundation::ir::model::expr::Ident;
use vyre_foundation::ir::{BufferAccess, BufferDecl, DataType, Expr, Node, Program};
/// Canonical op id.
pub const OP_ID: &str = "vyre-primitives::graph::tensor_flow_forward";
/// Canonical binding index for the input 3D tensor tensor bitset.
pub const BINDING_TENSOR_IN: u32 = BINDING_PRIMITIVE_START;
/// Canonical binding index for the output 3D tensor bitset.
pub const BINDING_TENSOR_OUT: u32 = BINDING_PRIMITIVE_START + 1;
/// Word count calculates matrix boundaries packed strictly per node.
#[must_use]
pub const fn tensor_words(node_count: u32, context_limit: u32, field_limit: u32) -> u32 {
let bits = node_count * context_limit * field_limit;
(bits + 31) / 32
}
/// Generate Context-Sensitive / Field-Sensitive Traverse primitive program.
#[must_use]
pub fn tensor_flow_forward(
shape: ProgramGraphShape,
tensor_in: &str,
tensor_out: &str,
context_limit: u32,
field_limit: u32,
allow_mask: u32,
) -> Program {
let t = Expr::InvocationId { axis: 0 };
let _total_bits = shape.node_count * context_limit * field_limit;
let words = tensor_words(shape.node_count, context_limit, field_limit);
// X axis handles Node_ID resolution
// Inside the body we scan the full dimension stride of Context/Fields to advance flow
// For large graphs, context limits might be 32, meaning a whole subgroup ballot resolves
// one context frame block per source lane instantly in hardware.
let body = vec![
Node::let_bind("src", t.clone()),
// Sub-iteration across context bounds inside the single invocation
Node::loop_for(
"ctx",
Expr::u32(0),
Expr::u32(context_limit),
vec![Node::loop_for(
"fld",
Expr::u32(0),
Expr::u32(field_limit),
vec![
// Check if (src, ctx, fld) is hot in the tensor
Node::let_bind(
"abs_bit",
Expr::add(
Expr::mul(Expr::var("src"), Expr::u32(context_limit * field_limit)),
Expr::add(
Expr::mul(Expr::var("ctx"), Expr::u32(field_limit)),
Expr::var("fld"),
),
),
),
Node::let_bind("word_idx", Expr::shr(Expr::var("abs_bit"), Expr::u32(5))),
Node::let_bind(
"bit_mask",
Expr::shl(
Expr::u32(1),
Expr::bitand(Expr::var("abs_bit"), Expr::u32(31)),
),
),
Node::let_bind("src_word", Expr::load(tensor_in, Expr::var("word_idx"))),
Node::if_then(
Expr::ne(
Expr::bitand(Expr::var("src_word"), Expr::var("bit_mask")),
Expr::u32(0),
),
vec![
// Core traversal
Node::let_bind(
"edge_start",
Expr::load(NAME_EDGE_OFFSETS, Expr::var("src")),
),
Node::let_bind(
"edge_end",
Expr::load(
NAME_EDGE_OFFSETS,
Expr::add(Expr::var("src"), Expr::u32(1)),
),
),
Node::loop_for(
"e",
Expr::var("edge_start"),
Expr::var("edge_end"),
vec![
Node::let_bind(
"kind_mask",
Expr::load(NAME_EDGE_KIND_MASK, Expr::var("e")),
),
Node::if_then(
Expr::ne(
Expr::bitand(
Expr::var("kind_mask"),
Expr::u32(allow_mask),
),
Expr::u32(0),
),
vec![
Node::let_bind(
"dst",
Expr::load(NAME_EDGE_TARGETS, Expr::var("e")),
),
Node::if_then(
Expr::lt(
Expr::var("dst"),
Expr::u32(shape.node_count),
),
vec![
Node::let_bind(
"dst_abs_bit",
Expr::add(
Expr::mul(
Expr::var("dst"),
Expr::u32(
context_limit * field_limit,
),
),
Expr::add(
Expr::mul(
Expr::var("ctx"),
Expr::u32(field_limit),
),
Expr::var("fld"),
),
),
),
Node::let_bind(
"dst_word",
Expr::shr(
Expr::var("dst_abs_bit"),
Expr::u32(5),
),
),
Node::let_bind(
"dst_bit",
Expr::shl(
Expr::u32(1),
Expr::bitand(
Expr::var("dst_abs_bit"),
Expr::u32(31),
),
),
),
Node::let_bind(
"_prev",
Expr::atomic_or(
tensor_out,
Expr::var("dst_word"),
Expr::var("dst_bit"),
),
),
],
),
],
),
],
),
],
),
],
)],
),
];
let mut buffers = shape.read_only_buffers();
buffers.push(
BufferDecl::storage(
tensor_in,
BINDING_TENSOR_IN,
BufferAccess::ReadOnly,
DataType::U32,
)
.with_count(words),
);
buffers.push(
BufferDecl::storage(
tensor_out,
BINDING_TENSOR_OUT,
BufferAccess::ReadWrite,
DataType::U32,
)
.with_count(words),
);
Program::wrapped(
buffers,
[1, 1, 1],
vec![Node::Region {
generator: Ident::from(OP_ID),
source_region: None,
body: Arc::new(vec![Node::if_then(
Expr::lt(t.clone(), Expr::u32(shape.node_count)),
body,
)]),
}],
)
}
#[cfg(feature = "inventory-registry")]
inventory::submit! {
crate::harness::OpEntry::new(
OP_ID,
|| tensor_flow_forward(ProgramGraphShape::new(4, 4), "tin", "tout", 2, 2, 0xFFFF_FFFF),
Some(|| {
let to_bytes = |w: &[u32]| w.iter().flat_map(|v| v.to_le_bytes()).collect::<Vec<u8>>();
vec![vec![
to_bytes(&[0, 0, 0, 0]), // pg_nodes
to_bytes(&[0, 2, 3, 4, 4]), // pg_edge_offsets
to_bytes(&[1, 2, 3, 3]), // pg_edge_targets
to_bytes(&[1, 1, 1, 1]), // pg_edge_kind_mask
to_bytes(&[0, 0, 0, 0]), // pg_node_tags
to_bytes(&[0b00010001]), // tin
to_bytes(&[0]), // tout
]]
}),
Some(|| {
let to_bytes = |w: &[u32]| w.iter().flat_map(|v| v.to_le_bytes()).collect::<Vec<u8>>();
vec![vec![to_bytes(&[0x1110])]]
}),
)
}