Buffers and memory

Parameter buffers are bound with T.match_buffer; scratch buffers are created in the body with one of two declaration APIs (below). Index a buffer with A[i, j], slice it with A[m0:m0+BM, 0:BK] (a BufferRegion), and take a pointer with A.ptr_to([i, j]) or the raw data pointer A.data.

Declaring buffers

Two fundamental APIs create a buffer:

T.alloc_buffer(shape, dtype, scope=..., ...) — allocates new storage (emits an AllocBuffer node) and returns the Buffer. T.alloc_shared / T.alloc_local are just alloc_buffer with scope="shared" / scope="local".
T.decl_buffer(shape, dtype, data=..., ...) — declares a view over an existing pointer data (no allocation); use it to alias or reinterpret storage — a sub-region of a pool, or a tensor-memory address. With data=None it allocates, like alloc_buffer.

A buffer’s data pointer is an immutable Var (alloc_buffer defines it; decl_buffer takes one). To back a buffer with a pointer expression, bind it first — see Data types and expressions.

Both share one descriptor; the parameters that matter most:

Parameter	Meaning
`dtype`	element type — `"float32"`, `"float16"`, `"float4_e2m1fn"`, …
`shape`	logical shape (a tuple of extents)
`layout`	physical mapping (TileLayout); `"default"` = dense row-major
`elem_offset` / `allocated_addr`	`elem_offset` (or `byte_offset`) places a view at an offset into `data`; `allocated_addr` carries a pre-assigned address (tensor memory)
`align`	alignment of the data pointer, in bytes

The scope argument selects the memory space:

Scope	Shorthand	Memory
`"global"`	(default)	device global memory
`"shared"`	`T.alloc_shared`	static shared memory (`__shared__`)
`"shared.dyn"`	(pool)	dynamic shared memory (pooled — see below)
`"local"`	`T.alloc_local`	per-thread registers
`"tmem"`	(TMEM pool)	Blackwell tensor memory (see below)

A = T.match_buffer(A_ptr, (M, K), "float16", align=16)   # parameter buffer
As = T.alloc_shared((BM, BK), "float16")                 # new shared tile
acc = T.alloc_local((4,), "float32")                     # register accumulator
view = T.decl_buffer((BM, BK), "float16", data=As.data)  # a view over As

A ptr-based buffer is just metadata over a pointer. For any non-tmem buffer, the declaration is a pointer plus a layout, and indexing resolves to an address:

addr(buffer[coord]) = buffer.data + elem_offset + layout.apply(coord, shape=shape)["m"]

(layout.apply returns the per-axis mapping; its "m" component is the element offset.) So the same logical access compiles to different address arithmetic depending purely on the buffer’s metadata. Writing B[i, j] = A[i, j] + 1 over a 4×8 region, with B declared four ways:

from tvm.tirx.layout import TileLayout, S

B = T.match_buffer(p, (4, 8), "float32")                                       # row-major
B = T.match_buffer(p, (4, 8), "float32", layout=TileLayout(S[(4, 8):(1, 4)]))  # column-major
B = T.match_buffer(p, (4, 8), "float32", elem_offset=64)                       # shifted view
B = T.match_buffer(p, (4, 8), "float32", layout=TileLayout(S[(4, 8):(16, 1)])) # row stride 16

each makes B[i, j] lower to a different index in the generated CUDA (the A[i, j] load stays i*8 + j — only B’s metadata changed):

B_ptr[((i * 8) + j)]        = ...;   // row-major:        i*8 + j
B_ptr[((j * 4) + i)]        = ...;   // column-major:     j*4 + i
B_ptr[(((i * 8) + j) + 64)] = ...;   // elem_offset=64:   i*8 + j + 64
B_ptr[((i * 16) + j)]       = ...;   // row stride 16:    i*16 + j

Shared memory

Shared memory comes in two flavors — static (fixed at compile time) and dynamic (sized at launch) — plus a pool helper that manages the dynamic case.

Static

The simplest shared buffer is a static one — T.alloc_shared (that is, scope="shared"), sized at compile time. Stage data into it, cta_sync so the whole block sees the writes, then read it back:

@T.prim_func
def smem_demo(A_ptr: T.handle, B_ptr: T.handle):
    A = T.match_buffer(A_ptr, (128,), "float32")
    B = T.match_buffer(B_ptr, (128,), "float32")
    T.device_entry()
    bx = T.cta_id([1])
    tx = T.thread_id([128])
    sm = T.alloc_shared((128,), "float32")   # static shared memory
    sm[tx] = A[tx]
    T.cuda.cta_sync()
    B[tx] = sm[tx] * T.float32(2.0)

It lowers to a plain __shared__ array (generated CUDA, boilerplate elided):

extern "C" __global__ void __launch_bounds__(128)
smem_demo_kernel(float* __restrict__ A_ptr, float* __restrict__ B_ptr) {
  int tx = ((int)threadIdx.x);
  __shared__ alignas(64) float sm_ptr[128];      // T.alloc_shared
  sm_ptr[tx] = A_ptr[tx];
  __syncthreads();                               // T.cuda.cta_sync()
  B_ptr[tx] = sm_ptr[tx] * 2.0f;
}

Dynamic

Dynamic shared memory (scope="shared.dyn") is sized per launch (the sharedMemBytes launch parameter), not at compile time. A kernel may have only one dynamic-shared allocation — the arena. So you allocate it once and decl each buffer as a view into it: T.decl_buffer with data= the arena pointer and an elem_offset:

arena = T.alloc_buffer((128,), "float32", scope="shared.dyn")   # the one arena
As = T.decl_buffer((64,), "float32", data=arena.data, scope="shared.dyn")                 # offset 0
Bs = T.decl_buffer((64,), "float32", data=arena.data, elem_offset=64, scope="shared.dyn") # offset 64
As[tx] = A[tx]
Bs[tx] = B[tx]
T.cuda.cta_sync()
C[tx] = As[tx] + Bs[tx]

Both views share the single extern __shared__ arena (generated CUDA, boilerplate elided; arena named smem for clarity):

extern __shared__ __align__(64) float smem[];   // the one dynamic-shared arena
smem[tx]      = A_ptr[tx];                       // As — view at offset 0
smem[tx + 64] = B_ptr[tx];                       // Bs — view at offset 64
__syncthreads();
C_ptr[tx] = smem[tx] + smem[tx + 64];

(Two separate alloc_buffer(scope="shared.dyn") is an error — only one dynamic shared memory allocation is allowed.) So static shared memory is sized at compile time (__shared__ T x[N];); dynamic shared memory is this one launch-sized arena with views decl’d at offsets inside it.

Note

How TVM annotates the dynamic-shared size. The arena’s size is known at compile time (here 128 floats = 512 bytes). During lowering TVM appends a "tirx.use_dyn_shared_memory" tag to the device kernel’s tirx.kernel_launch_params, and the host launcher computes the total bytes and passes them as the last launch argument:

# device kernel attribute:
"tirx.kernel_launch_params": ["blockIdx.x", "threadIdx.x", "tirx.use_dyn_shared_memory"]

# host-side launch call  (..., gridDim.x, blockDim.x, dyn_shared_bytes):
T.call_packed("dyn_kernel", A.data, B.data, C.data, 1, 64, 512)

At run time that 512 becomes config.sharedMemBytes in the cuLaunchKernelEx call. You never set it by hand — it is derived from the shared.dyn allocation’s size.

Pool sugar

T.SMEMPool automates that arena bookkeeping — it bump-allocates the offsets so you don’t decl views by hand. Beyond alloc / commit, it offers per-buffer align=, an alloc_mma helper that builds an MMA-compatible swizzle layout for you, and move_base_to to rewind the cursor and reuse space:

pool = T.SMEMPool()                          # bump allocator over shared.dyn
As = pool.alloc((BM, BK), "float16", align=128)   # carve a tile
Bs = pool.alloc((BK, BN), "float16", align=128)
Cs = pool.alloc_mma((BM, BN), "float16")     # MMA-compatible, swizzle inferred
pool.commit()                                 # finalize the pool's size
# pool.move_base_to(offset) rewinds the cursor to reuse space

The TMEM pool (Tensor memory, below) is layered on top of an SMEMPool.

Registers

Per-thread scratch lives in registers. Allocate it with T.alloc_local(shape, dtype) (i.e. scope="local"): it is private to each thread and lowers to a local array kept in registers.

r = T.alloc_local((4,), "float32")   # per-thread register array
for k in T.unroll(4):
    r[k] = A[tx, k]
# ... compute on r[0..3] ...

alignas(64) float r_ptr[4];          // per-thread, register-resident
r_ptr[0] = A_ptr[tx * 4 + 0];
r_ptr[1] = A_ptr[tx * 4 + 1];
// ...

Note

The alignas(64) is the default buffer alignment — a buffer’s data_alignment defaults to runtime::kAllocAlignment (64 bytes), and the CUDA codegen stamps it onto every allocation, including per-thread local arrays where it is meaningless. For these register-resident arrays it has no performance impact: a thread-local array with statically-resolvable indices is promoted to registers by nvcc/ptxas (scalar replacement of aggregates, SROA), so it never lives in addressable local memory and the alignment is a no-op. (A dynamically-indexed array that spilled to local memory would actually pick up the over-alignment, but that is the unusual case.) This over-alignment of register locals is a known rough edge we plan to fix (use the dtype’s natural alignment for local scope).

Scalar

A scalar is just a register array with one element — strictly, you don’t need a separate concept. You can allocate a size-1 local buffer and index [0]:

phase = T.alloc_local((1,), "int32")   # 1-element register array
phase[0] = 0
while phase[0] < 4:
    acc = acc + A[tx, phase[0]]
    phase[0] += 1

But writing phase[0] everywhere is clumsy, so a scalar is sugar for exactly this — a one-element register buffer you read and write by name:

phase: T.int32 = 0                 # mutable scalar (sugar for the above)
while phase < 4:
    acc = acc + A[tx, phase]
    phase += 1

s = T.local_scalar("int32")        # explicit form; assign by name (s = ..., not s[0])
acc: T.float32 = 0.0               # a type-annotated assignment also makes one

The two are not just similar — they parse to structurally identical TIRx. The sugar is resolved entirely in the parser: phase: T.int32 is that one-element local buffer, and phase / phase += 1 are phase[0] / phase[0] += 1. tvm.ir.assert_structural_equal on the two kernels passes, and the printer even renders the explicit alloc_local + [0] form back as the scalar form — so once parsing is done there is no difference at all. Both therefore lower to the same alignas(64) int phase_ptr[1];; the scalar just lets you drop the [0]. (T.local_scalar / T.shared_scalar / T.alloc_scalar choose the scope explicitly.)

Note

Why not a Var? A TIRx Var is immutable — a single static binding (it is exactly what T.let produces, below). A scalar needs to be mutable — you reassign it in loops and accumulators — so it must be backed by a one-element buffer you can store into repeatedly, not a Var.

`let`

A T.let binding is immutable — a single LetStmt (a named value, not a buffer). Use it for derived constants:

n: T.let = M * K               # immutable binding (LetStmt)
half: T.let[T.int32] = N // 2  # ... with an explicit type

It lowers to a plain scalar C variable — not a buffer (no array, no [0]). For half: T.let = m * 2 (with a runtime m):

int half = m * 2;     // the `let` -> a const-like local

Because the value is immutable, the simplifier is free to propagate and CSE it, so at the use sites you often see m * 2 substituted directly (or shared through a common-subexpression temporary) rather than a reference to half.

Note

Why have an immutable binding at all? Because the value cannot change, the arithmetic analyzer binds the var to it (analyzer.Bind(var, value) when it simplifies a LetStmt), so facts proven about the value — constant bounds, the modular set (divisibility / alignment), ranges — propagate through every use. That feeds index simplification, bounds-check elimination, and alignment/vectorization decisions. A mutable scalar is a memory load (buf[0]): the analyzer cannot assume it stays constant, so none of those properties carry through. A let is also a pure value — no allocation, and free to inline / substitute / CSE — whereas a scalar is a one-element buffer with load/store semantics.

Tensor memory

Blackwell tensor memory is not a plain scratch scope: it must be explicitly reserved and freed with the warp-uniform T.ptx.tcgen05.alloc / tcgen05.dealloc intrinsics, and each tensor is a view into it declared with T.decl_buffer(..., scope="tmem", allocated_addr=<column>, layout=<tmem layout>). The allocated_addr (a column offset) is mandatory — the tensor-core dispatch asserts it — so T.alloc_buffer(scope="tmem") (which does not set it) will not work. Unlike shared memory, tensor memory is not directly addressable: it is read and written only through tcgen05 mma / ld / st / cp.

By hand, one warp issues the allocation into a shared slot, you decl each tensor as a view at a column offset, and one warp frees it at the end:

addr = T.alloc_shared((1,), "uint32")             # slot for the allocated base
if warp_id == alloc_warp:                         # tcgen05.alloc is warp-uniform
    T.ptx.tcgen05.alloc(T.address_of(addr), n_cols=512, cta_group=cta_group)
acc = T.decl_buffer((CTA_M, 512), "float32", scope="tmem",
                    allocated_addr=0, layout=tmem_layout)   # view at column 0
# ... use acc as a gemm_async / copy_async operand ...
if warp_id == alloc_warp:
    T.ptx.tcgen05.relinquish_alloc_permit(cta_group=cta_group)
    T.ptx.tcgen05.dealloc(addr, n_cols=512, cta_group=cta_group)

You manage the column offsets and the tmem_layout (a datapath D/F layout) yourself. This is exactly the sequence the pool below emits.

Pool

T.TMEMPool wraps all of that — the warp-uniform alloc/dealloc, the column bump-allocation, and the datapath layout:

tmem_addr = pool.alloc((1,), "uint32")          # pool = the kernel's smem pool
tmem_pool = T.TMEMPool(pool, total_cols=512, cta_group=cta_group,
                       tmem_addr=tmem_addr)
acc = tmem_pool.alloc((CTA_M, 512), "float32")  # allocated_addr set for you
tmem_pool.commit()                               # emits tcgen05.alloc (one warp)
# ... use acc ...
tmem_pool.dealloc()                              # emits tcgen05.dealloc (one warp)

See the Tile Primitives walkthroughs for full examples.

Buffer APIs

A Buffer is metadata over a pointer (see Declaring buffers above), so most of its methods are compile-time reshapes/reinterprets that change index arithmetic or hand you a pointer — they emit no runtime op of their own. The common ones:

Method	What it is
`B.data`	the raw data pointer (a `Var`); prints as `B_ptr`
`B.ptr_to([i, j])`	a typed pointer to an element (`address_of`); prints as `&B_ptr[…]`
`B.vload([i], dtype="float32x4")` / `B.vstore([i], v)`	a vectorized load / store; prints as `(float4)(B_ptr + …)`
`B.view(*shape, layout=…)`	reinterpret the same storage under a new shape/layout (no copy)
`B.local(*shape, layout=…)`	the calling thread’s private register slice of a `local` buffer
`B.permute(*dims)`	a view with axes permuted (a transposed layout)
`B.access_ptr(mask, …)`	a masked access pointer (the `tvm_access_ptr` builtin), for passing a region to an intrinsic

Pointers — ``ptr_to`` / ``data``. ptr_to is how you hand an element address to an intrinsic or inline function; data is the base pointer:

B[tx] = T.cuda.func_call("ld", A.ptr_to([tx]), source_code=SRC, return_type="float32")

B_ptr[tx] = ld(&A_ptr[tx]);          // ptr_to([tx]) -> &A_ptr[tx];  A.data -> A_ptr

Vectorized access — ``vload`` / ``vstore``. Move several elements as one wide transfer (see also Data types and expressions):

B.vstore([tx * 4], A.vload([tx * 4], dtype="float32x4"))

*(float4*)(B_ptr + tx * 4) = *(float4*)(A_ptr + tx * 4);

Reshape / reinterpret — ``view`` / ``permute``. Both are pure metadata; the data pointer is unchanged, only the index arithmetic differs. A.view(64, 4) sees the 256-element buffer as 64×4; A.permute(1, 0) transposes the axes:

A2 = A.view(64, 4);     y = A2[tx, 0] + A2[tx, 3]   # A2[tx, j] -> A_ptr[tx*4 + j]
At = A.permute(1, 0);   z = At[i, j]                # At[i, j]  -> A_ptr[j*4 + i]

A2_ptr[tx * 4]  /* +3 */                 // view: row-major 64x4 index
At_ptr[(j * 4) + i]                       // permute: swapped strides

Registers — ``local``. Decomposes a thread-axis local layout into the calling thread’s flat register bundle (used pervasively by the tile primitives):

R  = T.alloc_buffer((32, 8), "float32", scope="local", layout=TileLayout(S[(32, 8) : (1 @ laneid, 1)]))
Rl = R.local(8)          # this lane's 8 registers

alignas(64) float Rl_ptr[8];             // the lane's private registers

Buffers and memory

Declaring buffers

Shared memory

Static

Dynamic

Pool sugar

Registers

Scalar

let

Tensor memory

Pool

Buffer APIs

`let`