tilelang.intrinsics.mma_macro_generator¶

Attributes¶

lift

Classes¶

`TensorCoreIntrinEmitter`	To eliminate Python syntax within TIR Macro.
`TensorCoreIntrinEmitterWithLadderTransform`	To eliminate Python syntax within TIR Macro.
`INT4TensorCoreIntrinEmitter`	To eliminate Python syntax within TIR Macro.
`INT4TensorCoreIntrinEmitterWithLadderTransform`	To eliminate Python syntax within TIR Macro.

Module Contents¶

tilelang.intrinsics.mma_macro_generator.lift¶

class tilelang.intrinsics.mma_macro_generator.TensorCoreIntrinEmitter(a_dtype='float16', b_dtype='float16', accum_dtype='float16', a_transposed=False, b_transposed=False, block_row_warps=2, block_col_warps=2, warp_row_tiles=8, warp_col_tiles=8, chunk=16, reduce_k=1, num_elems_per_byte=1, is_m_first=False)¶

Bases: object

To eliminate Python syntax within TIR Macro.

Parameters:

a_dtype (str)
b_dtype (str)
accum_dtype (str)
a_transposed (bool)
b_transposed (bool)
block_row_warps (int)
block_col_warps (int)
warp_row_tiles (int)
warp_col_tiles (int)
chunk (int)
reduce_k (int)
num_elems_per_byte (int)
is_m_first (Optional[bool])

M_DIM = 16¶

N_DIM = 16¶

WARP_SIZE = 32¶

dtype_abbrv¶

is_m_first = False¶

a_dtype = 'float16'¶

b_dtype = 'float16'¶

accum_dtype = 'float16'¶

a_transposed = False¶

b_transposed = False¶

block_row_warps = 2¶

block_col_warps = 2¶

warp_row_tiles = 8¶

warp_col_tiles = 8¶

chunk = 16¶

warp_rows = 0¶

warp_cols = 0¶

reduce_k = 1¶

threads = 128¶

num_elems_per_byte = 1¶

get_store_index_map(inverse=False)¶

Parameters:: inverse (bool)
Return type:: tvm.tir.IndexMap

extract_thread_binding(thread_id, is_m_first=None)¶

is_m_first: True if the thread binding is in the form of (tx, warp_n, warp_m) which represents [warp_size, block_row_warps (split n), block_col_warps (split m)] Otherwise, it is in the form of [warp_size, block_col_warps (split m), block_row_warps (split n)]

Parameters:

thread_id (tvm.tir.PrimExpr)
is_m_first (Optional[bool])

Return type:

Tuple[tvm.tir.PrimExpr, tvm.tir.PrimExpr, tvm.tir.PrimExpr]

ldmatrix_a(A_local_buf, A_shared_buf, ki, rk=0)¶

Parameters:

A_local_buf (tvm.tir.Buffer)
A_shared_buf (tvm.tir.Buffer)
ki (tvm.tir.PrimExpr)
rk (Optional[tvm.tir.PrimExpr])

ldmatrix_b(B_local_buf, B_shared_buf, ki, rk=0)¶

Parameters:

B_local_buf (tvm.tir.Buffer)
B_shared_buf (tvm.tir.Buffer)
ki (tvm.tir.PrimExpr)
rk (Optional[tvm.tir.PrimExpr])

mma(A_local_buf, B_local_buf, C_local_buf, k_inner=0)¶

Parameters:

A_local_buf (tvm.tir.Buffer)
B_local_buf (tvm.tir.Buffer)
C_local_buf (tvm.tir.Buffer)
k_inner (Optional[tvm.tir.PrimExpr])

stmatrix(C_local_buf, C_buf, pid_m=None, pid_n=None)¶

make_mma_load_layout(local_buf, matrix='A')¶

Create a layout function for storing MMA results into a fragment buffer. This layout is used in conjunction with inverse_mma_store_layout to map fragment indices to threads and local indices.

Parameters:

local_buf (tir.Buffer) – The local buffer representing a fragment of a matrix.
matrix (Literal['A', 'B'])

Returns:

A fragment object that describes how threads and indices in local_buf are laid out.

Return type:

T.Fragment

Raises:

AssertionError – If local_buf is not detected to be a fragment buffer.

make_mma_store_layout(local_buf)¶

Create a layout function for storing MMA results into a fragment buffer. This layout is used in conjunction with inverse_mma_store_layout to map fragment indices to threads and local indices.

Parameters:: local_buf (tir.Buffer) – The local buffer representing a fragment of a matrix.
Returns:: A fragment object that describes how threads and indices in local_buf are laid out.
Return type:: T.Fragment
Raises:: AssertionError – If local_buf is not detected to be a fragment buffer.

class tilelang.intrinsics.mma_macro_generator.TensorCoreIntrinEmitterWithLadderTransform(a_dtype='float16', b_dtype='float16', accum_dtype='float16', a_transposed=False, b_transposed=False, block_row_warps=2, block_col_warps=2, warp_row_tiles=8, warp_col_tiles=8, chunk=16, reduce_k=1, num_elems_per_byte=1, is_m_first=False, transform_kind_a=0, transform_kind_b=0)¶

Bases: TensorCoreIntrinEmitter

To eliminate Python syntax within TIR Macro. With Ladder Transform Plugin.

Parameters:

a_dtype (str)
b_dtype (str)
accum_dtype (str)
a_transposed (bool)
b_transposed (bool)
block_row_warps (int)
block_col_warps (int)
warp_row_tiles (int)
warp_col_tiles (int)
chunk (int)
reduce_k (int)
num_elems_per_byte (int)
is_m_first (Optional[bool])
transform_kind_a (Union[int, tilelang.common.TransformKind])
transform_kind_b (Union[int, tilelang.common.TransformKind])

ldmatrix_a(A_local_buf, A_shared_buf, ki, rk=0)¶

ldmatrix_b(B_local_buf, B_shared_buf, ki, rk=0)¶

mma(A_local_buf, B_local_buf, C_local_buf)¶

class tilelang.intrinsics.mma_macro_generator.INT4TensorCoreIntrinEmitter(a_dtype='float16', b_dtype='float16', accum_dtype='float16', a_transposed=False, b_transposed=False, block_row_warps=2, block_col_warps=2, warp_row_tiles=8, warp_col_tiles=8, chunk=16, reduce_k=1, num_elems_per_byte=1, is_m_first=False)¶

Bases: TensorCoreIntrinEmitter

To eliminate Python syntax within TIR Macro.

Parameters:

a_dtype (str)
b_dtype (str)
accum_dtype (str)
a_transposed (bool)
b_transposed (bool)
block_row_warps (int)
block_col_warps (int)
warp_row_tiles (int)
warp_col_tiles (int)
chunk (int)
reduce_k (int)
num_elems_per_byte (int)
is_m_first (Optional[bool])

mma(A_local_buf, B_local_buf, C_local_buf)¶

class tilelang.intrinsics.mma_macro_generator.INT4TensorCoreIntrinEmitterWithLadderTransform(a_dtype='float16', b_dtype='float16', accum_dtype='float16', a_transposed=False, b_transposed=False, block_row_warps=2, block_col_warps=2, warp_row_tiles=8, warp_col_tiles=8, chunk=16, reduce_k=1, num_elems_per_byte=1, is_m_first=False, transform_kind_a=0, transform_kind_b=0)¶

Bases: TensorCoreIntrinEmitterWithLadderTransform

To eliminate Python syntax within TIR Macro. With Ladder Transform Plugin.

Parameters:

a_dtype (str)
b_dtype (str)
accum_dtype (str)
a_transposed (bool)
b_transposed (bool)
block_row_warps (int)
block_col_warps (int)
warp_row_tiles (int)
warp_col_tiles (int)
chunk (int)
reduce_k (int)
num_elems_per_byte (int)
is_m_first (Optional[bool])
transform_kind_a (Union[int, tilelang.common.TransformKind])
transform_kind_b (Union[int, tilelang.common.TransformKind])

mma(A_local_buf, B_local_buf, C_local_buf)¶