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# mypy: allow-untyped-defs
import logging
import torch
from torch._inductor.codegen.rocm.ck_universal_gemm_template import CKGemmTemplate
from .. import ir, lowering as L
from ..select_algorithm import (
autotune_select_algorithm,
ExternKernelChoice,
TritonTemplate,
)
from ..utils import (
ceildiv as cdiv,
use_aten_gemm_kernels,
use_ck_template,
use_cpp_bmm_template,
use_cutlass_template,
use_triton_template,
)
from ..virtualized import V
from .mm_common import (
_is_static_problem,
addmm_epilogue,
mm_args,
mm_configs,
mm_options,
)
log = logging.getLogger(__name__)
aten = torch.ops.aten
def bmm_grid(b, m, n, meta):
return (cdiv(m, meta["BLOCK_M"]) * cdiv(n, meta["BLOCK_N"]), b, 1)
def _is_large_block_for_cpu(m, n, k):
# Thresholds are experimentally determined to reduce Triton CPU compile times
if m > 128 or n > 128 or k > 128:
return True
return m * n > 2**12
def bmm_configs(m, n, k, *, device_type):
if device_type == "cpu":
return mm_configs(m, n, k, scale=0.5, exclude=_is_large_block_for_cpu)
return mm_configs(m, n, k)
bmm_template = TritonTemplate(
name="bmm",
grid=bmm_grid,
source=r"""
{{def_kernel("A", "B")}}
M = {{size("A", -2)}}
N = {{size("B", -1)}}
K = {{size("A", -1)}}
stride_aq = {{stride("A", 0)}}
stride_am = {{stride("A", 1)}}
stride_ak = {{stride("A", 2)}}
stride_bq = {{stride("B", 0)}}
stride_bk = {{stride("B", 1)}}
stride_bn = {{stride("B", 2)}}
# based on triton.ops.matmul
pid = tl.program_id(0)
grid_m = (M + BLOCK_M - 1) // BLOCK_M
grid_n = (N + BLOCK_N - 1) // BLOCK_N
# re-order program ID for better L2 performance
width = GROUP_M * grid_n
group_id = pid // width
group_size = min(grid_m - group_id * GROUP_M, GROUP_M)
pid_m = group_id * GROUP_M + (pid % group_size)
pid_n = (pid % width) // (group_size)
rm = pid_m * BLOCK_M + tl.arange(0, BLOCK_M)
rn = pid_n * BLOCK_N + tl.arange(0, BLOCK_N)
if (stride_am == 1 and stride_ak == M) or (stride_am == K and stride_ak == 1):
ram = tl.max_contiguous(tl.multiple_of(rm % M, BLOCK_M), BLOCK_M)
else:
ram = rm % M
if (stride_bk == 1 and stride_bn == K) or (stride_bk == N and stride_bn == 1):
rbn = tl.max_contiguous(tl.multiple_of(rn % N, BLOCK_N), BLOCK_N)
else:
rbn = rn % N
rk = tl.arange(0, BLOCK_K)
idx_q = tl.program_id(1) # batch dimension for BMM
A = A + (ram[:, None] * stride_am + rk[None, :] * stride_ak + idx_q*stride_aq)
B = B + (rk[:, None] * stride_bk + rbn[None, :] * stride_bn + idx_q*stride_bq)
acc = tl.zeros((BLOCK_M, BLOCK_N), dtype=ACC_TYPE)
for k in range(K, 0, -BLOCK_K):
if EVEN_K:
a = tl.load(A)
b = tl.load(B)
else:
a = tl.load(A, mask=rk[None, :] < k, other=0.)
b = tl.load(B, mask=rk[:, None] < k, other=0.)
acc += tl.dot(a, b, allow_tf32=ALLOW_TF32)
A += BLOCK_K * stride_ak
B += BLOCK_K * stride_bk
# rematerialize rm and rn to save registers
rm = pid_m * BLOCK_M + tl.arange(0, BLOCK_M)
rn = pid_n * BLOCK_N + tl.arange(0, BLOCK_N)
idx_q = tl.program_id(1) # batch dimension for BMM
idx_m = rm[:, None]
idx_n = rn[None, :]
mask = (idx_m < M) & (idx_n < N)
# inductor generates a suffix
{{store_output(("idx_q", "idx_m", "idx_n"), "acc", "mask")}}
""",
)
aten_bmm = ExternKernelChoice(torch.bmm, "at::bmm_out")
aten_baddbmm = ExternKernelChoice(
torch.baddbmm, "at::baddbmm_out", op_overload=aten.baddbmm.out
)
@L.register_lowering(aten.bmm)
def tuned_bmm(mat1, mat2, *, layout=None):
if all(x.get_device().type == "cpu" for x in [mat1, mat2]):
# decompose to small ops when memory bound
if mat1.get_size()[1] == 1 or mat2.get_size()[2] == 1:
mat1 = L.unsqueeze(mat1, -1)
mat2 = L.unsqueeze(mat2, 1)
return L.sum_(L.mul(mat1, mat2), axis=2)
def is_valid_to_require_contiguous(t):
if not ir.is_storage_and_layout(t):
return True
_, layout = ir.as_storage_and_layout(t, freeze=False)
return isinstance(layout, ir.FlexibleLayout)
def is_preferred_layout_as_bmm_input(sizes, strides):
# contiguous on one of the last two dims
return (
strides[-1] == 1 and (sizes[-2] == 1 or strides[-2] >= sizes[-1])
) or (strides[-2] == 1 and (sizes[-1] == 1 or strides[-1] >= sizes[-2]))
# Make the input of bmm contiguous
# if it is not contiguous on either of the last two dims,
# because bmm cpu implementation would do contiguous() if not.
# This is to avoid additional copies in bmm.
def may_require_contiguous(t, meta_t):
sizes = meta_t.meta["val"].size()
strides = meta_t.meta["val"].stride()
if not is_preferred_layout_as_bmm_input(sizes, strides):
t = ir.ExternKernel.require_contiguous(t)
return t
if is_valid_to_require_contiguous(mat1):
meta_mat1 = V.graph.current_node.args[0]
mat1 = may_require_contiguous(mat1, meta_mat1)
if is_valid_to_require_contiguous(mat2):
meta_mat2 = V.graph.current_node.args[1]
mat2 = may_require_contiguous(mat2, meta_mat2)
m, n, k, layout, mat1, mat2 = mm_args(mat1, mat2, layout=layout)
# options to tune from
choices = [aten_bmm.bind((mat1, mat2), layout)] if use_aten_gemm_kernels() else []
if use_triton_template(layout):
for config in bmm_configs(m, n, k, device_type=ir.get_device_type(mat1)):
bmm_template.maybe_append_choice(
choices,
input_nodes=(mat1, mat2),
layout=layout,
**mm_options(config, m, n, k, layout),
)
static_shape, is_nonzero = _is_static_problem(layout)
if static_shape and is_nonzero and use_cutlass_template(layout, m, n, k):
from ..codegen.cuda.gemm_template import CUTLASS3xGemmTemplate
CUTLASS3xGemmTemplate.add_cutlass_gemm_choices(choices, layout, [mat1, mat2])
if use_cpp_bmm_template(layout, mat1, mat2):
from ..codegen.cpp_bmm_template import CppBmmTemplate
CppBmmTemplate.add_choices(
choices,
layout,
[mat1, mat2],
)
if use_ck_template(layout):
CKGemmTemplate.add_ck_gemm_choices(choices, layout, [mat1, mat2])
if len(choices) == 0:
log.warning("No choices for GEMM, using ATen backend as fallback")
choices.append(aten_bmm.bind((mat1, mat2), layout))
return autotune_select_algorithm("bmm", choices, [mat1, mat2], layout)
@L.register_lowering(aten.baddbmm)
def tuned_baddbmm(inp, mat1, mat2, *, alpha=1, beta=1, layout=None):
m, n, k, layout, mat1, mat2, inp = mm_args(mat1, mat2, inp, layout=layout)
# options to tune from
choices = (
[aten_baddbmm.bind((inp, mat1, mat2), layout, alpha=alpha, beta=beta)]
if use_aten_gemm_kernels()
else []
)
if use_triton_template(layout):
for config in bmm_configs(m, n, k, device_type=ir.get_device_type(mat1)):
bmm_template.maybe_append_choice(
choices,
input_nodes=(inp, mat1, mat2),
layout=layout,
**mm_options(config, m, n, k, layout),
prefix_args=1,
epilogue_fn=addmm_epilogue(layout.dtype, alpha, beta),
)
return autotune_select_algorithm("baddbmm", choices, [inp, mat1, mat2], layout)
|
