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# Copyright (c) Microsoft Corporation.
# Licensed under the MIT License.
"""A rule for GEMV and DecodeGEMV."""
from functools import reduce
from typing import List, Dict
from tvm.target import Target
from tvm.tir.function import PrimFunc
from tvm import DataType, tir
import logging
from ..base import (
normalize_prim_func,
get_output_blocks,
get_block,
)
from .base import GPUScheduleRule
from .matmul_analysis import auto_inline_producers, auto_inline_consumers
logger = logging.getLogger(__name__)
class GEMVWithDequantizeInfo(GPUScheduleRule):
"""A rule for Dequantized GEMV."""
def apply( # pylint: disable=too-many-locals,too-many-branches,too-many-return-statements
self,
func: tir.PrimFunc,
target: Target,
_: bool,
):
sch = tir.Schedule(func)
from .intrin import get_lop3_intrin_group
dequantize_info = func.attrs["dequantize_info"]
def check_dequantize_info(dequantize_info):
conditions = []
# currently only support weight only dequantization
conditions.append(len(dequantize_info) == 1)
# TODO(@lei) check if the dequantize value name is weight
return all(conditions)
if not check_dequantize_info(dequantize_info):
logger.debug("Dequantize info is not valid")
return None
(weight_decode_info,) = list(dequantize_info.values())
def check_weight_decode_info(weight_decode_info):
conditions = []
# check source format in ["int", "fp", "nf"]
conditions.append("source_format" in weight_decode_info)
conditions.append(weight_decode_info["source_format"]["format"] in
["uint", "int", "fp", "nf", "fp_e5m2", "fp_e4m3"])
# check source bits in [1, 2, 4, 8]
conditions.append(weight_decode_info["source_format"]["bits"] in [1, 2, 4, 8])
# check target format in ["float16", "int8"]
conditions.append("target_format" in weight_decode_info)
conditions.append(
weight_decode_info["target_format"] in ["float16", "bfloat16", "int8"])
return all(conditions)
if not check_weight_decode_info(weight_decode_info):
logger.debug("Weight Dequantize info is not valid")
return None
block_infos = normalize_prim_func(sch)
if block_infos is None:
return None
reduction_block: tir.schedule.BlockRV = None
for block in block_infos:
s_loops: List[tir.schedule.LoopRV] = []
r_loops: List[tir.schedule.LoopRV] = []
o_loops: List[tir.schedule.LoopRV] = []
dom_kind = block.dom_kind()
block = block.block_rv
if (any([
sch.get(loop_rv).thread_binding is not None for loop_rv in sch.get_loops(block)
]) or len(sch.get_loops(block)) == 0):
continue
for loop, iter_type in zip(sch.get_loops(block), dom_kind):
{"S": s_loops, "R": r_loops, "O": o_loops}[iter_type].append(loop)
if not s_loops:
s_loops.append(sch.add_unit_loop(block))
if len(r_loops) > 0:
reduction_block = block
def prod(iterable):
return reduce(lambda x, y: x * y, iterable, 1)
def get_vectorize_factor(target_format):
# coalesced access requires the vectorize factor to be the same as the transaction size
return 128 // DataType(target_format).bits
vec = get_vectorize_factor(weight_decode_info["target_format"])
num_warps = 1
warp_size = 32
block_b = reduction_block
output_blocks = get_output_blocks(sch, block_infos) # noqa: F841
B_decode_block = get_block(sch, block_infos, weight_decode_info["decode_block"])
block_decode_B = sch.cache_read(block_b, 1, "local")
sch.compute_inline(B_decode_block)
j, k = sch.get_loops(block_b)[-2:]
if len(sch.get_loops(block_b)) == 3:
i = sch.get_loops(block_b)[0]
sch.bind(i, "blockIdx.z")
elif len(sch.get_loops(block_b)) == 4:
# splitk case
sk, i = sch.get_loops(block_b)[:2]
sch.bind(sk, "blockIdx.y")
sch.bind(i, "blockIdx.z")
# get target dequantize buffer's idx
def get_idx(weight_decode_info: Dict):
# for LUT dequantize, the expr is LUT(w), the idx is 1
# maybe we can use a more general and structural based way
# to analysis the idx
if weight_decode_info["source_format"]["format"] == "nf":
return 1
return 0
block_shared_local_A = sch.cache_read(block_b, 0, "local")
block_shared_local_B = sch.cache_read(block_decode_B, get_idx(weight_decode_info), "local")
block_local_C = sch.cache_write(block_b, 0, "local")
auto_inline_producers(sch, block_shared_local_B)
auto_inline_consumers(sch, block_local_C)
bx, j = sch.split(j, factors=[None, num_warps])
k, tx, vk = sch.split(k, factors=[None, warp_size, vec])
# for dp4a/hfma2
inst_factor = 2 if weight_decode_info["target_format"] == "float16" else 4
_, vk = sch.split(vk, factors=[None, inst_factor])
sch.reorder(bx, j, k, tx)
sch.bind(bx, "blockIdx.x")
sch.bind(tx, "threadIdx.x")
sch.bind(j, "threadIdx.y")
self.block_size = [sch.get(tx).extent, sch.get(j).extent, 1]
self.grid_size = [sch.get(bx).extent, 1, 1]
sch.compute_at(block_decode_B, tx, preserve_unit_loops=True)
sch.compute_at(block_shared_local_A, tx, preserve_unit_loops=True)
sch.compute_at(block_shared_local_B, tx, preserve_unit_loops=True)
sch.reverse_compute_at(block_local_C, j, preserve_unit_loops=True)
block_local_a_v = sch.get_loops(block_shared_local_A)[-1]
sch.vectorize(block_local_a_v)
block_local_b_v = sch.get_loops(block_shared_local_B)[-1]
sch.vectorize(block_local_b_v)
skip_blocks = [block_shared_local_B]
if "with_scaling" in weight_decode_info and weight_decode_info["with_scaling"]:
block_local_scales = sch.cache_read(block_decode_B,
get_idx(weight_decode_info) + 1, "local")
sch.compute_at(block_local_scales, tx, preserve_unit_loops=True)
auto_inline_producers(sch, block_local_scales)
skip_blocks.append(block_local_scales)
if "with_zeros" in weight_decode_info and weight_decode_info["with_zeros"]:
block_local_zeros = sch.cache_read(block_decode_B,
get_idx(weight_decode_info) + 2, "local")
sch.compute_at(block_local_zeros, tx, preserve_unit_loops=True)
auto_inline_producers(sch, block_local_zeros)
skip_blocks.append(block_local_zeros)
auto_inline_producers(sch, block_decode_B, skip_blocks)
if ("fast_decoding" in weight_decode_info and weight_decode_info["fast_decoding"]):
source_bit = weight_decode_info["source_format"]["bits"]
out_dtype = weight_decode_info["target_format"]
intrin_info = get_lop3_intrin_group(
out_dtype=out_dtype,
storage_dtype=weight_decode_info["storage_dtype"],
source_format=weight_decode_info["source_format"]["format"],
source_bit=source_bit,
with_scaling=weight_decode_info["with_scaling"],
with_zeros=weight_decode_info["with_zeros"],
zeros_mode=weight_decode_info["zeros_mode"],
)
sch.tensorize(sch.get_loops(block_decode_B)[-1], intrin_info["compute"])
sch.annotate(block_b, ann_key="pragma_import_c", ann_val=intrin_info["c_source"])
return sch
def sch_inner_reduction_with_config( # pylint: disable=too-many-locals,too-many-branches,too-many-return-statements
self,
func: tir.PrimFunc,
config,
):
sch = tir.Schedule(func)
from .intrin import get_lop3_intrin_group
dequantize_info = func.attrs["dequantize_info"]
def check_dequantize_info(dequantize_info):
conditions = []
# currently only support weight only dequantization
conditions.append(len(dequantize_info) == 1)
# TODO(@lei) check if the dequantize value name is weight
return all(conditions)
if not check_dequantize_info(dequantize_info):
logger.debug("Dequantize info is not valid")
return None
(weight_decode_info,) = list(dequantize_info.values())
def check_weight_decode_info(weight_decode_info):
conditions = []
# check source format in ["int", "fp", "nf"]
conditions.append("source_format" in weight_decode_info)
conditions.append(weight_decode_info["source_format"]["format"] in
["uint", "int", "fp", "nf", "fp_e5m2", "fp_e4m3"])
# check source bits in [1, 2, 4, 8]
conditions.append(weight_decode_info["source_format"]["bits"] in [1, 2, 4, 8])
# check target format in ["float16", "int8"]
conditions.append("target_format" in weight_decode_info)
conditions.append(
weight_decode_info["target_format"] in ["float16", "bfloat16", "int8"])
return all(conditions)
if not check_weight_decode_info(weight_decode_info):
logger.debug("Weight Dequantize info is not valid")
return None
block_infos = normalize_prim_func(sch)
if block_infos is None:
return None
reduction_block: tir.schedule.BlockRV = None
for block in block_infos:
s_loops: List[tir.schedule.LoopRV] = []
r_loops: List[tir.schedule.LoopRV] = []
o_loops: List[tir.schedule.LoopRV] = []
dom_kind = block.dom_kind()
block = block.block_rv
if (any([
sch.get(loop_rv).thread_binding is not None for loop_rv in sch.get_loops(block)
]) or len(sch.get_loops(block)) == 0):
continue
for loop, iter_type in zip(sch.get_loops(block), dom_kind):
{"S": s_loops, "R": r_loops, "O": o_loops}[iter_type].append(loop)
if not s_loops:
s_loops.append(sch.add_unit_loop(block))
if len(r_loops) > 0:
reduction_block = block
def prod(iterable):
return reduce(lambda x, y: x * y, iterable, 1)
def get_vectorize_factor(target_format):
# coalesced access requires the vectorize factor to be the same as the transaction size
return config.arch.transaction_size[-1] // DataType(target_format).bits
vec = get_vectorize_factor(weight_decode_info["target_format"])
num_warps = int(prod(config.thread))
warp_size = int(prod(config.reduce_thread))
block_b = reduction_block
output_blocks = get_output_blocks(sch, block_infos) # noqa: F841
B_decode_block = get_block(sch, block_infos, weight_decode_info["decode_block"])
block_decode_B = sch.cache_read(block_b, 1, "local")
sch.compute_inline(B_decode_block)
j, k = sch.get_loops(block_b)[-2:]
if len(sch.get_loops(block_b)) == 3:
i = sch.get_loops(block_b)[0]
sch.bind(i, "blockIdx.z")
elif len(sch.get_loops(block_b)) == 4:
# splitk case
sk, i = sch.get_loops(block_b)[:2]
sch.bind(sk, "blockIdx.y")
sch.bind(i, "blockIdx.z")
assert len(config.thread) == 2, "SplitK only support 2D thread config"
num_warps = int(num_warps // config.thread[0])
# get target dequantize buffer's idx
def get_idx(weight_decode_info: Dict):
# for LUT dequantize, the expr is LUT(w), the idx is 1
# maybe we can use a more general and structural based way
# to analysis the idx
if weight_decode_info["source_format"]["format"] == "nf":
return 1
return 0
block_shared_local_A = sch.cache_read(block_b, 0, "local")
block_shared_local_B = sch.cache_read(block_decode_B, get_idx(weight_decode_info), "local")
block_local_C = sch.cache_write(block_b, 0, "local")
auto_inline_producers(sch, block_shared_local_B)
auto_inline_consumers(sch, block_local_C)
bx, j = sch.split(j, factors=[None, num_warps])
k, tx, vk = sch.split(k, factors=[None, warp_size, vec])
# for dp4a/hfma2
inst_factor = 2 if weight_decode_info["target_format"] == "float16" else 4
_, vk = sch.split(vk, factors=[None, inst_factor])
sch.reorder(bx, j, k, tx)
sch.bind(bx, "blockIdx.x")
sch.bind(tx, "threadIdx.x")
sch.bind(j, "threadIdx.y")
self.block_size = [sch.get(tx).extent, sch.get(j).extent, 1]
self.grid_size = [sch.get(bx).extent, 1, 1]
sch.compute_at(block_decode_B, tx, preserve_unit_loops=True)
sch.compute_at(block_shared_local_A, tx, preserve_unit_loops=True)
sch.compute_at(block_shared_local_B, tx, preserve_unit_loops=True)
sch.reverse_compute_at(block_local_C, j, preserve_unit_loops=True)
block_local_a_v = sch.get_loops(block_shared_local_A)[-1]
sch.vectorize(block_local_a_v)
block_local_b_v = sch.get_loops(block_shared_local_B)[-1]
sch.vectorize(block_local_b_v)
skip_blocks = [block_shared_local_B]
if "with_scaling" in weight_decode_info and weight_decode_info["with_scaling"]:
block_local_scales = sch.cache_read(block_decode_B,
get_idx(weight_decode_info) + 1, "local")
sch.compute_at(block_local_scales, tx, preserve_unit_loops=True)
auto_inline_producers(sch, block_local_scales)
skip_blocks.append(block_local_scales)
if "with_zeros" in weight_decode_info and weight_decode_info["with_zeros"]:
block_local_zeros = sch.cache_read(block_decode_B,
get_idx(weight_decode_info) + 2, "local")
sch.compute_at(block_local_zeros, tx, preserve_unit_loops=True)
auto_inline_producers(sch, block_local_zeros)
skip_blocks.append(block_local_zeros)
auto_inline_producers(sch, block_decode_B, skip_blocks)
if ("fast_decoding" in weight_decode_info and weight_decode_info["fast_decoding"]):
source_bit = weight_decode_info["source_format"]["bits"]
out_dtype = weight_decode_info["target_format"]
intrin_info = get_lop3_intrin_group(
out_dtype=out_dtype,
storage_dtype=weight_decode_info["storage_dtype"],
source_format=weight_decode_info["source_format"]["format"],
source_bit=source_bit,
with_scaling=weight_decode_info["with_scaling"],
with_zeros=weight_decode_info["with_zeros"],
zeros_mode=weight_decode_info["zeros_mode"],
)
sch.tensorize(sch.get_loops(block_decode_B)[-1], intrin_info["compute"])
sch.annotate(block_b, ann_key="pragma_import_c", ann_val=intrin_info["c_source"])
return sch
def apply_config(self, func: PrimFunc, config):
if any([t > 1 for t in config.reduce_thread]):
return self.sch_inner_reduction_with_config(func, config)
else:
return None
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