[rocm-libraries] ROCm/rocm-libraries#4797 (commit 1a30400)

[CK_TILE] Add CK Tile bwd weight profiler
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## Motivation

To compare old CK and CK Tile, we need to extend the current CK profiler
to support running also CK Tile instance with the same API. In order to
have the same instance coverage in CK Tile compared to the old CK, I've
added code generation from old CK configurations to CK Tile instances
using the CK Builder.

## Technical Details

- The codegen python script for CK Tile fwd convs is extended to support
also bwd weight and bwd data.
- The generated instances are added to the CMake build (target
`device_grouped_conv_bwd_weight_tile_instance`s).
- A new profiler op (`grouped_conv_bwd_weight_tile`) has been added to
the CK Profiler.
This commit is contained in:
Ville Pietilä
2026-03-04 21:50:29 +00:00
committed by assistant-librarian[bot]
parent fc1e1a5155
commit ae4e632c7d
68 changed files with 5194 additions and 196 deletions

View File

@@ -4,7 +4,6 @@
import argparse
from pathlib import Path
class ConvInstanceTemplateParams:
def __init__(
self,
@@ -84,12 +83,37 @@ def get_dtype(problem_name):
if problem_name.find("bf16") != -1:
return "ck_tile::bf16_t"
else:
raise RuntimeError("wrong dtype")
raise RuntimeError("Cannot parse data type from problem name: " + problem_name)
def get_k_mfma(dtype, m_per_xdl, n_per_xdl):
if m_per_xdl != n_per_xdl:
raise RuntimeError("Not supported")
if dtype == "float":
if m_per_xdl == 32:
return 2
else:
return 4
else:
if m_per_xdl == 32:
return 8
else:
return 16
def check_vectors(a_scalar_per_vector, b_scalar_per_vector, c_scalar_per_vector):
if a_scalar_per_vector != 1 and a_scalar_per_vector % 2 != 0:
return False
if b_scalar_per_vector != 1 and b_scalar_per_vector % 2 != 0:
return False
if c_scalar_per_vector != 1 and c_scalar_per_vector % 2 != 0:
return False
return True
def generate_calls_inc(instances, problem_name, direction, filter_pattern):
generate_dir = Path(__file__).resolve().parent
with open(f"{generate_dir}/{problem_name}_calls.inc", "w") as f:
output_dir = Path(f"{generate_dir}/instances/{direction}")
output_dir.mkdir(parents=True, exist_ok=True)
with open(f"{generate_dir}/instances/{direction}/{problem_name}_calls.inc", "w") as f:
if problem_name.find(filter_pattern) == -1:
return
for instance in instances:
@@ -99,7 +123,7 @@ def generate_calls_inc(instances, problem_name, direction, filter_pattern):
def generate_defs_inc(instances, problem_name, signature, direction, filter_pattern):
generate_dir = Path(__file__).resolve().parent
with open(f"{generate_dir}/{problem_name}.inc", "w") as f:
with open(f"{generate_dir}/instances/{direction}/{problem_name}.inc", "w") as f:
if problem_name.find(filter_pattern) == -1:
return
for instance in instances:
@@ -113,34 +137,29 @@ def generate_defs_inc(instances, problem_name, signature, direction, filter_patt
)
def generate_fwd_cpp(
instances, problem_name, config, direction, signature_name, filter_pattern
):
def generate_conv_cpp(
instances, problem_name, config, direction, signature_name, filter_pattern):
for instance in instances:
if problem_name.find(filter_pattern) == -1:
break
instance_name = problem_name + "_" + str(instance.id)
generate_dir = Path(__file__).resolve().parent
directory_path = Path(f"{generate_dir}/instances/{config}")
directory_path = Path(f"{generate_dir}/instances/{direction}/{config}")
directory_path.mkdir(parents=True, exist_ok=True)
with open(
f"{generate_dir}/instances/grouped_convolution_forward_tile.cpp.in",
"r",
) as f:
template_file = "grouped_convolution_tile.cpp.in"
with open(f"{generate_dir}/instances/{template_file}", "r",) as f:
content = f.read()
content = content.replace("gen_signature", signature_name)
content = content.replace("gen_instance_name", instance_name)
content = content.replace("gen_specialization", instance.get_specialization())
content = content.replace("gen_thread_block", instance.get_thread_block())
content = content.replace("gen_block_gemm_desc", instance.get_block_gemm_desc())
content = content.replace("gen_block_transfer", instance.get_block_transfer())
content = content.replace("gen_optimizations", instance.get_optimizations())
content = content.replace("gen_signature", signature_name)
content = content.replace("gen_instance_name", instance_name)
content = content.replace("gen_specialization", instance.get_specialization())
content = content.replace("gen_thread_block", instance.get_thread_block())
content = content.replace("gen_block_gemm_desc", instance.get_block_gemm_desc())
content = content.replace("gen_block_transfer", instance.get_block_transfer())
content = content.replace("gen_optimizations", instance.get_optimizations())
with open(
f"{generate_dir}/instances/{config}/{instance_name}.cpp",
"w",
) as f:
with open(f"{generate_dir}/instances/{direction}/{config}/{instance_name}.cpp","w",) as f:
f.write(content)
@@ -172,7 +191,7 @@ def parse_fwd_instances(instances, problem_name):
num_groups_to_merge = 1
split_image = instance.find("Large") != -1
double_smem_buffer = instance.find("BlkGemmPipelineVersion: v4") != -1
num_wave_groups = 2 if instance.find("BlkGemmPipelineVersion: v5") != -1 else 1
num_wave_groups = 1
scheduler = (
"Intrawave" if instance.find("BlkGemmPipelineScheduler") == -1 else args[14]
)
@@ -231,6 +250,189 @@ def parse_fwd_instances(instances, problem_name):
convs.append(conv)
return convs
def parse_instance_string(instance_string):
"""Parse instance string, treating Seq(...) as a single parameter."""
params = []
current_param = ""
paren_depth = 0
for char in instance_string:
if char == '(':
paren_depth += 1
current_param += char
elif char == ')':
paren_depth -= 1
current_param += char
elif char == ',' and paren_depth == 0:
# Only split on comma if we're not inside parentheses
params.append(current_param.strip())
current_param = ""
else:
current_param += char
# Add the last parameter
if current_param.strip():
params.append(current_param.strip())
return params
def parse_bwd_weight_instances(instances, problem_name):
convs = []
for instance_id, instance in enumerate(instances):
if instance.find("#") != -1 or instance.find(";") != -1:
continue
device_op_name = instance.split("<")[0]
start = instance.index('<') + 1
end = instance.rindex('>')
params_str = instance[start:end]
args = parse_instance_string(params_str)
is_v3_instance = instance.find("Xdl_CShuffleV3") != -1
is_two_stage_instance = instance.find("TwoStage") != -1
is_explicit_gemm = device_op_name.find("Explicit") != -1
if is_explicit_gemm:
gemm_params = device_op_name = instance.split("<")[2].split(">")[1].split(",")
args = [param.split(":")[1].strip() for param in gemm_params]
spec = "Default"
block_size = int(args[0])
mnk_per_block = args[1].split("x")
m_per_block = int(mnk_per_block[0])
n_per_block = int(mnk_per_block[1])
k_per_block = int(mnk_per_block[2])
wave_tile = args[2].split("x")
m_per_xdl = int(wave_tile[0])
n_per_xdl = int(wave_tile[1])
k1_values = args[3].split("x")
ak1 = int(k1_values[0])
bk1 = int(k1_values[1])
k1 = min(ak1, bk1)
wave_map = args[4].split("x")
m_xdl_per_wave = int(wave_map[0])
n_xdl_per_wave = int(wave_map[1])
vector_read = args[5].split("x")
a_scalar_per_vector = int(vector_read[0])
b_scalar_per_vector = int(vector_read[1])
c_scalar_per_vector_seq = [int(x) for x in vector_read[2].strip("Seq").strip("(").strip(")").split(",")]
if len(set(c_scalar_per_vector_seq)) != 1:
raise RuntimeError(f"c_scalar_per_vector must be the same across all waves for instance {instance_id} with device op {device_op_name}. Found values: {c_scalar_per_vector_seq}")
c_scalar_per_vector = c_scalar_per_vector_seq[0]
num_groups_to_merge = 1
# Block GEMM pipeline parameters
blk_gemm_pipeline_schduler = args[6]
blk_gemm_pipeline_version = args[7]
else:
spec = args[11]
block_size = int(args[12])
m_per_block = int(args[13])
n_per_block = int(args[14])
k1 = int(args[16])
m_per_xdl = int(args[17])
n_per_xdl = int(args[18])
m_xdl_per_wave = int(args[19])
n_xdl_per_wave = int(args[20])
a_scalar_per_vector = int(args[25])
b_scalar_per_vector = int(args[32])
c_scalar_per_vector = int(args[38])
if is_v3_instance or is_two_stage_instance:
k_per_block = int(args[15])
else:
k0_per_block = int(args[15])
k_per_block = k0_per_block * k1
if is_v3_instance:
if len(args) != 45:
raise RuntimeError(f"Wrong number of parameters in the V3 XDL CShuffle instance string: {instance}")
num_groups_to_merge = int(args[44])
# Block GEMM pipeline parameters
blk_gemm_pipeline_schduler = args[39]
blk_gemm_pipeline_version = args[40]
elif is_two_stage_instance:
print(f"Skipping instance {instance_id} with device op {device_op_name} since it's not supported yet.")
continue
else:
# Regular V1 XDL CShuffle instance
if len(args) != 43:
raise RuntimeError(f"Wrong number of parameters in the XDL CShuffle instance string: {instance}")
num_groups_to_merge = 1
# Block GEMM pipeline parameters
blk_gemm_pipeline_schduler = "Intrawave"
blk_gemm_pipeline_version = "v1"
# Common part to all solvers.
# Sanity check for Block GEMM pipeline parameters
# Scheduler must be either Intrawave or Interwave.
# Version must be from v1 to v5
if blk_gemm_pipeline_schduler not in ["Intrawave", "Interwave"]:
raise RuntimeError(f"Invalid Block GEMM pipeline scheduler: {blk_gemm_pipeline_schduler} in instance: {instance}")
if blk_gemm_pipeline_version not in ["v1", "v2", "v3", "v4", "v5"]:
raise RuntimeError(f"Invalid Block GEMM pipeline version: {blk_gemm_pipeline_version} in instance: {instance}")
split_image = instance.find("Large") != -1
double_smem_buffer = blk_gemm_pipeline_version == "v4"
num_wave_groups = 1
scheduler = blk_gemm_pipeline_schduler
pipeline_version = blk_gemm_pipeline_version.upper()
# OLd CK pipeline version V5 maps to V6 for CK Tile
if pipeline_version == "V5":
pipeline_version = "V6"
m_warp = int(m_per_block / (m_per_xdl * m_xdl_per_wave))
n_warp = int(n_per_block / (n_per_xdl * n_xdl_per_wave))
warp_size = 64
k_warp = int(block_size / (warp_size * m_warp * n_warp))
dtype = get_dtype(problem_name)
k_per_xdl = max(k1, get_k_mfma(dtype, m_per_xdl, n_per_xdl))
if check_vectors(a_scalar_per_vector, b_scalar_per_vector, c_scalar_per_vector) == False:
print(f"Skipping instance {instance_id} with irregular load since it's not supported yet.")
continue
conv = ConvInstanceTemplateParams(
spec,
[m_per_block, n_per_block, k_per_block],
[m_warp, n_warp, k_warp],
[m_per_xdl, n_per_xdl, k_per_xdl],
double_smem_buffer,
num_wave_groups,
pipeline_version,
scheduler,
[a_scalar_per_vector, b_scalar_per_vector, c_scalar_per_vector],
num_groups_to_merge,
split_image,
is_explicit_gemm,
instance_id,
)
convs.append(conv)
return convs
def parse_bwd_data_instances(instances, problem_name):
convs = []
print("Parsing backward data instances is not supported yet, skipping all instances.")
# TODO: Implement parsing logic for backward data instances.
return convs
def generate_instances_fwd(instances, problem_name, config, filter_pattern):
direction = "forward"
@@ -244,10 +446,62 @@ def generate_instances_fwd(instances, problem_name, config, filter_pattern):
direction,
filter_pattern,
)
generate_fwd_cpp(
generate_conv_cpp(
instances, problem_name, config, direction, signature_name, filter_pattern
)
def generate_instances_bwd_weight(instances, problem_name, config, filter_pattern):
direction = "backward_weight"
signature_name = f"SIGNATURE_{config.upper()}_BWD_WEIGHT"
instances = parse_bwd_weight_instances(instances, problem_name)
generate_calls_inc(instances, problem_name, direction, filter_pattern)
generate_defs_inc(
instances,
problem_name,
signature_name,
direction,
filter_pattern,
)
generate_conv_cpp(
instances, problem_name, config, direction, signature_name, filter_pattern
)
def generate_instances_bwd_data(instances, problem_name, config, filter_pattern):
direction = "backward_data"
signature_name = f"SIGNATURE_{config.upper()}_BWD_DATA"
instances = parse_bwd_data_instances(instances, problem_name)
generate_calls_inc(instances, problem_name, direction, filter_pattern)
generate_defs_inc(
instances,
problem_name,
signature_name,
direction,
filter_pattern,
)
generate_conv_cpp(
instances, problem_name, config, direction, signature_name, filter_pattern
)
def process_direction(configs, direction, generate_func, configs_prefix, filter_pattern):
"""Helper function to process a single direction."""
for config in configs:
instances = []
generate_dir = Path(__file__).resolve().parent
config_path = f"{generate_dir}/configs/{direction}/{configs_prefix}/{config}.conf"
with open(config_path, "r") as file:
instances = file.readlines()
# Determine problem name based on direction
if direction == "forward":
problem_name = f"grouped_convolution_forward_tile_{config}"
elif direction == "backward_weight":
problem_name = f"grouped_convolution_backward_weight_tile_{config}"
elif direction == "backward_data":
problem_name = f"grouped_convolution_backward_data_tile_{config}"
else:
raise RuntimeError(f"Unknown direction: {direction}")
generate_func(instances, problem_name, config, filter_pattern)
if __name__ == "__main__":
fwd_configs = [
@@ -259,6 +513,25 @@ if __name__ == "__main__":
"ndhwgc_bf16",
]
# FP32 doesn't work for bwd weigth currently
bwd_weight_configs = [
"nhwgc_fp32",
"nhwgc_fp16",
"nhwgc_bf16",
"ndhwgc_fp32",
"ndhwgc_fp16",
"ndhwgc_bf16",
]
bwd_data_configs = [
"nhwgc_fp32",
"nhwgc_fp16",
"nhwgc_bf16",
"ndhwgc_fp32",
"ndhwgc_fp16",
"ndhwgc_bf16",
]
parser = argparse.ArgumentParser(
description="Generate grouped conv CK Tile instances."
)
@@ -275,6 +548,13 @@ if __name__ == "__main__":
default="profiler",
help="Generator modes. compilation - empty instance list, tests - limited instance list, profiler - generate all instances",
)
parser.add_argument(
"--direction",
choices=["forward", "backward_weight", "backward_data", "all"],
type=str,
default="all",
help="Convolution direction for which to generate instances."
)
args = parser.parse_args()
# apply empty filter
@@ -288,11 +568,15 @@ if __name__ == "__main__":
else:
raise RuntimeError("wrong mode")
for config in fwd_configs:
instances = []
generate_dir = Path(__file__).resolve().parent
config_path = f"{generate_dir}/configs/{configs_prefix}/{config}.conf"
with open(config_path, "r") as file:
instances = file.readlines()
problem_name = f"grouped_convolution_forward_tile_{config}"
generate_instances_fwd(instances, problem_name, config, args.filter_pattern)
match args.direction:
case "forward":
process_direction(fwd_configs, args.direction, generate_instances_fwd, configs_prefix, args.filter_pattern)
case "backward_weight":
process_direction(bwd_weight_configs, args.direction, generate_instances_bwd_weight, configs_prefix, args.filter_pattern)
case "backward_data":
process_direction(bwd_data_configs, args.direction, generate_instances_bwd_data, configs_prefix, args.filter_pattern)
case "all":
process_direction(fwd_configs, "forward", generate_instances_fwd, configs_prefix, args.filter_pattern)
process_direction(bwd_weight_configs, "backward_weight", generate_instances_bwd_weight, configs_prefix, args.filter_pattern)
process_direction(bwd_data_configs, "backward_data", generate_instances_bwd_data, configs_prefix, args.filter_pattern)