mirror of
https://github.com/ROCm/composable_kernel.git
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ae4e632c7d
[CK_TILE] Add CK Tile bwd weight profiler MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit ## 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.
583 lines
22 KiB
Python
Executable File
583 lines
22 KiB
Python
Executable File
# Copyright (c) Advanced Micro Devices, Inc., or its affiliates.
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# SPDX-License-Identifier: MIT
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import argparse
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from pathlib import Path
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class ConvInstanceTemplateParams:
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def __init__(
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self,
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specialization,
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tile_size,
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warps,
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warp_tile,
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double_smem_buffer,
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num_wave_groups,
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pipeline_version,
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scheduler,
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scalar_per_vector,
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num_groups_to_merge,
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split_image,
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explicit_gemm,
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id,
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):
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self.specialization = specialization
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self.tile_size = tile_size
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self.warps = warps
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self.warp_tile = warp_tile
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self.double_smem_buffer = double_smem_buffer
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self.num_wave_groups = num_wave_groups
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self.pipeline_version = pipeline_version
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self.scheduler = scheduler
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self.scalar_per_vector = scalar_per_vector
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self.num_groups_to_merge = num_groups_to_merge
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self.split_image = split_image
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self.explicit_gemm = explicit_gemm
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self.id = id
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def get_optimizations(self):
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explicit_gemm = "true" if self.explicit_gemm else "false"
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split_image = "true" if self.split_image else "false"
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num_groups_to_merge = str(self.num_groups_to_merge)
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return f"ckt::TileOptimizations{{.num_groups_to_merge = {num_groups_to_merge}, .split_image = {split_image}, .explicit_gemm = {explicit_gemm}}}"
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def get_specialization(self):
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namespace = "ckb::TileConvSpecialization::"
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if self.specialization == "Default" or self.specialization == "OddC":
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return namespace + "DEFAULT"
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if self.specialization == "Filter1x1Pad0":
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return namespace + "FILTER_1X1_PAD0"
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if self.specialization == "Filter1x1Stride1Pad0":
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return namespace + "FILTER_1X1_STRIDE1_PAD0"
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if self.specialization == "Filter3x3":
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return namespace + "FILTER_3x3"
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else:
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raise RuntimeError("not supported specialization")
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def get_thread_block(self):
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return f"ckt::TileThreadBlock{{.tile_size = {{.m = {self.tile_size[0]}, .n = {self.tile_size[1]}, .k = {self.tile_size[2]}}}}}"
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def get_block_gemm_desc(self):
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double_smem_buffer = "true" if self.double_smem_buffer else "false"
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scheduler = (
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"INTRAWAVE" if self.scheduler.find("Intrawave") != -1 else "INTERWAVE"
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)
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return f"""ckt::TileBlockGemm{{
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.warps = {{.m = {self.warps[0]}, .n = {self.warps[1]}, .k = {self.warps[2]}}},
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.warp_tile = {{.m = {self.warp_tile[0]}, .n = {self.warp_tile[1]}, .k = {self.warp_tile[2]}}},
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.double_smem_buffer = {double_smem_buffer},
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.num_wave_groups = {self.num_wave_groups},
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.pipeline_version = ckb::PipelineVersion::{self.pipeline_version},
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.scheduler = ckb::PipelineScheduler::{scheduler}}}"""
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def get_block_transfer(self):
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return f"""ckt::TileTransfer{{.a_scalar_per_vector = {self.scalar_per_vector[0]},
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.b_scalar_per_vector = {self.scalar_per_vector[1]}, .c_scalar_per_vector = {self.scalar_per_vector[2]}}}"""
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def get_dtype(problem_name):
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if problem_name.find("fp32") != -1:
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return "float"
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if problem_name.find("fp16") != -1:
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return "ck_tile::half_t"
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if problem_name.find("bf16") != -1:
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return "ck_tile::bf16_t"
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else:
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raise RuntimeError("Cannot parse data type from problem name: " + problem_name)
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def get_k_mfma(dtype, m_per_xdl, n_per_xdl):
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if m_per_xdl != n_per_xdl:
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raise RuntimeError("Not supported")
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if dtype == "float":
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if m_per_xdl == 32:
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return 2
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else:
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return 4
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else:
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if m_per_xdl == 32:
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return 8
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else:
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return 16
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def check_vectors(a_scalar_per_vector, b_scalar_per_vector, c_scalar_per_vector):
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if a_scalar_per_vector != 1 and a_scalar_per_vector % 2 != 0:
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return False
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if b_scalar_per_vector != 1 and b_scalar_per_vector % 2 != 0:
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return False
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if c_scalar_per_vector != 1 and c_scalar_per_vector % 2 != 0:
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return False
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return True
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def generate_calls_inc(instances, problem_name, direction, filter_pattern):
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generate_dir = Path(__file__).resolve().parent
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output_dir = Path(f"{generate_dir}/instances/{direction}")
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output_dir.mkdir(parents=True, exist_ok=True)
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with open(f"{generate_dir}/instances/{direction}/{problem_name}_calls.inc", "w") as f:
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if problem_name.find(filter_pattern) == -1:
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return
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for instance in instances:
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instance_name = problem_name + "_" + str(instance.id)
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f.write(f"run_alg(run_{instance_name});\n")
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def generate_defs_inc(instances, problem_name, signature, direction, filter_pattern):
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generate_dir = Path(__file__).resolve().parent
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with open(f"{generate_dir}/instances/{direction}/{problem_name}.inc", "w") as f:
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if problem_name.find(filter_pattern) == -1:
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return
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for instance in instances:
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instance_name = problem_name + "_" + str(instance.id)
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f.write(
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f"std::tuple<bool, float, std::string> run_{instance_name}(\n"
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f" const ckt::Args<{signature}>& args,\n"
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f" const ckt::Inputs<{signature}>& inputs,\n"
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f" const ckt::Outputs<{signature}>& outputs,\n"
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f" const ck_tile::stream_config& s_conf);\n"
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)
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def generate_conv_cpp(
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instances, problem_name, config, direction, signature_name, filter_pattern):
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for instance in instances:
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if problem_name.find(filter_pattern) == -1:
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break
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instance_name = problem_name + "_" + str(instance.id)
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generate_dir = Path(__file__).resolve().parent
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directory_path = Path(f"{generate_dir}/instances/{direction}/{config}")
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directory_path.mkdir(parents=True, exist_ok=True)
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template_file = "grouped_convolution_tile.cpp.in"
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with open(f"{generate_dir}/instances/{template_file}", "r",) as f:
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content = f.read()
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content = content.replace("gen_signature", signature_name)
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content = content.replace("gen_instance_name", instance_name)
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content = content.replace("gen_specialization", instance.get_specialization())
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content = content.replace("gen_thread_block", instance.get_thread_block())
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content = content.replace("gen_block_gemm_desc", instance.get_block_gemm_desc())
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content = content.replace("gen_block_transfer", instance.get_block_transfer())
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content = content.replace("gen_optimizations", instance.get_optimizations())
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with open(f"{generate_dir}/instances/{direction}/{config}/{instance_name}.cpp","w",) as f:
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f.write(content)
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def parse_fwd_instances(instances, problem_name):
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convs = []
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for instance_id, instance in enumerate(instances):
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if instance.find("#") != -1 or instance.find(";") != -1:
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continue
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instance_args_list = instance[instance.find("<") + 1 : instance.find(">")]
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args = instance_args_list.split(", ")
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block_size = int(args[0])
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m_per_block = int(args[1])
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n_per_block = int(args[2])
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k_per_block = int(args[3])
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spec = args[4]
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m_per_xdl = int(args[5])
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n_per_xdl = int(args[6])
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m_xdl_per_wave = int(args[7])
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n_xdl_per_wave = int(args[8])
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a_scalar_per_vector = int(args[9])
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b_scalar_per_vector = int(args[10])
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c_scalar_per_vector = int(args[11])
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if len(args) == 15:
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num_groups_to_merge = int(args[14])
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elif len(args) != 16 and len(args) != 14:
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raise RuntimeError("wrong number of parameters")
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else:
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num_groups_to_merge = 1
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split_image = instance.find("Large") != -1
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double_smem_buffer = instance.find("BlkGemmPipelineVersion: v4") != -1
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num_wave_groups = 1
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scheduler = (
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"Intrawave" if instance.find("BlkGemmPipelineScheduler") == -1 else args[14]
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)
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pipeline_version = (
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"v1" if instance.find("BlkGemmPipelineVersion") == -1 else args[15]
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)
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# Replace pipeline if Direct Load
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if instance.find("DirectLoad") != -1:
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if instance.find("BlkGemmPipelineVersion: v1") != -1:
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pipeline_version = "ASYNC_V1"
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elif instance.find("BlkGemmPipelineVersion: v4") != -1:
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pipeline_version = "ASYNC_V4"
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else:
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raise RuntimeError("not supported pipeline for direct load")
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else:
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pipeline_version = f"""V{pipeline_version[-1:]}"""
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m_warp = int(m_per_block / (m_per_xdl * m_xdl_per_wave))
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n_warp = int(n_per_block / (n_per_xdl * n_xdl_per_wave))
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warp_size = 64
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k_warp = int(block_size / (warp_size * m_warp * n_warp))
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dtype = get_dtype(problem_name)
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# TODO: Make it more flexible
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# k_per_xdl = f"ck_tile::get_k_warp_tile<{dtype}, {m_per_xdl}>()"
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if dtype == "float":
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if m_per_xdl == 32:
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if instance.find("BlkGemmPipelineVersion") == -1:
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k_per_xdl = 4
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else:
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# Increase for universal gemm
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k_per_xdl = 8
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else:
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k_per_xdl = 8
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else:
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if m_per_xdl == 32:
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k_per_xdl = 16
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else:
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k_per_xdl = 32
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k_per_xdl = min(k_per_xdl, k_per_block)
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conv = ConvInstanceTemplateParams(
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spec,
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[m_per_block, n_per_block, k_per_block],
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[m_warp, n_warp, k_warp],
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[m_per_xdl, n_per_xdl, k_per_xdl],
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double_smem_buffer,
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num_wave_groups,
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pipeline_version,
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scheduler,
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[a_scalar_per_vector, b_scalar_per_vector, c_scalar_per_vector],
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num_groups_to_merge,
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split_image,
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False,
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instance_id,
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)
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convs.append(conv)
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return convs
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def parse_instance_string(instance_string):
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"""Parse instance string, treating Seq(...) as a single parameter."""
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params = []
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current_param = ""
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paren_depth = 0
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for char in instance_string:
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if char == '(':
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paren_depth += 1
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current_param += char
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elif char == ')':
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paren_depth -= 1
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current_param += char
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elif char == ',' and paren_depth == 0:
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# Only split on comma if we're not inside parentheses
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params.append(current_param.strip())
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current_param = ""
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else:
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current_param += char
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# Add the last parameter
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if current_param.strip():
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params.append(current_param.strip())
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return params
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def parse_bwd_weight_instances(instances, problem_name):
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convs = []
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for instance_id, instance in enumerate(instances):
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if instance.find("#") != -1 or instance.find(";") != -1:
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continue
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device_op_name = instance.split("<")[0]
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start = instance.index('<') + 1
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end = instance.rindex('>')
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params_str = instance[start:end]
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args = parse_instance_string(params_str)
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is_v3_instance = instance.find("Xdl_CShuffleV3") != -1
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is_two_stage_instance = instance.find("TwoStage") != -1
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is_explicit_gemm = device_op_name.find("Explicit") != -1
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if is_explicit_gemm:
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gemm_params = device_op_name = instance.split("<")[2].split(">")[1].split(",")
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args = [param.split(":")[1].strip() for param in gemm_params]
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spec = "Default"
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block_size = int(args[0])
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mnk_per_block = args[1].split("x")
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m_per_block = int(mnk_per_block[0])
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n_per_block = int(mnk_per_block[1])
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k_per_block = int(mnk_per_block[2])
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wave_tile = args[2].split("x")
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m_per_xdl = int(wave_tile[0])
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n_per_xdl = int(wave_tile[1])
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k1_values = args[3].split("x")
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ak1 = int(k1_values[0])
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bk1 = int(k1_values[1])
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k1 = min(ak1, bk1)
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wave_map = args[4].split("x")
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m_xdl_per_wave = int(wave_map[0])
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n_xdl_per_wave = int(wave_map[1])
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vector_read = args[5].split("x")
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a_scalar_per_vector = int(vector_read[0])
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b_scalar_per_vector = int(vector_read[1])
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c_scalar_per_vector_seq = [int(x) for x in vector_read[2].strip("Seq").strip("(").strip(")").split(",")]
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if len(set(c_scalar_per_vector_seq)) != 1:
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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}")
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c_scalar_per_vector = c_scalar_per_vector_seq[0]
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num_groups_to_merge = 1
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# Block GEMM pipeline parameters
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blk_gemm_pipeline_schduler = args[6]
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blk_gemm_pipeline_version = args[7]
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else:
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spec = args[11]
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block_size = int(args[12])
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m_per_block = int(args[13])
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n_per_block = int(args[14])
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k1 = int(args[16])
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m_per_xdl = int(args[17])
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n_per_xdl = int(args[18])
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m_xdl_per_wave = int(args[19])
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n_xdl_per_wave = int(args[20])
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a_scalar_per_vector = int(args[25])
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b_scalar_per_vector = int(args[32])
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c_scalar_per_vector = int(args[38])
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if is_v3_instance or is_two_stage_instance:
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k_per_block = int(args[15])
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else:
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k0_per_block = int(args[15])
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k_per_block = k0_per_block * k1
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if is_v3_instance:
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if len(args) != 45:
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raise RuntimeError(f"Wrong number of parameters in the V3 XDL CShuffle instance string: {instance}")
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num_groups_to_merge = int(args[44])
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# Block GEMM pipeline parameters
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blk_gemm_pipeline_schduler = args[39]
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blk_gemm_pipeline_version = args[40]
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elif is_two_stage_instance:
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print(f"Skipping instance {instance_id} with device op {device_op_name} since it's not supported yet.")
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continue
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else:
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# Regular V1 XDL CShuffle instance
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if len(args) != 43:
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raise RuntimeError(f"Wrong number of parameters in the XDL CShuffle instance string: {instance}")
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num_groups_to_merge = 1
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# Block GEMM pipeline parameters
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blk_gemm_pipeline_schduler = "Intrawave"
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blk_gemm_pipeline_version = "v1"
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# Common part to all solvers.
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# Sanity check for Block GEMM pipeline parameters
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# Scheduler must be either Intrawave or Interwave.
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# Version must be from v1 to v5
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if blk_gemm_pipeline_schduler not in ["Intrawave", "Interwave"]:
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raise RuntimeError(f"Invalid Block GEMM pipeline scheduler: {blk_gemm_pipeline_schduler} in instance: {instance}")
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if blk_gemm_pipeline_version not in ["v1", "v2", "v3", "v4", "v5"]:
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raise RuntimeError(f"Invalid Block GEMM pipeline version: {blk_gemm_pipeline_version} in instance: {instance}")
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split_image = instance.find("Large") != -1
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double_smem_buffer = blk_gemm_pipeline_version == "v4"
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num_wave_groups = 1
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scheduler = blk_gemm_pipeline_schduler
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pipeline_version = blk_gemm_pipeline_version.upper()
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# OLd CK pipeline version V5 maps to V6 for CK Tile
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if pipeline_version == "V5":
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pipeline_version = "V6"
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m_warp = int(m_per_block / (m_per_xdl * m_xdl_per_wave))
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n_warp = int(n_per_block / (n_per_xdl * n_xdl_per_wave))
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warp_size = 64
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k_warp = int(block_size / (warp_size * m_warp * n_warp))
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dtype = get_dtype(problem_name)
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k_per_xdl = max(k1, get_k_mfma(dtype, m_per_xdl, n_per_xdl))
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if check_vectors(a_scalar_per_vector, b_scalar_per_vector, c_scalar_per_vector) == False:
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print(f"Skipping instance {instance_id} with irregular load since it's not supported yet.")
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continue
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conv = ConvInstanceTemplateParams(
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spec,
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[m_per_block, n_per_block, k_per_block],
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[m_warp, n_warp, k_warp],
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[m_per_xdl, n_per_xdl, k_per_xdl],
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double_smem_buffer,
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num_wave_groups,
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pipeline_version,
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scheduler,
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[a_scalar_per_vector, b_scalar_per_vector, c_scalar_per_vector],
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num_groups_to_merge,
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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"
|
|
signature_name = f"SIGNATURE_{config.upper()}_FWD"
|
|
instances = parse_fwd_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_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 = [
|
|
"nhwgc_fp32",
|
|
"nhwgc_fp16",
|
|
"nhwgc_bf16",
|
|
"ndhwgc_fp32",
|
|
"ndhwgc_fp16",
|
|
"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."
|
|
)
|
|
parser.add_argument(
|
|
"--filter_pattern",
|
|
type=str,
|
|
default="convolution",
|
|
help="Filter pattern for configs.",
|
|
)
|
|
parser.add_argument(
|
|
"--mode",
|
|
choices=["compilation", "tests", "profiler"],
|
|
type=str,
|
|
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
|
|
if args.mode == "compilation":
|
|
args.filter_pattern = "empty"
|
|
configs_prefix = "profiler"
|
|
elif args.mode == "tests":
|
|
configs_prefix = "tests"
|
|
elif args.mode == "profiler":
|
|
configs_prefix = "profiler"
|
|
else:
|
|
raise RuntimeError("wrong mode")
|
|
|
|
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)
|
|
|