mirror of
https://github.com/ROCm/composable_kernel.git
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a33d98f8e2
* Renaming old code * Adding GEMM code with new Architecture * Partial Progress : Errors * Partial Progress : Working code * Changes to element wise function * Removing Debugging statements * Working GEMM Multi D code * Removing Stale Code * Address Copilot review comments * Address Copilot review comments * Changes to validation file * Changes to common code snippets * Creating common folder * Removing duplicate files * Pointing to right common file * Pointing to right common file * Pointing to right common file * Changing to VERBOSE * Changing CMAKE messages to verbose * Updating Cmake with right layout datatype configs * Working code for GEMM Multi D
603 lines
17 KiB
Python
603 lines
17 KiB
Python
#!/usr/bin/env python
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# SPDX-License-Identifier: MIT
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# Copyright (c) 2024, Advanced Micro Devices, Inc. All rights reserved.
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"""
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Validation utilities for GEMM kernel generation.
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Extracted from tile_engine_develop for consistency.
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"""
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import logging
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from typing import Tuple, List
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# Element size mapping for different data types
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ELEMENT_SIZE_MAP = {
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"fp16": 2,
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"bf16": 2,
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"int8": 1,
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"fp8": 1,
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"bf8": 1,
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"int4": 0.5,
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"int32": 4,
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"fp32": 4,
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"fp64": 8,
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}
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WARP_SUPPORTED_COMBINATIONS = {
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"gfx90a": [
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[1, 4, 1],
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[2, 2, 1],
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[4, 1, 1],
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],
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"gfx942": [
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[1, 4, 1],
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[2, 2, 1],
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[4, 1, 1],
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],
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"gfx950": [
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[1, 4, 1],
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[2, 2, 1],
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[4, 1, 1],
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],
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"gfx1201": [
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[2, 4, 1],
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[1, 8, 1],
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[8, 1, 1],
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[4, 2, 1],
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],
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}
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# [TODO] Handle this while moving code to commons
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# Supported warp tile combinations for different GPU architectures and data types
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WARP_TILE_SUPPORTED_COMBINATIONS = {
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"gfx90a": {
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"fp16_fp16_fp16": [
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[32, 32, 8],
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[16, 16, 16],
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[32, 32, 16],
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[16, 16, 32],
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[4, 64, 16],
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[64, 4, 16],
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],
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"bf16_bf16_bf16": [
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[32, 32, 8],
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[16, 16, 16],
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[32, 32, 16],
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[16, 16, 32],
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[4, 64, 16],
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[64, 4, 16],
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],
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"fp8_fp8_fp16": [[32, 32, 16], [32, 32, 32]],
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"bf8_bf8_fp16": [[32, 32, 16], [32, 32, 32]],
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},
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"gfx942": {
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"fp16_fp16_fp16": [
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[32, 32, 8],
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[16, 16, 16],
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[32, 32, 16],
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[16, 16, 32],
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[4, 64, 16],
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[64, 4, 16],
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],
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"bf16_bf16_bf16": [
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[32, 32, 8],
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[16, 16, 16],
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[32, 32, 16],
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[16, 16, 32],
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[4, 64, 16],
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[64, 4, 16],
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],
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"fp8_fp8_fp16": [[32, 32, 16], [32, 32, 32], [16, 16, 32], [16, 16, 64]],
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"bf8_bf8_fp16": [[32, 32, 16], [32, 32, 32], [16, 16, 64], [16, 16, 32]],
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"int8_int8_int32": [[16, 16, 32], [32, 32, 16]],
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},
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"gfx950": {
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"fp16_fp16_fp16": [
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[32, 32, 8],
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[16, 16, 16],
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[32, 32, 16],
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[16, 16, 32],
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[4, 64, 16],
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[64, 4, 16],
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],
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"bf16_bf16_bf16": [
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[32, 32, 8],
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[16, 16, 16],
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[32, 32, 16],
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[16, 16, 32],
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[4, 64, 16],
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[64, 4, 16],
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],
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"fp8_fp8_fp16": [
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[32, 32, 16],
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[32, 32, 32],
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[16, 16, 32],
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[16, 16, 64],
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[16, 16, 128],
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[32, 32, 64],
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],
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"bf8_bf8_fp16": [
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[32, 32, 16],
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[32, 32, 32],
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[16, 16, 64],
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[16, 16, 32],
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[16, 16, 128],
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[32, 32, 64],
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],
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},
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"gfx1201": { # Check how to handle for GEMM and Multi D
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"fp16_fp16_fp16": [
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[16, 16, 16],
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],
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},
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}
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# Unsupported trait combinations
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TRAIT_UNSUPPORTED_COMBINATIONS = {
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("compv3", "cshuffle", "interwave"),
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("compv3", "default", "interwave"),
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("compv4", "cshuffle", "interwave"),
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("compv4", "default", "interwave"),
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}
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def element_size(data_type: str) -> float:
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"""Calculate the size (in bytes) of a single element for given data type."""
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data_type = data_type.lower()
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if data_type not in ELEMENT_SIZE_MAP:
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raise ValueError(f"Unsupported data type: {data_type}")
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return ELEMENT_SIZE_MAP[data_type]
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def is_trait_combination_valid(pipeline: str, epilogue: str, scheduler: str) -> bool:
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"""Check if a trait combination is valid."""
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return (pipeline, epilogue, scheduler) not in TRAIT_UNSUPPORTED_COMBINATIONS
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def validate_warp_configuration(
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warp_m: int,
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warp_n: int,
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warp_k: int,
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gpu_name: str,
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) -> bool:
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"""Validate warp configuration."""
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current_combination = [warp_m, warp_n, warp_k]
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allowed_combinations = WARP_SUPPORTED_COMBINATIONS.get(gpu_name, {})
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if not allowed_combinations:
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# If GPU not recognized, try to be permissive but log warning
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logging.warning(f"No warp_[m/n/k] combinations found for GPU: {gpu_name}")
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return True
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# Check if current combination is in the allowed list
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if current_combination not in allowed_combinations:
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return False
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return True
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def validate_dimension_alignment(
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tile_m: int,
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tile_n: int,
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tile_k: int,
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warp_m: int,
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warp_n: int,
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warp_k: int,
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warp_tile_m: int,
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warp_tile_n: int,
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warp_tile_k: int,
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) -> Tuple[bool, List[str]]:
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"""Check if tile dimensions are properly aligned with warp dimensions."""
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alignment_issues = []
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if tile_m % (warp_m * warp_tile_m) != 0:
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alignment_issues.append(
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f"tile_m({tile_m}) % [{warp_m}x{warp_tile_m}] = {tile_m % (warp_m * warp_tile_m)}"
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)
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if tile_n % (warp_n * warp_tile_n) != 0:
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alignment_issues.append(
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f"tile_n({tile_n}) % [{warp_n}x{warp_tile_n}] = {tile_n % (warp_n * warp_tile_n)}"
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)
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if tile_k % (warp_k * warp_tile_k) != 0:
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alignment_issues.append(
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f"tile_k({tile_k}) % [{warp_k}x{warp_tile_k}] = {tile_k % (warp_k * warp_tile_k)}"
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)
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return len(alignment_issues) == 0, alignment_issues
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def validate_lds_capacity(
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tile_m: int,
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tile_n: int,
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tile_k: int,
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a_datatype: str,
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b_datatype: str,
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pipeline: str,
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) -> Tuple[bool, str]:
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"""Validate LDS capacity requirements."""
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matrix_a_size = (tile_m * tile_k) * element_size(a_datatype)
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matrix_b_size = (tile_n * tile_k) * element_size(b_datatype)
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total_tile_in_lds = matrix_a_size + matrix_b_size
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max_tile_size = 2**15 if pipeline == "compv4" else 2**16
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if total_tile_in_lds > max_tile_size:
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error_msg = (
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f"LDS capacity exceeded: Total required {total_tile_in_lds:,}B ({total_tile_in_lds / 1024:.1f}KB) > "
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f"maximum allowed {max_tile_size:,}B ({max_tile_size / 1024}KB). Breakdown:\n"
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f"- Matrix A ({a_datatype}): {tile_m}x{tile_k} = {matrix_a_size:,}B\n"
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f"- Matrix B ({b_datatype}): {tile_n}x{tile_k} = {matrix_b_size:,}B"
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)
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return False, error_msg
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return True, ""
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def validate_warp_tile_combination(
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warp_tile_m: int,
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warp_tile_n: int,
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warp_tile_k: int,
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a_datatype: str,
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b_datatype: str,
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c_datatype: str,
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gpu_name: str,
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) -> Tuple[bool, str]:
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"""Validate warp tile combination against GPU-specific supported combinations."""
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# Construct the key for looking up supported combinations
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warp_tile_key = f"{a_datatype}_{b_datatype}_{c_datatype}"
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current_combination = [warp_tile_m, warp_tile_n, warp_tile_k]
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# Check if we have GPU-specific combinations
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gpu_warp_tile_combinations = WARP_TILE_SUPPORTED_COMBINATIONS.get(gpu_name, {})
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if not gpu_warp_tile_combinations:
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# If GPU not recognized, try to be permissive but log warning
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logging.warning(f"No warp tile combinations found for GPU: {gpu_name}")
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return True, ""
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# Check if we have combinations for this data type combination
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allowed_combinations = gpu_warp_tile_combinations.get(warp_tile_key, [])
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if not allowed_combinations:
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# For data type combinations not in the list, be permissive
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logging.debug(
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f"No warp tile combinations found for data types: {warp_tile_key}"
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)
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return True, ""
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# Check if current combination is in the allowed list
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if current_combination not in allowed_combinations:
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error_msg = (
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f"Invalid warp tile combination: {current_combination} not in allowed list. "
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f"Valid combinations for '{warp_tile_key}' on {gpu_name}: {allowed_combinations}"
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)
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return False, error_msg
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return True, ""
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def is_tile_config_valid(
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tile_m: int,
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tile_n: int,
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tile_k: int,
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warp_m: int,
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warp_n: int,
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warp_k: int,
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warp_tile_m: int,
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warp_tile_n: int,
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warp_tile_k: int,
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a_datatype: str,
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b_datatype: str,
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c_datatype: str,
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pipeline: str,
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layout: str,
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gpu_target: str,
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trait_name: str = None,
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) -> bool:
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"""
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Comprehensive tile configuration validation.
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Returns True if configuration is valid, False otherwise.
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"""
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# Basic sanity checks
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if tile_m <= 0 or tile_n <= 0 or tile_k <= 0:
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return False
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if warp_m <= 0 or warp_n <= 0 or warp_k <= 0:
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return False
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if warp_tile_m <= 0 or warp_tile_n <= 0 or warp_tile_k <= 0:
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return False
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# Check that warp tiles fit within block tiles
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if warp_m * warp_tile_m > tile_m:
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return False
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if warp_n * warp_tile_n > tile_n:
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return False
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if warp_k * warp_tile_k > tile_k:
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return False
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# Validate warp configuration
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if not validate_warp_configuration(warp_m, warp_n, warp_k, gpu_target):
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logging.debug(
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f"Invalid warp configuration: warp_m({warp_m}), warp_n({warp_n}), warp_k({warp_k})"
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)
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return False
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# Validate dimension alignment
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is_aligned, alignment_issues = validate_dimension_alignment(
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tile_m,
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tile_n,
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tile_k,
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warp_m,
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warp_n,
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warp_k,
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warp_tile_m,
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warp_tile_n,
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warp_tile_k,
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)
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if not is_aligned:
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logging.debug(
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f"Dimension alignment failed: {', '.join(alignment_issues)}. "
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f"Tile dimensions {tile_m}x{tile_n}x{tile_k} must be divisible by "
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f"[warp]: {warp_m}x{warp_n}x{warp_k} x [warp_tile]: {warp_tile_m}x{warp_tile_n}x{warp_tile_k}"
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)
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return False
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# Validate LDS capacity
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lds_valid, lds_error = validate_lds_capacity(
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tile_m, tile_n, tile_k, a_datatype, b_datatype, pipeline
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)
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if not lds_valid:
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logging.debug(f"LDS validation failed: {lds_error}")
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return False
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# Validate whole workgroup cover configuration
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wr_cover_valid, wg_cover_error = validate_whole_wg_cover_configuration(
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tile_m,
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tile_n,
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tile_k,
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warp_m,
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warp_n,
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warp_k,
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layout,
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a_datatype,
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b_datatype,
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)
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if not wr_cover_valid:
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logging.debug(
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f"Whole workgroup cover configuration validation failed: {wg_cover_error}"
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)
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return False
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# Validate warp tile combination
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warp_tile_valid, warp_tile_error = validate_warp_tile_combination(
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warp_tile_m,
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warp_tile_n,
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warp_tile_k,
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a_datatype,
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b_datatype,
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c_datatype,
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gpu_target,
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)
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if not warp_tile_valid:
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logging.debug(f"Warp tile validation failed: {warp_tile_error}")
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return False
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return True
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# [TODO] Handle this while moving code to commons Add more datatype to this function if needed
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def get_dtype_string(datatype: str) -> str:
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"""Get C++ type string for datatype"""
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dtype_map = {
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"fp16": "ck_tile::fp16_t",
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"fp8": "ck_tile::fp8_t",
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"bf8": "ck_tile::bf8_t",
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"bf16": "ck_tile::bf16_t",
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"fp32": "float",
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"fp64": "double",
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}
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return dtype_map.get(datatype, "float")
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LAYOUT_MAP = {
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"r": "ck_tile::tensor_layout::gemm::RowMajor",
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"c": "ck_tile::tensor_layout::gemm::ColumnMajor",
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}
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def get_abc_layouts(layout_code: str) -> Tuple[str, str, str]:
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"""
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Return (ALayout, BLayout, CLayout) from a 3-letter code like 'rcr', 'ccr', 'crr', 'rrr'.
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"""
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code = str(layout_code).strip().lower()
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a_layout = LAYOUT_MAP[code[0]]
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b_layout = LAYOUT_MAP[code[1]]
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c_layout = LAYOUT_MAP[code[2]]
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return a_layout, b_layout, c_layout
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def get_abcd_layouts(layout_code: str) -> Tuple[str, str, str, List[str]]:
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"""
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Return (ALayout, BLayout, CLayout) from a 3-letter code like 'rcrr', 'ccrr', 'crrr', 'rrrr'.
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"""
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code = str(layout_code).strip().lower()
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a_layout = LAYOUT_MAP[code[0]]
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b_layout = LAYOUT_MAP[code[1]]
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c_layout = LAYOUT_MAP[code[2]]
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d0_layout = LAYOUT_MAP[code[3]]
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d1_layout = LAYOUT_MAP[code[3]]
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return a_layout, b_layout, c_layout, [d0_layout, d1_layout]
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def validate_whole_wg_cover_configuration(
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tile_m,
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tile_n,
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tile_k,
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warp_m,
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warp_n,
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warp_k,
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layout,
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a_datatype,
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b_datatype,
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) -> Tuple[bool, str]:
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# Validate whole workgroup cover configuration
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warp_size = 64
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NumWarps = warp_m * warp_n * warp_k
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BlockSize = NumWarps * warp_size
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XPerTile = 0
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YPerTile = 0
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vector_load_size = 0
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# A matrix validation
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if layout[0] == "r":
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vector_load_size = get_global_vector_load_size(
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BlockSize, tile_k, a_datatype, tile_m, tile_k
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)
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XPerTile = tile_k
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YPerTile = tile_m
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elif layout[0] == "c":
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vector_load_size = get_global_vector_load_size(
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BlockSize, tile_k, a_datatype, tile_m, tile_m
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)
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# Validate distribution
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XPerTile = tile_k
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YPerTile = tile_m
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wg_cover_core_valid, wg_cover_core_error = wg_cover_core_validation(
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XPerTile, YPerTile, BlockSize, vector_load_size, warp_size
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)
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if not wg_cover_core_valid:
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logging.debug(
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f"whole workgroup cover failed for Matrix A distribution: {wg_cover_core_error}"
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)
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return False, wg_cover_core_error
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XPerTile = tile_m
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YPerTile = tile_k
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wg_cover_core_valid, wg_cover_core_error = wg_cover_core_validation(
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XPerTile, YPerTile, BlockSize, vector_load_size, warp_size
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)
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if not wg_cover_core_valid:
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logging.debug(
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f"whole workgroup cover failed for Matrix A: {wg_cover_core_error}"
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)
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return False, wg_cover_core_error
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# B matrix validation
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if layout[1] == "r":
|
|
vector_load_size = get_global_vector_load_size(
|
|
BlockSize, tile_k, b_datatype, tile_n, tile_n
|
|
)
|
|
|
|
# Validate distribution
|
|
XPerTile = tile_k
|
|
YPerTile = tile_n
|
|
|
|
wg_cover_core_valid, wg_cover_core_error = wg_cover_core_validation(
|
|
XPerTile, YPerTile, BlockSize, vector_load_size, warp_size
|
|
)
|
|
|
|
if not wg_cover_core_valid:
|
|
print("I am here 3")
|
|
logging.debug(
|
|
f"whole workgroup cover failed for Matrix B distribution: {wg_cover_core_error}"
|
|
)
|
|
return False, wg_cover_core_error
|
|
|
|
XPerTile = tile_n
|
|
YPerTile = tile_k
|
|
|
|
elif layout[1] == "c":
|
|
XPerTile = tile_k
|
|
YPerTile = tile_n
|
|
|
|
vector_load_size = get_global_vector_load_size(
|
|
BlockSize, tile_k, b_datatype, tile_n, tile_k
|
|
)
|
|
|
|
wg_cover_core_valid, wg_cover_core_error = wg_cover_core_validation(
|
|
XPerTile, YPerTile, BlockSize, vector_load_size, warp_size
|
|
)
|
|
if not wg_cover_core_valid:
|
|
logging.debug(
|
|
f"whole workgroup cover failed for Matrix B: {wg_cover_core_error}"
|
|
)
|
|
return False, wg_cover_core_error
|
|
|
|
return True, ""
|
|
|
|
|
|
def wg_cover_core_validation(
|
|
XPerTile: int,
|
|
YPerTile: int,
|
|
BlockSize: int,
|
|
vector_load_size: int,
|
|
warp_size: int,
|
|
) -> Tuple[bool, str]:
|
|
if XPerTile % vector_load_size != 0:
|
|
return False, "XPerTile is not divisible by vector_load_size"
|
|
|
|
num_warps = BlockSize / warp_size
|
|
LargestVec = (XPerTile * YPerTile) / (num_warps * warp_size)
|
|
|
|
X1 = LargestVec if vector_load_size > LargestVec else vector_load_size
|
|
X0 = XPerTile / X1
|
|
Y1 = warp_size // X0
|
|
|
|
if X0 * Y1 != warp_size:
|
|
return False, "X0 * Y1 != warp_size"
|
|
|
|
return True, ""
|
|
|
|
|
|
def get_global_vector_load_size(
|
|
BlockSize: int,
|
|
KPerBlock: int,
|
|
DataType: str,
|
|
MNPerBlock: int,
|
|
XPerTile: int,
|
|
) -> int:
|
|
elements_per_thread = MNPerBlock * KPerBlock / BlockSize
|
|
PackedSize = 1
|
|
|
|
if (
|
|
PackedSize == 2
|
|
and XPerTile % (PackedSize * 32 / element_size(DataType)) == 0
|
|
and elements_per_thread % (PackedSize * 32 / element_size(DataType)) == 0
|
|
):
|
|
return PackedSize * 32 / element_size(DataType)
|
|
elif (
|
|
XPerTile % (PackedSize * 16 / element_size(DataType)) == 0
|
|
and elements_per_thread % (PackedSize * 16 / element_size(DataType)) == 0
|
|
):
|
|
return int(PackedSize * 16 / element_size(DataType))
|
|
|
|
elif (
|
|
XPerTile % (PackedSize * 8 / element_size(DataType)) == 0
|
|
and elements_per_thread % (PackedSize * 8 / element_size(DataType)) == 0
|
|
):
|
|
return int(PackedSize * 8 / element_size(DataType))
|
|
elif (
|
|
element_size(DataType) >= PackedSize * 4
|
|
and XPerTile % (PackedSize * 4 / element_size(DataType)) == 0
|
|
and elements_per_thread % (PackedSize * 4 / element_size(DataType)) == 0
|
|
):
|
|
return int(PackedSize * 4 / element_size(DataType))
|
|
elif (
|
|
element_size(DataType) >= PackedSize * 2
|
|
and XPerTile % (PackedSize * 2 / element_size(DataType)) == 0
|
|
and elements_per_thread % (PackedSize * 2 / element_size(DataType)) == 0
|
|
):
|
|
return int(PackedSize * 2 / element_size(DataType))
|
|
else:
|
|
return PackedSize
|