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# flake8: noqa: B950
# fmt: off
# This file was generated by AutoHeuristic. Do not modify it manually!
# To regenerate this file, take a look at the steps in the README.md file inside torchgen/_autoheuristic/pad_mm/
from torch._inductor.autoheuristic.autoheuristic_utils import AHContext, AHMetadata, Choice, CHOICE_COL
from torch._inductor.autoheuristic.learnedheuristic_interface import (
LearnedHeuristicRegression,
)
class PadMMA100(LearnedHeuristicRegression):
def __init__(self) -> None:
pass
def check_precondition(self, metadata: AHMetadata, context: AHContext,) -> bool:
return (
metadata.name == self.get_name()
and metadata.shared_memory == 166912
and str(metadata.device_capa) == "(8, 0)"
)
def get_feedback(self, context: AHContext, choice: Choice) -> float:
context.context_dict[CHOICE_COL] = choice
return self.predict(context)
def get_confidence_threshold(self) -> float:
return 1.7025303314066
def get_name(self) -> str:
return 'pad_mm'
def predict(self, context: AHContext) -> float:
if str(context.get_value('choice')) != 'pad':
if str(context.get_value('using_tf32')) != 'False':
if context.get_value('m*n') <= 4171264.0:
if context.get_value('m*k') <= 3999308.0:
return 1.8751469764071178
else:
if str(context.get_value('n_multiple_32')) != 'True':
return 0.9117231355626345
else:
return 1.1607689608873861
else:
if str(context.get_value('n_multiple_2')) != 'True':
if str(context.get_value('using_tf32')) != 'True':
return 0.7430382200435992
else:
return 0.8531269794448678
else:
if str(context.get_value('k_multiple_2')) != 'True':
return 0.7577181972719917
else:
return 0.8977349440424219
else:
if context.get_value('m*n') <= 1299712.0:
return 1.1669723418995592
else:
if context.get_value('mat2_stride_1') <= 45217.5:
if context.get_value('m*n') <= 55884158.0:
return 1.0262769936909601
else:
return 1.0022677428470845
else:
if context.get_value('m') <= 18478.0:
return 1.1127066261894312
else:
return 1.0337740659894263
else:
if str(context.get_value('mat1_dtype')) != 'torch.float32':
if str(context.get_value('n_multiple_2')) != 'False':
if str(context.get_value('k_multiple_2')) != 'True':
if context.get_value('mat1_stride_0') <= 561.0:
return 1.2900382135142956
else:
return 1.5761737616057887
else:
if context.get_value('num_dims_needs_padding') <= 1.5:
return 1.0472263310239422
else:
return 1.1727673465762514
else:
if context.get_value('k') <= 28238.5:
if context.get_value('k/(m*n)') <= 0.00026227018679492176:
return 1.6770542505397175
else:
return 1.3974785435105923
else:
if str(context.get_value('mat1_dtype')) != 'torch.bfloat16':
return 1.3952699800111992
else:
return 1.5759286511628336
else:
if str(context.get_value('using_tf32')) != 'False':
if context.get_value('m*n') <= 14119424.0:
return 0.8875772670422478
else:
if str(context.get_value('mat2_innermost_needs_padding')) != 'True':
return 1.1467728924377265
else:
return 1.215842963532998
else:
if context.get_value('arith_intensity') <= 396.8774871826172:
return 0.89940161869551
else:
if context.get_value('mat2_stride_1') <= 45217.5:
return 0.9964328169353532
else:
return 0.9493479238294826
|
