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CUTLASS 3.1 (#915)

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Co-authored-by: Aniket Shivam <ashivam@nvidia.com>
This commit is contained in:
ANIKET SHIVAM
2023-04-14 23:19:34 -04:00
committed by GitHub
co-authored by Aniket Shivam
parent 9b8166e3f0
commit d572cc1aab
482 changed files with 37175 additions and 16410 deletions
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examples/40_cutlass_py/customizable/gemm_grouped.py
+16 -16
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@@ -30,9 +30,9 @@
#
################################################################################
import numpy as np
import pycutlass
from pycutlass import *
from pycutlass.utils.device import device_cc
import cutlass.backend as pycutlass
from cutlass.backend import *
from cutlass.backend.utils.device import device_cc
import csv
import sys
@@ -61,7 +61,7 @@ parser.add_argument("-tacc", "--element_acc", default="float32", type=str,
help='Data type of accumulator')
parser.add_argument('-m', "--math", default="multiply_add",
type=str, choices=["multiply_add", "multiply_add_fast_bf16", "multiply_add_fast_f32"], help="math instruction")
parser.add_argument('-op', "--opcode", default="simt", type=str,
parser.add_argument('-op', "--opcode", default="Simt", type=str,
choices=["Simt", 'TensorOp'], help='This option describes whether you want to use tensor \
cores (TensorOp) or regular SIMT cores (Simt) on GPU SM')
# tile description
@@ -111,7 +111,7 @@ parser.add_argument("-pm", "--precompute_mode",
default="Device", type=str, choices=["Host", "Device"],
help="Grouped Gemm Scheduing on device only (Device) or using host precompute (Host)")
# arguments
parser.add_argument("-p", "--problem_size_dir", type=str,
parser.add_argument("-p", "--problem_size_dir", type=str, default="grouped_gemm_problem_size.csv",
help="path to the csv file contains the problem sizes")
parser.add_argument("-alpha", "--alpha", default=1.0, type=float, help="alpha")
parser.add_argument("-beta", "--beta", default=0.0, type=float, help="beta")
@@ -139,12 +139,12 @@ pycutlass.get_memory_pool(init_pool_size=2**30, max_pool_size=2**32)
np.random.seed(0)
element_a = getattr(cutlass, args.element_a)
element_b = getattr(cutlass, args.element_b)
element_c = getattr(cutlass, args.element_c)
element_acc = getattr(cutlass, args.element_acc)
element_a = getattr(cutlass_bindings, args.element_a)
element_b = getattr(cutlass_bindings, args.element_b)
element_c = getattr(cutlass_bindings, args.element_c)
element_acc = getattr(cutlass_bindings, args.element_acc)
math_operation = getattr(MathOperation, args.math)
opclass = getattr(cutlass.OpClass, args.opcode)
opclass = getattr(cutlass_bindings.OpClass, args.opcode)
math_inst = MathInstruction(
args.instruction_shape, element_a, element_b,
@@ -156,9 +156,9 @@ tile_description = TileDescription(
math_inst
)
layout_a = getattr(cutlass, args.layout_a)
layout_b = getattr(cutlass, args.layout_b)
layout_c = getattr(cutlass, args.layout_c)
layout_a = getattr(cutlass_bindings, args.layout_a)
layout_b = getattr(cutlass_bindings, args.layout_b)
layout_c = getattr(cutlass_bindings, args.layout_c)
A = TensorDescription(
element_a, layout_a, args.alignment_a
@@ -172,7 +172,7 @@ C = TensorDescription(
element_c, layout_c, args.alignment_c
)
element_epilogue = getattr(cutlass, args.element_epilogue)
element_epilogue = getattr(cutlass_bindings, args.element_epilogue)
if args.activation_function == "identity":
epilogue_functor = getattr(pycutlass, args.epilogue_functor)(
C.element, C.alignment, math_inst.element_accumulator, element_epilogue)
@@ -180,7 +180,7 @@ else:
epilogue_functor = getattr(pycutlass, "LinearCombinationGeneric")(
getattr(pycutlass, args.activation_function)(element_epilogue),
C.element, C.alignment, math_inst.element_accumulator, element_epilogue)
swizzling_functor = getattr(cutlass, args.swizzling_functor)
swizzling_functor = getattr(cutlass_bindings, args.swizzling_functor)
precompute_mode = getattr(SchedulerMode, args.precompute_mode)
operation = GemmOperationGrouped(
@@ -203,7 +203,7 @@ with open(args.problem_size_dir) as csv_file:
reader = csv.reader(csv_file)
for row in reader:
problem_sizes.append(
cutlass.gemm.GemmCoord(int(row[0]), int(row[1]), int(row[2]))
cutlass_bindings.gemm.GemmCoord(int(row[0]), int(row[1]), int(row[2]))
)
problem_count = len(problem_sizes)