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cutlass/test/python/cutlass/evt/evt_mixed_sm80_90.py
Junkai-Wu a49a78ffef v4.2 release. (#2587) 
* Fix default cluster callback values to 1 to avoid profiler failure when these values are not set in command line.

* v4.2 release.
2025-08-22 18:11:24 -04:00

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################################################################################
#
# Copyright (c) 2023 - 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
# SPDX-License-Identifier: BSD-3-Clause
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# 1. Redistributions of source code must retain the above copyright notice, this
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# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
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################################################################################
"""
Unittest for mixed types of nodes in SM90
"""
import logging
import unittest
import cutlass_cppgen
from cutlass_cppgen.backend import *
from cutlass_cppgen.epilogue import *
from cutlass_cppgen.swizzle import ThreadblockSwizzleStreamK
from utils.evt_testbed import EVTTestBed, EVTTestCaseBase
cutlass_cppgen.set_log_level(logging.WARNING)
@unittest.skipIf(device_cc() not in [80, 86, 89, 90], "This unittest is only supported on CC [80, 86, 89, 90]")
class TestEVTMixed(EVTTestCaseBase):
def test_same_variable_used_multiple_times(self):
"""
The same variable z0 is used multiple times
"""
def evt_aux_store(accum):
z0 = relu(accum)
D = z0 + z0
return z0, D
for m, n, k, l in self.get_problem_sizes(8):
example_inputs = {
"accum": self.fake_tensor(self.element, (l, m, n)),
"D": self.fake_tensor(self.element, (l, m, n)),
"z0": self.fake_tensor(self.element, (l, m, n)),
}
launcher = EVTTestBed(self.element, evt_aux_store, example_inputs)
input_keys = ["accum"]
result_keys = ["z0", "D"]
launcher.verify((m, n, k), input_keys, result_keys, l)
def test_no_lca(self):
"""
The same variable z0 is used multiple times
"""
def evt_no_lca(accum, bias):
E = relu(accum)
F = E + bias
tmp_2 = E + 2
D = tmp_2 + E
return D
for m, n, k, l in self.get_problem_sizes(8):
example_inputs = {
"accum": self.fake_tensor(self.element, (l, m, n)),
"D": self.fake_tensor(self.element, (l, m, n)),
"bias": self.fake_tensor(self.element, (m,1), stride=(1,0)),
}
launcher = EVTTestBed(self.element, evt_no_lca, example_inputs)
input_keys = ["accum", "bias"]
result_keys = ["D"]
launcher.verify((m, n, k), input_keys, result_keys, l)
def test_mixed_dag(self):
def evt_mixed_dag(accum, alpha, C, beta, aux, cbias, rbias):
F = alpha * accum + (beta * C + aux)
F_row_max = max(F, dim=[0, 1])
E = relu(F + 1) + cbias + rbias
E_col_max = max(E, dim=[0, 2])
D = E + F
return D, F, F_row_max, E_col_max
if device_cc() == 80:
alignments = [2, 4, 8]
else:
# Sm90 EVT currently only supports 128-bit alignment
alignments = [8,]
for align in alignments:
for m, n, k, l in self.get_problem_sizes(align):
example_inputs = {
"accum": self.fake_tensor(self.element, (l, m, n)),
"alpha": 1.0,
"C": self.fake_tensor(self.element, (l, m, n)),
"beta": 1.0,
"aux": self.fake_tensor(self.element, (l, m, n)),
"cbias": self.fake_tensor(self.element, (m, 1)),
"rbias": self.fake_tensor(self.element, (n,)),
"D": self.fake_tensor(self.element, (l, m, n)),
"F": self.fake_tensor(self.element, (l, m, n)),
"F_row_max": self.fake_tensor(DataType.f32, (n,)),
"E_col_max": self.fake_tensor(DataType.f32, (m, 1))
}
launcher = EVTTestBed(self.element, evt_mixed_dag, example_inputs)
input_keys = ["alpha", "C", "beta", "aux", "cbias", "rbias"]
result_keys = ["D", "F", "F_row_max", "E_col_max"]
launcher.verify((m, n, k), input_keys, result_keys, l)
@unittest.skipIf(device_cc() not in [80, 89], "This unittest is for cc 80 and 89 only")
def test_mixed_dag_float(self):
def evt_mixed_dag(accum, alpha, C, beta, aux, cbias, rbias):
F = alpha * accum + (beta * C + aux)
F_row_max = max(F, dim=[0, 1])
E = relu(F + 1) + cbias + rbias
E_col_max = max(E, dim=[0, 2])
D = E + F
return D, F, F_row_max, E_col_max
for align in [3, 2, 4]:
for m, n, k, l in self.get_problem_sizes(align):
example_inputs = {
"accum": self.fake_tensor(np.float32, (l, m, n)),
"alpha": 1.0,
"C": self.fake_tensor(np.float32, (l, m, n)),
"beta": 1.0,
"aux": self.fake_tensor(np.float32, (l, m, n)),
"cbias": self.fake_tensor(np.float32, (m, 1)),
"rbias": self.fake_tensor(np.float32, (n,)),
"D": self.fake_tensor(np.float32, (l, m, n)),
"F": self.fake_tensor(np.float32, (l, m, n)),
"F_row_max": self.fake_tensor(np.float32, (n,)),
"E_col_max": self.fake_tensor(np.float32, (m, 1))
}
launcher = EVTTestBed(DataType.f32, evt_mixed_dag, example_inputs)
input_keys = ["alpha", "C", "beta", "aux", "cbias", "rbias"]
result_keys = ["D", "F", "F_row_max", "E_col_max"]
launcher.verify((m, n, k), input_keys, result_keys, l)
@unittest.skipIf(device_cc() not in [80, 89], "This unittest is for cc 80 and 89 only")
def test_mixed_dag_stage2(self):
def evt_mixed_dag(accum, alpha, C, beta, aux, cbias, rbias):
F = alpha * accum + (beta * C + aux)
F_row_max = max(F, dim=[0, 1])
E = relu(F + 1) + cbias + rbias
E_col_max = max(E, dim=[0, 2])
D = E + F
return D, F, F_row_max, E_col_max
for m, n, k, l in self.get_problem_sizes(8):
example_inputs = {
"accum": self.fake_tensor(self.element, (l, m, n)),
"alpha": 1.0,
"C": self.fake_tensor(self.element, (l, m, n)),
"beta": 1.0,
"aux": self.fake_tensor(self.element, (l, m, n)),
"cbias": self.fake_tensor(self.element, (m, 1)),
"rbias": self.fake_tensor(self.element, (n,)),
"D": self.fake_tensor(self.element, (l, m, n)),
"F": self.fake_tensor(self.element, (l, m, n)),
"F_row_max": self.fake_tensor(DataType.f32, (n,)),
"E_col_max": self.fake_tensor(DataType.f32, (m, 1))
}
launcher = EVTTestBed(self.element, evt_mixed_dag, example_inputs, epilogue_stages=2)
input_keys = ["alpha", "C", "beta", "aux", "cbias", "rbias"]
result_keys = ["D", "F", "F_row_max", "E_col_max"]
launcher.verify((m, n, k), input_keys, result_keys, l)
@unittest.skipIf(device_cc() not in [80, 89], "This unittest is for cc 80 and 89 only")
def test_mixed_dag_partition_k(self):
def evt_mixed_dag(accum, alpha, C, beta, aux, cbias, rbias):
F = alpha * accum + (beta * C + aux)
F_row_max = max(F, dim=[0, 1])
E = relu(F + 1) + cbias + rbias
E_col_max = max(E, dim=[0, 2])
D = E + F
return D, F, F_row_max, E_col_max
for m, n, k, l in self.get_problem_sizes(8):
example_inputs = {
"accum": self.fake_tensor(self.element, (l, m, n)),
"alpha": 1.0,
"C": self.fake_tensor(self.element, (l, m, n)),
"beta": 1.0,
"aux": self.fake_tensor(self.element, (l, m, n)),
"cbias": self.fake_tensor(self.element, (m, 1)),
"rbias": self.fake_tensor(self.element, (n,)),
"D": self.fake_tensor(self.element, (l, m, n)),
"F": self.fake_tensor(self.element, (l, m, n)),
"F_row_max": self.fake_tensor(DataType.f32, (n,)),
"E_col_max": self.fake_tensor(DataType.f32, (m, 1))
}
tile_description = {
"threadblock_shape": [128, 128, 64],
"warp_count": [2, 2, 2]
}
launcher = EVTTestBed(self.element, evt_mixed_dag, example_inputs, tile_description=tile_description, epilogue_stages=2)
input_keys = ["alpha", "C", "beta", "aux", "cbias", "rbias"]
result_keys = ["D", "F", "F_row_max", "E_col_max"]
launcher.verify((m, n, k), input_keys, result_keys, l)
@unittest.skipIf(device_cc() not in [80, 89], "This unittest is for cc 80 and 89 only")
def test_mixed_dag_stream_k(self):
def evt_mixed_dag(accum, alpha, C, beta, aux, cbias, rbias):
F = alpha * accum + (beta * C + aux)
F_row_max = max(F, dim=[0, 1])
E = relu(F + 1) + cbias + rbias
E_col_max = max(E, dim=[0, 2])
D = E + F
return D, F, F_row_max, E_col_max
# High per-sm occupancy tile_description
tile_description = {
"threadblock_shape": [128, 128, 32],
"warp_count": [2, 2, 1],
"stages": 3
}
tds = [None, tile_description]
for td in tds:
for m, n, k, l in self.get_problem_sizes(8, k=960, batch_count=[1, 3]):
if l == 1:
example_inputs = {
"accum": self.fake_tensor(self.element, (m, n)),
"alpha": 1.0,
"C": self.fake_tensor(self.element, (m, n)),
"beta": 1.0,
"aux": self.fake_tensor(self.element, (m, n)),
"cbias": self.fake_tensor(self.element, (m, 1)),
"rbias": self.fake_tensor(self.element, (n,)),
"D": self.fake_tensor(self.element, (m, n)),
"F": self.fake_tensor(self.element, (m, n)),
"F_row_max": self.fake_tensor(DataType.f32, (n,)),
"E_col_max": self.fake_tensor(DataType.f32, (m, 1))
}
else:
example_inputs = {
"accum": self.fake_tensor(self.element, (l, m, n)),
"alpha": 1.0,
"C": self.fake_tensor(self.element, (l, m, n)),
"beta": 1.0,
"aux": self.fake_tensor(self.element, (l, m, n)),
"cbias": self.fake_tensor(self.element, (m, 1)),
"rbias": self.fake_tensor(self.element, (n,)),
"D": self.fake_tensor(self.element, (l, m, n)),
"F": self.fake_tensor(self.element, (l, m, n)),
"F_row_max": self.fake_tensor(DataType.f32, (n,)),
"E_col_max": self.fake_tensor(DataType.f32, (m, 1))
}
if td is not None:
launcher = EVTTestBed(
self.element, evt_mixed_dag, example_inputs,
tile_description=td,
swizzling_functor=ThreadblockSwizzleStreamK, backend="torch")
else:
launcher = EVTTestBed(
self.element, evt_mixed_dag, example_inputs,
swizzling_functor=ThreadblockSwizzleStreamK, backend="torch")
input_keys = ["alpha", "C", "beta", "aux", "cbias", "rbias"]
result_keys = ["D", "F", "F_row_max", "E_col_max"]
launcher.verify((m, n, k), input_keys, result_keys, l)
def test_mixed_dag_no_batch(self):
def evt_mixed_dag_no_batch(accum, alpha, C, beta, aux, cbias, rbias):
F = alpha * accum + (beta * C + aux)
F_row_max = max(F, dim=[0, 1])
E = relu(F + 1) + cbias + rbias
E_col_max = max(E, dim=[0, 2])
D = E + F
return D, F, F_row_max, E_col_max
for m, n, k, _ in self.get_problem_sizes(8):
example_inputs = {
"accum": self.fake_tensor(self.element, (m, n)),
"alpha": 1.0,
"C": self.fake_tensor(self.element, (m, n)),
"beta": 1.0,
"aux": self.fake_tensor(self.element, (m, n)),
"cbias": self.fake_tensor(self.element, (m, 1)),
"rbias": self.fake_tensor(self.element, (n,)),
"D": self.fake_tensor(self.element, (m, n)),
"F": self.fake_tensor(self.element, (m, n)),
"F_row_max": self.fake_tensor(DataType.f32, (n,)),
"E_col_max": self.fake_tensor(DataType.f32, (m, 1))
}
launcher = EVTTestBed(self.element, evt_mixed_dag_no_batch, example_inputs)
input_keys = ["alpha", "C", "beta", "aux", "cbias", "rbias"]
result_keys = ["D", "F", "F_row_max", "E_col_max"]
launcher.verify((m, n, k), input_keys, result_keys, 1)
if __name__ == '__main__':
unittest.main()
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