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[ACL] Enable int8 data type in CONCATENATE #13497

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Merged
lhutton1 merged 1 commit into from
Nov 28, 2022
Merged

[ACL] Enable int8 data type in CONCATENATE #13497

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2 changes: 1 addition & 1 deletion python/tvm/relay/op/contrib/arm_compute_lib.py
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Original file line number Diff line number Diff line change
Expand Up @@ -285,7 +285,7 @@ def check_concatenate(expr):
return False
attrs, type_args = expr.attrs, expr.type_args
for idx in range(len(type_args[0].fields)):
if type_args[0].fields[idx].dtype not in ["float32", "uint8"]:
if type_args[0].fields[idx].dtype not in ["float32", "uint8", "int8"]:
return False
# ACL concatenate only supports maximum 4 dimensions input tensor
if attrs.axis not in [-4, -3, -2, -1, 0, 1, 2, 3]:
Expand Down
72 changes: 40 additions & 32 deletions tests/python/contrib/test_arm_compute_lib/test_concatenate.py
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Original file line number Diff line number Diff line change
Expand Up @@ -17,6 +17,7 @@
"""Arm Compute Library integration concatenate tests."""

import numpy as np
import pytest

import tvm
from tvm import relay
Expand Down Expand Up @@ -88,16 +89,9 @@ def _get_expected_codegen(input_shape_a, input_shape_b, input_shape_c, axis, dty
return [input_a, input_b, input_c, node]


def test_concatenate():
Device.load("test_config.json")

if skip_runtime_test():
return

device = Device()
np.random.seed(0)

for input_shape_a, input_shape_b, input_shape_c, axis, dtype in [
@pytest.mark.parametrize(
"input_shape_a, input_shape_b, input_shape_c, axis, dtype",
[
([1, 234, 234, 256], [2, 234, 234, 256], [3, 234, 234, 256], 0, "float32"),
([1, 1, 234, 256], [1, 2, 234, 256], [1, 3, 234, 256], 1, "float32"),
([1, 234, 234, 1], [1, 234, 234, 2], [1, 234, 234, 3], -1, "float32"),
Expand All @@ -106,29 +100,43 @@ def test_concatenate():
([1, 1, 234, 256], [1, 2, 234, 256], [1, 3, 234, 256], 1, "uint8"),
([1, 234, 234, 1], [1, 234, 234, 2], [1, 234, 234, 3], -1, "uint8"),
([1, 234, 234, 256], [2, 234, 234, 256], [3, 234, 234, 256], -4, "uint8"),
]:
outputs = []
inputs = {
"a": tvm.nd.array(np.random.randn(*input_shape_a).astype(dtype)),
"b": tvm.nd.array(np.random.randn(*input_shape_b).astype(dtype)),
"c": tvm.nd.array(np.random.randn(*input_shape_c).astype(dtype)),
}
func = _get_model(
inputs["a"].shape, inputs["b"].shape, inputs["c"].shape, axis, dtype, iter(inputs)
([1, 234, 234, 256], [2, 234, 234, 256], [3, 234, 234, 256], 0, "int8"),
([1, 1, 234, 256], [1, 2, 234, 256], [1, 3, 234, 256], 1, "int8"),
([1, 234, 234, 1], [1, 234, 234, 2], [1, 234, 234, 3], -1, "int8"),
([1, 234, 234, 256], [2, 234, 234, 256], [3, 234, 234, 256], -4, "int8"),
],
)
def test_concatenate(input_shape_a, input_shape_b, input_shape_c, axis, dtype):
Device.load("test_config.json")

if skip_runtime_test():
return

device = Device()
np.random.seed(0)

outputs = []
inputs = {
"a": tvm.nd.array(np.random.randn(*input_shape_a).astype(dtype)),
"b": tvm.nd.array(np.random.randn(*input_shape_b).astype(dtype)),
"c": tvm.nd.array(np.random.randn(*input_shape_c).astype(dtype)),
}
func = _get_model(
inputs["a"].shape, inputs["b"].shape, inputs["c"].shape, axis, dtype, iter(inputs)
)
for acl in [False, True]:
outputs.append(
build_and_run(func, inputs, 1, None, device, enable_acl=acl, disabled_ops=[])[0]
)
for acl in [False, True]:
outputs.append(
build_and_run(func, inputs, 1, None, device, enable_acl=acl, disabled_ops=[])[0]
)

config = {
"input_shape_a": input_shape_a,
"input_shape_b": input_shape_b,
"input_shape_c": input_shape_c,
"axis": axis,
"dtype": dtype,
}
verify(outputs, atol=1e-7, rtol=1e-7, config=config)

config = {
"input_shape_a": input_shape_a,
"input_shape_b": input_shape_b,
"input_shape_c": input_shape_c,
"axis": axis,
"dtype": dtype,
}
verify(outputs, atol=1e-7, rtol=1e-7, config=config)


def test_codegen_concatenate():
Expand Down