This is an automated email from the ASF dual-hosted git repository.
mousius pushed a commit to branch main
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The following commit(s) were added to refs/heads/main by this push:
new e2680142ef [CMSIS-NN] Global function that provides range based on
dtype (#13652)
e2680142ef is described below
commit e2680142ef8d301ae5dfc10b89594fd388219a7e
Author: Ashutosh Parkhi <[email protected]>
AuthorDate: Sun Dec 25 16:15:35 2022 +0000
[CMSIS-NN] Global function that provides range based on dtype (#13652)
Range for dtype was being sought differently from
inside various CMSIS-NN tests. This commit has created
a common global function inside aot.py under
tvm.testing that can provide (min, max) values based
on the dtype. In future, other AOT based targets can
make use of this function to obtain the range.
---
python/tvm/testing/aot.py | 31 +++++++++++++++++++-
.../python/contrib/test_cmsisnn/test_binary_ops.py | 14 ++++-----
tests/python/contrib/test_cmsisnn/test_conv2d.py | 21 ++++++-------
.../contrib/test_cmsisnn/test_fully_connected.py | 10 +++----
.../python/contrib/test_cmsisnn/test_fuse_pads.py | 22 ++++++++------
.../test_cmsisnn/test_generate_constants.py | 7 +++--
.../contrib/test_cmsisnn/test_invalid_graphs.py | 5 ++--
tests/python/contrib/test_cmsisnn/test_networks.py | 6 ++--
tests/python/contrib/test_cmsisnn/test_pooling.py | 4 +--
.../contrib/test_cmsisnn/test_remove_reshapes.py | 4 +--
tests/python/contrib/test_cmsisnn/test_softmax.py | 5 ++--
tests/python/contrib/test_cmsisnn/utils.py | 34 ++++------------------
.../relay/aot/test_crt_forward_declarations.py | 24 ---------------
13 files changed, 85 insertions(+), 102 deletions(-)
diff --git a/python/tvm/testing/aot.py b/python/tvm/testing/aot.py
index 563a7dff4a..30d3c78ae4 100644
--- a/python/tvm/testing/aot.py
+++ b/python/tvm/testing/aot.py
@@ -24,7 +24,7 @@ import shutil
import subprocess
import tarfile
import logging
-from typing import Any, NamedTuple, Union, Optional, List, Dict
+from typing import Any, NamedTuple, Union, Tuple, Optional, List, Dict
import numpy as np
import tvm
@@ -901,6 +901,35 @@ def compile_and_run(
)
+def get_dtype_range(dtype: str) -> Tuple[int, int]:
+ """
+ Produces the min,max for a give data type.
+
+ Parameters
+ ----------
+ dtype : str
+ a type string (e.g., int8, float64)
+
+ Returns
+ -------
+ type_info.min : int
+ the minimum of the range
+ type_info.max : int
+ the maximum of the range
+ """
+ type_info = None
+ np_dtype = np.dtype(dtype)
+ kind = np_dtype.kind
+
+ if kind == "f":
+ type_info = np.finfo(np_dtype)
+ elif kind in ["i", "u"]:
+ type_info = np.iinfo(np_dtype)
+ else:
+ raise TypeError(f"dtype ({dtype}) must indicate some floating-point or
integral data type.")
+ return type_info.min, type_info.max
+
+
def generate_ref_data(mod, input_data, params=None, target="llvm"):
"""Generate reference data through executing the relay module"""
with tvm.transform.PassContext(opt_level=3,
config={"tir.disable_vectorize": True}):
diff --git a/tests/python/contrib/test_cmsisnn/test_binary_ops.py
b/tests/python/contrib/test_cmsisnn/test_binary_ops.py
index 29335072bf..663a1bd45d 100644
--- a/tests/python/contrib/test_cmsisnn/test_binary_ops.py
+++ b/tests/python/contrib/test_cmsisnn/test_binary_ops.py
@@ -25,7 +25,7 @@ import pytest
import tvm
from tvm import relay
from tvm.relay.op.contrib import cmsisnn
-from tvm.testing.aot import generate_ref_data, AOTTestModel, compile_and_run
+from tvm.testing.aot import get_dtype_range, generate_ref_data, AOTTestModel,
compile_and_run
from tvm.micro.testing.aot_test_utils import (
AOT_USMP_CORSTONE300_RUNNER,
)
@@ -34,7 +34,6 @@ from .utils import (
skip_if_no_reference_system,
make_module,
make_qnn_relu,
- get_range_for_dtype_str,
assert_partitioned_function,
assert_no_external_function,
create_test_runner,
@@ -45,9 +44,8 @@ def generate_tensor_constant():
rng = np.random.default_rng(12321)
dtype = "int8"
shape = (1, 16, 16, 3)
- values = tvm.nd.array(
- rng.integers(np.iinfo(dtype).min, high=np.iinfo(dtype).max,
size=shape, dtype=dtype)
- )
+ in_min, in_max = get_dtype_range(dtype)
+ values = tvm.nd.array(rng.integers(in_min, high=in_max, size=shape,
dtype=dtype))
return relay.const(values, dtype)
@@ -136,7 +134,7 @@ def test_op_int8(
assert_partitioned_function(orig_mod, cmsisnn_mod)
# validate the output
- in_min, in_max = get_range_for_dtype_str(dtype)
+ in_min, in_max = get_dtype_range(dtype)
inputs = {
"input_0": np.random.randint(in_min, high=in_max, size=shape,
dtype=dtype),
"input_1": np.random.randint(in_min, high=in_max, size=shape,
dtype=dtype),
@@ -196,7 +194,7 @@ def test_same_input_to_binary_op(op, relu_type):
), "Composite function for the binary op should have only 1 parameter."
# validate the output
- in_min, in_max = get_range_for_dtype_str(dtype)
+ in_min, in_max = get_dtype_range(dtype)
inputs = {
"input": np.random.randint(in_min, high=in_max, size=shape,
dtype=dtype),
}
@@ -275,7 +273,7 @@ def test_constant_input_int8(op, input_0, input_1):
assert_partitioned_function(orig_mod, cmsisnn_mod)
# validate the output
- in_min, in_max = get_range_for_dtype_str(dtype)
+ in_min, in_max = get_dtype_range(dtype)
inputs = {}
if isinstance(input_0, tvm.relay.expr.Var):
inputs.update({"input_0": np.random.randint(in_min, high=in_max,
size=shape, dtype=dtype)})
diff --git a/tests/python/contrib/test_cmsisnn/test_conv2d.py
b/tests/python/contrib/test_cmsisnn/test_conv2d.py
index 66ff5d7938..20e7b9ed2f 100644
--- a/tests/python/contrib/test_cmsisnn/test_conv2d.py
+++ b/tests/python/contrib/test_cmsisnn/test_conv2d.py
@@ -24,6 +24,7 @@ from tvm import relay
from tvm.relay.op.contrib import cmsisnn
from tvm.testing.aot import (
+ get_dtype_range,
generate_ref_data,
AOTTestModel,
compile_models,
@@ -33,7 +34,6 @@ from tvm.testing.aot import (
from tvm.micro.testing.aot_test_utils import AOT_USMP_CORSTONE300_RUNNER
from .utils import (
make_module,
- get_range_for_dtype_str,
get_same_padding,
get_conv2d_qnn_params,
get_kernel_bias_dtype,
@@ -82,10 +82,11 @@ def make_model(
p = get_same_padding((shape[1], shape[2]), (kernel_h, kernel_w),
dilation, strides)
rng = np.random.default_rng(12321)
+ kmin, kmax = get_dtype_range(kernel_dtype)
kernel = tvm.nd.array(
rng.integers(
- np.iinfo(kernel_dtype).min,
- high=np.iinfo(kernel_dtype).max,
+ kmin,
+ high=kmax,
size=kernel_shape,
dtype=kernel_dtype,
)
@@ -157,7 +158,7 @@ def test_conv2d_number_primfunc_args(
kernel_w = kernel_size[1]
kernel_shape = (kernel_h, kernel_w, ifm_shape[3] // groups, out_channels)
kernel_zero_point = 0
- in_min, in_max = get_range_for_dtype_str(dtype)
+ in_min, in_max = get_dtype_range(dtype)
relu_type = "RELU"
kernel_dtype, bias_dtype = get_kernel_bias_dtype(dtype)
@@ -264,7 +265,7 @@ def test_conv2d_symmetric_padding(
kernel_w = kernel_size[1]
kernel_shape = (kernel_h, kernel_w, ifm_shape[3] // groups, out_channels)
kernel_zero_point = 0
- in_min, in_max = get_range_for_dtype_str(dtype)
+ in_min, in_max = get_dtype_range(dtype)
kernel_dtype, bias_dtype = get_kernel_bias_dtype(dtype)
@@ -358,7 +359,7 @@ def test_conv2d_asymmetric_padding(
kernel_w = kernel_size[1]
kernel_shape = (kernel_h, kernel_w, ifm_shape[3] // groups, out_channels)
kernel_zero_point = 0
- in_min, in_max = get_range_for_dtype_str(dtype)
+ in_min, in_max = get_dtype_range(dtype)
kernel_dtype, bias_dtype = get_kernel_bias_dtype(dtype)
@@ -454,7 +455,7 @@ def test_pad_conv2d_fusion_int8(
kernel_w = kernel_size[1]
kernel_shape = (kernel_h, kernel_w, ifm_shape[3] // groups, out_channels)
kernel_zero_point = 0
- in_min, in_max = get_range_for_dtype_str(dtype)
+ in_min, in_max = get_dtype_range(dtype)
kernel_dtype, bias_dtype = get_kernel_bias_dtype(dtype)
output_scale, output_zero_point = get_conv2d_qnn_params(
@@ -567,7 +568,7 @@ def test_invalid_pad_conv2d_fusion_int8(
kernel_w = kernel_size[1]
kernel_shape = (kernel_h, kernel_w, ifm_shape[3] // groups, out_channels)
kernel_zero_point = 0
- in_min, in_max = get_range_for_dtype_str(dtype)
+ in_min, in_max = get_dtype_range(dtype)
kernel_dtype, bias_dtype = get_kernel_bias_dtype(dtype)
@@ -740,7 +741,7 @@ def test_depthwise(
kernel_w = kernel_size[1]
kernel_shape = (kernel_h, kernel_w, ifm_shape[3] // groups, out_channels)
kernel_zero_point = 0
- in_min, in_max = get_range_for_dtype_str(dtype)
+ in_min, in_max = get_dtype_range(dtype)
groups = ifm_shape[3]
kernel_layout = "HWOI"
@@ -844,7 +845,7 @@ def test_relay_conv2d_cmsisnn_depthwise_int8(
test_runner = AOT_USMP_CORSTONE300_RUNNER
dtype = "int8"
- in_min, in_max = get_range_for_dtype_str(dtype)
+ in_min, in_max = get_dtype_range(dtype)
ifm_shape = (1, 24, 24, 1)
groups = ifm_shape[3]
diff --git a/tests/python/contrib/test_cmsisnn/test_fully_connected.py
b/tests/python/contrib/test_cmsisnn/test_fully_connected.py
index 3b220eb42c..46b1488eb3 100644
--- a/tests/python/contrib/test_cmsisnn/test_fully_connected.py
+++ b/tests/python/contrib/test_cmsisnn/test_fully_connected.py
@@ -23,10 +23,9 @@ import tvm
from tvm import relay
from tvm.relay.op.contrib import cmsisnn
-from tvm.testing.aot import generate_ref_data, AOTTestModel, compile_and_run
+from tvm.testing.aot import get_dtype_range, generate_ref_data, AOTTestModel,
compile_and_run
from .utils import (
make_module,
- get_range_for_dtype_str,
get_conv2d_qnn_params,
make_qnn_relu,
assert_partitioned_function,
@@ -55,10 +54,11 @@ def make_model(
"""Return a model and any parameters it may have"""
input_ = relay.var("input", shape=in_shape, dtype=dtype)
rng = np.random.default_rng(12321)
+ kmin, kmax = get_dtype_range(kernel_dtype)
weight = tvm.nd.array(
rng.integers(
- np.iinfo(kernel_dtype).min,
- high=np.iinfo(kernel_dtype).max,
+ kmin,
+ high=kmax,
size=kernel_shape,
dtype=kernel_dtype,
)
@@ -123,7 +123,7 @@ def test_ops(
kernel_zero_point = 0
kernel_shape = [out_channels, in_shape[1]]
conv2d_kernel_shape = (1, 1, kernel_shape[0], kernel_shape[1])
- in_min, in_max = get_range_for_dtype_str(dtype)
+ in_min, in_max = get_dtype_range(dtype)
output_scale, output_zero_point = get_conv2d_qnn_params(
conv2d_kernel_shape,
diff --git a/tests/python/contrib/test_cmsisnn/test_fuse_pads.py
b/tests/python/contrib/test_cmsisnn/test_fuse_pads.py
index f57dc5cd5b..4ea306cc43 100644
--- a/tests/python/contrib/test_cmsisnn/test_fuse_pads.py
+++ b/tests/python/contrib/test_cmsisnn/test_fuse_pads.py
@@ -19,7 +19,7 @@
import numpy as np
import pytest
import tvm
-import tvm.testing
+from tvm.testing.aot import get_dtype_range
from tvm import relay
from .utils import CheckForPadsWithinCompositeFunc
@@ -59,10 +59,11 @@ def test_invalid_padding_for_fusion(ifm_shape, pad_width,
conv2d_padding, ofm_sh
pad_mode="constant",
)
rng = np.random.default_rng(12321)
+ in_min, in_max = get_dtype_range(dtype)
local_weight = tvm.nd.array(
rng.integers(
- np.iinfo(dtype).min,
- high=np.iinfo(dtype).max,
+ in_min,
+ high=in_max,
size=(ofm_channels, kernel_size[0], kernel_size[1], ifm_shape[3]),
dtype=dtype,
)
@@ -139,10 +140,11 @@ def test_pad_conv2d_fusion_noncmsisnn_target(ifm_shape,
pad_width, conv2d_paddin
pad_mode="constant",
)
rng = np.random.default_rng(12321)
+ in_min, in_max = get_dtype_range(dtype)
local_weight = tvm.nd.array(
rng.integers(
- np.iinfo(dtype).min,
- high=np.iinfo(dtype).max,
+ in_min,
+ high=in_max,
size=(ofm_channels, kernel_size[0], kernel_size[1], ifm_shape[3]),
dtype=dtype,
)
@@ -217,10 +219,11 @@ def test_pad_conv2d_fusion(ifm_shape, pad_width,
conv2d_padding, ofm_shape):
pad_mode="constant",
)
rng = np.random.default_rng(12321)
+ kmin, kmax = get_dtype_range(dtype)
local_weight = tvm.nd.array(
rng.integers(
- np.iinfo(dtype).min,
- high=np.iinfo(dtype).max,
+ kmin,
+ high=kmax,
size=(ofm_channels, kernel_size[0], kernel_size[1], ifm_shape[3]),
dtype=dtype,
)
@@ -281,10 +284,11 @@ def test_without_preceding_pad():
ofm_shape = (1, 56, 56, 64)
local_input = relay.var("local_input", shape=ifm_shape, dtype=dtype)
rng = np.random.default_rng(12321)
+ kmin, kmax = get_dtype_range(dtype)
local_weight = tvm.nd.array(
rng.integers(
- np.iinfo(dtype).min,
- high=np.iinfo(dtype).max,
+ kmin,
+ high=kmax,
size=(64, 3, 3, 64),
dtype=dtype,
)
diff --git a/tests/python/contrib/test_cmsisnn/test_generate_constants.py
b/tests/python/contrib/test_cmsisnn/test_generate_constants.py
index 86737370bc..b838841284 100644
--- a/tests/python/contrib/test_cmsisnn/test_generate_constants.py
+++ b/tests/python/contrib/test_cmsisnn/test_generate_constants.py
@@ -20,7 +20,7 @@ import math
import numpy as np
import pytest
import tvm
-import tvm.testing
+from tvm.testing.aot import get_dtype_range
from tvm import relay
from tvm.relay.op.contrib import cmsisnn
@@ -107,10 +107,11 @@ def make_model(
weight_shape = (kernel_h, kernel_w, shape[3] // groups, out_channels)
rng = np.random.default_rng(12321)
+ kmin, kmax = get_dtype_range(kernel_dtype)
weight = tvm.nd.array(
rng.integers(
- np.iinfo(kernel_dtype).min,
- high=np.iinfo(kernel_dtype).max,
+ kmin,
+ high=kmax,
size=weight_shape,
dtype=kernel_dtype,
)
diff --git a/tests/python/contrib/test_cmsisnn/test_invalid_graphs.py
b/tests/python/contrib/test_cmsisnn/test_invalid_graphs.py
index c66f9d0e07..ace1db7811 100644
--- a/tests/python/contrib/test_cmsisnn/test_invalid_graphs.py
+++ b/tests/python/contrib/test_cmsisnn/test_invalid_graphs.py
@@ -19,13 +19,12 @@
import numpy as np
import tvm
-from tvm.testing.aot import AOTTestModel, compile_and_run, generate_ref_data
+from tvm.testing.aot import AOTTestModel, get_dtype_range, compile_and_run,
generate_ref_data
from tvm.micro.testing.aot_test_utils import (
AOT_USMP_CORSTONE300_RUNNER,
)
from .utils import (
skip_if_no_reference_system,
- get_range_for_dtype_str,
)
@@ -58,7 +57,7 @@ def @main(%data : Tensor[(16, 29), int8]) -> Tensor[(16, 29),
int8] {
use_unpacked_api = True
test_runner = AOT_USMP_CORSTONE300_RUNNER
dtype = "int8"
- in_min, in_max = get_range_for_dtype_str(dtype)
+ in_min, in_max = get_dtype_range(dtype)
rng = np.random.default_rng(12345)
inputs = {"data": rng.integers(in_min, high=in_max, size=(16, 29),
dtype=dtype)}
outputs = generate_ref_data(orig_mod["main"], inputs, params)
diff --git a/tests/python/contrib/test_cmsisnn/test_networks.py
b/tests/python/contrib/test_cmsisnn/test_networks.py
index 6f9f3743a6..9f64be2461 100644
--- a/tests/python/contrib/test_cmsisnn/test_networks.py
+++ b/tests/python/contrib/test_cmsisnn/test_networks.py
@@ -24,12 +24,12 @@ import tvm.testing
from tvm import relay
from tvm.contrib.download import download_testdata
from tvm.relay.op.contrib import cmsisnn
-from tvm.testing.aot import AOTTestModel, compile_and_run, generate_ref_data
+from tvm.testing.aot import AOTTestModel, get_dtype_range, compile_and_run,
generate_ref_data
from tvm.micro.testing.aot_test_utils import (
AOT_CORSTONE300_RUNNER,
AOT_USMP_CORSTONE300_RUNNER,
)
-from .utils import skip_if_no_reference_system, get_range_for_dtype_str
+from .utils import skip_if_no_reference_system
# pylint: disable=import-outside-toplevel
def _convert_to_relay(
@@ -93,7 +93,7 @@ def test_cnn_small(test_runner):
input_shape = (1, 490)
dtype = "int8"
- in_min, in_max = get_range_for_dtype_str(dtype)
+ in_min, in_max = get_dtype_range(dtype)
rng = np.random.default_rng(12345)
input_data = rng.integers(in_min, high=in_max, size=input_shape,
dtype=dtype)
diff --git a/tests/python/contrib/test_cmsisnn/test_pooling.py
b/tests/python/contrib/test_cmsisnn/test_pooling.py
index 7657e0e632..c6e5f02e71 100644
--- a/tests/python/contrib/test_cmsisnn/test_pooling.py
+++ b/tests/python/contrib/test_cmsisnn/test_pooling.py
@@ -23,6 +23,7 @@ from tvm import relay
from tvm.relay.op.contrib import cmsisnn
from tvm.testing.aot import (
+ get_dtype_range,
generate_ref_data,
AOTTestModel,
compile_and_run,
@@ -30,7 +31,6 @@ from tvm.testing.aot import (
from tvm.micro.testing.aot_test_utils import AOT_USMP_CORSTONE300_RUNNER
from .utils import (
make_module,
- get_range_for_dtype_str,
get_same_padding,
make_qnn_relu,
assert_partitioned_function,
@@ -128,7 +128,7 @@ def test_ops(
assert_partitioned_function(orig_mod, cmsisnn_mod)
# validate the output
- in_min, in_max = get_range_for_dtype_str(dtype)
+ in_min, in_max = get_dtype_range(dtype)
np.random.seed(0)
inputs = {
"input": np.random.randint(in_min, high=in_max, size=in_shape,
dtype=dtype),
diff --git a/tests/python/contrib/test_cmsisnn/test_remove_reshapes.py
b/tests/python/contrib/test_cmsisnn/test_remove_reshapes.py
index 8b33a8a90b..3cd60341eb 100644
--- a/tests/python/contrib/test_cmsisnn/test_remove_reshapes.py
+++ b/tests/python/contrib/test_cmsisnn/test_remove_reshapes.py
@@ -23,6 +23,7 @@ from tvm import relay
from tvm.relay.op.contrib import cmsisnn
from tvm.testing.aot import (
+ get_dtype_range,
generate_ref_data,
AOTTestModel,
compile_models,
@@ -31,7 +32,6 @@ from tvm.testing.aot import (
from tvm.micro.testing.aot_test_utils import AOT_USMP_CORSTONE300_RUNNER
from .utils import (
make_module,
- get_range_for_dtype_str,
get_same_padding,
make_qnn_relu,
assert_partitioned_function,
@@ -126,7 +126,7 @@ def test_reshape_removal(padding):
# generate reference output
rng = np.random.default_rng(12345)
- in_min, in_max = get_range_for_dtype_str("int8")
+ in_min, in_max = get_dtype_range("int8")
inputs = {"input": rng.integers(in_min, high=in_max, size=in_shape,
dtype="int8")}
output_list = generate_ref_data(orig_mod["main"], inputs, params=None)
diff --git a/tests/python/contrib/test_cmsisnn/test_softmax.py
b/tests/python/contrib/test_cmsisnn/test_softmax.py
index d048723529..0316d567ad 100644
--- a/tests/python/contrib/test_cmsisnn/test_softmax.py
+++ b/tests/python/contrib/test_cmsisnn/test_softmax.py
@@ -24,12 +24,11 @@ import pytest
import tvm.testing
from tvm import relay
from tvm.relay.op.contrib import cmsisnn
-from tvm.testing.aot import AOTTestModel, compile_and_run, generate_ref_data
+from tvm.testing.aot import get_dtype_range, AOTTestModel, compile_and_run,
generate_ref_data
from .utils import (
skip_if_no_reference_system,
make_module,
- get_range_for_dtype_str,
assert_partitioned_function,
assert_no_external_function,
create_test_runner,
@@ -78,7 +77,7 @@ def test_op_int8(zero_point, scale, compiler_cpu, cpu_flags):
assert_partitioned_function(orig_mod, cmsisnn_mod)
# validate the output
- in_min, in_max = get_range_for_dtype_str(dtype)
+ in_min, in_max = get_dtype_range(dtype)
np.random.seed(0)
input_data = np.random.randint(in_min, high=in_max, size=shape,
dtype=dtype)
inputs = {"in0": input_data}
diff --git a/tests/python/contrib/test_cmsisnn/utils.py
b/tests/python/contrib/test_cmsisnn/utils.py
index f3a6b0c134..1ec3e609f1 100644
--- a/tests/python/contrib/test_cmsisnn/utils.py
+++ b/tests/python/contrib/test_cmsisnn/utils.py
@@ -23,7 +23,7 @@ import numpy as np
import tvm
from tvm import relay
-from tvm.testing.aot import AOTTestRunner
+from tvm.testing.aot import AOTTestRunner, get_dtype_range
def skip_if_no_reference_system(func):
@@ -86,30 +86,6 @@ def assert_no_external_function(mod):
assert not any(attrs), "No function should have an external attribute."
-def get_range_for_dtype_str(dtype):
- """
- Produces the min,max for a give data type.
-
- Parameters
- ----------
- dtype : str
- a type string (e.g., int8)
-
- Returns
- -------
- type_info.min : int
- the minimum of the range
- type_info.max : int
- the maximum of the range
- """
-
- try:
- type_info = np.iinfo(dtype)
- except ValueError:
- type_info = np.finfo(dtype)
- return type_info.min, type_info.max
-
-
def make_module(func):
"""Creates IRModule from Function"""
func = relay.Function(relay.analysis.free_vars(func), func)
@@ -193,11 +169,11 @@ def get_conv2d_qnn_params(
output_zp : int
zero point of the output tensor
"""
- input_dtype_min, input_dtype_max = get_range_for_dtype_str(input_dtype)
+ input_dtype_min, input_dtype_max = get_dtype_range(input_dtype)
input_max = input_scale * (input_dtype_max - input_zp)
input_min = input_scale * (input_dtype_min - input_zp)
- kernel_dtype_min, kernel_dtype_max = get_range_for_dtype_str(kernel_dtype)
+ kernel_dtype_min, kernel_dtype_max = get_dtype_range(kernel_dtype)
kernel_sc_max = np.max(kernel_scale)
kernel_max = kernel_sc_max * (kernel_dtype_max - kernel_zp)
@@ -222,7 +198,7 @@ def get_conv2d_qnn_params(
output_max = max(output_limits)
output_min = min(output_limits)
- output_dtype_min, output_dtype_max = get_range_for_dtype_str(output_dtype)
+ output_dtype_min, output_dtype_max = get_dtype_range(output_dtype)
output_scale = (output_max - output_min) / (output_dtype_max -
output_dtype_min)
output_zp = int(output_dtype_min - (output_min / output_scale))
@@ -236,7 +212,7 @@ def make_qnn_relu(expr, fused_activation_fn, scale,
zero_point, dtype):
# Get min/max of the output dtype. This will be used to ensure that clip
a_min/a_max are not
# beyond the dtype range.
- qmin, qmax = get_range_for_dtype_str(dtype)
+ qmin, qmax = get_dtype_range(dtype)
# The input expr is a quantized tensor with its scale and zero point. We
calculate the
# suitable clip off points based on these scale and zero point.
diff --git a/tests/python/relay/aot/test_crt_forward_declarations.py
b/tests/python/relay/aot/test_crt_forward_declarations.py
index 7454f85ed1..e54846f3aa 100644
--- a/tests/python/relay/aot/test_crt_forward_declarations.py
+++ b/tests/python/relay/aot/test_crt_forward_declarations.py
@@ -34,30 +34,6 @@ from tvm.micro.testing.aot_test_utils import (
)
-def get_range_for_dtype_str(dtype):
- """
- Produces the min,max for a give data type.
-
- Parameters
- ----------
- dtype : str
- a type string (e.g., int8)
-
- Returns
- -------
- type_info.min : int
- the minimum of the range
- type_info.max : int
- the maximum of the range
- """
-
- try:
- type_info = np.iinfo(dtype)
- except ValueError:
- type_info = np.finfo(dtype)
- return type_info.min, type_info.max
-
-
def _change_ndarray_layout(arr, src_layout, dst_layout):
"""Makes a copy of an ndarray, reshaping it to a new data layout.
