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Posted to commits@tvm.apache.org by GitBox <gi...@apache.org> on 2022/11/25 16:40:05 UTC
[GitHub] [tvm] leandron opened a new pull request, #13488: [ACL] Enable int8 data type in pooling operators
leandron opened a new pull request, #13488:
URL: https://github.com/apache/tvm/pull/13488
This enables int8 data type to be used in Compute Library for the Arm(r) Architecture (ACL) BYOC integration.
This PR covers:
- `nn.max_pool2d`
- `nn.avg_pool2d`
- `nn.l2_pool2d`
- `nn.global_avg_pool2d`
- `nn.global_max_pool2d`
cc @lhutton1 for reviews.
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[GitHub] [tvm] lhutton1 merged pull request #13488: [ACL] Enable int8 data type in pooling operators
Posted by GitBox <gi...@apache.org>.
lhutton1 merged PR #13488:
URL: https://github.com/apache/tvm/pull/13488
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[GitHub] [tvm] leandron commented on a diff in pull request #13488: [ACL] Enable int8 data type in pooling operators
Posted by GitBox <gi...@apache.org>.
leandron commented on code in PR #13488:
URL: https://github.com/apache/tvm/pull/13488#discussion_r1033365074
##########
tests/python/contrib/test_arm_compute_lib/test_pooling.py:
##########
@@ -169,91 +224,79 @@ def test_pooling():
device = Device()
np.random.seed(0)
- fp32_dtype = ("float32", -127, 128, 0.001, 0.001)
- uint8_dtype = ("uint8", 0, 255, 1, 0)
- # fmt: off
- trials = [
- ["nn.max_pool2d", fp32_dtype, (3, 3), (2, 2), (1, 1), (0, 0), False, False, (27, 27, 512), (0, 1),],
- ["nn.max_pool2d", fp32_dtype, (2, 2), (2, 2), (1, 1), (0, 0), False, True, (16, 16, 16), (0, 1),],
- ["nn.max_pool2d", fp32_dtype, (3, 3), (2, 2), (1, 1), (1, 1), True, True, (15, 15, 16), (0, 1),],
- ["nn.max_pool2d", fp32_dtype, (2, 2), (2, 2), (1, 1), (0, 1), False, False, (16, 16, 16), (0, 1),],
- ["nn.max_pool2d", uint8_dtype, (3, 3), (2, 2), (1, 1), (0, 1), False, False, (16, 16, 16), (0, 1),],
- ["nn.max_pool2d", uint8_dtype, (2, 2), (2, 2), (1, 1), (1, 1), True, True, (15, 15, 16), (0, 1),],
- ["nn.max_pool2d", uint8_dtype, (2, 2), (2, 2), (3, 2), (1, 1), True, True, (15, 15, 16), (1, 0),],
- ["nn.avg_pool2d", fp32_dtype, (2, 2), (2, 2), (1, 1), (1, 1), False, False, (16, 16, 16), (0, 1),],
- ["nn.avg_pool2d", fp32_dtype, (2, 2), (2, 2), (1, 1), (0, 0), False, True, (16, 16, 16), (0, 1),],
- ["nn.avg_pool2d", fp32_dtype, (3, 3), (2, 2), (3, 2), (0, 1), True, False, (15, 15, 16), (1, 0),],
- # 20.05: "exclude_padding equal false is not supported for AVG Pooling with padding on quantized types"
- # ["nn.avg_pool2d", uint8_dtype, (2, 2), (2, 2), (1, 1), False, True, (16, 16, 16)],
- ["nn.avg_pool2d", uint8_dtype, (3, 3), (2, 2), (1, 1), (0, 1), False, False, (16, 16, 16), (0, 1),],
- ["nn.l2_pool2d", fp32_dtype, (2, 2), (2, 2), (1, 1), (0, 1), True, False, (16, 16, 16), (0, 1),],
- ["nn.l2_pool2d", fp32_dtype, (3, 3), (2, 2), (1, 1), (0, 0), False, False, (16, 16, 16), (0, 1),],
- ["nn.l2_pool2d", fp32_dtype, (2, 2), (2, 2), (1, 1), (1, 1), False, True, (15, 15, 16), (0, 1),],
- ]
- # fmt: on
- for (
+ low, high, atol, rtol = _get_low_high_atol_rtol(dtype)
+ tvm_ops, acl_partitions = expected_ops
+
+ shape = (1, *input_shape)
+ outputs = []
+ inputs = {
+ "a": tvm.nd.array(np.random.uniform(low, high, shape).astype(dtype)),
+ }
+
+ func = _get_pooling_model(
+ shape,
+ dtype,
typef,
- (dtype, low, high, atol, rtol),
size,
stride,
dilation,
pad,
ceil_mode,
count_include_pad,
- input_shape,
- (tvm_ops, acl_partitions),
- ) in trials:
- shape = (1, *input_shape)
- outputs = []
- inputs = {
- "a": tvm.nd.array(np.random.uniform(low, high, shape).astype(dtype)),
- }
-
- func = _get_pooling_model(
- shape,
- dtype,
- typef,
- size,
- stride,
- dilation,
- pad,
- ceil_mode,
- count_include_pad,
- iter(inputs),
+ iter(inputs),
+ )
+
+ config = {
+ "size": size,
+ "stride": stride,
+ "shape": shape,
+ "pooling type": typef,
+ "dtype": dtype,
+ "padding": pad,
+ "dilation": dilation,
+ "ceil_mode": ceil_mode,
+ "count_include_pad": count_include_pad,
+ "inputs": inputs,
+ }
+ verify_saturation = True if dtype == "uint8" else False
+ for acl in [False, True]:
+ outputs.append(
+ build_and_run(
+ func,
+ inputs,
+ 1,
+ None,
+ device,
+ enable_acl=acl,
+ tvm_ops=tvm_ops,
+ acl_partitions=acl_partitions,
+ config=config,
+ )[0]
)
- config = {
- "size": size,
- "stride": stride,
- "shape": shape,
- "pooling type": typef,
- "dtype": dtype,
- "padding": pad,
- "dilation": dilation,
- "ceil_mode": ceil_mode,
- "count_include_pad": count_include_pad,
- "inputs": inputs,
- }
- verify_saturation = True if dtype == "uint8" else False
- for acl in [False, True]:
- outputs.append(
- build_and_run(
- func,
- inputs,
- 1,
- None,
- device,
- enable_acl=acl,
- tvm_ops=tvm_ops,
- acl_partitions=acl_partitions,
- config=config,
- )[0]
- )
-
- verify(outputs, atol=atol, rtol=rtol, config=config, verify_saturation=verify_saturation)
-
-
-def test_global_pooling():
+ verify(outputs, atol=atol, rtol=rtol, config=config, verify_saturation=verify_saturation)
+
+
+@pytest.mark.parametrize(
+ "typef,dtype,input_shape",
+ [
+ ["nn.global_max_pool2d", "float32", (8, 8, 16)],
+ ["nn.global_max_pool2d", "float32", (9, 9, 16)],
+ ["nn.global_max_pool2d", "float32", (8, 8, 16)],
+ ["nn.global_max_pool2d", "uint8", (8, 8, 16)],
+ ["nn.global_max_pool2d", "uint8", (9, 9, 16)],
+ ["nn.global_max_pool2d", "int8", (8, 8, 16)],
+ ["nn.global_max_pool2d", "int8", (9, 9, 16)],
+ ["nn.global_avg_pool2d", "float32", (8, 8, 16)],
+ ["nn.global_avg_pool2d", "float32", (8, 8, 16)],
+ ["nn.global_avg_pool2d", "float32", (9, 9, 16)],
+ ["nn.global_avg_pool2d", "uint8", (8, 8, 16)],
+ ["nn.global_avg_pool2d", "uint8", (9, 9, 16)],
+ ["nn.global_avg_pool2d", "int8", (8, 8, 16)],
+ ["nn.global_avg_pool2d", "int8", (9, 9, 16)],
Review Comment:
Thanks!
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[GitHub] [tvm] lhutton1 commented on a diff in pull request #13488: [ACL] Enable int8 data type in pooling operators
Posted by GitBox <gi...@apache.org>.
lhutton1 commented on code in PR #13488:
URL: https://github.com/apache/tvm/pull/13488#discussion_r1033307151
##########
tests/python/contrib/test_arm_compute_lib/test_pooling.py:
##########
@@ -169,91 +224,79 @@ def test_pooling():
device = Device()
np.random.seed(0)
- fp32_dtype = ("float32", -127, 128, 0.001, 0.001)
- uint8_dtype = ("uint8", 0, 255, 1, 0)
- # fmt: off
- trials = [
- ["nn.max_pool2d", fp32_dtype, (3, 3), (2, 2), (1, 1), (0, 0), False, False, (27, 27, 512), (0, 1),],
- ["nn.max_pool2d", fp32_dtype, (2, 2), (2, 2), (1, 1), (0, 0), False, True, (16, 16, 16), (0, 1),],
- ["nn.max_pool2d", fp32_dtype, (3, 3), (2, 2), (1, 1), (1, 1), True, True, (15, 15, 16), (0, 1),],
- ["nn.max_pool2d", fp32_dtype, (2, 2), (2, 2), (1, 1), (0, 1), False, False, (16, 16, 16), (0, 1),],
- ["nn.max_pool2d", uint8_dtype, (3, 3), (2, 2), (1, 1), (0, 1), False, False, (16, 16, 16), (0, 1),],
- ["nn.max_pool2d", uint8_dtype, (2, 2), (2, 2), (1, 1), (1, 1), True, True, (15, 15, 16), (0, 1),],
- ["nn.max_pool2d", uint8_dtype, (2, 2), (2, 2), (3, 2), (1, 1), True, True, (15, 15, 16), (1, 0),],
- ["nn.avg_pool2d", fp32_dtype, (2, 2), (2, 2), (1, 1), (1, 1), False, False, (16, 16, 16), (0, 1),],
- ["nn.avg_pool2d", fp32_dtype, (2, 2), (2, 2), (1, 1), (0, 0), False, True, (16, 16, 16), (0, 1),],
- ["nn.avg_pool2d", fp32_dtype, (3, 3), (2, 2), (3, 2), (0, 1), True, False, (15, 15, 16), (1, 0),],
- # 20.05: "exclude_padding equal false is not supported for AVG Pooling with padding on quantized types"
- # ["nn.avg_pool2d", uint8_dtype, (2, 2), (2, 2), (1, 1), False, True, (16, 16, 16)],
- ["nn.avg_pool2d", uint8_dtype, (3, 3), (2, 2), (1, 1), (0, 1), False, False, (16, 16, 16), (0, 1),],
- ["nn.l2_pool2d", fp32_dtype, (2, 2), (2, 2), (1, 1), (0, 1), True, False, (16, 16, 16), (0, 1),],
- ["nn.l2_pool2d", fp32_dtype, (3, 3), (2, 2), (1, 1), (0, 0), False, False, (16, 16, 16), (0, 1),],
- ["nn.l2_pool2d", fp32_dtype, (2, 2), (2, 2), (1, 1), (1, 1), False, True, (15, 15, 16), (0, 1),],
- ]
- # fmt: on
- for (
+ low, high, atol, rtol = _get_low_high_atol_rtol(dtype)
+ tvm_ops, acl_partitions = expected_ops
+
+ shape = (1, *input_shape)
+ outputs = []
+ inputs = {
+ "a": tvm.nd.array(np.random.uniform(low, high, shape).astype(dtype)),
+ }
+
+ func = _get_pooling_model(
+ shape,
+ dtype,
typef,
- (dtype, low, high, atol, rtol),
size,
stride,
dilation,
pad,
ceil_mode,
count_include_pad,
- input_shape,
- (tvm_ops, acl_partitions),
- ) in trials:
- shape = (1, *input_shape)
- outputs = []
- inputs = {
- "a": tvm.nd.array(np.random.uniform(low, high, shape).astype(dtype)),
- }
-
- func = _get_pooling_model(
- shape,
- dtype,
- typef,
- size,
- stride,
- dilation,
- pad,
- ceil_mode,
- count_include_pad,
- iter(inputs),
+ iter(inputs),
+ )
+
+ config = {
+ "size": size,
+ "stride": stride,
+ "shape": shape,
+ "pooling type": typef,
+ "dtype": dtype,
+ "padding": pad,
+ "dilation": dilation,
+ "ceil_mode": ceil_mode,
+ "count_include_pad": count_include_pad,
+ "inputs": inputs,
+ }
+ verify_saturation = True if dtype == "uint8" else False
+ for acl in [False, True]:
+ outputs.append(
+ build_and_run(
+ func,
+ inputs,
+ 1,
+ None,
+ device,
+ enable_acl=acl,
+ tvm_ops=tvm_ops,
+ acl_partitions=acl_partitions,
+ config=config,
+ )[0]
)
- config = {
- "size": size,
- "stride": stride,
- "shape": shape,
- "pooling type": typef,
- "dtype": dtype,
- "padding": pad,
- "dilation": dilation,
- "ceil_mode": ceil_mode,
- "count_include_pad": count_include_pad,
- "inputs": inputs,
- }
- verify_saturation = True if dtype == "uint8" else False
- for acl in [False, True]:
- outputs.append(
- build_and_run(
- func,
- inputs,
- 1,
- None,
- device,
- enable_acl=acl,
- tvm_ops=tvm_ops,
- acl_partitions=acl_partitions,
- config=config,
- )[0]
- )
-
- verify(outputs, atol=atol, rtol=rtol, config=config, verify_saturation=verify_saturation)
-
-
-def test_global_pooling():
+ verify(outputs, atol=atol, rtol=rtol, config=config, verify_saturation=verify_saturation)
+
+
+@pytest.mark.parametrize(
+ "typef,dtype,input_shape",
+ [
+ ["nn.global_max_pool2d", "float32", (8, 8, 16)],
+ ["nn.global_max_pool2d", "float32", (9, 9, 16)],
+ ["nn.global_max_pool2d", "float32", (8, 8, 16)],
+ ["nn.global_max_pool2d", "uint8", (8, 8, 16)],
+ ["nn.global_max_pool2d", "uint8", (9, 9, 16)],
+ ["nn.global_max_pool2d", "int8", (8, 8, 16)],
+ ["nn.global_max_pool2d", "int8", (9, 9, 16)],
+ ["nn.global_avg_pool2d", "float32", (8, 8, 16)],
+ ["nn.global_avg_pool2d", "float32", (8, 8, 16)],
+ ["nn.global_avg_pool2d", "float32", (9, 9, 16)],
+ ["nn.global_avg_pool2d", "uint8", (8, 8, 16)],
+ ["nn.global_avg_pool2d", "uint8", (8, 8, 16)],
+ ["nn.global_avg_pool2d", "int8", (8, 8, 16)],
+ ["nn.global_avg_pool2d", "int8", (8, 8, 16)],
Review Comment:
nit: looks like there are some duplicated cases here
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[GitHub] [tvm] leandron commented on a diff in pull request #13488: [ACL] Enable int8 data type in pooling operators
Posted by GitBox <gi...@apache.org>.
leandron commented on code in PR #13488:
URL: https://github.com/apache/tvm/pull/13488#discussion_r1033323695
##########
tests/python/contrib/test_arm_compute_lib/test_pooling.py:
##########
@@ -169,91 +224,79 @@ def test_pooling():
device = Device()
np.random.seed(0)
- fp32_dtype = ("float32", -127, 128, 0.001, 0.001)
- uint8_dtype = ("uint8", 0, 255, 1, 0)
- # fmt: off
- trials = [
- ["nn.max_pool2d", fp32_dtype, (3, 3), (2, 2), (1, 1), (0, 0), False, False, (27, 27, 512), (0, 1),],
- ["nn.max_pool2d", fp32_dtype, (2, 2), (2, 2), (1, 1), (0, 0), False, True, (16, 16, 16), (0, 1),],
- ["nn.max_pool2d", fp32_dtype, (3, 3), (2, 2), (1, 1), (1, 1), True, True, (15, 15, 16), (0, 1),],
- ["nn.max_pool2d", fp32_dtype, (2, 2), (2, 2), (1, 1), (0, 1), False, False, (16, 16, 16), (0, 1),],
- ["nn.max_pool2d", uint8_dtype, (3, 3), (2, 2), (1, 1), (0, 1), False, False, (16, 16, 16), (0, 1),],
- ["nn.max_pool2d", uint8_dtype, (2, 2), (2, 2), (1, 1), (1, 1), True, True, (15, 15, 16), (0, 1),],
- ["nn.max_pool2d", uint8_dtype, (2, 2), (2, 2), (3, 2), (1, 1), True, True, (15, 15, 16), (1, 0),],
- ["nn.avg_pool2d", fp32_dtype, (2, 2), (2, 2), (1, 1), (1, 1), False, False, (16, 16, 16), (0, 1),],
- ["nn.avg_pool2d", fp32_dtype, (2, 2), (2, 2), (1, 1), (0, 0), False, True, (16, 16, 16), (0, 1),],
- ["nn.avg_pool2d", fp32_dtype, (3, 3), (2, 2), (3, 2), (0, 1), True, False, (15, 15, 16), (1, 0),],
- # 20.05: "exclude_padding equal false is not supported for AVG Pooling with padding on quantized types"
- # ["nn.avg_pool2d", uint8_dtype, (2, 2), (2, 2), (1, 1), False, True, (16, 16, 16)],
- ["nn.avg_pool2d", uint8_dtype, (3, 3), (2, 2), (1, 1), (0, 1), False, False, (16, 16, 16), (0, 1),],
- ["nn.l2_pool2d", fp32_dtype, (2, 2), (2, 2), (1, 1), (0, 1), True, False, (16, 16, 16), (0, 1),],
- ["nn.l2_pool2d", fp32_dtype, (3, 3), (2, 2), (1, 1), (0, 0), False, False, (16, 16, 16), (0, 1),],
- ["nn.l2_pool2d", fp32_dtype, (2, 2), (2, 2), (1, 1), (1, 1), False, True, (15, 15, 16), (0, 1),],
- ]
- # fmt: on
- for (
+ low, high, atol, rtol = _get_low_high_atol_rtol(dtype)
+ tvm_ops, acl_partitions = expected_ops
+
+ shape = (1, *input_shape)
+ outputs = []
+ inputs = {
+ "a": tvm.nd.array(np.random.uniform(low, high, shape).astype(dtype)),
+ }
+
+ func = _get_pooling_model(
+ shape,
+ dtype,
typef,
- (dtype, low, high, atol, rtol),
size,
stride,
dilation,
pad,
ceil_mode,
count_include_pad,
- input_shape,
- (tvm_ops, acl_partitions),
- ) in trials:
- shape = (1, *input_shape)
- outputs = []
- inputs = {
- "a": tvm.nd.array(np.random.uniform(low, high, shape).astype(dtype)),
- }
-
- func = _get_pooling_model(
- shape,
- dtype,
- typef,
- size,
- stride,
- dilation,
- pad,
- ceil_mode,
- count_include_pad,
- iter(inputs),
+ iter(inputs),
+ )
+
+ config = {
+ "size": size,
+ "stride": stride,
+ "shape": shape,
+ "pooling type": typef,
+ "dtype": dtype,
+ "padding": pad,
+ "dilation": dilation,
+ "ceil_mode": ceil_mode,
+ "count_include_pad": count_include_pad,
+ "inputs": inputs,
+ }
+ verify_saturation = True if dtype == "uint8" else False
+ for acl in [False, True]:
+ outputs.append(
+ build_and_run(
+ func,
+ inputs,
+ 1,
+ None,
+ device,
+ enable_acl=acl,
+ tvm_ops=tvm_ops,
+ acl_partitions=acl_partitions,
+ config=config,
+ )[0]
)
- config = {
- "size": size,
- "stride": stride,
- "shape": shape,
- "pooling type": typef,
- "dtype": dtype,
- "padding": pad,
- "dilation": dilation,
- "ceil_mode": ceil_mode,
- "count_include_pad": count_include_pad,
- "inputs": inputs,
- }
- verify_saturation = True if dtype == "uint8" else False
- for acl in [False, True]:
- outputs.append(
- build_and_run(
- func,
- inputs,
- 1,
- None,
- device,
- enable_acl=acl,
- tvm_ops=tvm_ops,
- acl_partitions=acl_partitions,
- config=config,
- )[0]
- )
-
- verify(outputs, atol=atol, rtol=rtol, config=config, verify_saturation=verify_saturation)
-
-
-def test_global_pooling():
+ verify(outputs, atol=atol, rtol=rtol, config=config, verify_saturation=verify_saturation)
+
+
+@pytest.mark.parametrize(
+ "typef,dtype,input_shape",
+ [
+ ["nn.global_max_pool2d", "float32", (8, 8, 16)],
+ ["nn.global_max_pool2d", "float32", (9, 9, 16)],
+ ["nn.global_max_pool2d", "float32", (8, 8, 16)],
+ ["nn.global_max_pool2d", "uint8", (8, 8, 16)],
+ ["nn.global_max_pool2d", "uint8", (9, 9, 16)],
+ ["nn.global_max_pool2d", "int8", (8, 8, 16)],
+ ["nn.global_max_pool2d", "int8", (9, 9, 16)],
+ ["nn.global_avg_pool2d", "float32", (8, 8, 16)],
+ ["nn.global_avg_pool2d", "float32", (8, 8, 16)],
+ ["nn.global_avg_pool2d", "float32", (9, 9, 16)],
+ ["nn.global_avg_pool2d", "uint8", (8, 8, 16)],
+ ["nn.global_avg_pool2d", "uint8", (8, 8, 16)],
+ ["nn.global_avg_pool2d", "int8", (8, 8, 16)],
+ ["nn.global_avg_pool2d", "int8", (8, 8, 16)],
Review Comment:
Good spot. I've de-duplicated them now.
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[GitHub] [tvm] leandron commented on a diff in pull request #13488: [ACL] Enable int8 data type in pooling operators
Posted by GitBox <gi...@apache.org>.
leandron commented on code in PR #13488:
URL: https://github.com/apache/tvm/pull/13488#discussion_r1033322644
##########
tests/python/contrib/test_arm_compute_lib/test_pooling.py:
##########
@@ -169,91 +224,79 @@ def test_pooling():
device = Device()
np.random.seed(0)
- fp32_dtype = ("float32", -127, 128, 0.001, 0.001)
- uint8_dtype = ("uint8", 0, 255, 1, 0)
- # fmt: off
- trials = [
- ["nn.max_pool2d", fp32_dtype, (3, 3), (2, 2), (1, 1), (0, 0), False, False, (27, 27, 512), (0, 1),],
- ["nn.max_pool2d", fp32_dtype, (2, 2), (2, 2), (1, 1), (0, 0), False, True, (16, 16, 16), (0, 1),],
- ["nn.max_pool2d", fp32_dtype, (3, 3), (2, 2), (1, 1), (1, 1), True, True, (15, 15, 16), (0, 1),],
- ["nn.max_pool2d", fp32_dtype, (2, 2), (2, 2), (1, 1), (0, 1), False, False, (16, 16, 16), (0, 1),],
- ["nn.max_pool2d", uint8_dtype, (3, 3), (2, 2), (1, 1), (0, 1), False, False, (16, 16, 16), (0, 1),],
- ["nn.max_pool2d", uint8_dtype, (2, 2), (2, 2), (1, 1), (1, 1), True, True, (15, 15, 16), (0, 1),],
- ["nn.max_pool2d", uint8_dtype, (2, 2), (2, 2), (3, 2), (1, 1), True, True, (15, 15, 16), (1, 0),],
- ["nn.avg_pool2d", fp32_dtype, (2, 2), (2, 2), (1, 1), (1, 1), False, False, (16, 16, 16), (0, 1),],
- ["nn.avg_pool2d", fp32_dtype, (2, 2), (2, 2), (1, 1), (0, 0), False, True, (16, 16, 16), (0, 1),],
- ["nn.avg_pool2d", fp32_dtype, (3, 3), (2, 2), (3, 2), (0, 1), True, False, (15, 15, 16), (1, 0),],
- # 20.05: "exclude_padding equal false is not supported for AVG Pooling with padding on quantized types"
- # ["nn.avg_pool2d", uint8_dtype, (2, 2), (2, 2), (1, 1), False, True, (16, 16, 16)],
- ["nn.avg_pool2d", uint8_dtype, (3, 3), (2, 2), (1, 1), (0, 1), False, False, (16, 16, 16), (0, 1),],
- ["nn.l2_pool2d", fp32_dtype, (2, 2), (2, 2), (1, 1), (0, 1), True, False, (16, 16, 16), (0, 1),],
- ["nn.l2_pool2d", fp32_dtype, (3, 3), (2, 2), (1, 1), (0, 0), False, False, (16, 16, 16), (0, 1),],
- ["nn.l2_pool2d", fp32_dtype, (2, 2), (2, 2), (1, 1), (1, 1), False, True, (15, 15, 16), (0, 1),],
- ]
- # fmt: on
- for (
+ low, high, atol, rtol = _get_low_high_atol_rtol(dtype)
+ tvm_ops, acl_partitions = expected_ops
+
+ shape = (1, *input_shape)
+ outputs = []
+ inputs = {
+ "a": tvm.nd.array(np.random.uniform(low, high, shape).astype(dtype)),
+ }
+
+ func = _get_pooling_model(
+ shape,
+ dtype,
typef,
- (dtype, low, high, atol, rtol),
size,
stride,
dilation,
pad,
ceil_mode,
count_include_pad,
- input_shape,
- (tvm_ops, acl_partitions),
- ) in trials:
- shape = (1, *input_shape)
- outputs = []
- inputs = {
- "a": tvm.nd.array(np.random.uniform(low, high, shape).astype(dtype)),
- }
-
- func = _get_pooling_model(
- shape,
- dtype,
- typef,
- size,
- stride,
- dilation,
- pad,
- ceil_mode,
- count_include_pad,
- iter(inputs),
+ iter(inputs),
+ )
+
+ config = {
+ "size": size,
+ "stride": stride,
+ "shape": shape,
+ "pooling type": typef,
+ "dtype": dtype,
+ "padding": pad,
+ "dilation": dilation,
+ "ceil_mode": ceil_mode,
+ "count_include_pad": count_include_pad,
+ "inputs": inputs,
+ }
+ verify_saturation = True if dtype == "uint8" else False
+ for acl in [False, True]:
+ outputs.append(
+ build_and_run(
+ func,
+ inputs,
+ 1,
+ None,
+ device,
+ enable_acl=acl,
+ tvm_ops=tvm_ops,
+ acl_partitions=acl_partitions,
+ config=config,
+ )[0]
)
- config = {
- "size": size,
- "stride": stride,
- "shape": shape,
- "pooling type": typef,
- "dtype": dtype,
- "padding": pad,
- "dilation": dilation,
- "ceil_mode": ceil_mode,
- "count_include_pad": count_include_pad,
- "inputs": inputs,
- }
- verify_saturation = True if dtype == "uint8" else False
- for acl in [False, True]:
- outputs.append(
- build_and_run(
- func,
- inputs,
- 1,
- None,
- device,
- enable_acl=acl,
- tvm_ops=tvm_ops,
- acl_partitions=acl_partitions,
- config=config,
- )[0]
- )
-
- verify(outputs, atol=atol, rtol=rtol, config=config, verify_saturation=verify_saturation)
-
-
-def test_global_pooling():
+ verify(outputs, atol=atol, rtol=rtol, config=config, verify_saturation=verify_saturation)
+
+
+@pytest.mark.parametrize(
+ "typef,dtype,input_shape",
+ [
+ ["nn.global_max_pool2d", "float32", (8, 8, 16)],
+ ["nn.global_max_pool2d", "float32", (9, 9, 16)],
+ ["nn.global_max_pool2d", "float32", (8, 8, 16)],
+ ["nn.global_max_pool2d", "uint8", (8, 8, 16)],
+ ["nn.global_max_pool2d", "uint8", (9, 9, 16)],
+ ["nn.global_max_pool2d", "int8", (8, 8, 16)],
+ ["nn.global_max_pool2d", "int8", (9, 9, 16)],
+ ["nn.global_avg_pool2d", "float32", (8, 8, 16)],
+ ["nn.global_avg_pool2d", "float32", (8, 8, 16)],
+ ["nn.global_avg_pool2d", "float32", (9, 9, 16)],
+ ["nn.global_avg_pool2d", "uint8", (8, 8, 16)],
+ ["nn.global_avg_pool2d", "uint8", (8, 8, 16)],
+ ["nn.global_avg_pool2d", "int8", (8, 8, 16)],
+ ["nn.global_avg_pool2d", "int8", (8, 8, 16)],
Review Comment:
```suggestion
["nn.global_avg_pool2d", "uint8", (8, 8, 16)],
["nn.global_avg_pool2d", "uint8", (9, 9, 16)],
["nn.global_avg_pool2d", "int8", (8, 8, 16)],
["nn.global_avg_pool2d", "int8", (9, 9, 16)],
```
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[GitHub] [tvm] tvm-bot commented on pull request #13488: [ACL] Enable int8 data type in pooling operators
Posted by GitBox <gi...@apache.org>.
tvm-bot commented on PR #13488:
URL: https://github.com/apache/tvm/pull/13488#issuecomment-1327687514
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Thanks for contributing to TVM! Please refer to the contributing guidelines https://tvm.apache.org/docs/contribute/ for useful information and tips. Please request code reviews from [Reviewers](https://github.com/apache/incubator-tvm/blob/master/CONTRIBUTORS.md#reviewers) by @-ing them in a comment.
<!--bot-comment-ccs-start-->
* cc @ashutosh-arm, @lhutton1 <sub>See [#10317](https://github.com/apache/tvm/issues/10317) for details</sub><!--bot-comment-ccs-end-->
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[GitHub] [tvm] lhutton1 commented on a diff in pull request #13488: [ACL] Enable int8 data type in pooling operators
Posted by GitBox <gi...@apache.org>.
lhutton1 commented on code in PR #13488:
URL: https://github.com/apache/tvm/pull/13488#discussion_r1033339170
##########
tests/python/contrib/test_arm_compute_lib/test_pooling.py:
##########
@@ -169,91 +224,79 @@ def test_pooling():
device = Device()
np.random.seed(0)
- fp32_dtype = ("float32", -127, 128, 0.001, 0.001)
- uint8_dtype = ("uint8", 0, 255, 1, 0)
- # fmt: off
- trials = [
- ["nn.max_pool2d", fp32_dtype, (3, 3), (2, 2), (1, 1), (0, 0), False, False, (27, 27, 512), (0, 1),],
- ["nn.max_pool2d", fp32_dtype, (2, 2), (2, 2), (1, 1), (0, 0), False, True, (16, 16, 16), (0, 1),],
- ["nn.max_pool2d", fp32_dtype, (3, 3), (2, 2), (1, 1), (1, 1), True, True, (15, 15, 16), (0, 1),],
- ["nn.max_pool2d", fp32_dtype, (2, 2), (2, 2), (1, 1), (0, 1), False, False, (16, 16, 16), (0, 1),],
- ["nn.max_pool2d", uint8_dtype, (3, 3), (2, 2), (1, 1), (0, 1), False, False, (16, 16, 16), (0, 1),],
- ["nn.max_pool2d", uint8_dtype, (2, 2), (2, 2), (1, 1), (1, 1), True, True, (15, 15, 16), (0, 1),],
- ["nn.max_pool2d", uint8_dtype, (2, 2), (2, 2), (3, 2), (1, 1), True, True, (15, 15, 16), (1, 0),],
- ["nn.avg_pool2d", fp32_dtype, (2, 2), (2, 2), (1, 1), (1, 1), False, False, (16, 16, 16), (0, 1),],
- ["nn.avg_pool2d", fp32_dtype, (2, 2), (2, 2), (1, 1), (0, 0), False, True, (16, 16, 16), (0, 1),],
- ["nn.avg_pool2d", fp32_dtype, (3, 3), (2, 2), (3, 2), (0, 1), True, False, (15, 15, 16), (1, 0),],
- # 20.05: "exclude_padding equal false is not supported for AVG Pooling with padding on quantized types"
- # ["nn.avg_pool2d", uint8_dtype, (2, 2), (2, 2), (1, 1), False, True, (16, 16, 16)],
- ["nn.avg_pool2d", uint8_dtype, (3, 3), (2, 2), (1, 1), (0, 1), False, False, (16, 16, 16), (0, 1),],
- ["nn.l2_pool2d", fp32_dtype, (2, 2), (2, 2), (1, 1), (0, 1), True, False, (16, 16, 16), (0, 1),],
- ["nn.l2_pool2d", fp32_dtype, (3, 3), (2, 2), (1, 1), (0, 0), False, False, (16, 16, 16), (0, 1),],
- ["nn.l2_pool2d", fp32_dtype, (2, 2), (2, 2), (1, 1), (1, 1), False, True, (15, 15, 16), (0, 1),],
- ]
- # fmt: on
- for (
+ low, high, atol, rtol = _get_low_high_atol_rtol(dtype)
+ tvm_ops, acl_partitions = expected_ops
+
+ shape = (1, *input_shape)
+ outputs = []
+ inputs = {
+ "a": tvm.nd.array(np.random.uniform(low, high, shape).astype(dtype)),
+ }
+
+ func = _get_pooling_model(
+ shape,
+ dtype,
typef,
- (dtype, low, high, atol, rtol),
size,
stride,
dilation,
pad,
ceil_mode,
count_include_pad,
- input_shape,
- (tvm_ops, acl_partitions),
- ) in trials:
- shape = (1, *input_shape)
- outputs = []
- inputs = {
- "a": tvm.nd.array(np.random.uniform(low, high, shape).astype(dtype)),
- }
-
- func = _get_pooling_model(
- shape,
- dtype,
- typef,
- size,
- stride,
- dilation,
- pad,
- ceil_mode,
- count_include_pad,
- iter(inputs),
+ iter(inputs),
+ )
+
+ config = {
+ "size": size,
+ "stride": stride,
+ "shape": shape,
+ "pooling type": typef,
+ "dtype": dtype,
+ "padding": pad,
+ "dilation": dilation,
+ "ceil_mode": ceil_mode,
+ "count_include_pad": count_include_pad,
+ "inputs": inputs,
+ }
+ verify_saturation = True if dtype == "uint8" else False
+ for acl in [False, True]:
+ outputs.append(
+ build_and_run(
+ func,
+ inputs,
+ 1,
+ None,
+ device,
+ enable_acl=acl,
+ tvm_ops=tvm_ops,
+ acl_partitions=acl_partitions,
+ config=config,
+ )[0]
)
- config = {
- "size": size,
- "stride": stride,
- "shape": shape,
- "pooling type": typef,
- "dtype": dtype,
- "padding": pad,
- "dilation": dilation,
- "ceil_mode": ceil_mode,
- "count_include_pad": count_include_pad,
- "inputs": inputs,
- }
- verify_saturation = True if dtype == "uint8" else False
- for acl in [False, True]:
- outputs.append(
- build_and_run(
- func,
- inputs,
- 1,
- None,
- device,
- enable_acl=acl,
- tvm_ops=tvm_ops,
- acl_partitions=acl_partitions,
- config=config,
- )[0]
- )
-
- verify(outputs, atol=atol, rtol=rtol, config=config, verify_saturation=verify_saturation)
-
-
-def test_global_pooling():
+ verify(outputs, atol=atol, rtol=rtol, config=config, verify_saturation=verify_saturation)
+
+
+@pytest.mark.parametrize(
+ "typef,dtype,input_shape",
+ [
+ ["nn.global_max_pool2d", "float32", (8, 8, 16)],
+ ["nn.global_max_pool2d", "float32", (9, 9, 16)],
+ ["nn.global_max_pool2d", "float32", (8, 8, 16)],
+ ["nn.global_max_pool2d", "uint8", (8, 8, 16)],
+ ["nn.global_max_pool2d", "uint8", (9, 9, 16)],
+ ["nn.global_max_pool2d", "int8", (8, 8, 16)],
+ ["nn.global_max_pool2d", "int8", (9, 9, 16)],
+ ["nn.global_avg_pool2d", "float32", (8, 8, 16)],
+ ["nn.global_avg_pool2d", "float32", (8, 8, 16)],
+ ["nn.global_avg_pool2d", "float32", (9, 9, 16)],
+ ["nn.global_avg_pool2d", "uint8", (8, 8, 16)],
+ ["nn.global_avg_pool2d", "uint8", (9, 9, 16)],
+ ["nn.global_avg_pool2d", "int8", (8, 8, 16)],
+ ["nn.global_avg_pool2d", "int8", (9, 9, 16)],
Review Comment:
Missed a couple for float ;)
```suggestion
["nn.global_max_pool2d", "float32", (8, 8, 16)],
["nn.global_max_pool2d", "float32", (9, 9, 16)],
["nn.global_max_pool2d", "uint8", (8, 8, 16)],
["nn.global_max_pool2d", "uint8", (9, 9, 16)],
["nn.global_max_pool2d", "int8", (8, 8, 16)],
["nn.global_max_pool2d", "int8", (9, 9, 16)],
["nn.global_avg_pool2d", "float32", (8, 8, 16)],
["nn.global_avg_pool2d", "float32", (9, 9, 16)],
["nn.global_avg_pool2d", "uint8", (8, 8, 16)],
["nn.global_avg_pool2d", "uint8", (9, 9, 16)],
["nn.global_avg_pool2d", "int8", (8, 8, 16)],
["nn.global_avg_pool2d", "int8", (9, 9, 16)],
```
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