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Subset of Reduction Ops for Pytorch Frontend #3927

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merged 7 commits into from
Sep 13, 2022
Merged

Subset of Reduction Ops for Pytorch Frontend #3927

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34bb74e
implmented 4 reduction ops
ahmedo42 Sep 1, 2022
20a6c0d
added more functions, tests
ahmedo42 Sep 5, 2022
3a91c05
removed functions and tests,will do another PR
ahmedo42 Sep 9, 2022
3c79314
remove None
ahmedo42 Sep 9, 2022
9fdc6f3
revert change
ahmedo42 Sep 10, 2022
503dbfa
Merge remote-tracking branch 'upstream/master' into torch-reduction-ops
ahmedo42 Sep 12, 2022
4db748a
refactoring tests and adding None to amax,amin
ahmedo42 Sep 12, 2022
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33 changes: 33 additions & 0 deletions ivy/functional/frontends/torch/reduction_ops.py
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Original file line number Diff line number Diff line change
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import ivy


def argmax(input, dim=None, keepdim=False):
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return ivy.argmax(input, axis=dim, keepdims=keepdim)


def argmin(input, dim=None, keepdim=False):
return ivy.argmin(input, axis=dim, keepdims=keepdim)


def amax(input, dim=None, keepdim=False, *, out=None):
return ivy.max(input, axis=dim, keepdims=keepdim, out=out)


def amin(input, dim=None, keepdim=False, *, out=None):
return ivy.min(input, axis=dim, keepdims=keepdim, out=out)


def all(input, dim=None, keepdim=False, *, out=None):
input_dtype = ivy.as_ivy_dtype(input.dtype)
ret = ivy.all(input, axis=dim, keepdims=keepdim, out=out)
if ivy.is_uint_dtype(input_dtype):
ret = ivy.astype(ret, input_dtype, out=out)
return ret


def any(input, dim=None, keepdim=False, *, out=None):
input_dtype = ivy.as_ivy_dtype(input.dtype)
ret = ivy.any(input, axis=dim, keepdims=keepdim, out=out)
if ivy.is_uint_dtype(input_dtype):
ret = ivy.astype(ret, input_dtype, out=out)
return ret
241 changes: 241 additions & 0 deletions ivy_tests/test_ivy/test_frontends/test_torch/test_reduction_ops.py
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# global
import numpy as np
from hypothesis import given, strategies as st

# local
import ivy_tests.test_ivy.helpers as helpers
from ivy_tests.test_ivy.helpers import handle_cmd_line_args


@handle_cmd_line_args
@given(
dtype_input_axis=helpers.dtype_values_axis(
available_dtypes=helpers.get_dtypes("numeric"),
force_int_axis=True,
min_num_dims=1,
min_axis=-1,
max_axis=0,
),
num_positional_args=helpers.num_positional_args(
fn_name="ivy.functional.frontends.torch.argmax"
),
keepdims=st.booleans(),
)
def test_torch_argmax(
dtype_input_axis,
as_variable,
num_positional_args,
native_array,
keepdims,
fw,
):
input_dtype, x, axis = dtype_input_axis
helpers.test_frontend_function(
input_dtypes=input_dtype,
as_variable_flags=as_variable,
with_out=False,
num_positional_args=num_positional_args,
native_array_flags=native_array,
fw=fw,
frontend="torch",
fn_tree="argmax",
input=np.asarray(x, dtype=input_dtype),
dim=axis,
keepdim=keepdims,
)


@handle_cmd_line_args
@given(
dtype_input_axis=helpers.dtype_values_axis(
available_dtypes=helpers.get_dtypes("numeric"),
force_int_axis=True,
min_num_dims=1,
min_axis=-1,
max_axis=0,
),
num_positional_args=helpers.num_positional_args(
fn_name="ivy.functional.frontends.torch.argmin"
),
keepdims=st.booleans(),
)
def test_torch_argmin(
dtype_input_axis,
as_variable,
num_positional_args,
native_array,
keepdims,
fw,
):
input_dtype, x, axis = dtype_input_axis
helpers.test_frontend_function(
input_dtypes=input_dtype,
as_variable_flags=as_variable,
with_out=False,
num_positional_args=num_positional_args,
native_array_flags=native_array,
fw=fw,
frontend="torch",
fn_tree="argmin",
input=np.asarray(x, dtype=input_dtype),
dim=axis,
keepdim=keepdims,
)


@handle_cmd_line_args
@given(
dtype_input_axis=helpers.dtype_values_axis(
available_dtypes=helpers.get_dtypes("numeric"),
min_num_dims=1,
min_axis=-1,
max_axis=0,
),
num_positional_args=helpers.num_positional_args(
fn_name="ivy.functional.frontends.torch.amax"
),
keepdims=st.booleans(),
)
def test_torch_amax(
dtype_input_axis,
as_variable,
num_positional_args,
native_array,
keepdims,
with_out,
fw,
):
input_dtype, x, axis = dtype_input_axis
helpers.test_frontend_function(
input_dtypes=input_dtype,
as_variable_flags=as_variable,
with_out=with_out,
num_positional_args=num_positional_args,
native_array_flags=native_array,
fw=fw,
frontend="torch",
fn_tree="amax",
input=np.asarray(x, dtype=input_dtype),
dim=axis,
keepdim=keepdims,
out=None,
)


@handle_cmd_line_args
@given(
dtype_input_axis=helpers.dtype_values_axis(
available_dtypes=helpers.get_dtypes("numeric"),
min_num_dims=1,
min_axis=-1,
max_axis=0,
),
num_positional_args=helpers.num_positional_args(
fn_name="ivy.functional.frontends.torch.amin"
),
keepdims=st.booleans(),
)
def test_torch_amin(
dtype_input_axis,
as_variable,
num_positional_args,
native_array,
keepdims,
with_out,
fw,
):
input_dtype, x, axis = dtype_input_axis
helpers.test_frontend_function(
input_dtypes=input_dtype,
as_variable_flags=as_variable,
with_out=with_out,
num_positional_args=num_positional_args,
native_array_flags=native_array,
fw=fw,
frontend="torch",
fn_tree="amin",
input=np.asarray(x, dtype=input_dtype),
dim=axis,
keepdim=keepdims,
out=None,
)


@handle_cmd_line_args
@given(
dtype_input_axis=helpers.dtype_values_axis(
available_dtypes=helpers.get_dtypes("valid"),
min_axis=-1,
max_axis=0,
min_num_dims=1,
allow_inf=False,
),
num_positional_args=helpers.num_positional_args(
fn_name="ivy.functional.frontends.torch.all"
),
keepdims=st.booleans(),
)
def test_torch_all(
dtype_input_axis,
as_variable,
num_positional_args,
native_array,
keepdims,
with_out,
fw,
):
input_dtype, x, axis = dtype_input_axis
helpers.test_frontend_function(
input_dtypes=input_dtype,
as_variable_flags=as_variable,
with_out=with_out,
num_positional_args=num_positional_args,
native_array_flags=native_array,
fw=fw,
frontend="torch",
fn_tree="all",
input=np.asarray(x, dtype=input_dtype),
dim=axis,
keepdim=keepdims,
out=None,
)


@handle_cmd_line_args
@given(
dtype_input_axis=helpers.dtype_values_axis(
available_dtypes=helpers.get_dtypes("valid"),
min_axis=-1,
max_axis=0,
min_num_dims=1,
allow_inf=False,
),
num_positional_args=helpers.num_positional_args(
fn_name="ivy.functional.frontends.torch.any"
),
keepdims=st.booleans(),
)
def test_torch_any(
dtype_input_axis,
as_variable,
num_positional_args,
native_array,
keepdims,
with_out,
fw,
):
input_dtype, x, axis = dtype_input_axis
helpers.test_frontend_function(
input_dtypes=input_dtype,
as_variable_flags=as_variable,
with_out=with_out,
num_positional_args=num_positional_args,
native_array_flags=native_array,
fw=fw,
frontend="torch",
fn_tree="any",
input=np.asarray(x, dtype=input_dtype),
dim=axis,
keepdim=keepdims,
out=None,
)