Skip to content

[Relay][Frontend] Add a few mxnet ops in relay frontend #2704

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Merged
merged 5 commits into from
Mar 3, 2019
Merged

[Relay][Frontend] Add a few mxnet ops in relay frontend #2704

Show file tree
Hide file tree
Changes from all commits
Commits
Show all changes
5 commits
Select commit Hold shift + click to select a range
eeabf52
Add a few mxnet ops in relay frontend
icemelon Feb 28, 2019
7260403
Remove debug logging
icemelon Feb 28, 2019
b837251
Use checked_type for safety check
icemelon Feb 28, 2019
e68dacc
add missing bcast max/min
icemelon Feb 28, 2019
786bcc7
fix indent
icemelon Feb 28, 2019
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
79 changes: 53 additions & 26 deletions python/tvm/relay/frontend/mxnet.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -64,6 +64,13 @@ def _stable_softrelu(x):
raise RuntimeError("Do not support act_type: {}".format(act_type))


def _mx_compare(new_op, wrapper):
def impl(inputs, attrs):
dtype = ir_pass.infer_type(inputs[0]).checked_type.dtype
return wrapper(new_op)(inputs, attrs).astype(dtype)
return impl


def _mx_conv2d(inputs, attrs):
kernel_size = attrs.get_int_tuple("kernel")
if len(kernel_size) != 2:
Expand Down Expand Up @@ -321,32 +328,52 @@ def _mx_roi_align(inputs, attrs):
]

_convert_map = {
"_copy" : _rename(_op.copy),
"relu" : _rename(_op.nn.relu),
"broadcast_add" : _rename(_op.add),
"broadcast_sub" : _rename(_op.subtract),
"broadcast_mul" : _rename(_op.multiply),
"broadcast_div" : _rename(_op.divide),
"elemwise_add" : _rename(_op.add),
"elemwise_sub" : _rename(_op.subtract),
"elemwise_mul" : _rename(_op.multiply),
"elemwise_div" : _rename(_op.divide),
"flatten" : _rename(_op.nn.batch_flatten),
"Flatten" : _rename(_op.nn.batch_flatten),
"_plus_scalar" : _binop_scalar(_op.add),
"__add_scalar__": _binop_scalar(_op.add),
"__sub_scalar__": _binop_scalar(_op.subtract),
"_minus_scalar" : _binop_scalar(_op.subtract),
"__mul_scalar__": _binop_scalar(_op.multiply),
"_mul_scalar" : _binop_scalar(_op.multiply),
"__div_scalar__": _binop_scalar(_op.divide),
"_div_scalar" : _binop_scalar(_op.divide),
"__pow_scalar__": _binop_scalar(_op.power),
"_rminus_scalar": _rbinop_scalar(_op.subtract),
"__rsub_scalar__": _rbinop_scalar(_op.subtract),
"_rdiv_scalar" : _rbinop_scalar(_op.divide),
"__rdiv_scalar__" : _rbinop_scalar(_op.divide),
"__rpow_scalar__": _rbinop_scalar(_op.power),
"_copy" : _rename(_op.copy),
"relu" : _rename(_op.nn.relu),
"broadcast_add" : _rename(_op.add),
"broadcast_sub" : _rename(_op.subtract),
"broadcast_mul" : _rename(_op.multiply),
"broadcast_div" : _rename(_op.divide),
"broadcast_mod" : _rename(_op.mod),
"broadcast_maximum" : _rename(_op.maximum),
"broadcast_minimum" : _rename(_op.minimum),
"broadcast_equal" : _mx_compare(_op.equal, _rename),
"broadcast_not_equal" : _mx_compare(_op.not_equal, _rename),
"broadcast_greater" : _mx_compare(_op.greater, _rename),
"broadcast_greater_equal": _mx_compare(_op.greater_equal, _rename),
"broadcast_lesser" : _mx_compare(_op.less, _rename),
"broadcast_lesser_equal" : _mx_compare(_op.less_equal, _rename),
"elemwise_add" : _rename(_op.add),
"elemwise_sub" : _rename(_op.subtract),
"elemwise_mul" : _rename(_op.multiply),
"elemwise_div" : _rename(_op.divide),
"_maximum" : _rename(_op.maximum),
"_minimum" : _rename(_op.minimum),
"flatten" : _rename(_op.nn.batch_flatten),
"Flatten" : _rename(_op.nn.batch_flatten),
"__add_scalar__" : _binop_scalar(_op.add),
"_plus_scalar" : _binop_scalar(_op.add),
"__sub_scalar__" : _binop_scalar(_op.subtract),
"_minus_scalar" : _binop_scalar(_op.subtract),
"__mul_scalar__" : _binop_scalar(_op.multiply),
"_mul_scalar" : _binop_scalar(_op.multiply),
"__div_scalar__" : _binop_scalar(_op.divide),
"_div_scalar" : _binop_scalar(_op.divide),
"__pow_scalar__" : _binop_scalar(_op.power),
"_power_scalar" : _binop_scalar(_op.power),
"__rsub_scalar__" : _rbinop_scalar(_op.subtract),
"_rminus_scalar" : _rbinop_scalar(_op.subtract),
"__rdiv_scalar__" : _rbinop_scalar(_op.divide),
"_rdiv_scalar" : _rbinop_scalar(_op.divide),
"__rpow_scalar__" : _rbinop_scalar(_op.power),
"_equal_scalar" : _mx_compare(_op.equal, _binop_scalar),
"_not_equal_scalar" : _mx_compare(_op.not_equal, _binop_scalar),
"_greater_scalar" : _mx_compare(_op.greater, _binop_scalar),
"_greater_equal_scalar" : _mx_compare(_op.greater_equal, _binop_scalar),
"_lesser_scalar" : _mx_compare(_op.less, _binop_scalar),
"_lesser_equal_scalar" : _mx_compare(_op.less_equal, _binop_scalar),
"_maximum_scalar" : _binop_scalar(_op.maximum),
"_minimum_scalar" : _binop_scalar(_op.minimum),
# reduction ops
"max" : _reduce(_op.max),
"min" : _reduce(_op.min),
Expand Down
83 changes: 83 additions & 0 deletions tests/python/frontend/mxnet/test_forward.py
View file
Open in desktop
Original file line number Diff line number Diff line change
@@ -1,4 +1,5 @@
import numpy as np
import operator

import tvm
from tvm.contrib import graph_runtime
Expand Down Expand Up @@ -256,6 +257,85 @@ def verify(start, stop, step):
verify(20, 1, -1)
verify(20, 1, -1.5)

def _mx_symbol(F, op_name, inputs):
op = getattr(F, op_name)
return op(*inputs)

def test_forward_broadcast_ops():
for op in ["broadcast_add", "broadcast_sub", "broadcast_mul",
"broadcast_div", "broadcast_mod", "broadcast_maximum",
"broadcast_minimum", "broadcast_equal", "broadcast_not_equal",
"broadcast_greater", "broadcast_greater_equal",
"broadcast_lesser", "broadcast_lesser_equal"]:
a_shape = (3, 4, 5)
b_shape = (4, 5)
if op == "broadcast_mod":
dtype = 'int32'
a_np = np.random.randint(1, 100, size=a_shape).astype(dtype)
b_np = np.random.randint(1, 100, size=b_shape).astype(dtype)
else:
dtype = 'float32'
a_np = np.random.uniform(size=a_shape).astype(dtype)
b_np = np.random.uniform(size=b_shape).astype(dtype)
mx_sym = _mx_symbol(mx.sym, op, [mx.sym.var('a'), mx.sym.var('b')])
ref_res = _mx_symbol(mx.nd, op, [mx.nd.array(a_np), mx.nd.array(b_np)])
shapes = {'a': a_shape, 'b': b_shape}
new_sym, _ = relay.frontend.from_mxnet(mx_sym, shapes, dtype)
for target, ctx in ctx_list():
for kind in ["graph", "debug"]:
intrp = relay.create_executor(kind, ctx=ctx, target=target)
op_res = intrp.evaluate(new_sym)(a_np, b_np)
tvm.testing.assert_allclose(op_res.asnumpy(), ref_res.asnumpy())

def test_forward_elemwise_ops():
for op in ["elemwise_add", "elemwise_sub", "elemwise_mul",
"elemwise_div", "maximum", "minimum"]:
shape = (3, 4, 5)
dtype = 'float32'
a_np = np.random.uniform(size=shape).astype(dtype)
b_np = np.random.uniform(size=shape).astype(dtype)
mx_sym = _mx_symbol(mx.sym, op, [mx.sym.var('a'), mx.sym.var('b')])
ref_res = _mx_symbol(mx.nd, op, [mx.nd.array(a_np), mx.nd.array(b_np)])
shapes = {'a': shape, 'b': shape}
new_sym, _ = relay.frontend.from_mxnet(mx_sym, shapes, dtype)
for target, ctx in ctx_list():
for kind in ["graph", "debug"]:
intrp = relay.create_executor(kind, ctx=ctx, target=target)
op_res = intrp.evaluate(new_sym)(a_np, b_np)
tvm.testing.assert_allclose(op_res.asnumpy(), ref_res.asnumpy())

def test_forward_scalar_ops():
for op in [operator.add, operator.sub, operator.mul, operator.truediv,
operator.pow, operator.lt, operator.le, operator.eq,
operator.ne, operator.gt, operator.ge]:
dtype='float32'
a_shape = (3, 4, 5)
a_np = np.random.uniform(size=a_shape).astype(dtype)
b_scalar = 2.3
mx_sym = op(mx.sym.var('a'), b_scalar)
ref_res = op(mx.nd.array(a_np), b_scalar)
shapes = {'a': a_shape}
new_sym, _ = relay.frontend.from_mxnet(mx_sym, shapes, dtype)
for target, ctx in ctx_list():
for kind in ["graph", "debug"]:
intrp = relay.create_executor(kind, ctx=ctx, target=target)
op_res = intrp.evaluate(new_sym)(a_np)
tvm.testing.assert_allclose(op_res.asnumpy(), ref_res.asnumpy())
for op in ["maximum", "minimum"]:
dtype='float32'
a_shape = (3, 4, 5)
a_np = np.random.uniform(size=a_shape).astype(dtype)
b_scalar = 2.3
mx_sym = _mx_symbol(mx.sym, op, [mx.sym.var('a'), b_scalar])
ref_res = _mx_symbol(mx.nd, op, [mx.nd.array(a_np), b_scalar])
shapes = {'a': a_shape}
new_sym, _ = relay.frontend.from_mxnet(mx_sym, shapes, dtype)
for target, ctx in ctx_list():
for kind in ["graph", "debug"]:
intrp = relay.create_executor(kind, ctx=ctx, target=target)
op_res = intrp.evaluate(new_sym)(a_np)
tvm.testing.assert_allclose(op_res.asnumpy(), ref_res.asnumpy())


if __name__ == '__main__':
test_forward_mlp()
Expand All @@ -280,3 +360,6 @@ def verify(start, stop, step):
test_forward_argmin()
test_forward_where()
test_forward_arange()
test_forward_broadcast_ops()
test_forward_elemwise_ops()
test_forward_scalar_ops()