yzhliu commented on a change in pull request #6054:
URL: https://github.com/apache/incubator-tvm/pull/6054#discussion_r461254852
##########
File path: tests/python/frontend/mxnet/test_forward.py
##########
@@ -1410,6 +1411,221 @@ def verify(data_shape, axis, use_length, length):
verify((2, 3, 4), 2, True, np.array([[3, 4, 2], [1, 2,
1]]).astype('int32'))
[email protected](not hasattr(mx.sym.np, 'pad'), reason="mx.sym.np.pad
hasn't been publish yet")
[email protected](
+ "data_shape, pad_width",
+ [((1,1,3,5),(0,0,0,0,1,2,3,4)), ((1,1,3,5,7),(0,0,0,0,1,2,3,4,5,6))]
+)
[email protected]("mode", ["constant", "edge", "reflect"])
[email protected]("dtype", ['float64', 'float32', 'int64', 'int32'])
[email protected]("constant_value", [0.0, 3.0])
+def test_forward_npi_pad(data_shape, pad_width, mode, dtype, constant_value):
+ data_np = np.random.uniform(size=data_shape).astype(dtype)
+ data = mx.sym.var('data')
+ if mode == 'constant':
+ ref_res = mx.ndarray.pad(mx.nd.array(data_np),
mode=mode,pad_width=pad_width, constant_value=constant_value)
+ mx_sym = mx.sym.np.pad(data.as_np_ndarray(), mode=mode,
pad_width=pad_width, constant_values=constant_value)
+ else:
+ ref_res = mx.ndarray.pad(mx.nd.array(data_np),
mode=mode,pad_width=pad_width)
+ mx_sym = mx.sym.np.pad(data.as_np_ndarray(), mode=mode,
pad_width=pad_width)
+ mod, _ = relay.frontend.from_mxnet(mx_sym, {"data": data_shape},
dtype=dtype)
+ for target, ctx in ctx_list():
+ for kind in ["debug"]:
Review comment:
why is this one using debug only?
##########
File path: tests/python/frontend/mxnet/test_forward.py
##########
@@ -1410,6 +1411,221 @@ def verify(data_shape, axis, use_length, length):
verify((2, 3, 4), 2, True, np.array([[3, 4, 2], [1, 2,
1]]).astype('int32'))
[email protected](not hasattr(mx.sym.np, 'pad'), reason="mx.sym.np.pad
hasn't been publish yet")
[email protected](
+ "data_shape, pad_width",
+ [((1,1,3,5),(0,0,0,0,1,2,3,4)), ((1,1,3,5,7),(0,0,0,0,1,2,3,4,5,6))]
+)
[email protected]("mode", ["constant", "edge", "reflect"])
[email protected]("dtype", ['float64', 'float32', 'int64', 'int32'])
[email protected]("constant_value", [0.0, 3.0])
+def test_forward_npi_pad(data_shape, pad_width, mode, dtype, constant_value):
+ data_np = np.random.uniform(size=data_shape).astype(dtype)
+ data = mx.sym.var('data')
+ if mode == 'constant':
+ ref_res = mx.ndarray.pad(mx.nd.array(data_np),
mode=mode,pad_width=pad_width, constant_value=constant_value)
+ mx_sym = mx.sym.np.pad(data.as_np_ndarray(), mode=mode,
pad_width=pad_width, constant_values=constant_value)
+ else:
+ ref_res = mx.ndarray.pad(mx.nd.array(data_np),
mode=mode,pad_width=pad_width)
+ mx_sym = mx.sym.np.pad(data.as_np_ndarray(), mode=mode,
pad_width=pad_width)
+ mod, _ = relay.frontend.from_mxnet(mx_sym, {"data": data_shape},
dtype=dtype)
+ for target, ctx in ctx_list():
+ for kind in ["debug"]:
Review comment:
maybe we can also use `@pytest.mark.parametrize` for ctx_list and kind.
##########
File path: tests/python/frontend/mxnet/test_forward.py
##########
@@ -1410,6 +1411,221 @@ def verify(data_shape, axis, use_length, length):
verify((2, 3, 4), 2, True, np.array([[3, 4, 2], [1, 2,
1]]).astype('int32'))
[email protected](not hasattr(mx.sym.np, 'pad'), reason="mx.sym.np.pad
hasn't been publish yet")
[email protected](
+ "data_shape, pad_width",
+ [((1,1,3,5),(0,0,0,0,1,2,3,4)), ((1,1,3,5,7),(0,0,0,0,1,2,3,4,5,6))]
+)
[email protected]("mode", ["constant", "edge", "reflect"])
[email protected]("dtype", ['float64', 'float32', 'int64', 'int32'])
[email protected]("constant_value", [0.0, 3.0])
+def test_forward_npi_pad(data_shape, pad_width, mode, dtype, constant_value):
+ data_np = np.random.uniform(size=data_shape).astype(dtype)
+ data = mx.sym.var('data')
+ if mode == 'constant':
+ ref_res = mx.ndarray.pad(mx.nd.array(data_np),
mode=mode,pad_width=pad_width, constant_value=constant_value)
+ mx_sym = mx.sym.np.pad(data.as_np_ndarray(), mode=mode,
pad_width=pad_width, constant_values=constant_value)
+ else:
+ ref_res = mx.ndarray.pad(mx.nd.array(data_np),
mode=mode,pad_width=pad_width)
+ mx_sym = mx.sym.np.pad(data.as_np_ndarray(), mode=mode,
pad_width=pad_width)
+ mod, _ = relay.frontend.from_mxnet(mx_sym, {"data": data_shape},
dtype=dtype)
+ for target, ctx in ctx_list():
+ for kind in ["debug"]:
+ intrp = relay.create_executor(kind, mod=mod, ctx=ctx,
target=target)
+ op_res = intrp.evaluate()(data_np)
+ tvm.testing.assert_allclose(op_res.asnumpy(), ref_res.asnumpy(),
rtol=1e-5)
+
+
[email protected]("data_shape", [(2,2,2),(2,7,2)])
[email protected]("dtype", ['float64', 'float32', 'int64', 'int32',
'bool'])
[email protected]("axes", [(1,0,2),None])
+def test_forward_npi_transpose(data_shape, axes, dtype):
+ def verify(data_shape, axes=None):
+ data_np = np.random.uniform(size=data_shape).astype(dtype)
+ data = mx.sym.var('data')
+ ref_res = mx.np.transpose(mx.np.array(data_np), axes=axes)
+ mx_sym = mx.sym.np.transpose(data.as_np_ndarray(), axes=axes)
+ mod, _ = relay.frontend.from_mxnet(mx_sym, {"data": data_shape},
dtype=dtype)
+ for target, ctx in ctx_list():
+ for kind in ["graph", "vm", "debug"]:
+ intrp = relay.create_executor(kind, mod=mod, ctx=ctx,
target=target)
+ op_res = intrp.evaluate()(data_np)
+ tvm.testing.assert_allclose(op_res.asnumpy(),
ref_res.asnumpy(), rtol=1e-5)
+
+
[email protected](
+ "data_shape1, data_shape2, axis",
+
[((2,2),(2,2),1),((2,4),(2,3),1),((1,3,2),(1,3,5),2),((1,3,3),(1,3,3),1),((1,3),(1,3),0)]
+)
[email protected]("dtype", ['float64', 'float32', 'int64', 'int32'])
+def test_forward_npi_concatenate(data_shape1, data_shape2, axis, dtype):
+ data_np1 = np.random.uniform(size=data_shape1).astype(dtype)
+ data_np2 = np.random.uniform(size=data_shape2).astype(dtype)
+ data1 = mx.sym.var('data1')
+ data2 = mx.sym.var('data2')
+ ref_res = mx.np.concatenate([mx.np.array(data_np1),
mx.np.array(data_np2)], axis=axis)
+ mx_sym = mx.sym.np.concatenate([data1.as_np_ndarray(),
data2.as_np_ndarray()], axis=axis)
+ mod, _ = relay.frontend.from_mxnet(mx_sym, shape={"data1": data_shape1,
"data2": data_shape2}, dtype=dtype)
+ for target, ctx in ctx_list():
+ for kind in ["graph", "vm", "debug"]:
+ intrp = relay.create_executor(kind, mod=mod, ctx=ctx,
target=target)
+ op_res = intrp.evaluate()(data_np1, data_np2)
+ tvm.testing.assert_allclose(op_res.asnumpy(), ref_res.asnumpy(),
rtol=1e-5)
+
+
[email protected]("data_shape", [(2,2,2),(2,7,2),(2,2,2,1,2,3,1),(1,8)])
[email protected]("dtype", ['float64', 'float32', 'int64', 'int32',
'bool'])
+def test_forward_np_copy(data_shape,dtype):
+ data_np = np.random.uniform(size=data_shape).astype(dtype)
+ data = mx.sym.var('data')
+ ref_res = mx.np.copy(mx.np.array(data_np))
+ mx_sym = mx.sym.np.copy(data.as_np_ndarray())
+ mod, _ = relay.frontend.from_mxnet(mx_sym, {"data": data_shape},
dtype=dtype)
+ for target, ctx in ctx_list():
+ for kind in ["graph", "vm", "debug"]:
+ intrp = relay.create_executor(kind, mod=mod, ctx=ctx,
target=target)
+ op_res = intrp.evaluate()(data_np)
+ tvm.testing.assert_allclose(op_res.asnumpy(), ref_res.asnumpy(),
rtol=1e-5)
+
+
[email protected]("dtype", ['float64', 'float32', 'int64', 'int32',
'bool'])
+def test_forward_npx_reshape(dtype):
+ def verify(data_shape,out_shape,reverse=False):
+ data_np = np.random.uniform(size=data_shape).astype(dtype)
+ data = mx.sym.var('data')
+ ref_res = mx.npx.reshape(mx.np.array(data_np), newshape=out_shape,
reverse=reverse)
+ mx_sym = mx.sym.npx.reshape(data.as_np_ndarray(), newshape=out_shape,
reverse=reverse)
+ mod, _ = relay.frontend.from_mxnet(mx_sym, {"data": data_shape},
dtype=dtype)
+ for target, ctx in ctx_list():
+ for kind in ["graph", "vm", "debug"]:
+ intrp = relay.create_executor(kind, mod=mod, ctx=ctx,
target=target)
+ op_res = intrp.evaluate()(data_np)
+ tvm.testing.assert_allclose(op_res.asnumpy(),
ref_res.asnumpy(), rtol=1e-5)
+
+ verify(data_shape=(2, 3, 8), out_shape=(-2, -2, 2, -1))
+ verify(data_shape=(8, 3, 3, 3, 4, 4), out_shape=(-6, 2, -1, -4))
+ verify(data_shape=(8, 3, 3, 3, 4, 4), out_shape=(-5, -4))
+ verify(data_shape=(8, 3, 3, 3, 3, 8), out_shape=(-4, -5), reverse=True)
+ verify(data_shape=(8, 3, 2, 4, 8), out_shape=(-4, -1, 2, -6), reverse=True)
+
+
[email protected]("data_shape",
[(2,2,2),(2,7,2),(2,2,2,1,2,3,1),(1,8),(2,2),(1,3)])
[email protected]("dtype", ['float64', 'float32', 'int64', 'int32'])
+def test_forward_npi_binary(data_shape,dtype):
+ ref_ops = [mx.np.power, mx.np.multiply, mx.np.add, mx.np.less]
+ mx_ops = [mx.sym.np.power, mx.sym.np.multiply, mx.sym.np.add,
mx.sym.np.less]
+ for i in range(len(ref_ops)):
+ ref_op = ref_ops[i]
+ mx_op = mx_ops[i]
+ # mx.np.power only support float type
+ if ref_op == mx.np.power and dtype not in ['float64', 'float32']:
+ continue
+ data_np1 = np.random.uniform(size=data_shape).astype(dtype)
+ data_np2 = np.random.uniform(size=data_shape).astype(dtype)
+ data1 = mx.sym.var('lhs')
+ data2 = mx.sym.var('rhs')
+ ref_res = ref_op(mx.np.array(data_np1), mx.np.array(data_np2))
+ mx_sym = mx_op(data1.as_np_ndarray(), data2.as_np_ndarray())
+ mod, _ = relay.frontend.from_mxnet(mx_sym, shape={"lhs": data_shape,
"rhs": data_shape}, dtype=dtype)
+ for target, ctx in ctx_list():
+ for kind in ["graph", "vm", "debug"]:
+ intrp = relay.create_executor(kind, mod=mod, ctx=ctx,
target=target)
+ op_res = intrp.evaluate()(data_np1, data_np2)
+ tvm.testing.assert_allclose(op_res.asnumpy(),
ref_res.asnumpy(), rtol=1e-5)
+
+
[email protected]("data_shape",
[(2,2,2),(2,7,2),(2,2,2,1,2,3,1),(1,8),(2,2),(1,3)])
[email protected]("dtype", ['float64', 'float32', 'int64', 'int32'])
[email protected]("scalar", [1.0,2.0,3.0,4.0])
+def test_forward_npi_binary_scalar(data_shape,dtype,scalar):
+ ref_ops = [mx.np.power, mx.np.multiply, mx.np.add, mx.np.true_divide]
+ mx_ops = [mx.sym.np.power, mx.sym.np.multiply, mx.sym.np.add,
mx.sym.np.true_divide]
+ for i in range(len(ref_ops)):
+ ref_op = ref_ops[i]
+ mx_op = mx_ops[i]
+ # mx.np.power only support float type
+ if ref_op == mx.np.power and dtype not in ['float64', 'float32']:
+ continue
+ data_np1 = np.random.uniform(size=data_shape).astype(dtype)
+ data1 = mx.sym.var('lhs')
+ ref_res = ref_op(mx.np.array(data_np1), scalar)
+ mx_sym = mx_op(data1.as_np_ndarray(), scalar)
+ mod, _ = relay.frontend.from_mxnet(mx_sym, shape={"lhs": data_shape},
dtype=dtype)
+ for target, ctx in ctx_list():
+ for kind in ["graph", "vm", "debug"]:
+ intrp = relay.create_executor(kind, mod=mod, ctx=ctx,
target=target)
+ op_res = intrp.evaluate()(data_np1)
+ tvm.testing.assert_allclose(op_res.asnumpy(),
ref_res.asnumpy(), rtol=1e-5)
+
+
[email protected]("data_shape",
[(2,2,2),(2,7,2),(2,2,2,1,2,3,1),(1,8),(2,2),(1,3)])
[email protected]("dtype", ['float64', 'float32'])
+def test_forward_npi_tanh(data_shape,dtype):
+ data_np1 = np.random.uniform(size=data_shape).astype(dtype)
+ data1 = mx.sym.var('data')
+ ref_res = mx.np.tanh(mx.np.array(data_np1))
+ mx_sym = mx.sym.np.tanh(data1.as_np_ndarray())
+ mod, _ = relay.frontend.from_mxnet(mx_sym, shape={"data": data_shape},
dtype=dtype)
+ for target, ctx in ctx_list():
+ for kind in ["graph", "vm", "debug"]:
+ intrp = relay.create_executor(kind, mod=mod, ctx=ctx,
target=target)
+ op_res = intrp.evaluate()(data_np1)
+ tvm.testing.assert_allclose(op_res.asnumpy(), ref_res.asnumpy(),
rtol=1e-5)
+
+
[email protected](not hasattr(mx.np, 'where'), reason="mx.np.where hasn't
been publish yet")
[email protected]("data_shape", [(2,2,2),(2,7,2),(1,8),(2,2),(1,3)])
[email protected]("cond_dtype", ['float64', 'float32', 'int64',
'int32', 'bool'])
[email protected]("data_dtype", ['float64', 'float32', 'int64',
'int32', 'bool'])
[email protected]("scalar", [1.0,2.0])
+def test_forward_npi_where_rscalar(data_shape,cond_dtype,data_dtype,scalar):
+ if data_dtype == 'bool':
+ scalar = scalar == 0.0
+ cond_np = np.random.uniform(size=data_shape).astype(cond_dtype)
+ data_np = np.random.uniform(size=data_shape).astype(data_dtype)
+ cond = mx.sym.var('condition')
+ data = mx.sym.var('x')
+ ref_res = mx.np.where(mx.np.array(cond_np), mx.np.array(data_np), scalar)
+ mx_sym = mx.sym.np.where(cond.as_np_ndarray(), data.as_np_ndarray(),
scalar)
+ dtypeDic = {}
+ dtypeDic["condition"] = cond_dtype
+ dtypeDic["x"] = data_dtype
+ mod, _ = relay.frontend.from_mxnet(
+ mx_sym, shape={"condition": data_shape, "x": data_shape},
+ dtype=dtypeDic)
+ for target, ctx in ctx_list():
+ for kind in ["graph", "vm", "debug"]:
+ intrp = relay.create_executor(kind, mod=mod, ctx=ctx,
target=target)
+ op_res = intrp.evaluate()(cond_np, data_np)
+ tvm.testing.assert_allclose(op_res.asnumpy(), ref_res.asnumpy(),
rtol=1e-5)
+
+
[email protected]("dtype", ['float64', 'float32', 'int64', 'int32',
'bool'])
+def test_forward_split_v2(dtype):
+ def verify(data_shape, axis=0, indices_or_sections=1, squeeze_axis=False):
+ data_np = np.random.uniform(size=data_shape).astype(dtype)
+ data = mx.sym.var('data')
+ ref_res = mx.ndarray.split_v2(mx.nd.array(data_np),
indices_or_sections, axis=axis, squeeze_axis=squeeze_axis)
+ mx_sym = mx.sym.split_v2(data.as_nd_ndarray(), indices_or_sections,
axis=axis, squeeze_axis=squeeze_axis)
+ mod, _ = relay.frontend.from_mxnet(mx_sym, {"data": data_shape},
dtype=dtype)
+ for target, ctx in ctx_list():
+ for kind in ["graph", "vm", "debug"]:
+ intrp = relay.create_executor(kind, mod=mod, ctx=ctx,
target=target)
+ op_res = intrp.evaluate()(data_np)
+ op_res_ = []
+ for arr in op_res:
+ op_res_.append(arr.asnumpy().tolist())
+ ref_res_ = []
+ for arr in ref_res:
+ ref_res_.append(arr.asnumpy().tolist())
+ tvm.testing.assert_allclose(op_res_, ref_res_, rtol=1e-5)
+
+ verify((3, 2, 1), axis=1, indices_or_sections=2)
+ verify((3, 2, 1), axis=0, indices_or_sections=3)
+ verify((3, 2, 1), axis=0, indices_or_sections=3, squeeze_axis=True)
+ verify((3, 2, 1), axis=0, indices_or_sections=(1, 2))
+
+
if __name__ == '__main__':
test_forward_mlp()
Review comment:
remove all the test_xxx function calls and add `pytest.main([__file__])`
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