def test_convert_recursive():
is_match = lambda obj: obj == "foo"
convert_item = lambda obj: obj.upper()
obj = {
"a": {
("b", "foo"): {
"c": "foo",
"d": ["foo", {
"e": "foo",
"f": (1, "foo")
}]
}
}
}
result = convert_recursive(is_match, convert_item, obj)
assert result["a"][("b", "FOO")]["c"] == "FOO"
assert result["a"][("b",
"FOO")]["d"] == ["FOO", {
"e": "FOO",
"f": (1, "FOO")
}]
obj = {"a": ArgsKwargs(("foo", [{"b": "foo"}]), {"a": ["x", "foo"]})}
result = convert_recursive(is_match, convert_item, obj)
assert result["a"].args == ("FOO", [{"b": "FOO"}])
assert result["a"].kwargs == {"a": ["x", "FOO"]}
def convert_transformer_inputs(model, tokens: TokensPlus, is_train):
kwargs = {
"input_ids": tokens.input_ids,
"attention_mask": tokens.attention_mask,
"token_type_ids": tokens.token_type_ids,
}
return ArgsKwargs(args=(), kwargs=kwargs), lambda dX: []
def backprop(d_tokvecs: List[Floats2d]) -> ArgsKwargs:
# Restore entries for bos and eos markers.
row = model.ops.alloc2f(1, d_tokvecs[0].shape[1])
d_tokvecs = [model.ops.xp.vstack((row, arr, row)) for arr in d_tokvecs]
return ArgsKwargs(
args=(torch_tokvecs,),
kwargs={"grad_tensors": xp2torch(model.ops.pad(d_tokvecs))},
)
def _convert_transformer_inputs(model, tokens: BatchEncoding, is_train):
# Adapter for the PyTorchWrapper. See https://thinc.ai/docs/usage-frameworks
kwargs = {
"input_ids": tokens["input_ids"],
"attention_mask": tokens["attention_mask"],
}
if "token_type_ids" in tokens:
kwargs["token_type_ids"] = tokens["token_type_ids"]
return ArgsKwargs(args=(), kwargs=kwargs), lambda dX: []
def _convert_transformer_inputs(model, wps: WordpieceBatch, is_train):
# Adapter for the PyTorchWrapper. See https://thinc.ai/docs/usage-frameworks
kwargs = {
"input_ids": xp2torch(wps.input_ids),
"attention_mask": xp2torch(wps.attention_mask),
}
if wps.token_type_ids is not None:
kwargs["token_type_ids"] = xp2torch(wps.token_type_ids)
return ArgsKwargs(args=(), kwargs=kwargs), lambda dX: []
def _convert_transformer_inputs(model, wps: WordpieceBatch, is_train):
# Adapter for the HFWrapper. See https://thinc.ai/docs/usage-frameworks
hf_device = model.shims[0]._hfmodel.transformer.device
kwargs = {
# Note: remove conversion to long when PyTorch >= 1.8.0.
"input_ids": xp2torch(wps.input_ids).long().to(device=hf_device),
"attention_mask": xp2torch(wps.attention_mask).to(device=hf_device),
}
if wps.token_type_ids is not None:
kwargs["token_type_ids"] = (xp2torch(
wps.token_type_ids).long().to(device=hf_device))
return ArgsKwargs(args=(), kwargs=kwargs), lambda dX: []
def backprop(d_tensors: List[torch.Tensor]) -> ArgsKwargs:
return ArgsKwargs(args=(tensors, ), kwargs={"grad_tensors": d_tensors})
def backprop(d_model_output: ModelOutput) -> ArgsKwargs:
return ArgsKwargs(
args=(model_output.last_hidden_state, ),
kwargs={"grad_tensors": d_model_output.values()},
)
