def test_rc_pynative_fp16_int32():
context.set_context(mode=context.PYNATIVE_MODE, device_target=TARGET)
x = Tensor(np.array([[1, 2, 3, 4, 5], [6, 7, 8, 9, 10]]), ms.float16)
num_sample = 10
seed = 5
dtype = ms.int32
expect = np.array(
[[4, 3, 2, 4, 4, 4, 3, 4, 1, 3], [4, 3, 2, 4, 4, 4, 3, 4, 1, 3]],
dtype=np.int32)
output = P.RandomCategorical(dtype)(x, num_sample, seed)
diff = output.asnumpy() - expect
assert expect.dtype == output.asnumpy().dtype
assert np.all(diff == 0)
def __init__(self, num_sample, seed=0, dtype=ms.int64):
super(RCnet, self).__init__()
self.rc = P.RandomCategorical(dtype)
self.num_sample = num_sample
self.seed = seed
def __init__(self,
decoder:Model,
model_config=None,
generate_length:int=1,
tokenizer:Optional[GPT2Tokenizer]=None,
topk_num:int=0,
topp_prob:float=1.0,
temperature:float=1.0,
min_tokens_to_keep:int=1,
early_stop:bool=False,
demo_mode:bool=False,
return_ids:bool=False,
return_last_token_logits:bool=False,
append_eos:bool=False):
assert model_config is not None, 'Config is a must for sampling.'
self.model_config = model_config
self.topk_num = topk_num
self.topp_prob = topp_prob
self.temperature = temperature
self.min_tokens_to_keep = min_tokens_to_keep
self.decoder = decoder
self.tokenizer = tokenizer
self.reshape = P.Reshape()
self.cumsum = P.CumSum()
self.onehot = P.OneHot()
self.generate_length = generate_length
self.seq_length = model_config.seq_length
self.batch_size = model_config.batch_size
self.vocab_size = model_config.vocab_size
self.on_value = Tensor(1.0, mstype.float32)
self.off_value = Tensor(0.0, mstype.float32)
self.cast = P.Cast()
self.concat = P.Concat()
self.early_stop = early_stop
self.demo_mode = demo_mode
self.return_ids = return_ids
self.return_last_token_logits = return_last_token_logits
self.append_eos = append_eos
self.device_target = get_context("device_target")
#different choice of sample function for adjusting several device target types
if self.device_target == "GPU":
self.sample_function = P.Multinomial(seed=1)
elif self.device_target == "Ascend":
self.sample_function = P.RandomCategorical(mstype.int32)
else:
raise NotImplementedError("Device Target {} not supported.".format(self.device_target))
self.filter_distribution = TopKTopP_Filter(self.batch_size,
self.vocab_size,
k=self.topk_num,
p=self.topp_prob,
temperature=self.temperature,
min_tokens_to_keep=self.min_tokens_to_keep)
if self.tokenizer is not None:
self.eos_id = self.tokenizer.eos_token_id
else:
self.eos_id = model_config.vocab_size-1
if self.tokenizer is not None:
self.eos_text = self.tokenizer.eos_token
else:
self.eos_text = "<|endoftext|>"
if self.demo_mode is True:
assert self.batch_size == 1, 'Demo mode requires batchsize euqals to 1, but get batch_size={}'.format(
self.batch_size)
def __init__(self, num_sample):
super(RandomCategoricalNet, self).__init__()
self.random_categorical = P.RandomCategorical(mstype.int64)
self.num_sample = num_sample
def __init__(
self,
decoder,
config=None,
batch_size=None,
tokenizer=None,
generate_length=1,
topk_num=0,
topp_prob=1.0,
temperature=1.0,
min_tokens_to_keep=1,
early_stop=False,
demo_mode=False,
return_ids=False,
return_last_token_logits=False,
append_eos=False,
):
assert config is not None, 'Config is a must for sampling.'
self.decoder = decoder
self.config = config
self.tokenizer = tokenizer
self.generate_length = generate_length
self.topk_num = topk_num
self.topp_prob = topp_prob
self.temperature = temperature
self.min_tokens_to_keep = min_tokens_to_keep
self.early_stop = early_stop
self.demo_mode = demo_mode
self.return_ids = return_ids
self.return_last_token_logits = return_last_token_logits
self.append_eos = append_eos
self.seq_length = config.seq_length
self.batch_size = config.batch_size if batch_size is None else batch_size
self.vocab_size = config.vocab_size
self.on_value = Tensor(1.0, mstype.float32)
self.off_value = Tensor(0.0, mstype.float32)
self.reshape = P.Reshape()
self.cumsum = P.CumSum()
self.onehot = P.OneHot()
self.cast = P.Cast()
self.concat = P.Concat()
self.sample_function = P.RandomCategorical(mstype.int32)
self.filter_distribution = TopKTopP_Filter(
batch_size=self.batch_size,
vocab_size=self.vocab_size,
k=self.topk_num,
p=self.topp_prob,
temperature=self.temperature,
min_tokens_to_keep=self.min_tokens_to_keep)
if self.tokenizer is not None:
self.eos_id = self.tokenizer.eos_token_id
else:
self.eos_id = config.vocab_size - 1
if self.tokenizer is not None:
self.eos_text = self.tokenizer.eos_token
else:
self.eos_text = "<|endoftext|>"
if self.demo_mode is True:
assert self.batch_size == 1, 'Demo mode requires batchsize euqals to 1, but get batch_size={}'.format(
self.batch_size)