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change llama/modeling.py to opt npu performence #8342

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merged 7 commits into from
Apr 30, 2024
Merged

change llama/modeling.py to opt npu performence #8342

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5db1b03
change llama/modeling.py to opt npu performence
wanghuancoder Apr 28, 2024
48a1338
update
wanghuancoder Apr 28, 2024
11a52e0
update
wanghuancoder Apr 28, 2024
f2252e8
Update modeling.py
Galaxy1458 Apr 29, 2024
198e58e
Merge branch 'develop' of https://github.com/PaddlePaddle/PaddleNLP i…
wanghuancoder Apr 30, 2024
e557b5a
Merge branch 'develop' of https://github.com/Galaxy1458/PaddleNLP int…
wanghuancoder Apr 30, 2024
672f4aa
add judge
wanghuancoder Apr 30, 2024
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16 changes: 11 additions & 5 deletions paddlenlp/transformers/llama/modeling.py
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Original file line number Diff line number Diff line change
Expand Up @@ -97,6 +97,7 @@ def swiglu(x, y=None):
"LlamaPretrainingCriterion",
]

npu_is_casual = False

def _get_interleave(n):
def _get_interleave_power_of_2(n):
Expand Down Expand Up @@ -244,7 +245,7 @@ def scaled_dot_product_attention(
attention_mask is None,
True,
False,
False,
npu_is_casual,
)[0]
else:
attn_output = F.scaled_dot_product_attention(
Expand Down Expand Up @@ -1118,6 +1119,7 @@ def __init__(self, config, layerwise_recompute: bool = False):
self.layerwise_recompute = layerwise_recompute
self.recompute_granularity = config.recompute_granularity


def forward(
self,
hidden_states: paddle.Tensor,
Expand Down Expand Up @@ -1612,11 +1614,12 @@ def forward(
attention_mask, (batch_size, seq_length), cache_length, inputs_embeds.dtype
) # [bs, 1, seq_len, seq_len]
if self.config.use_flash_attention:
is_casual = is_casual_mask(attention_mask)
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if get_env_device() != "npu":
is_casual = is_casual_mask(attention_mask)
if is_casual and alibi is None:
attention_mask = None
else:
npu_is_casual = is_casual
attention_mask = attention_mask.astype("bool")
hidden_states = inputs_embeds
# decoder layers
Expand Down Expand Up @@ -1722,9 +1725,12 @@ def forward(self, prediction_scores, masked_lm_labels):
_hcg = fleet.get_hybrid_communicate_group()
masked_lm_loss = ConcatSePMaskedLoss.apply(masked_lm_loss, axis=1, group=_hcg.get_sep_parallel_group())
# skip ignore_index which loss == 0
masked_lm_loss = masked_lm_loss[masked_lm_loss > 0]
loss = paddle.mean(masked_lm_loss)

# masked_lm_loss = masked_lm_loss[masked_lm_loss > 0]
# loss = paddle.mean(masked_lm_loss)
binary_sequence = paddle.where(masked_lm_loss > 0, paddle.ones_like(masked_lm_loss), paddle.zeros_like(masked_lm_loss))
sum_ = paddle.sum(binary_sequence)
loss = 0 if sum_ == 0 else paddle.sum(masked_lm_loss * binary_sequence) / sum_

return loss


Expand Down
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