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Specify dtype as int32 for paddle.cumsum due to API Change #3692

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Merged
Jiang-Jia-Jun merged 1 commit into from
Aug 29, 2025
Merged

Specify dtype as int32 for paddle.cumsum due to API Change #3692

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2 changes: 1 addition & 1 deletion custom_ops/xpu_ops/test/test_speculate_get_output_padding_offset.py
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Original file line number Diff line number Diff line change
Expand Up @@ -30,7 +30,7 @@ def test_speculate_get_output_padding_offset():

seq_lens_output = paddle.to_tensor(seq_lens_output, dtype="int32")
out_token_num = paddle.sum(seq_lens_output)
output_cum_offsets_tmp = paddle.cumsum(max_seq_len - seq_lens_output)
output_cum_offsets_tmp = paddle.cumsum(max_seq_len - seq_lens_output, dtype="int32")

output_padding_offset_xpu, output_cum_offsets_xpu = speculate_get_output_padding_offset(
output_cum_offsets_tmp, out_token_num, seq_lens_output, max_seq_len
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2 changes: 1 addition & 1 deletion fastdeploy/engine/common_engine.py
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Expand Up @@ -397,7 +397,7 @@ def update_mm_requests_chunk_size(self, requests):
image_type_ids = paddle.to_tensor(inputs["image_type_ids"], dtype="int32")
image_mask = input_ids == self.data_processor.image_patch_id
image_token_sum = paddle.full(shape=[len(input_ids) + 1], fill_value=0, dtype="int32")
image_token_sum[1:] = paddle.cumsum(image_mask.cast("int32"))
image_token_sum[1:] = paddle.cumsum(image_mask.cast("int32"), dtype="int32")
grid_thw = []
for one in inputs["grid_thw"]:
if one[0] == 1:
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4 changes: 2 additions & 2 deletions fastdeploy/model_executor/layers/utils.py
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Expand Up @@ -257,7 +257,7 @@ def remove_padding(
- The key sequence lengths (paddle.Tensor).
"""
if current_platform.is_cuda():
cum_offsets_now = paddle.cumsum(max_len - seq_lens_this_time)
cum_offsets_now = paddle.cumsum(max_len - seq_lens_this_time, dtype="int32")
token_num = paddle.sum(seq_lens_this_time)
(
ids_remove_padding,
Expand Down Expand Up @@ -301,7 +301,7 @@ def speculate_remove_padding(
- Key sequence lengths (paddle.Tensor).
"""
if current_platform.is_cuda():
cum_offsets_now = paddle.cumsum(max_len - seq_lens_this_time)
cum_offsets_now = paddle.cumsum(max_len - seq_lens_this_time, dtype="int32")
token_num = paddle.sum(seq_lens_this_time)
(
ids_remove_padding,
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4 changes: 2 additions & 2 deletions fastdeploy/model_executor/pre_and_post_process.py
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Expand Up @@ -106,7 +106,7 @@ def pre_process(
"""
# Remove padding
max_len = input_ids.shape[1]
cum_offsets_now = paddle.cumsum(max_len - seq_lens_this_time)
cum_offsets_now = paddle.cumsum(max_len - seq_lens_this_time, dtype="int32")
token_num = paddle.sum(seq_lens_this_time)
output_padding_offset = None
output_cum_offsets = None
Expand All @@ -132,7 +132,7 @@ def pre_process(
if isinstance(seq_lens_output, list):
seq_lens_output = seq_lens_output[0]
output_token_num = paddle.sum(seq_lens_output)
output_cum_offsets_tmp = paddle.cumsum(max_len - seq_lens_output)
output_cum_offsets_tmp = paddle.cumsum(max_len - seq_lens_output, dtype="int32")
output_padding_offset, output_cum_offsets = speculate_get_output_padding_offset(
output_cum_offsets_tmp,
output_token_num,
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2 changes: 1 addition & 1 deletion fastdeploy/worker/xpu_model_runner.py
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Expand Up @@ -63,7 +63,7 @@ def xpu_pre_process(
) -> XPUForwardMeta:
""" """
max_len = input_ids.shape[1]
cum_offsets_now = paddle.cumsum(max_len - seq_lens_this_time)
cum_offsets_now = paddle.cumsum(max_len - seq_lens_this_time, dtype="int32")
token_num = paddle.sum(seq_lens_this_time)

(
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2 changes: 1 addition & 1 deletion tests/layers/test_append_attention.py
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Expand Up @@ -197,7 +197,7 @@ def naive_attention_impl(


def get_padding_offset(bsz, max_seq_len, seq_lens_this_time):
cum_offsets_now = paddle.cumsum(max_seq_len - seq_lens_this_time)
cum_offsets_now = paddle.cumsum(max_seq_len - seq_lens_this_time, dtype="int32")
cum_offsets = paddle.zeros(shape=(bsz + 1), dtype="int32")
cum_offsets[1:] = cum_offsets_now
token_num = paddle.sum(seq_lens_this_time)
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2 changes: 1 addition & 1 deletion tests/layers/test_append_attention_with_output.py
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Expand Up @@ -197,7 +197,7 @@ def naive_attention_impl(


def get_padding_offset(bsz, max_seq_len, seq_lens_this_time):
cum_offsets_now = paddle.cumsum(max_seq_len - seq_lens_this_time)
cum_offsets_now = paddle.cumsum(max_seq_len - seq_lens_this_time, dtype="int32")
cum_offsets = paddle.zeros(shape=(bsz + 1), dtype="int32")
cum_offsets[1:] = cum_offsets_now
token_num = paddle.sum(seq_lens_this_time)
Expand Down