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Fix one_hot bug in torch compile mode #427

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merged 4 commits into from
Oct 29, 2024
Merged

Fix one_hot bug in torch compile mode #427

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7fc910f
vllm/worker/hpu_model_runner.py
yuwenzho Oct 25, 2024
44583b5
Update vllm/worker/hpu_model_runner.py
yuwenzho Oct 28, 2024
91ab89b
update vllm/worker/hpu_model_runner.py
yuwenzho Oct 28, 2024
bce42f3
remove redundant code
yuwenzho Oct 28, 2024
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14 changes: 8 additions & 6 deletions vllm/worker/hpu_model_runner.py
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Original file line number Diff line number Diff line change
Expand Up @@ -282,6 +282,7 @@ def __init__(self, model, block_size, dtype, enforce_eager):
'0').lower() in ['1', 'true']
self.block_size = block_size
self.dtype = dtype
self.enforce_eager = enforce_eager
if not is_fake_hpu() and not htorch.utils.internal.is_lazy(
) and not enforce_eager:
self.model = torch.compile(self.model,
Expand Down Expand Up @@ -317,14 +318,15 @@ def _set_block_mapping(self, metadata, batch_size, device, dtype):
mask = mask >= metadata.block_usage.unsqueeze(-1)
attn_bias = (torch.zeros_like(mask, dtype=dtype).masked_fill_(
mask, -math.inf))
if is_fake_hpu():
# Unfortunately one_hot on CPU doesn't handle
# out of bounds classes. We need to mask those
# values manually
oob_values = metadata.block_mapping.lt(0)
block_mapping = metadata.block_mapping.masked_fill(oob_values, 0)
if is_fake_hpu() or (not htorch.utils.internal.is_lazy()
and not self.enforce_eager):
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# Unfortunately one_hot on CPU or in torch compile mode
# doesn't handle out of bounds classes.
# We need to mask those values manually
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block_mapping = torch.nn.functional.relu(metadata.block_mapping)
block_mapping = torch.nn.functional.one_hot(block_mapping,
num_classes=batch_size)
oob_values = metadata.block_mapping.lt(0)
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this and next line were left accidentally ?

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This and next line cannot be deleted. Negative values in metadata.block_mapping must be represented as zeros after one_hot, otherwise it will cause error in further processing.
Here is an example for better understanding:
metadata.block_mapping=[0, -1, -1] --relu()--> [0, 0, 0] --one_hot with num_classes=1--> [[1], [1], [1]] --oob value mask--> [[1], [0], [0]]

block_mapping.masked_fill_(oob_values.unsqueeze(-1), 0)
else:
block_mapping = torch.nn.functional.one_hot(metadata.block_mapping,
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