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fix whisper inference on Ascend #937

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merged 1 commit into from
Mar 19, 2024
Merged

fix whisper inference on Ascend #937

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30 changes: 20 additions & 10 deletions mindnlp/injection.py
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Original file line number Diff line number Diff line change
Expand Up @@ -628,17 +628,13 @@ def __init__(self,
raise ValueError(f"The argument 'group' should be divisible by 'in_channels' " \
f"and 'out_channels', but got group:{group}, in_channels:{in_channels}, " \
f"out_channels:{out_channels}.")
kernel_size = (kernel_size,)
if mindspore.__version__ == '2.0.0':
stride = (1, stride,)
else:
stride = (stride,)
stride = (1, stride)
dilation = (1, dilation)

dilation = (dilation,)
super().__init__(
in_channels,
out_channels,
kernel_size,
(kernel_size,),
stride,
pad_mode,
padding,
Expand All @@ -647,12 +643,26 @@ def __init__(self,
has_bias,
None,
None)
self.padding = padding
self.padding = (0, 0, padding, padding)
self.padding = (0, 0, padding, padding)
Validator.check_string(pad_mode, ['valid', 'same', 'pad'], 'pad_mode', self.cls_name)
self.conv2d = ops.Conv2D(out_channel=self.out_channels,
kernel_size=(1, kernel_size),
mode=1,
pad_mode=self.pad_mode,
pad=self.padding,
stride=self.stride,
dilation=self.dilation,
group=self.group)

def construct(self, x):
return ops.conv1d(x, self.weight, self.bias, stride=self.stride, pad_mode=self.pad_mode,
padding=self.padding, dilation=self.dilation, groups=self.group)
x = x.expand_dims(2)
output = self.conv2d(x, self.weight.expand_dims(2))
if self.has_bias:
output = ops.bias_add(output, self.bias)

output = output.squeeze(2)
return output

class Conv1dTranspose(_Conv):
"""patched Conv1dTranspose"""
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2 changes: 1 addition & 1 deletion mindnlp/transformers/generation/logits_process.py
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Original file line number Diff line number Diff line change
Expand Up @@ -1302,7 +1302,7 @@ def __call__(self, input_ids: mindspore.Tensor, scores: mindspore.Tensor) -> min
for k in range(input_ids.shape[0]):
timestamp_logprob = logprobs[k, self.timestamp_begin :].logsumexp(axis=-1)
max_text_token_logprob = logprobs[k, : self.timestamp_begin].max()
if timestamp_logprob > max_text_token_logprob:
if not ops.isnan(timestamp_logprob) and timestamp_logprob > max_text_token_logprob:
scores[k, : self.timestamp_begin] = -float("inf")

return scores
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