On Device Sampling #350
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On Device Sampling #350
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Signed-off-by: quic-sanising <quic_sanising@quicinc.com>
Signed-off-by: quic-sanising <quic_sanising@quicinc.com>
Signed-off-by: quic-sanising <quic_sanising@quicinc.com>
Signed-off-by: quic-sanising <quic_sanising@quicinc.com>
Signed-off-by: quic-sanising <quic_sanising@quicinc.com>
Signed-off-by: quic-sanising <quic_sanising@quicinc.com>
Signed-off-by: quic-sanising <quic_sanising@quicinc.com>
Signed-off-by: quic-sanising <quic_sanising@quicinc.com>
Signed-off-by: quic-sanising <quic_sanising@quicinc.com>
Signed-off-by: quic-sanising <quic_sanising@quicinc.com>
Signed-off-by: quic-sanising <quic_sanising@quicinc.com>
Signed-off-by: quic-sanising <quic_sanising@quicinc.com>
Signed-off-by: quic-sanising <quic_sanising@quicinc.com>
Signed-off-by: quic-sanising <quic_sanising@quicinc.com>
Signed-off-by: quic-sanising <quic_sanising@quicinc.com>
Signed-off-by: quic-sanising <quic_sanising@quicinc.com>
Signed-off-by: quic-sanising <quic_sanising@quicinc.com>
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Signed-off-by: quic-sanising <quic_sanising@quicinc.com>
Signed-off-by: quic-sanising <quic_sanising@quicinc.com>
| @@ -75,7 +76,7 @@ def __repr__(self) -> str: | |||
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| @classmethod | |||
| @with_replaced_quantizers | |||
| def from_pretrained(cls, pretrained_model_name_or_path: str, is_tlm: bool = False, *args, **kwargs): | |||
| def from_pretrained(cls, pretrained_model_name_or_path: str, is_tlm: bool = False, include_sampler: bool = False, return_pdfs: bool = False, *args, **kwargs): | |||
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Should we make them optional parameters
| @@ -1317,8 +1322,14 @@ def __init__( | |||
| if is_tlm: | |||
| # TODO: It is possible to always apply this transform and make value of indices as last indices by default in PyTorch | |||
| self.model, transformed = SpDTransform.apply(self.model) | |||
| self.model.return_pdfs = True | |||
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where is the code for handling is_tlm == FALSE condition for population of return_pdfs
| dynamic_axes["top_ks"] = {0: "batch_size"} | ||
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| example_inputs["top_ps"] = torch.ones((bs, 1), dtype=torch.float) * 0.80 | ||
| dynamic_axes["top_ps"] = {0: "batch_size"} |
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Can we define constants for 0.80 and 0.99
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| @classmethod | ||
| def apply(cls, model: nn.Module) -> Tuple[nn.Module, bool]: | ||
| transformed = False |
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Please add doc string
Signed-off-by: quic-sanising <quic_sanising@quicinc.com>
Signed-off-by: quic-sanising <quic_sanising@quicinc.com>
* Initial commit * Reformat code * Fix bug * Add Gumbel-Max trick based random sampling * Bring up to date * Use Gumbel-Max Trick based Random Sampling as default * Clip k to max value * Add docstring for sampling parameters * Fix bug * Add support for continuous batching * Fix ONNX error for batch_size 1 treated as a Constant * Undo docstring deletion * Remove device and unncessary reshapes * Revert batch_size to 1 * Remove vocab_size from dynamic axes * Change condition * Change size of each sampling parameter to (batch_size, 1) * Reformat code * Add optimizations * Identify optimizations * Fix bug * Fix merge issue * Optimizations: Perform random sampling only on topk_values_asc Only need logits for probs when self.return_pdfs is True * Remove where clause for temperature * Remove boolean type casting for retain state * Always return next_tokens * Fix bug * Reformat code * Initialize retain states * Optimize imports * Remove torch.index_select() * Change dtype of penalty buffers to bool --------- Signed-off-by: quic-sanising <quic_sanising@quicinc.com>
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Signed-off-by: quic-sanising <quic_sanising@quicinc.com>
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