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yizhuoz004
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| query: The query tensor with shape ``[batchSize, numHeadsQuery, sequenceLengthQuery, dimHead]``. | ||
| key: The key tensor with shape ``[batchSize, numHeadsKeyValue, sequenceLengthKeyValue, dimHead]``. | ||
| value: The value tensor with shape ``[batchSize, numHeadsKeyValue, sequenceLengthKeyValue, dimHead]``. |
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I need to verify why in TRT we use numHeadsQuery and numHeadsKeyValue separately.
| 5. Matrix multiplication with value (BMM2) | ||
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| query: The query tensor with shape ``[batchSize, numHeadsQuery, sequenceLengthQuery, dimHead]``. |
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nit: can we use snake_case to be consistent with the rest of the documentation?
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| def get_trt_dtype_enum_str(dtype: "nvtripy.dtype") -> str: |
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I think we should make this a property of dtype so we don't have to update multiple places when adding new dtypes.
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| assert output.shape == (batch_size, num_heads, seq_len, head_dim) | ||
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| .. code-block:: python |
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Since the inputs to all 3 examples are the same, can we omit the input initialization in the docs so that it is easier to tell what is changing between the samples? Also, can we have the quantization sample omit the mask?
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I'm conflicted on this - on one hand, it will make the examples much cleaner, but on the other, it'll mean that you can't just copy-paste the example code and have it work.
If all the tensors are the same shape, maybe a compromise could be:
query = key = value = tp.iota(...)although we would need to clarify that it's only being done for the sake of brevity and they don't all need to be the same tensor.
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