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Posted to commits@tvm.apache.org by GitBox <gi...@apache.org> on 2022/08/19 09:08:34 UTC
[GitHub] [tvm] gayatripk1 opened a new pull request, #12499: [TOPI][Hexagon] Implement quantized depthwise conv2d
gayatripk1 opened a new pull request, #12499:
URL: https://github.com/apache/tvm/pull/12499
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[GitHub] [tvm] gayatripk1 commented on pull request #12499: [TOPI][Hexagon] Implement quantized depthwise conv2d
Posted by GitBox <gi...@apache.org>.
gayatripk1 commented on PR #12499:
URL: https://github.com/apache/tvm/pull/12499#issuecomment-1292190325
> @gayatripk1 please update the PR description
Done.
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[GitHub] [tvm] masahi commented on a diff in pull request #12499: [TOPI][Hexagon] Implement quantized depthwise conv2d
Posted by GitBox <gi...@apache.org>.
masahi commented on code in PR #12499:
URL: https://github.com/apache/tvm/pull/12499#discussion_r1008452745
##########
python/tvm/topi/hexagon/utils.py:
##########
@@ -235,6 +241,19 @@ def get_fixed_point_value(flp: float, dtype: str = "int16") -> Tuple[int, int]:
best scaling factor for 'int16' type that can be used to convert the floating-point value to
fixed-point with the least amount of precision loss.
+
+ Here is a more rigorous explanation of the above, for non-negative scale values, which are of
+ interest. M < 2, so M * 2^(E-Bias+x) < 2 ^ (E-Bias+x+1) [Note: LHS is a fraction, RHS int]
+ => round(M * 2^(E-Bias+x)) <= 2 ^ (E-Bias+x+1) [Note the "<=", not "<"]
+ We want x s.t. round(M * 2^(E-Bias+x)) <= 2^15 - 1
+ We know round(M * 2^(E-Bias+x)) <= 2^(E-Bias+x+1)
+ It will be sufficient to choose x s.t. 2^(E-Bias+x+1) <= 2^15 - 1
+ That is, max x. s.t. 2^(E-Bias+x+1) < 2^15
+ E-Bias+x+1 < 15
+ E-Bias+x+1 <= 14
+ Max x will make E-Bias+x+1 = 14
+ x = 13 - E + Bias
Review Comment:
cc @ibsidorenko - I'm curious how the requantize operation done in QC "slice ops" (such as this PR) compares to the one done by QNN canonicalization.
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[GitHub] [tvm] mehrdadh commented on pull request #12499: [TOPI][Hexagon] Implement quantized depthwise conv2d
Posted by GitBox <gi...@apache.org>.
mehrdadh commented on PR #12499:
URL: https://github.com/apache/tvm/pull/12499#issuecomment-1287549322
@gayatripk1 please update the PR description
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[GitHub] [tvm] masahi commented on a diff in pull request #12499: [TOPI][Hexagon] Implement quantized depthwise conv2d
Posted by GitBox <gi...@apache.org>.
masahi commented on code in PR #12499:
URL: https://github.com/apache/tvm/pull/12499#discussion_r1008452745
##########
python/tvm/topi/hexagon/utils.py:
##########
@@ -235,6 +241,19 @@ def get_fixed_point_value(flp: float, dtype: str = "int16") -> Tuple[int, int]:
best scaling factor for 'int16' type that can be used to convert the floating-point value to
fixed-point with the least amount of precision loss.
+
+ Here is a more rigorous explanation of the above, for non-negative scale values, which are of
+ interest. M < 2, so M * 2^(E-Bias+x) < 2 ^ (E-Bias+x+1) [Note: LHS is a fraction, RHS int]
+ => round(M * 2^(E-Bias+x)) <= 2 ^ (E-Bias+x+1) [Note the "<=", not "<"]
+ We want x s.t. round(M * 2^(E-Bias+x)) <= 2^15 - 1
+ We know round(M * 2^(E-Bias+x)) <= 2^(E-Bias+x+1)
+ It will be sufficient to choose x s.t. 2^(E-Bias+x+1) <= 2^15 - 1
+ That is, max x. s.t. 2^(E-Bias+x+1) < 2^15
+ E-Bias+x+1 < 15
+ E-Bias+x+1 <= 14
+ Max x will make E-Bias+x+1 = 14
+ x = 13 - E + Bias
Review Comment:
cc @ibsidorenko - I'm curious how requantize operation done in QC "slice ops" (such as this PR) compare to the one done by QNN canonicalization.
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[GitHub] [tvm] tvm-bot commented on pull request #12499: [TOPI][Hexagon] Implement quantized depthwise conv2d
Posted by GitBox <gi...@apache.org>.
tvm-bot commented on PR #12499:
URL: https://github.com/apache/tvm/pull/12499#issuecomment-1282933127
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Thanks for contributing to TVM! Please refer to the contributing guidelines https://tvm.apache.org/docs/contribute/ for useful information and tips. Please request code reviews from [Reviewers](https://github.com/apache/incubator-tvm/blob/master/CONTRIBUTORS.md#reviewers) by @-ing them in a comment.
<!--bot-comment-docs-start-->
* Built docs for commit e68d59080e7e570cbaa7a9e118340f35f59adce2 can be found [here](https://pr-docs.tlcpack.ai/PR-12499/9/docs/index.html).<!--bot-comment-docs-end-->
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[GitHub] [tvm] masahi merged pull request #12499: [TOPI][Hexagon] Implement quantized depthwise conv2d
Posted by GitBox <gi...@apache.org>.
masahi merged PR #12499:
URL: https://github.com/apache/tvm/pull/12499
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