[QNNPACK, Sparsity] Sparse kernel with 4x8 blocking #50590
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Summary: Larger blocking across M dim such as 8 in previous PR is likely introducing wasted compute on the shapes being benchmarked. Here we introduced 4x8 blocking of mrxnr. This helps 1) in packing smaller data for small values of M and 2) for compute kernel it writes same number of bytes but more contiguously. It is not certain but it likely helps. Test Plan: q8gemm-sparse-test fully-connected-sparse-test Reviewers: Subscribers: Tasks: Tags: [ghstack-poisoned]
This was referenced Jan 15, 2021
Summary: Larger blocking across M dim such as 8 in previous PR is likely introducing wasted compute on the shapes being benchmarked. Here we introduced 4x8 blocking of mrxnr. This helps 1) in packing smaller data for small values of M and 2) for compute kernel it writes same number of bytes but more contiguously. It is not certain but it likely helps. Test Plan: q8gemm-sparse-test fully-connected-sparse-test Reviewers: Subscribers: Tasks: Tags: Differential Revision: [D25925499](https://our.internmc.facebook.com/intern/diff/D25925499) [ghstack-poisoned]
This was referenced Jan 26, 2021
Summary: Larger blocking across M dim such as 8 in previous PR is likely introducing wasted compute on the shapes being benchmarked. Here we introduced 4x8 blocking of mrxnr. This helps 1) in packing smaller data for small values of M and 2) for compute kernel it writes same number of bytes but more contiguously. It is not certain but it likely helps. Test Plan: q8gemm-sparse-test fully-connected-sparse-test Reviewers: Subscribers: Tasks: Tags: Differential Revision: [D25925499](https://our.internmc.facebook.com/intern/diff/D25925499) [ghstack-poisoned]
Summary: Larger blocking across M dim such as 8 in previous PR is likely introducing wasted compute on the shapes being benchmarked. Here we introduced 4x8 blocking of mrxnr. This helps 1) in packing smaller data for small values of M and 2) for compute kernel it writes same number of bytes but more contiguously. It is not certain but it likely helps. Test Plan: q8gemm-sparse-test fully-connected-sparse-test Reviewers: Subscribers: Tasks: Tags: Differential Revision: [D25925499](https://our.internmc.facebook.com/intern/diff/D25925499) [ghstack-poisoned]
This was referenced Jan 26, 2021
Summary: Larger blocking across M dim such as 8 in previous PR is likely introducing wasted compute on the shapes being benchmarked. Here we introduced 4x8 blocking of mrxnr. This helps 1) in packing smaller data for small values of M and 2) for compute kernel it writes same number of bytes but more contiguously. It is not certain but it likely helps. Test Plan: q8gemm-sparse-test fully-connected-sparse-test Reviewers: Subscribers: Tasks: Tags: Differential Revision: [D25925499](https://our.internmc.facebook.com/intern/diff/D25925499) [ghstack-poisoned]
Summary: Larger blocking across M dim such as 8 in previous PR is likely introducing wasted compute on the shapes being benchmarked. Here we introduced 4x8 blocking of mrxnr. This helps 1) in packing smaller data for small values of M and 2) for compute kernel it writes same number of bytes but more contiguously. It is not certain but it likely helps. Test Plan: q8gemm-sparse-test fully-connected-sparse-test Reviewers: Subscribers: Tasks: Tags: Differential Revision: [D25925499](https://our.internmc.facebook.com/intern/diff/D25925499) [ghstack-poisoned]
AshkanAliabadi
approved these changes
Jan 27, 2021
Summary: Larger blocking across M dim such as 8 in previous PR is likely introducing wasted compute on the shapes being benchmarked. Here we introduced 4x8 blocking of mrxnr. This helps 1) in packing smaller data for small values of M and 2) for compute kernel it writes same number of bytes but more contiguously. It is not certain but it likely helps. Test Plan: q8gemm-sparse-test fully-connected-sparse-test Reviewers: Subscribers: Tasks: Tags: Differential Revision: [D25925499](https://our.internmc.facebook.com/intern/diff/D25925499) [ghstack-poisoned]
Summary: Larger blocking across M dim such as 8 in previous PR is likely introducing wasted compute on the shapes being benchmarked. Here we introduced 4x8 blocking of mrxnr. This helps 1) in packing smaller data for small values of M and 2) for compute kernel it writes same number of bytes but more contiguously. It is not certain but it likely helps. Test Plan: q8gemm-sparse-test fully-connected-sparse-test Reviewers: Subscribers: Tasks: Tags: Differential Revision: [D25925499](https://our.internmc.facebook.com/intern/diff/D25925499) [ghstack-poisoned]
Summary: Larger blocking across M dim such as 8 in previous PR is likely introducing wasted compute on the shapes being benchmarked. Here we introduced 4x8 blocking of mrxnr. This helps 1) in packing smaller data for small values of M and 2) for compute kernel it writes same number of bytes but more contiguously. It is not certain but it likely helps. Test Plan: q8gemm-sparse-test fully-connected-sparse-test Reviewers: Subscribers: Tasks: Tags: Differential Revision: [D25925499](https://our.internmc.facebook.com/intern/diff/D25925499) [ghstack-poisoned]
Summary: Larger blocking across M dim such as 8 in previous PR is likely introducing wasted compute on the shapes being benchmarked. Here we introduced 4x8 blocking of mrxnr. This helps 1) in packing smaller data for small values of M and 2) for compute kernel it writes same number of bytes but more contiguously. It is not certain but it likely helps. Test Plan: q8gemm-sparse-test fully-connected-sparse-test Reviewers: Subscribers: Tasks: Tags: Differential Revision: [D25925499](https://our.internmc.facebook.com/intern/diff/D25925499) [ghstack-poisoned]
Summary: Larger blocking across M dim such as 8 in previous PR is likely introducing wasted compute on the shapes being benchmarked. Here we introduced 4x8 blocking of mrxnr. This helps 1) in packing smaller data for small values of M and 2) for compute kernel it writes same number of bytes but more contiguously. It is not certain but it likely helps. Test Plan: q8gemm-sparse-test fully-connected-sparse-test Reviewers: Subscribers: Tasks: Tags: Differential Revision: [D25925499](https://our.internmc.facebook.com/intern/diff/D25925499) [ghstack-poisoned]
Summary: Larger blocking across M dim such as 8 in previous PR is likely introducing wasted compute on the shapes being benchmarked. Here we introduced 4x8 blocking of mrxnr. This helps 1) in packing smaller data for small values of M and 2) for compute kernel it writes same number of bytes but more contiguously. It is not certain but it likely helps. Test Plan: q8gemm-sparse-test fully-connected-sparse-test Reviewers: Subscribers: Tasks: Tags: Differential Revision: [D25925499](https://our.internmc.facebook.com/intern/diff/D25925499) [ghstack-poisoned]
Summary: Larger blocking across M dim such as 8 in previous PR is likely introducing wasted compute on the shapes being benchmarked. Here we introduced 4x8 blocking of mrxnr. This helps 1) in packing smaller data for small values of M and 2) for compute kernel it writes same number of bytes but more contiguously. It is not certain but it likely helps. Test Plan: q8gemm-sparse-test fully-connected-sparse-test Reviewers: Subscribers: Tasks: Tags: Differential Revision: [D25925499](https://our.internmc.facebook.com/intern/diff/D25925499) [ghstack-poisoned]
Summary: Larger blocking across M dim such as 8 in previous PR is likely introducing wasted compute on the shapes being benchmarked. Here we introduced 4x8 blocking of mrxnr. This helps 1) in packing smaller data for small values of M and 2) for compute kernel it writes same number of bytes but more contiguously. It is not certain but it likely helps. Test Plan: q8gemm-sparse-test fully-connected-sparse-test Reviewers: Subscribers: Tasks: Tags: Differential Revision: [D25925499](https://our.internmc.facebook.com/intern/diff/D25925499) [ghstack-poisoned]
Summary: Larger blocking across M dim such as 8 in previous PR is likely introducing wasted compute on the shapes being benchmarked. Here we introduced 4x8 blocking of mrxnr. This helps 1) in packing smaller data for small values of M and 2) for compute kernel it writes same number of bytes but more contiguously. It is not certain but it likely helps. Test Plan: q8gemm-sparse-test fully-connected-sparse-test Reviewers: Subscribers: Tasks: Tags: Differential Revision: [D25925499](https://our.internmc.facebook.com/intern/diff/D25925499) [ghstack-poisoned]
Summary: Larger blocking across M dim such as 8 in previous PR is likely introducing wasted compute on the shapes being benchmarked. Here we introduced 4x8 blocking of mrxnr. This helps 1) in packing smaller data for small values of M and 2) for compute kernel it writes same number of bytes but more contiguously. It is not certain but it likely helps. Test Plan: q8gemm-sparse-test fully-connected-sparse-test Reviewers: Subscribers: Tasks: Tags: Differential Revision: [D25925499](https://our.internmc.facebook.com/intern/diff/D25925499) [ghstack-poisoned]
Summary: Larger blocking across M dim such as 8 in previous PR is likely introducing wasted compute on the shapes being benchmarked. Here we introduced 4x8 blocking of mrxnr. This helps 1) in packing smaller data for small values of M and 2) for compute kernel it writes same number of bytes but more contiguously. It is not certain but it likely helps. Test Plan: q8gemm-sparse-test fully-connected-sparse-test Reviewers: Subscribers: Tasks: Tags: Differential Revision: [D25925499](https://our.internmc.facebook.com/intern/diff/D25925499) [ghstack-poisoned]
Summary: Larger blocking across M dim such as 8 in previous PR is likely introducing wasted compute on the shapes being benchmarked. Here we introduced 4x8 blocking of mrxnr. This helps 1) in packing smaller data for small values of M and 2) for compute kernel it writes same number of bytes but more contiguously. It is not certain but it likely helps. Test Plan: q8gemm-sparse-test fully-connected-sparse-test Reviewers: Subscribers: Tasks: Tags: Differential Revision: [D25925499](https://our.internmc.facebook.com/intern/diff/D25925499) [ghstack-poisoned]
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This pull request has been merged in 70830b5. |
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Stack from ghstack:
Summary:
Larger blocking across M dim such as 8 in previous PR is likely
introducing wasted compute on the shapes being benchmarked.
Here we introduced 4x8 blocking of mrxnr. This helps 1) in packing
smaller data for small values of M and 2) for compute kernel it writes
same number of bytes but more contiguously. It is not certain but it
likely helps.
Test Plan:
q8gemm-sparse-test
fully-connected-sparse-test
Reviewers:
Subscribers:
Tasks:
Tags:
Differential Revision: D25925499