cbalint13 opened a new pull request #5805: URL: https://github.com/apache/incubator-tvm/pull/5805
This PR adds ```nn.batch_flatten``` as quantizable layer. **Description** * ```nn/batch_flatten``` is commonly used before ```nn.dense``` in final layers. * Proposed PR allows it to be included in quantization process avoiding re-cast to ```float32```. **Outcome** * Before ``` %19 = nn.max_pool2d(%18, pool_size=[2, 2], strides=[2, 2], padding=[0, 0, 0, 0]) /* ty=Tensor[(1, 50, 4, 4), int8] */; %20 = cast(%19, dtype="int8") /* ty=Tensor[(1, 50, 4, 4), int8] */; %21 = annotation.stop_fusion(%20) /* ty=Tensor[(1, 50, 4, 4), int8] */; %22 = cast(%21, dtype="float32") /* ty=Tensor[(1, 50, 4, 4), float32] */; %23 = multiply(%22, 0.0625f /* ty=float32 */) /* ty=Tensor[(1, 50, 4, 4), float32] */; %24 = nn.batch_flatten(%23) /* ty=Tensor[(1, 800), float32] */; %25 = nn.batch_flatten(%24) /* ty=Tensor[(1, 800), float32] */; %26 = nn.batch_flatten(%25) /* ty=Tensor[(1, 800), float32] */; %27 = nn.dense(%26, meta[relay.Constant][2] /* ty=Tensor[(512, 800), float32] */ /* ty=Tensor[(512, 800), float32] */, units=512) /* ty=Tensor[(1, 512), float32] */; %28 = nn.relu(%27) /* ty=Tensor[(1, 512), float32] */; %29 = nn.batch_flatten(%28) /* ty=Tensor[(1, 512), float32] */; %30 = nn.batch_flatten(%29) /* ty=Tensor[(1, 512), float32] */; nn.dense(%30, meta[relay.Constant][3] /* ty=Tensor[(10, 512), float32] */ /* ty=Tensor[(10, 512), float32] */, units=10) /* ty=Tensor[(1, 10), float32] */ ``` * After ``` %19 = nn.max_pool2d(%18, pool_size=[2, 2], strides=[2, 2], padding=[0, 0, 0, 0]) /* ty=Tensor[(1, 50, 4, 4), int8] */; %20 = cast(%19, dtype="int8") /* ty=Tensor[(1, 50, 4, 4), int8] */; %21 = annotation.stop_fusion(%20) /* ty=Tensor[(1, 50, 4, 4), int8] */; %22 = nn.batch_flatten(%21) /* ty=Tensor[(1, 800), int8] */; %23 = nn.batch_flatten(%22) /* ty=Tensor[(1, 800), int8] */; %24 = nn.batch_flatten(%23) /* ty=Tensor[(1, 800), int8] */; %25 = clip(%24, a_min=-127f, a_max=127f) /* ty=Tensor[(1, 800), int8] */; %26 = nn.dense(%25, meta[relay.Constant][2] /* ty=Tensor[(512, 800), int8] */ /* ty=Tensor[(512, 800), int8] */, units=512, out_dtype="int32") /* ty=Tensor[(1, 512), int32] */; %27 = nn.relu(%26) /* ty=Tensor[(1, 512), int32] */; %28 = nn.batch_flatten(%27) /* ty=Tensor[(1, 512), int32] */; %29 = nn.batch_flatten(%28) /* ty=Tensor[(1, 512), int32] */; %30 = add(%29, 512 /* ty=int32 */) /* ty=Tensor[(1, 512), int32] */; %31 = right_shift(%30, 10 /* ty=int32 */) /* ty=Tensor[(1, 512), int32] */; %32 = clip(%31, a_min=-127f, a_max=127f) /* ty=Tensor[(1, 512), int32] */; %33 = cast(%32, dtype="int8") /* ty=Tensor[(1, 512), int8] */; %34 = nn.dense(%33, meta[relay.Constant][3] /* ty=Tensor[(10, 512), int8] */ /* ty=Tensor[(10, 512), int8] */, units=10, out_dtype="int32") /* ty=Tensor[(1, 10), int32] */; ``` @vinx13, @siju-samuel @masahi @FrozenGene @ZihengJiang please help with the review. Thank You ! ---------------------------------------------------------------- This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org