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 !
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