| # Licensed to the Apache Software Foundation (ASF) under one |
| # or more contributor license agreements. See the NOTICE file |
| # distributed with this work for additional information |
| # regarding copyright ownership. The ASF licenses this file |
| # to you under the Apache License, Version 2.0 (the |
| # "License"); you may not use this file except in compliance |
| # with the License. You may obtain a copy of the License at |
| # |
| # http://www.apache.org/licenses/LICENSE-2.0 |
| # |
| # Unless required by applicable law or agreed to in writing, |
| # software distributed under the License is distributed on an |
| # "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY |
| # KIND, either express or implied. See the License for the |
| # specific language governing permissions and limitations |
| # under the License. |
| |
| import tvm |
| import tvm.testing |
| from tvm.relax.transform import ConvertLayout, Normalize |
| from tvm.script.parser import ir as I, relax as R, tir as T |
| |
| |
| def verify(input, expected, extra_ops={}): |
| desired_layouts = {"relax.nn.conv2d": ["NHWC", "OHWI"]} |
| desired_layouts.update(extra_ops) |
| mod = ConvertLayout(desired_layouts)(input) |
| mod = Normalize()(mod) |
| tvm.ir.assert_structural_equal(mod, expected) |
| |
| |
| def test_conv2d(): |
| @I.ir_module |
| class Input: |
| @R.function |
| def main( |
| x: R.Tensor((2, 3, 28, 28), "float32"), w: R.Tensor((4, 3, 3, 3), "float32") |
| ) -> R.Tensor(None, "float32", ndim=4): |
| with R.dataflow(): |
| gv: R.Tensor((2, 4, 26, 26), "float32") = R.nn.conv2d(x, w, out_dtype="float32") |
| R.output(gv) |
| return gv |
| |
| @I.ir_module |
| class Expected: |
| @R.function |
| def main( |
| x: R.Tensor((2, 3, 28, 28), dtype="float32"), w: R.Tensor((4, 3, 3, 3), dtype="float32") |
| ) -> R.Tensor(None, dtype="float32", ndim=4): |
| with R.dataflow(): |
| lv: R.Tensor((2, 28, 28, 3), dtype="float32") = R.permute_dims(x, axes=[0, 2, 3, 1]) |
| lv1: R.Tensor((4, 3, 3, 3), dtype="float32") = R.permute_dims(w, axes=[0, 2, 3, 1]) |
| lv2: R.Tensor((2, 26, 26, 4), dtype="float32") = R.nn.conv2d( |
| lv, |
| lv1, |
| strides=[1, 1], |
| padding=[0, 0, 0, 0], |
| dilation=[1, 1], |
| groups=1, |
| data_layout="NHWC", |
| kernel_layout="OHWI", |
| out_layout="NHWC", |
| out_dtype="float32", |
| ) |
| gv: R.Tensor((2, 4, 26, 26), dtype="float32") = R.permute_dims( |
| lv2, axes=[0, 3, 1, 2] |
| ) |
| R.output(gv) |
| return gv |
| |
| verify(Input, Expected) |
| |
| |
| def test_conv2d_onlydim(): |
| @I.ir_module |
| class Input: |
| @R.function |
| def main( |
| x: R.Tensor("float32", ndim=4), w: R.Tensor("float32", ndim=4) |
| ) -> R.Tensor(None, "float32", ndim=4): |
| with R.dataflow(): |
| gv: R.Tensor("float32", ndim=4) = R.nn.conv2d(x, w, out_dtype="float32") |
| R.output(gv) |
| return gv |
| |
| @I.ir_module |
| class Expected: |
| @R.function |
| def main( |
| x: R.Tensor(dtype="float32", ndim=4), w: R.Tensor(dtype="float32", ndim=4) |
| ) -> R.Tensor(dtype="float32", ndim=4): |
| with R.dataflow(): |
| lv: R.Tensor(dtype="float32", ndim=4) = R.permute_dims(x, axes=[0, 2, 3, 1]) |
| lv1: R.Tensor(dtype="float32", ndim=4) = R.permute_dims(w, axes=[0, 2, 3, 1]) |
| lv2: R.Tensor(dtype="float32", ndim=4) = R.nn.conv2d( |
| lv, |
| lv1, |
| strides=[1, 1], |
| padding=[0, 0, 0, 0], |
| dilation=[1, 1], |
| groups=1, |
| data_layout="NHWC", |
| kernel_layout="OHWI", |
| out_layout="NHWC", |
| out_dtype="float32", |
| ) |
| gv: R.Tensor(dtype="float32", ndim=4) = R.permute_dims(lv2, axes=[0, 3, 1, 2]) |
| R.output(gv) |
| return gv |
| |
| verify(Input, Expected) |
| |
| |
| def test_conv2d_symbolic(): |
| @I.ir_module |
| class Input: |
| @R.function |
| def main( |
| x: R.Tensor("float32", ndim=4), w: R.Tensor("float32", ndim=4) |
| ) -> R.Tensor(None, "float32", ndim=4): |
| with R.dataflow(): |
| N, C, H, W = T.int64(), T.int64(), T.int64(), T.int64() |
| lv0 = R.match_cast(x, R.Tensor((N, C, H, W), "float32")) |
| gv: R.Tensor("float32", ndim=4) = R.nn.conv2d(lv0, w, out_dtype="float32") |
| R.output(gv) |
| return gv |
| |
| @I.ir_module |
| class Expected: |
| @R.function |
| def main( |
| x: R.Tensor(dtype="float32", ndim=4), w: R.Tensor(dtype="float32", ndim=4) |
| ) -> R.Tensor(dtype="float32", ndim=4): |
| N = T.int64() |
| C = T.int64() |
| H = T.int64() |
| W = T.int64() |
| with R.dataflow(): |
| lv0: R.Tensor((N, C, H, W), dtype="float32") = R.match_cast( |
| x, R.Tensor((N, C, H, W), dtype="float32") |
| ) |
| lv: R.Tensor((N, H, W, C), dtype="float32") = R.permute_dims(lv0, axes=[0, 2, 3, 1]) |
| lv1: R.Tensor(dtype="float32", ndim=4) = R.permute_dims(w, axes=[0, 2, 3, 1]) |
| lv2: R.Tensor(dtype="float32", ndim=4) = R.nn.conv2d( |
| lv, |
| lv1, |
| strides=[1, 1], |
| padding=[0, 0, 0, 0], |
| dilation=[1, 1], |
| groups=1, |
| data_layout="NHWC", |
| kernel_layout="OHWI", |
| out_layout="NHWC", |
| out_dtype="float32", |
| ) |
| gv: R.Tensor(dtype="float32", ndim=4) = R.permute_dims(lv2, axes=[0, 3, 1, 2]) |
| R.output(gv) |
| return gv |
| |
| verify(Input, Expected) |
| |
| |
| def test_conv2d_matchcast_bias(): |
| @I.ir_module |
| class Input: |
| @R.function |
| def main( |
| x: R.Tensor("float32", ndim=4), w: R.Tensor("float32", ndim=4) |
| ) -> R.Tensor(None, "float32", ndim=4): |
| with R.dataflow(): |
| lv0: R.Tensor("float32", ndim=4) = R.nn.conv2d(x, w, out_dtype="float32") |
| N, C, H, W = T.int64(), T.int64(), T.int64(), T.int64() |
| lv1 = R.match_cast(lv0, R.Tensor((N, C, H, W), "float32")) |
| gv = R.add(lv1, w) |
| R.output(gv) |
| return gv |
| |
| @I.ir_module |
| class Expected: |
| @R.function |
| def main( |
| x: R.Tensor(dtype="float32", ndim=4), w: R.Tensor(dtype="float32", ndim=4) |
| ) -> R.Tensor(dtype="float32", ndim=4): |
| N = T.int64() |
| H = T.int64() |
| W = T.int64() |
| C = T.int64() |
| with R.dataflow(): |
| lv: R.Tensor(dtype="float32", ndim=4) = R.permute_dims(x, axes=[0, 2, 3, 1]) |
| lv1: R.Tensor(dtype="float32", ndim=4) = R.permute_dims(w, axes=[0, 2, 3, 1]) |
| lv0: R.Tensor(dtype="float32", ndim=4) = R.nn.conv2d( |
| lv, |
| lv1, |
| strides=[1, 1], |
| padding=[0, 0, 0, 0], |
| dilation=[1, 1], |
| groups=1, |
| data_layout="NHWC", |
| kernel_layout="OHWI", |
| out_layout="NHWC", |
| out_dtype="float32", |
| ) |
| lv2: R.Tensor((N, H, W, C), dtype="float32") = R.match_cast( |
| lv0, R.Tensor((N, H, W, C), dtype="float32") |
| ) |
| lv3: R.Tensor(dtype="float32", ndim=4) = R.permute_dims(w, axes=[0, 2, 3, 1]) |
| lv4: R.Tensor(dtype="float32", ndim=4) = R.add(lv2, lv3) |
| gv: R.Tensor(dtype="float32", ndim=4) = R.permute_dims(lv4, axes=[0, 3, 1, 2]) |
| R.output(gv) |
| return gv |
| |
| verify(Input, Expected) |
| |
| |
| def test_conv2d_relu(): |
| @I.ir_module |
| class Input: |
| @R.function |
| def main( |
| x: R.Tensor((2, 3, 28, 28), "float32"), w: R.Tensor((4, 3, 3, 3), "float32") |
| ) -> R.Tensor(None, "float32", ndim=4): |
| with R.dataflow(): |
| gv: R.Tensor((2, 4, 26, 26), "float32") = R.nn.conv2d(x, w, out_dtype="float32") |
| gv2: R.Tensor((2, 4, 26, 26), "float32") = R.nn.relu(gv) |
| R.output(gv2) |
| return gv2 |
| |
| @I.ir_module |
| class Expected: |
| @R.function |
| def main( |
| x: R.Tensor((2, 3, 28, 28), dtype="float32"), w: R.Tensor((4, 3, 3, 3), dtype="float32") |
| ) -> R.Tensor(None, dtype="float32", ndim=4): |
| with R.dataflow(): |
| lv: R.Tensor((2, 28, 28, 3), dtype="float32") = R.permute_dims(x, axes=[0, 2, 3, 1]) |
| lv1: R.Tensor((4, 3, 3, 3), dtype="float32") = R.permute_dims(w, axes=[0, 2, 3, 1]) |
| gv: R.Tensor((2, 26, 26, 4), dtype="float32") = R.nn.conv2d( |
| lv, |
| lv1, |
| strides=[1, 1], |
| padding=[0, 0, 0, 0], |
| dilation=[1, 1], |
| groups=1, |
| data_layout="NHWC", |
| kernel_layout="OHWI", |
| out_layout="NHWC", |
| out_dtype="float32", |
| ) |
| lv2: R.Tensor((2, 26, 26, 4), dtype="float32") = R.nn.relu(gv) |
| gv2: R.Tensor((2, 4, 26, 26), dtype="float32") = R.permute_dims( |
| lv2, axes=[0, 3, 1, 2] |
| ) |
| R.output(gv2) |
| return gv2 |
| |
| verify(Input, Expected) |
| |
| |
| def test_relu_conv2d_relu(): |
| @I.ir_module |
| class Input: |
| @R.function |
| def main( |
| x: R.Tensor((2, 3, 28, 28), "float32"), w: R.Tensor((4, 3, 3, 3), "float32") |
| ) -> R.Tensor(None, "float32", ndim=4): |
| with R.dataflow(): |
| x0: R.Tensor((2, 3, 28, 28), "float32") = R.nn.relu(x) |
| gv: R.Tensor((2, 4, 26, 26), "float32") = R.nn.conv2d(x0, w, out_dtype="float32") |
| gv2: R.Tensor((2, 4, 26, 26), "float32") = R.nn.relu(gv) |
| R.output(gv2) |
| return gv2 |
| |
| @tvm.script.ir_module |
| class Expected: |
| @R.function |
| def main( |
| x: R.Tensor((2, 3, 28, 28), dtype="float32"), w: R.Tensor((4, 3, 3, 3), dtype="float32") |
| ) -> R.Tensor(None, dtype="float32", ndim=4): |
| with R.dataflow(): |
| x0: R.Tensor((2, 3, 28, 28), dtype="float32") = R.nn.relu(x) |
| lv: R.Tensor((2, 28, 28, 3), dtype="float32") = R.permute_dims( |
| x0, axes=[0, 2, 3, 1] |
| ) |
| lv1: R.Tensor((4, 3, 3, 3), dtype="float32") = R.permute_dims(w, axes=[0, 2, 3, 1]) |
| gv: R.Tensor((2, 26, 26, 4), dtype="float32") = R.nn.conv2d( |
| lv, |
| lv1, |
| strides=[1, 1], |
| padding=[0, 0, 0, 0], |
| dilation=[1, 1], |
| groups=1, |
| data_layout="NHWC", |
| kernel_layout="OHWI", |
| out_layout="NHWC", |
| out_dtype="float32", |
| ) |
| lv2: R.Tensor((2, 26, 26, 4), dtype="float32") = R.nn.relu(gv) |
| gv2: R.Tensor((2, 4, 26, 26), dtype="float32") = R.permute_dims( |
| lv2, axes=[0, 3, 1, 2] |
| ) |
| R.output(gv2) |
| return gv2 |
| |
| verify(Input, Expected) |
| |
| |
| def test_conv2d_relu_tanh(): |
| @I.ir_module |
| class Input: |
| @R.function |
| def main( |
| x: R.Tensor((2, 3, 28, 28), "float32"), w: R.Tensor((4, 3, 3, 3), "float32") |
| ) -> R.Tensor(None, "float32", ndim=4): |
| with R.dataflow(): |
| gv: R.Tensor((2, 4, 26, 26), "float32") = R.nn.conv2d(x, w, out_dtype="float32") |
| gv2: R.Tensor((2, 4, 26, 26), "float32") = R.nn.relu(gv) |
| gv3: R.Tensor((2, 4, 26, 26), "float32") = R.tanh(gv2) |
| R.output(gv3) |
| return gv3 |
| |
| @I.ir_module |
| class Expected: |
| @R.function |
| def main( |
| x: R.Tensor((2, 3, 28, 28), dtype="float32"), w: R.Tensor((4, 3, 3, 3), dtype="float32") |
| ) -> R.Tensor(None, dtype="float32", ndim=4): |
| with R.dataflow(): |
| lv: R.Tensor((2, 28, 28, 3), dtype="float32") = R.permute_dims(x, axes=[0, 2, 3, 1]) |
| lv1: R.Tensor((4, 3, 3, 3), dtype="float32") = R.permute_dims(w, axes=[0, 2, 3, 1]) |
| gv: R.Tensor((2, 26, 26, 4), dtype="float32") = R.nn.conv2d( |
| lv, |
| lv1, |
| strides=[1, 1], |
| padding=[0, 0, 0, 0], |
| dilation=[1, 1], |
| groups=1, |
| data_layout="NHWC", |
| kernel_layout="OHWI", |
| out_layout="NHWC", |
| out_dtype="float32", |
| ) |
| gv2: R.Tensor((2, 26, 26, 4), dtype="float32") = R.nn.relu(gv) |
| lv2: R.Tensor((2, 26, 26, 4), dtype="float32") = R.tanh(gv2) |
| gv3: R.Tensor((2, 4, 26, 26), dtype="float32") = R.permute_dims( |
| lv2, axes=[0, 3, 1, 2] |
| ) |
| R.output(gv3) |
| return gv3 |
| |
| verify(Input, Expected) |
| |
| |
| def test_conv2d_add(): |
| @I.ir_module |
| class Input: |
| @R.function |
| def main( |
| x: R.Tensor((2, 3, 28, 28), "float32"), |
| w: R.Tensor((4, 3, 3, 3), "float32"), |
| bias: R.Tensor((2, 4, 26, 26), "float32"), |
| ) -> R.Tensor(None, "float32", ndim=4): |
| with R.dataflow(): |
| gv: R.Tensor((2, 4, 26, 26), "float32") = R.nn.conv2d(x, w, out_dtype="float32") |
| gv2: R.Tensor((2, 4, 26, 26), "float32") = R.add(gv, bias) |
| R.output(gv2) |
| return gv2 |
| |
| @I.ir_module |
| class Expected: |
| @R.function |
| def main( |
| x: R.Tensor((2, 3, 28, 28), dtype="float32"), |
| w: R.Tensor((4, 3, 3, 3), dtype="float32"), |
| bias: R.Tensor((2, 4, 26, 26), dtype="float32"), |
| ) -> R.Tensor(None, dtype="float32", ndim=4): |
| with R.dataflow(): |
| lv: R.Tensor((2, 28, 28, 3), dtype="float32") = R.permute_dims(x, axes=[0, 2, 3, 1]) |
| lv1: R.Tensor((4, 3, 3, 3), dtype="float32") = R.permute_dims(w, axes=[0, 2, 3, 1]) |
| gv: R.Tensor((2, 26, 26, 4), dtype="float32") = R.nn.conv2d( |
| lv, |
| lv1, |
| strides=[1, 1], |
| padding=[0, 0, 0, 0], |
| dilation=[1, 1], |
| groups=1, |
| data_layout="NHWC", |
| kernel_layout="OHWI", |
| out_layout="NHWC", |
| out_dtype="float32", |
| ) |
| lv2: R.Tensor((2, 26, 26, 4), dtype="float32") = R.permute_dims( |
| bias, axes=[0, 2, 3, 1] |
| ) |
| lv3: R.Tensor((2, 26, 26, 4), dtype="float32") = R.add(gv, lv2) |
| gv2: R.Tensor((2, 4, 26, 26), dtype="float32") = R.permute_dims( |
| lv3, axes=[0, 3, 1, 2] |
| ) |
| R.output(gv2) |
| return gv2 |
| |
| verify(Input, Expected) |
| |
| |
| def test_conv2d_add_relu_conv2d(): |
| @I.ir_module |
| class Input: |
| @R.function |
| def main( |
| x: R.Tensor((2, 4, 28, 28), "float32"), |
| w: R.Tensor((4, 4, 3, 3), "float32"), |
| bias: R.Tensor((2, 4, 26, 26), "float32"), |
| ) -> R.Tensor(None, "float32", ndim=4): |
| with R.dataflow(): |
| gv: R.Tensor((2, 4, 26, 26), "float32") = R.nn.conv2d(x, w, out_dtype="float32") |
| gv2: R.Tensor((2, 4, 26, 26), "float32") = R.add(gv, bias) |
| gv3: R.Tensor((2, 4, 26, 26), "float32") = R.nn.relu(gv2) |
| gv4: R.Tensor((2, 4, 24, 24), "float32") = R.nn.conv2d(gv3, w, out_dtype="float32") |
| R.output(gv4) |
| return gv4 |
| |
| @I.ir_module |
| class Expected: |
| @R.function |
| def main( |
| x: R.Tensor((2, 4, 28, 28), dtype="float32"), |
| w: R.Tensor((4, 4, 3, 3), dtype="float32"), |
| bias: R.Tensor((2, 4, 26, 26), dtype="float32"), |
| ) -> R.Tensor((2, 4, 24, 24), dtype="float32"): |
| with R.dataflow(): |
| lv: R.Tensor((2, 28, 28, 4), dtype="float32") = R.permute_dims(x, axes=[0, 2, 3, 1]) |
| lv1: R.Tensor((4, 3, 3, 4), dtype="float32") = R.permute_dims(w, axes=[0, 2, 3, 1]) |
| gv: R.Tensor((2, 26, 26, 4), dtype="float32") = R.nn.conv2d( |
| lv, |
| lv1, |
| strides=[1, 1], |
| padding=[0, 0, 0, 0], |
| dilation=[1, 1], |
| groups=1, |
| data_layout="NHWC", |
| kernel_layout="OHWI", |
| out_layout="NHWC", |
| out_dtype="float32", |
| ) |
| lv2: R.Tensor((2, 26, 26, 4), dtype="float32") = R.permute_dims( |
| bias, axes=[0, 2, 3, 1] |
| ) |
| gv2: R.Tensor((2, 26, 26, 4), dtype="float32") = R.add(gv, lv2) |
| gv3: R.Tensor((2, 26, 26, 4), dtype="float32") = R.nn.relu(gv2) |
| lv3: R.Tensor((4, 3, 3, 4), dtype="float32") = R.permute_dims(w, axes=[0, 2, 3, 1]) |
| lv4: R.Tensor((2, 24, 24, 4), dtype="float32") = R.nn.conv2d( |
| gv3, |
| lv3, |
| strides=[1, 1], |
| padding=[0, 0, 0, 0], |
| dilation=[1, 1], |
| groups=1, |
| data_layout="NHWC", |
| kernel_layout="OHWI", |
| out_layout="NHWC", |
| out_dtype="float32", |
| ) |
| gv4: R.Tensor((2, 4, 24, 24), dtype="float32") = R.permute_dims( |
| lv4, axes=[0, 3, 1, 2] |
| ) |
| R.output(gv4) |
| return gv4 |
| |
| verify(Input, Expected) |
| |
| |
| def test_conv2d_fma_relu_conv2d(): |
| @I.ir_module |
| class Input: |
| @R.function |
| def main( |
| x: R.Tensor((2, 4, 28, 28), "float32"), |
| w: R.Tensor((4, 4, 3, 3), "float32"), |
| scale: R.Tensor((2, 4, 26, 26), dtype="float32"), |
| bias: R.Tensor((2, 4, 26, 26), "float32"), |
| ) -> R.Tensor(None, "float32", ndim=4): |
| with R.dataflow(): |
| gv: R.Tensor((2, 4, 26, 26), "float32") = R.nn.conv2d(x, w, out_dtype="float32") |
| gv2: R.Tensor((2, 4, 26, 26), "float32") = R.ewise_fma(gv, scale, bias) |
| gv3: R.Tensor((2, 4, 26, 26), "float32") = R.nn.relu(gv2) |
| gv4: R.Tensor((2, 4, 24, 24), "float32") = R.nn.conv2d(gv3, w, out_dtype="float32") |
| R.output(gv4) |
| return gv4 |
| |
| @I.ir_module |
| class Expected: |
| @R.function |
| def main( |
| x: R.Tensor((2, 4, 28, 28), dtype="float32"), |
| w: R.Tensor((4, 4, 3, 3), dtype="float32"), |
| scale: R.Tensor((2, 4, 26, 26), dtype="float32"), |
| bias: R.Tensor((2, 4, 26, 26), dtype="float32"), |
| ) -> R.Tensor((2, 4, 24, 24), dtype="float32"): |
| with R.dataflow(): |
| lv: R.Tensor((2, 28, 28, 4), dtype="float32") = R.permute_dims(x, axes=[0, 2, 3, 1]) |
| lv1: R.Tensor((4, 3, 3, 4), dtype="float32") = R.permute_dims(w, axes=[0, 2, 3, 1]) |
| gv: R.Tensor((2, 26, 26, 4), dtype="float32") = R.nn.conv2d( |
| lv, |
| lv1, |
| strides=[1, 1], |
| padding=[0, 0, 0, 0], |
| dilation=[1, 1], |
| groups=1, |
| data_layout="NHWC", |
| kernel_layout="OHWI", |
| out_layout="NHWC", |
| out_dtype="float32", |
| ) |
| lv2: R.Tensor((2, 4, 26, 26), dtype="float32") = R.permute_dims( |
| gv, axes=[0, 3, 1, 2] |
| ) |
| gv2: R.Tensor((2, 4, 26, 26), dtype="float32") = R.ewise_fma(lv2, scale, bias) |
| gv3: R.Tensor((2, 4, 26, 26), dtype="float32") = R.nn.relu(gv2) |
| lv3: R.Tensor((2, 26, 26, 4), dtype="float32") = R.permute_dims( |
| gv3, axes=[0, 2, 3, 1] |
| ) |
| lv4: R.Tensor((4, 3, 3, 4), dtype="float32") = R.permute_dims(w, axes=[0, 2, 3, 1]) |
| lv5: R.Tensor((2, 24, 24, 4), dtype="float32") = R.nn.conv2d( |
| lv3, |
| lv4, |
| strides=[1, 1], |
| padding=[0, 0, 0, 0], |
| dilation=[1, 1], |
| groups=1, |
| data_layout="NHWC", |
| kernel_layout="OHWI", |
| out_layout="NHWC", |
| out_dtype="float32", |
| ) |
| gv4: R.Tensor((2, 4, 24, 24), dtype="float32") = R.permute_dims( |
| lv5, axes=[0, 3, 1, 2] |
| ) |
| R.output(gv4) |
| return gv4 |
| |
| verify(Input, Expected) |
| |
| |
| def test_conv2d_sum(): |
| @I.ir_module |
| class Input: |
| @R.function |
| def main( |
| x: R.Tensor((2, 3, 28, 28), "float32"), w: R.Tensor((4, 3, 3, 3), "float32") |
| ) -> R.Tensor(None, "float32", ndim=2): |
| with R.dataflow(): |
| gv: R.Tensor((2, 4, 26, 26), "float32") = R.nn.conv2d(x, w, out_dtype="float32") |
| gv2: R.Tensor((2, 4), "float32") = R.sum(gv, axis=[2, 3]) |
| R.output(gv2) |
| return gv2 |
| |
| @I.ir_module |
| class Expected: |
| @R.function |
| def main( |
| x: R.Tensor((2, 3, 28, 28), dtype="float32"), w: R.Tensor((4, 3, 3, 3), dtype="float32") |
| ) -> R.Tensor(None, dtype="float32", ndim=2): |
| with R.dataflow(): |
| lv: R.Tensor((2, 28, 28, 3), dtype="float32") = R.permute_dims(x, axes=[0, 2, 3, 1]) |
| lv1: R.Tensor((4, 3, 3, 3), dtype="float32") = R.permute_dims(w, axes=[0, 2, 3, 1]) |
| gv: R.Tensor((2, 26, 26, 4), dtype="float32") = R.nn.conv2d( |
| lv, |
| lv1, |
| strides=[1, 1], |
| padding=[0, 0, 0, 0], |
| dilation=[1, 1], |
| groups=1, |
| data_layout="NHWC", |
| kernel_layout="OHWI", |
| out_layout="NHWC", |
| out_dtype="float32", |
| ) |
| gv2: R.Tensor((2, 4), dtype="float32") = R.sum(gv, axis=[1, 2], keepdims=False) |
| R.output(gv2) |
| return gv2 |
| |
| verify(Input, Expected) |
| |
| |
| def test_conv2d_sum_keepdim(): |
| @I.ir_module |
| class Input: |
| @R.function |
| def main( |
| x: R.Tensor((2, 3, 28, 28), "float32"), w: R.Tensor((4, 3, 3, 3), "float32") |
| ) -> R.Tensor(None, "float32", ndim=4): |
| with R.dataflow(): |
| gv: R.Tensor((2, 4, 26, 26), "float32") = R.nn.conv2d(x, w, out_dtype="float32") |
| gv2: R.Tensor((2, 4, 1, 1), "float32") = R.sum(gv, axis=[2, 3], keepdims=True) |
| R.output(gv2) |
| return gv2 |
| |
| @I.ir_module |
| class Expected: |
| @R.function |
| def main( |
| x: R.Tensor((2, 3, 28, 28), dtype="float32"), w: R.Tensor((4, 3, 3, 3), dtype="float32") |
| ) -> R.Tensor(None, dtype="float32", ndim=4): |
| with R.dataflow(): |
| lv: R.Tensor((2, 28, 28, 3), dtype="float32") = R.permute_dims(x, axes=[0, 2, 3, 1]) |
| lv1: R.Tensor((4, 3, 3, 3), dtype="float32") = R.permute_dims(w, axes=[0, 2, 3, 1]) |
| gv: R.Tensor((2, 26, 26, 4), dtype="float32") = R.nn.conv2d( |
| lv, |
| lv1, |
| strides=[1, 1], |
| padding=[0, 0, 0, 0], |
| dilation=[1, 1], |
| groups=1, |
| data_layout="NHWC", |
| kernel_layout="OHWI", |
| out_layout="NHWC", |
| out_dtype="float32", |
| ) |
| lv2: R.Tensor((2, 1, 1, 4), dtype="float32") = R.sum(gv, axis=[1, 2], keepdims=True) |
| gv2: R.Tensor((2, 4, 1, 1), dtype="float32") = R.permute_dims( |
| lv2, axes=[0, 3, 1, 2] |
| ) |
| R.output(gv2) |
| return gv2 |
| |
| verify(Input, Expected) |
| |
| |
| def test_conv2d_sum_negative_dims(): |
| @I.ir_module |
| class Input: |
| @R.function |
| def main(x: R.Tensor((2, 3, 28, 28), "float32"), w: R.Tensor((4, 3, 3, 3), "float32")): |
| with R.dataflow(): |
| gv: R.Tensor((2, 4, 26, 26), "float32") = R.nn.conv2d(x, w, out_dtype="float32") |
| gv2: R.Tensor((2, 4), "float32") = R.sum(gv, axis=[-2, -1]) |
| R.output(gv2) |
| return gv2 |
| |
| @I.ir_module |
| class Expected: |
| @R.function |
| def main( |
| x: R.Tensor((2, 3, 28, 28), dtype="float32"), w: R.Tensor((4, 3, 3, 3), dtype="float32") |
| ): |
| with R.dataflow(): |
| lv: R.Tensor((2, 28, 28, 3), dtype="float32") = R.permute_dims(x, axes=[0, 2, 3, 1]) |
| lv1: R.Tensor((4, 3, 3, 3), dtype="float32") = R.permute_dims(w, axes=[0, 2, 3, 1]) |
| gv: R.Tensor((2, 26, 26, 4), dtype="float32") = R.nn.conv2d( |
| lv, |
| lv1, |
| strides=[1, 1], |
| padding=[0, 0, 0, 0], |
| dilation=[1, 1], |
| groups=1, |
| data_layout="NHWC", |
| kernel_layout="OHWI", |
| out_layout="NHWC", |
| out_dtype="float32", |
| ) |
| gv2: R.Tensor((2, 4), dtype="float32") = R.sum(gv, axis=[1, 2]) |
| R.output(gv2) |
| return gv2 |
| |
| verify(Input, Expected) |
| |
| |
| def test_conv2d_transpose(): |
| @I.ir_module |
| class Input: |
| @R.function |
| def main( |
| x: R.Tensor((2, 3, 28, 28), "float32"), w: R.Tensor((4, 3, 3, 3), "float32") |
| ) -> R.Tensor(None, "float32", ndim=4): |
| with R.dataflow(): |
| gv: R.Tensor((2, 4, 26, 26), "float32") = R.nn.conv2d(x, w, out_dtype="float32") |
| gv2: R.Tensor((26, 26, 4, 2), "float32") = R.permute_dims(gv, axes=[3, 2, 1, 0]) |
| R.output(gv2) |
| return gv2 |
| |
| @I.ir_module |
| class Expected: |
| @R.function |
| def main( |
| x: R.Tensor((2, 3, 28, 28), dtype="float32"), w: R.Tensor((4, 3, 3, 3), dtype="float32") |
| ) -> R.Tensor(None, dtype="float32", ndim=4): |
| with R.dataflow(): |
| lv: R.Tensor((2, 28, 28, 3), dtype="float32") = R.permute_dims(x, axes=[0, 2, 3, 1]) |
| lv1: R.Tensor((4, 3, 3, 3), dtype="float32") = R.permute_dims(w, axes=[0, 2, 3, 1]) |
| gv: R.Tensor((2, 26, 26, 4), dtype="float32") = R.nn.conv2d( |
| lv, |
| lv1, |
| strides=[1, 1], |
| padding=[0, 0, 0, 0], |
| dilation=[1, 1], |
| groups=1, |
| data_layout="NHWC", |
| kernel_layout="OHWI", |
| out_layout="NHWC", |
| out_dtype="float32", |
| ) |
| gv2: R.Tensor((26, 26, 4, 2), dtype="float32") = R.permute_dims( |
| gv, axes=[2, 1, 3, 0] |
| ) |
| R.output(gv2) |
| return gv2 |
| |
| verify(Input, Expected) |
| |
| |
| def test_expand_dims_scalar(): |
| @I.ir_module |
| class Input: |
| @R.function |
| def main() -> R.Tensor((1,), dtype="int64"): |
| with R.dataflow(): |
| gv: R.Tensor((1,), dtype="int64") = R.expand_dims(R.const(0, "int64"), axis=[0]) |
| R.output(gv) |
| return gv |
| |
| verify(Input, Input) |
| |
| |
| def test_conv2d_expand_dims(): |
| @I.ir_module |
| class Input: |
| @R.function |
| def main( |
| x: R.Tensor((2, 3, 28, 28), "float32"), w: R.Tensor((4, 3, 3, 3), "float32") |
| ) -> R.Tensor(None, "float32", ndim=6): |
| with R.dataflow(): |
| gv: R.Tensor((2, 4, 26, 26), "float32") = R.nn.conv2d(x, w, out_dtype="float32") |
| gv2: R.Tensor((2, 1, 4, 1, 26, 26), "float32") = R.expand_dims(gv, axis=(-3, 1)) |
| R.output(gv2) |
| return gv2 |
| |
| @I.ir_module |
| class Expected: |
| @R.function |
| def main( |
| x: R.Tensor((2, 3, 28, 28), dtype="float32"), w: R.Tensor((4, 3, 3, 3), dtype="float32") |
| ) -> R.Tensor(None, dtype="float32", ndim=6): |
| with R.dataflow(): |
| lv: R.Tensor((2, 28, 28, 3), dtype="float32") = R.permute_dims(x, axes=[0, 2, 3, 1]) |
| lv1: R.Tensor((4, 3, 3, 3), dtype="float32") = R.permute_dims(w, axes=[0, 2, 3, 1]) |
| gv: R.Tensor((2, 26, 26, 4), dtype="float32") = R.nn.conv2d( |
| lv, |
| lv1, |
| strides=[1, 1], |
| padding=[0, 0, 0, 0], |
| dilation=[1, 1], |
| groups=1, |
| data_layout="NHWC", |
| kernel_layout="OHWI", |
| out_layout="NHWC", |
| out_dtype="float32", |
| ) |
| lv2: R.Tensor((2, 1, 26, 1, 26, 4), dtype="float32") = R.expand_dims( |
| gv, axis=[-3, 1] |
| ) |
| gv2: R.Tensor((2, 1, 4, 1, 26, 26), dtype="float32") = R.permute_dims( |
| lv2, axes=[0, 1, 5, 3, 2, 4] |
| ) |
| R.output(gv2) |
| return gv2 |
| |
| verify(Input, Expected) |
| |
| |
| def test_conv2d_expand_dims_squeeze(): |
| @I.ir_module |
| class Input: |
| @R.function |
| def main( |
| x: R.Tensor((2, 3, 28, 28), "float32"), w: R.Tensor((4, 3, 3, 3), "float32") |
| ) -> R.Tensor(None, "float32", ndim=4): |
| with R.dataflow(): |
| gv: R.Tensor((2, 4, 26, 26), "float32") = R.nn.conv2d(x, w, out_dtype="float32") |
| gv2: R.Tensor((2, 1, 4, 1, 26, 26), "float32") = R.expand_dims(gv, axis=(-3, 1)) |
| gv3: R.Tensor((2, 4, 26, 26), "float32") = R.squeeze(gv2, axis=[1, 3]) |
| R.output(gv3) |
| return gv3 |
| |
| @I.ir_module |
| class Expected: |
| @R.function |
| def main( |
| x: R.Tensor((2, 3, 28, 28), dtype="float32"), w: R.Tensor((4, 3, 3, 3), dtype="float32") |
| ) -> R.Tensor(None, dtype="float32", ndim=4): |
| with R.dataflow(): |
| lv: R.Tensor((2, 28, 28, 3), dtype="float32") = R.permute_dims(x, axes=[0, 2, 3, 1]) |
| lv1: R.Tensor((4, 3, 3, 3), dtype="float32") = R.permute_dims(w, axes=[0, 2, 3, 1]) |
| gv: R.Tensor((2, 26, 26, 4), dtype="float32") = R.nn.conv2d( |
| lv, |
| lv1, |
| strides=[1, 1], |
| padding=[0, 0, 0, 0], |
| dilation=[1, 1], |
| groups=1, |
| data_layout="NHWC", |
| kernel_layout="OHWI", |
| out_layout="NHWC", |
| out_dtype="float32", |
| ) |
| gv2: R.Tensor((2, 1, 26, 1, 26, 4), dtype="float32") = R.expand_dims( |
| gv, axis=[-3, 1] |
| ) |
| lv2: R.Tensor((2, 26, 26, 4), dtype="float32") = R.squeeze(gv2, axis=[1, 3]) |
| gv3: R.Tensor((2, 4, 26, 26), dtype="float32") = R.permute_dims( |
| lv2, axes=[0, 3, 1, 2] |
| ) |
| R.output(gv3) |
| return gv3 |
| |
| verify(Input, Expected) |
| |
| |
| def test_conv2d_strided_slice(): |
| @I.ir_module |
| class Input: |
| @R.function |
| def main( |
| x: R.Tensor((2, 3, 28, 28), "float32"), w: R.Tensor((4, 3, 3, 3), "float32") |
| ) -> R.Tensor(None, "float32", ndim=4): |
| with R.dataflow(): |
| gv: R.Tensor((2, 4, 26, 26), "float32") = R.nn.conv2d(x, w, out_dtype="float32") |
| gv2: R.Tensor((2, 2, 9, 7), dtype="float32") = R.strided_slice( |
| gv, begin=[0, 0, 0], end=[4, 26, 26], strides=[2, 3, 4], axes=[1, 2, 3] |
| ) |
| R.output(gv2) |
| return gv2 |
| |
| @I.ir_module |
| class Expected: |
| @R.function |
| def main( |
| x: R.Tensor((2, 3, 28, 28), dtype="float32"), w: R.Tensor((4, 3, 3, 3), dtype="float32") |
| ) -> R.Tensor(None, dtype="float32", ndim=4): |
| with R.dataflow(): |
| lv: R.Tensor((2, 28, 28, 3), dtype="float32") = R.permute_dims(x, axes=[0, 2, 3, 1]) |
| lv1: R.Tensor((4, 3, 3, 3), dtype="float32") = R.permute_dims(w, axes=[0, 2, 3, 1]) |
| gv: R.Tensor((2, 26, 26, 4), dtype="float32") = R.nn.conv2d( |
| lv, |
| lv1, |
| strides=[1, 1], |
| padding=[0, 0, 0, 0], |
| dilation=[1, 1], |
| groups=1, |
| data_layout="NHWC", |
| kernel_layout="OHWI", |
| out_layout="NHWC", |
| out_dtype="float32", |
| ) |
| lv2: R.Tensor((2, 9, 7, 2), dtype="float32") = R.strided_slice( |
| gv, axes=[3, 1, 2], begin=[0, 0, 0], end=[4, 26, 26], strides=[2, 3, 4] |
| ) |
| gv2: R.Tensor((2, 2, 9, 7), dtype="float32") = R.permute_dims( |
| lv2, axes=[0, 3, 1, 2] |
| ) |
| R.output(gv2) |
| return gv2 |
| |
| verify(Input, Expected) |
| |
| |
| def test_conv2d_relu_concat(): |
| @I.ir_module |
| class Input: |
| @R.function |
| def main( |
| x: R.Tensor((2, 3, 28, 28), "float32"), w: R.Tensor((4, 3, 3, 3), "float32") |
| ) -> R.Tensor(None, "float32", ndim=4): |
| with R.dataflow(): |
| gv: R.Tensor((2, 4, 26, 26), "float32") = R.nn.conv2d(x, w, out_dtype="float32") |
| gv2: R.Tensor((2, 4, 26, 26), "float32") = R.nn.relu(gv) |
| gv3: R.Tensor((2, 8, 26, 26), "float32") = R.concat((gv, gv2), axis=1) |
| R.output(gv3) |
| return gv3 |
| |
| @I.ir_module |
| class Expected: |
| @R.function |
| def main( |
| x: R.Tensor((2, 3, 28, 28), dtype="float32"), w: R.Tensor((4, 3, 3, 3), dtype="float32") |
| ) -> R.Tensor(None, dtype="float32", ndim=4): |
| with R.dataflow(): |
| lv: R.Tensor((2, 28, 28, 3), dtype="float32") = R.permute_dims(x, axes=[0, 2, 3, 1]) |
| lv1: R.Tensor((4, 3, 3, 3), dtype="float32") = R.permute_dims(w, axes=[0, 2, 3, 1]) |
| gv: R.Tensor((2, 26, 26, 4), dtype="float32") = R.nn.conv2d( |
| lv, |
| lv1, |
| strides=[1, 1], |
| padding=[0, 0, 0, 0], |
| dilation=[1, 1], |
| groups=1, |
| data_layout="NHWC", |
| kernel_layout="OHWI", |
| out_layout="NHWC", |
| out_dtype="float32", |
| ) |
| gv2: R.Tensor((2, 26, 26, 4), dtype="float32") = R.nn.relu(gv) |
| lv2: R.Tensor((2, 26, 26, 8), dtype="float32") = R.concat((gv, gv2), axis=3) |
| gv3: R.Tensor((2, 8, 26, 26), dtype="float32") = R.permute_dims( |
| lv2, axes=[0, 3, 1, 2] |
| ) |
| R.output(gv3) |
| return gv3 |
| |
| verify(Input, Expected) |
| |
| |
| def test_conv2d_relu_concat_split(): |
| @I.ir_module |
| class Input: |
| @R.function |
| def main(x: R.Tensor((2, 3, 28, 28), "float32"), w: R.Tensor((4, 3, 3, 3), "float32")): |
| with R.dataflow(): |
| gv: R.Tensor((2, 4, 26, 26), "float32") = R.nn.conv2d(x, w, out_dtype="float32") |
| gv2: R.Tensor((2, 4, 26, 26), "float32") = R.nn.relu(gv) |
| gv3: R.Tensor((2, 8, 26, 26), "float32") = R.concat((gv, gv2), axis=1) |
| gv4 = R.split(gv3, indices_or_sections=2, axis=1) |
| R.output(gv4) |
| return gv4 |
| |
| @I.ir_module |
| class Expected: |
| @R.function |
| def main( |
| x: R.Tensor((2, 3, 28, 28), dtype="float32"), w: R.Tensor((4, 3, 3, 3), dtype="float32") |
| ): |
| with R.dataflow(): |
| lv: R.Tensor((2, 28, 28, 3), dtype="float32") = R.permute_dims(x, axes=[0, 2, 3, 1]) |
| lv1: R.Tensor((4, 3, 3, 3), dtype="float32") = R.permute_dims(w, axes=[0, 2, 3, 1]) |
| gv: R.Tensor((2, 26, 26, 4), dtype="float32") = R.nn.conv2d( |
| lv, |
| lv1, |
| strides=[1, 1], |
| padding=[0, 0, 0, 0], |
| dilation=[1, 1], |
| groups=1, |
| data_layout="NHWC", |
| kernel_layout="OHWI", |
| out_layout="NHWC", |
| out_dtype="float32", |
| ) |
| gv2: R.Tensor((2, 26, 26, 4), dtype="float32") = R.nn.relu(gv) |
| gv3: R.Tensor((2, 26, 26, 8), dtype="float32") = R.concat((gv, gv2), axis=3) |
| gv4: R.Tuple( |
| R.Tensor((2, 26, 26, 4), dtype="float32"), |
| R.Tensor((2, 26, 26, 4), dtype="float32"), |
| ) = R.split(gv3, indices_or_sections=2, axis=3) |
| lv2: R.Tensor((2, 26, 26, 4), dtype="float32") = gv4[0] |
| lv3: R.Tensor((2, 4, 26, 26), dtype="float32") = R.permute_dims( |
| lv2, axes=[0, 3, 1, 2] |
| ) |
| lv4: R.Tensor((2, 26, 26, 4), dtype="float32") = gv4[1] |
| lv5: R.Tensor((2, 4, 26, 26), dtype="float32") = R.permute_dims( |
| lv4, axes=[0, 3, 1, 2] |
| ) |
| gv5 = (lv3, lv5) |
| R.output(gv5) |
| return gv5 |
| |
| verify(Input, Expected) |
| |
| |
| def test_conv2d_maxpool2d(): |
| @I.ir_module |
| class Input: |
| @R.function |
| def main( |
| x: R.Tensor((2, 3, 28, 28), "float32"), w: R.Tensor((4, 3, 3, 3), "float32") |
| ) -> R.Tensor(None, "float32", ndim=4): |
| with R.dataflow(): |
| gv: R.Tensor((2, 4, 26, 26), "float32") = R.nn.conv2d(x, w, out_dtype="float32") |
| gv2 = R.nn.max_pool2d( |
| gv, |
| pool_size=[2, 2], |
| strides=[2, 2], |
| padding=[0, 0], |
| layout="NCHW", |
| out_layout="NCHW", |
| ) |
| R.output(gv2) |
| return gv2 |
| |
| @I.ir_module |
| class Expected: |
| @R.function |
| def main( |
| x: R.Tensor((2, 3, 28, 28), dtype="float32"), w: R.Tensor((4, 3, 3, 3), dtype="float32") |
| ) -> R.Tensor(None, dtype="float32", ndim=4): |
| with R.dataflow(): |
| lv: R.Tensor((2, 28, 28, 3), dtype="float32") = R.permute_dims(x, axes=[0, 2, 3, 1]) |
| lv1: R.Tensor((4, 3, 3, 3), dtype="float32") = R.permute_dims(w, axes=[0, 2, 3, 1]) |
| gv: R.Tensor((2, 26, 26, 4), dtype="float32") = R.nn.conv2d( |
| lv, |
| lv1, |
| strides=[1, 1], |
| padding=[0, 0, 0, 0], |
| dilation=[1, 1], |
| groups=1, |
| data_layout="NHWC", |
| kernel_layout="OHWI", |
| out_layout="NHWC", |
| out_dtype="float32", |
| ) |
| lv2: R.Tensor((2, 13, 13, 4), dtype="float32") = R.nn.max_pool2d( |
| gv, |
| pool_size=[2, 2], |
| strides=[2, 2], |
| dilation=[1, 1], |
| padding=[0, 0, 0, 0], |
| ceil_mode=False, |
| layout="NHWC", |
| out_layout="NHWC", |
| ) |
| gv2: R.Tensor((2, 4, 13, 13), dtype="float32") = R.permute_dims( |
| lv2, axes=[0, 3, 1, 2] |
| ) |
| R.output(gv2) |
| return gv2 |
| |
| verify(Input, Expected) |
| |
| |
| def test_conv2d_avgpool2d(): |
| @I.ir_module |
| class Input: |
| @R.function |
| def main( |
| x: R.Tensor((2, 3, 28, 28), "float32"), w: R.Tensor((4, 3, 3, 3), "float32") |
| ) -> R.Tensor(None, "float32", ndim=4): |
| with R.dataflow(): |
| gv: R.Tensor((2, 4, 26, 26), "float32") = R.nn.conv2d(x, w, out_dtype="float32") |
| gv2 = R.nn.adaptive_avg_pool2d(gv, output_size=[13, 13], layout="NCHW") |
| R.output(gv2) |
| return gv2 |
| |
| @I.ir_module |
| class Expected: |
| @R.function |
| def main( |
| x: R.Tensor((2, 3, 28, 28), dtype="float32"), w: R.Tensor((4, 3, 3, 3), dtype="float32") |
| ) -> R.Tensor(None, dtype="float32", ndim=4): |
| with R.dataflow(): |
| lv: R.Tensor((2, 28, 28, 3), dtype="float32") = R.permute_dims(x, axes=[0, 2, 3, 1]) |
| lv1: R.Tensor((4, 3, 3, 3), dtype="float32") = R.permute_dims(w, axes=[0, 2, 3, 1]) |
| gv: R.Tensor((2, 26, 26, 4), dtype="float32") = R.nn.conv2d( |
| lv, |
| lv1, |
| strides=[1, 1], |
| padding=[0, 0, 0, 0], |
| dilation=[1, 1], |
| groups=1, |
| data_layout="NHWC", |
| kernel_layout="OHWI", |
| out_layout="NHWC", |
| out_dtype="float32", |
| ) |
| lv2: R.Tensor((2, 13, 13, 4), dtype="float32") = R.nn.adaptive_avg_pool2d( |
| gv, output_size=[13, 13], layout="NHWC", out_layout="NHWC" |
| ) |
| gv2: R.Tensor((2, 4, 13, 13), dtype="float32") = R.permute_dims( |
| lv2, axes=[0, 3, 1, 2] |
| ) |
| R.output(gv2) |
| return gv2 |
| |
| verify(Input, Expected) |
| |
| |
| def test_conv2d_softmax(): |
| @I.ir_module |
| class Input: |
| @R.function |
| def main( |
| x: R.Tensor((2, 3, 28, 28), "float32"), w: R.Tensor((4, 3, 3, 3), "float32") |
| ) -> R.Tensor(None, "float32", ndim=4): |
| with R.dataflow(): |
| gv: R.Tensor((2, 4, 26, 26), "float32") = R.nn.conv2d(x, w, out_dtype="float32") |
| gv2 = R.nn.softmax(gv, axis=1) |
| R.output(gv2) |
| return gv2 |
| |
| @I.ir_module |
| class Expected: |
| @R.function |
| def main( |
| x: R.Tensor((2, 3, 28, 28), dtype="float32"), w: R.Tensor((4, 3, 3, 3), dtype="float32") |
| ) -> R.Tensor(None, dtype="float32", ndim=4): |
| with R.dataflow(): |
| lv: R.Tensor((2, 28, 28, 3), dtype="float32") = R.permute_dims(x, axes=[0, 2, 3, 1]) |
| lv1: R.Tensor((4, 3, 3, 3), dtype="float32") = R.permute_dims(w, axes=[0, 2, 3, 1]) |
| gv: R.Tensor((2, 26, 26, 4), dtype="float32") = R.nn.conv2d( |
| lv, |
| lv1, |
| strides=[1, 1], |
| padding=[0, 0, 0, 0], |
| dilation=[1, 1], |
| groups=1, |
| data_layout="NHWC", |
| kernel_layout="OHWI", |
| out_layout="NHWC", |
| out_dtype="float32", |
| ) |
| lv2: R.Tensor((2, 26, 26, 4), dtype="float32") = R.nn.softmax(gv, axis=3) |
| gv2: R.Tensor((2, 4, 26, 26), dtype="float32") = R.permute_dims( |
| lv2, axes=[0, 3, 1, 2] |
| ) |
| R.output(gv2) |
| return gv2 |
| |
| verify(Input, Expected) |
| |
| |
| def test_conv2d_batchnorm(): |
| @I.ir_module |
| class Input: |
| @R.function |
| def main( |
| x: R.Tensor((2, 3, 28, 28), "float32"), |
| w: R.Tensor((4, 3, 3, 3), "float32"), |
| gamma: R.Tensor((4,), dtype="float32"), |
| beta: R.Tensor((4,), dtype="float32"), |
| moving_mean: R.Tensor((4,), dtype="float32"), |
| moving_var: R.Tensor((4,), dtype="float32"), |
| ): |
| with R.dataflow(): |
| gv: R.Tensor((2, 4, 26, 26), "float32") = R.nn.conv2d(x, w, out_dtype="float32") |
| gv2: R.Tuple( |
| R.Tensor((2, 4, 26, 26), dtype="float32"), |
| R.Tensor((4,), dtype="float32"), |
| R.Tensor((4,), dtype="float32"), |
| ) = R.nn.batch_norm(gv, gamma, beta, moving_mean, moving_var, axis=1) |
| R.output(gv2) |
| return gv2 |
| |
| @I.ir_module |
| class Expected: |
| @R.function |
| def main( |
| x: R.Tensor((2, 3, 28, 28), dtype="float32"), |
| w: R.Tensor((4, 3, 3, 3), dtype="float32"), |
| gamma: R.Tensor((4,), dtype="float32"), |
| beta: R.Tensor((4,), dtype="float32"), |
| moving_mean: R.Tensor((4,), dtype="float32"), |
| moving_var: R.Tensor((4,), dtype="float32"), |
| ): |
| with R.dataflow(): |
| lv: R.Tensor((2, 28, 28, 3), dtype="float32") = R.permute_dims(x, axes=[0, 2, 3, 1]) |
| lv1: R.Tensor((4, 3, 3, 3), dtype="float32") = R.permute_dims(w, axes=[0, 2, 3, 1]) |
| gv: R.Tensor((2, 26, 26, 4), dtype="float32") = R.nn.conv2d( |
| lv, |
| lv1, |
| strides=[1, 1], |
| padding=[0, 0, 0, 0], |
| dilation=[1, 1], |
| groups=1, |
| data_layout="NHWC", |
| kernel_layout="OHWI", |
| out_layout="NHWC", |
| out_dtype="float32", |
| ) |
| gv2: R.Tuple( |
| R.Tensor((2, 26, 26, 4), dtype="float32"), |
| R.Tensor((4,), dtype="float32"), |
| R.Tensor((4,), dtype="float32"), |
| ) = R.nn.batch_norm( |
| gv, |
| gamma, |
| beta, |
| moving_mean, |
| moving_var, |
| axis=3, |
| epsilon=1.0000000000000001e-05, |
| center=True, |
| scale=True, |
| ) |
| lv2: R.Tensor((2, 26, 26, 4), dtype="float32") = gv2[0] |
| lv3: R.Tensor((2, 4, 26, 26), dtype="float32") = R.permute_dims( |
| lv2, axes=[0, 3, 1, 2] |
| ) |
| lv4: R.Tensor((4,), dtype="float32") = gv2[1] |
| lv5: R.Tensor((4,), dtype="float32") = gv2[2] |
| gv3 = (lv3, lv4, lv5) |
| R.output(gv3) |
| return gv3 |
| |
| verify(Input, Expected) |
| |
| |
| def test_conv2d_layernorm(): |
| @I.ir_module |
| class Input: |
| @R.function |
| def main( |
| x: R.Tensor((2, 3, 28, 28), "float32"), |
| w: R.Tensor((4, 3, 3, 3), "float32"), |
| gamma: R.Tensor((26, 26), dtype="float32"), |
| beta: R.Tensor((26, 26), dtype="float32"), |
| ) -> R.Tensor(None, "float32", ndim=4): |
| with R.dataflow(): |
| gv: R.Tensor((2, 4, 26, 26), "float32") = R.nn.conv2d(x, w, out_dtype="float32") |
| gv2: R.Tensor((2, 4, 26, 26), dtype="float32") = R.nn.layer_norm( |
| gv, gamma, beta, axes=[-2, -1] |
| ) |
| R.output(gv2) |
| return gv2 |
| |
| @I.ir_module |
| class Expected: |
| @R.function |
| def main( |
| x: R.Tensor((2, 3, 28, 28), dtype="float32"), |
| w: R.Tensor((4, 3, 3, 3), dtype="float32"), |
| gamma: R.Tensor((26, 26), dtype="float32"), |
| beta: R.Tensor((26, 26), dtype="float32"), |
| ) -> R.Tensor(None, dtype="float32", ndim=4): |
| with R.dataflow(): |
| lv: R.Tensor((2, 28, 28, 3), dtype="float32") = R.permute_dims(x, axes=[0, 2, 3, 1]) |
| lv1: R.Tensor((4, 3, 3, 3), dtype="float32") = R.permute_dims(w, axes=[0, 2, 3, 1]) |
| gv: R.Tensor((2, 26, 26, 4), dtype="float32") = R.nn.conv2d( |
| lv, |
| lv1, |
| strides=[1, 1], |
| padding=[0, 0, 0, 0], |
| dilation=[1, 1], |
| groups=1, |
| data_layout="NHWC", |
| kernel_layout="OHWI", |
| out_layout="NHWC", |
| out_dtype="float32", |
| ) |
| lv2: R.Tensor((2, 26, 26, 4), dtype="float32") = R.nn.layer_norm( |
| gv, |
| gamma, |
| beta, |
| axes=[1, 2], |
| epsilon=1.0000000000000001e-05, |
| center=True, |
| scale=True, |
| ) |
| gv2: R.Tensor((2, 4, 26, 26), dtype="float32") = R.permute_dims( |
| lv2, axes=[0, 3, 1, 2] |
| ) |
| R.output(gv2) |
| return gv2 |
| |
| verify(Input, Expected) |
| |
| |
| def test_conv2d_resize2d(): |
| @I.ir_module |
| class Input: |
| @R.function |
| def main( |
| x: R.Tensor((2, 3, 28, 28), "float32"), w: R.Tensor((4, 3, 3, 3), "float32") |
| ) -> R.Tensor(None, "float32", ndim=4): |
| with R.dataflow(): |
| gv: R.Tensor((2, 4, 26, 26), "float32") = R.nn.conv2d(x, w, out_dtype="float32") |
| gv2 = R.image.resize2d(gv, (52, 52), layout="NCHW") |
| R.output(gv2) |
| return gv2 |
| |
| @I.ir_module |
| class Expected: |
| @R.function |
| def main( |
| x: R.Tensor((2, 3, 28, 28), dtype="float32"), w: R.Tensor((4, 3, 3, 3), dtype="float32") |
| ) -> R.Tensor(None, dtype="float32", ndim=4): |
| with R.dataflow(): |
| lv: R.Tensor((2, 28, 28, 3), dtype="float32") = R.permute_dims(x, axes=[0, 2, 3, 1]) |
| lv1: R.Tensor((4, 3, 3, 3), dtype="float32") = R.permute_dims(w, axes=[0, 2, 3, 1]) |
| gv: R.Tensor((2, 26, 26, 4), dtype="float32") = R.nn.conv2d( |
| lv, |
| lv1, |
| strides=[1, 1], |
| padding=[0, 0, 0, 0], |
| dilation=[1, 1], |
| groups=1, |
| data_layout="NHWC", |
| kernel_layout="OHWI", |
| out_layout="NHWC", |
| out_dtype="float32", |
| ) |
| lv2: R.Tensor((2, 52, 52, 4), dtype="float32") = R.image.resize2d( |
| gv, |
| (52, 52), |
| roi=[T.float32(0), T.float32(0), T.float32(0), T.float32(0)], |
| layout="NHWC", |
| method="linear", |
| coordinate_transformation_mode="half_pixel", |
| rounding_method="round", |
| cubic_alpha=-0.5, |
| cubic_exclude=0, |
| extrapolation_value=0, |
| out_dtype="void", |
| ) |
| gv2: R.Tensor((2, 4, 52, 52), dtype="float32") = R.permute_dims( |
| lv2, axes=[0, 3, 1, 2] |
| ) |
| R.output(gv2) |
| return gv2 |
| |
| verify(Input, Expected) |
| |
| |
| def test_resize2d_conv2d(): |
| @I.ir_module |
| class Input: |
| @R.function |
| def main( |
| x: R.Tensor((2, 3, 28, 28), "float32"), w: R.Tensor((4, 3, 3, 3), "float32") |
| ) -> R.Tensor(None, "float32", ndim=4): |
| with R.dataflow(): |
| gv = R.image.resize2d(x, (52, 52), layout="NCHW") |
| gv2: R.Tensor((2, 4, 50, 50), "float32") = R.nn.conv2d(gv, w, out_dtype="float32") |
| R.output(gv2) |
| return gv2 |
| |
| @I.ir_module |
| class Expected: |
| @R.function |
| def main( |
| x: R.Tensor((2, 3, 28, 28), dtype="float32"), w: R.Tensor((4, 3, 3, 3), dtype="float32") |
| ) -> R.Tensor((2, 4, 50, 50), dtype="float32"): |
| with R.dataflow(): |
| lv: R.Tensor((2, 28, 28, 3), dtype="float32") = R.permute_dims(x, axes=[0, 2, 3, 1]) |
| gv: R.Tensor((2, 52, 52, 3), dtype="float32") = R.image.resize2d( |
| lv, |
| R.shape([52, 52]), |
| roi=[T.float32(0), T.float32(0), T.float32(0), T.float32(0)], |
| layout="NHWC", |
| method="linear", |
| coordinate_transformation_mode="half_pixel", |
| rounding_method="round", |
| cubic_alpha=-0.5, |
| cubic_exclude=0, |
| extrapolation_value=0, |
| out_dtype="void", |
| ) |
| lv1: R.Tensor((4, 3, 3, 3), dtype="float32") = R.permute_dims(w, axes=[0, 2, 3, 1]) |
| lv2: R.Tensor((2, 50, 50, 4), dtype="float32") = R.nn.conv2d( |
| gv, |
| lv1, |
| strides=[1, 1], |
| padding=[0, 0, 0, 0], |
| dilation=[1, 1], |
| groups=1, |
| data_layout="NHWC", |
| kernel_layout="OHWI", |
| out_layout="NHWC", |
| out_dtype="float32", |
| ) |
| gv2: R.Tensor((2, 4, 50, 50), dtype="float32") = R.permute_dims( |
| lv2, axes=[0, 3, 1, 2] |
| ) |
| R.output(gv2) |
| return gv2 |
| |
| verify(Input, Expected, extra_ops={"relax.image.resize2d": ["NHWC"]}) |
| |
| |
| def test_conv2d_unknown_bias_dim(): |
| @I.ir_module |
| class Input: |
| @R.function |
| def main( |
| x: R.Tensor((2, 3, 28, 28), "float32"), |
| w: R.Tensor((4, 3, 3, 3), "float32"), |
| w2: R.Tensor(dtype="float32"), |
| ) -> R.Tensor(None, "float32"): |
| with R.dataflow(): |
| gv: R.Tensor((2, 4, 26, 26), "float32") = R.nn.conv2d(x, w, out_dtype="float32") |
| gv2 = w2 + gv |
| R.output(gv2) |
| return gv2 |
| |
| @tvm.script.ir_module |
| class Expected: |
| @R.function |
| def main( |
| x: R.Tensor((2, 3, 28, 28), dtype="float32"), |
| w: R.Tensor((4, 3, 3, 3), dtype="float32"), |
| w2: R.Tensor(dtype="float32"), |
| ) -> R.Tensor(dtype="float32"): |
| with R.dataflow(): |
| lv: R.Tensor((2, 28, 28, 3), dtype="float32") = R.permute_dims(x, axes=[0, 2, 3, 1]) |
| lv1: R.Tensor((4, 3, 3, 3), dtype="float32") = R.permute_dims(w, axes=[0, 2, 3, 1]) |
| gv: R.Tensor((2, 26, 26, 4), dtype="float32") = R.nn.conv2d( |
| lv, |
| lv1, |
| strides=[1, 1], |
| padding=[0, 0, 0, 0], |
| dilation=[1, 1], |
| groups=1, |
| data_layout="NHWC", |
| kernel_layout="OHWI", |
| out_layout="NHWC", |
| out_dtype="float32", |
| ) |
| lv2: R.Tensor((2, 4, 26, 26), dtype="float32") = R.permute_dims( |
| gv, axes=[0, 3, 1, 2] |
| ) |
| gv2: R.Tensor(dtype="float32") = R.add(w2, lv2) |
| R.output(gv2) |
| return gv2 |
| |
| verify(Input, Expected) |
| |
| |
| def test_binary_broadcast(): |
| @I.ir_module |
| class Input: |
| @R.function |
| def main( |
| x: R.Tensor((2, 3, 28, 28), "float32"), |
| w: R.Tensor((4, 3, 3, 3), "float32"), |
| bias: R.Tensor((26, 26), "float32"), |
| ) -> R.Tensor(None, "float32", ndim=4): |
| with R.dataflow(): |
| gv: R.Tensor((2, 4, 26, 26), "float32") = R.nn.conv2d(x, w, out_dtype="float32") |
| gv2: R.Tensor((2, 4, 26, 26), "float32") = R.add(gv, bias) |
| R.output(gv2) |
| return gv2 |
| |
| @tvm.script.ir_module |
| class Expected: |
| @R.function |
| def main( |
| x: R.Tensor((2, 3, 28, 28), dtype="float32"), |
| w: R.Tensor((4, 3, 3, 3), dtype="float32"), |
| bias: R.Tensor((26, 26), dtype="float32"), |
| ) -> R.Tensor((2, 4, 26, 26), dtype="float32"): |
| with R.dataflow(): |
| lv: R.Tensor((2, 28, 28, 3), dtype="float32") = R.permute_dims(x, axes=[0, 2, 3, 1]) |
| lv1: R.Tensor((4, 3, 3, 3), dtype="float32") = R.permute_dims(w, axes=[0, 2, 3, 1]) |
| gv: R.Tensor((2, 26, 26, 4), dtype="float32") = R.nn.conv2d( |
| lv, |
| lv1, |
| strides=[1, 1], |
| padding=[0, 0, 0, 0], |
| dilation=[1, 1], |
| groups=1, |
| data_layout="NHWC", |
| kernel_layout="OHWI", |
| out_layout="NHWC", |
| out_dtype="float32", |
| ) |
| lv2: R.Tensor((2, 4, 26, 26), dtype="float32") = R.permute_dims( |
| gv, axes=[0, 3, 1, 2] |
| ) |
| gv2: R.Tensor((2, 4, 26, 26), dtype="float32") = R.add(lv2, bias) |
| R.output(gv2) |
| return gv2 |
| |
| verify(Input, Expected) |
| |
| |
| def test_binary_ewise_scalar(): |
| @I.ir_module |
| class Input: |
| @R.function |
| def main( |
| x: R.Tensor((2, 3, 28, 28), "float32"), w: R.Tensor((4, 3, 3, 3), "float32") |
| ) -> R.Tensor(None, "float32", ndim=4): |
| with R.dataflow(): |
| gv: R.Tensor((2, 4, 26, 26), "float32") = R.nn.conv2d(x, w, out_dtype="float32") |
| gv2: R.Tensor((2, 4, 26, 26), "float32") = R.add(gv, R.const(1, "float32")) |
| R.output(gv2) |
| return gv2 |
| |
| @I.ir_module |
| class Expected: |
| @R.function |
| def main( |
| x: R.Tensor((2, 3, 28, 28), dtype="float32"), w: R.Tensor((4, 3, 3, 3), dtype="float32") |
| ) -> R.Tensor((2, 4, 26, 26), dtype="float32"): |
| with R.dataflow(): |
| lv: R.Tensor((2, 28, 28, 3), dtype="float32") = R.permute_dims(x, axes=[0, 2, 3, 1]) |
| lv1: R.Tensor((4, 3, 3, 3), dtype="float32") = R.permute_dims(w, axes=[0, 2, 3, 1]) |
| gv: R.Tensor((2, 26, 26, 4), dtype="float32") = R.nn.conv2d( |
| lv, |
| lv1, |
| strides=[1, 1], |
| padding=[0, 0, 0, 0], |
| dilation=[1, 1], |
| groups=1, |
| data_layout="NHWC", |
| kernel_layout="OHWI", |
| out_layout="NHWC", |
| out_dtype="float32", |
| ) |
| lv2: R.Tensor((2, 26, 26, 4), dtype="float32") = R.add(gv, R.const(1, "float32")) |
| gv2: R.Tensor((2, 4, 26, 26), dtype="float32") = R.permute_dims( |
| lv2, axes=[0, 3, 1, 2] |
| ) |
| R.output(gv2) |
| return gv2 |
| |
| verify(Input, Expected) |
| |
| |
| if __name__ == "__main__": |
| tvm.testing.main() |
