quic-sanirudh commented on code in PR #12204: URL: https://github.com/apache/tvm/pull/12204#discussion_r945446968
########## tests/python/contrib/test_hexagon/topi/test_conv2d_fp16_intrin.py: ########## @@ -0,0 +1,256 @@ +# 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 numpy as np +import pytest +import sys + +import tvm +import tvm.contrib.hexagon +from tvm.topi.testing import conv2d_nhwc_python + + +def build_conv2d(target): + an, ah, aw, ac = ( + tvm.te.var("an"), + tvm.te.var("ah"), + tvm.te.var("aw"), + tvm.te.var("ac"), + ) + fh, fw, fo = tvm.te.var("fh"), tvm.te.var("fw"), tvm.te.var("fo") + off_l, off_t = tvm.te.var("off_l"), tvm.te.var("off_t") + stride_h, stride_w = tvm.te.var("stride_h"), tvm.te.var("stride_w") + + act_flat = tvm.te.placeholder(shape=(an, ah, aw, ac), dtype="float16", name="act_flat") + wgt_flat = tvm.te.placeholder(shape=(fh, fw, ac, fo), dtype="float16", name="wgt_flat") + + out_flat = tvm.te.extern( + shape=(an, (ah - fh) // stride_h + 1, (aw - fw) // stride_w + 1, fo), + inputs=[act_flat, wgt_flat], + fcompute=lambda ins, outs: tvm.tir.call_cpacked( + "conv2d_packed", # Function from TVM runtime + ins[0], + ins[1], + off_t, + off_l, + stride_h, + stride_w, + outs[0], + tvm.runtime.const(0), # resource_handle (unused) + ), + dtype="float16", + ) + + s = tvm.te.create_schedule(out_flat.op) + + func_name = "extern_conv" + with tvm.transform.PassContext(opt_level=3): + module = tvm.build( + s, + [act_flat, wgt_flat, off_t, off_l, stride_h, stride_w, out_flat], + target=target, + name=func_name, + ) + + return module + + +shape_parameters = [ Review Comment: Thanks. I've done it the first way (using `tvm.testing.parameters(...)` and not `parameter(...)` because there are a few restrictions on the compatibility of inputs, weights and stride to make sure that we don't get an incorrect output shape. So, using cartesian products to generate the input/weight/stride combinations was creating problems. ########## tests/python/contrib/test_hexagon/topi/test_conv2d_fp16_intrin.py: ########## @@ -0,0 +1,256 @@ +# 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 numpy as np +import pytest +import sys + +import tvm +import tvm.contrib.hexagon +from tvm.topi.testing import conv2d_nhwc_python + + +def build_conv2d(target): + an, ah, aw, ac = ( + tvm.te.var("an"), + tvm.te.var("ah"), + tvm.te.var("aw"), + tvm.te.var("ac"), + ) + fh, fw, fo = tvm.te.var("fh"), tvm.te.var("fw"), tvm.te.var("fo") + off_l, off_t = tvm.te.var("off_l"), tvm.te.var("off_t") + stride_h, stride_w = tvm.te.var("stride_h"), tvm.te.var("stride_w") + + act_flat = tvm.te.placeholder(shape=(an, ah, aw, ac), dtype="float16", name="act_flat") + wgt_flat = tvm.te.placeholder(shape=(fh, fw, ac, fo), dtype="float16", name="wgt_flat") + + out_flat = tvm.te.extern( + shape=(an, (ah - fh) // stride_h + 1, (aw - fw) // stride_w + 1, fo), + inputs=[act_flat, wgt_flat], + fcompute=lambda ins, outs: tvm.tir.call_cpacked( + "conv2d_packed", # Function from TVM runtime + ins[0], + ins[1], + off_t, + off_l, + stride_h, + stride_w, + outs[0], + tvm.runtime.const(0), # resource_handle (unused) + ), + dtype="float16", + ) + + s = tvm.te.create_schedule(out_flat.op) + + func_name = "extern_conv" + with tvm.transform.PassContext(opt_level=3): + module = tvm.build( + s, + [act_flat, wgt_flat, off_t, off_l, stride_h, stride_w, out_flat], + target=target, + name=func_name, + ) + + return module + + +shape_parameters = [ + { + "act_shape": (1, 8, 4, 3), + "wgt_shape": (3, 3, 3, 3), + "inp_offset": (0, 0), + "inp_stride": (1, 1), + }, + { + "act_shape": (1, 10, 14, 3), + "wgt_shape": (3, 3, 3, 3), + "inp_offset": (0, 0), + "inp_stride": (1, 1), + }, + { + "act_shape": (1, 14, 6, 3), + "wgt_shape": (3, 3, 3, 3), + "inp_offset": (0, 0), + "inp_stride": (1, 1), + }, + { + "act_shape": (1, 14, 6, 3), + "wgt_shape": (3, 3, 3, 64), + "inp_offset": (0, 0), + "inp_stride": (1, 1), + }, + { + "act_shape": (1, 14, 6, 3), + "wgt_shape": (5, 5, 3, 3), + "inp_offset": (0, 0), + "inp_stride": (1, 1), + }, + { + "act_shape": (1, 8, 8, 3), + "wgt_shape": (2, 2, 3, 3), + "inp_offset": (0, 0), + "inp_stride": (1, 1), + }, + { + "act_shape": (1, 14, 6, 64), + "wgt_shape": (3, 3, 64, 3), + "inp_offset": (0, 0), + "inp_stride": (1, 1), + }, + { + "act_shape": (1, 4, 4, 40), + "wgt_shape": (3, 3, 40, 3), + "inp_offset": (0, 0), + "inp_stride": (1, 1), + }, + { + "act_shape": (1, 4, 4, 3), + "wgt_shape": (3, 3, 3, 3), + "inp_offset": (0, 0), + "inp_stride": (1, 1), + }, + { + "act_shape": (1, 5, 5, 3), + "wgt_shape": (3, 3, 3, 3), + "inp_offset": (0, 0), + "inp_stride": (1, 1), + }, + { + "act_shape": (1, 6, 6, 3), + "wgt_shape": (3, 3, 3, 3), + "inp_offset": (0, 0), + "inp_stride": (1, 1), + }, + { + "act_shape": (1, 7, 7, 3), + "wgt_shape": (3, 3, 3, 3), + "inp_offset": (0, 0), + "inp_stride": (1, 1), + }, + { + "act_shape": (1, 8, 8, 3), + "wgt_shape": (3, 3, 3, 3), + "inp_offset": (0, 0), + "inp_stride": (1, 1), + }, + { + "act_shape": (1, 8, 8, 3), + "wgt_shape": (5, 5, 3, 3), + "inp_offset": (0, 0), + "inp_stride": (1, 1), + }, + { + "act_shape": (1, 8, 8, 64), + "wgt_shape": (2, 2, 64, 64), + "inp_offset": (0, 0), + "inp_stride": (1, 1), + }, + { + "act_shape": (1, 8, 4, 3), + "wgt_shape": (3, 3, 3, 3), + "inp_offset": (0, 0), + "inp_stride": (2, 2), + }, + { + "act_shape": (1, 14, 6, 3), + "wgt_shape": (3, 3, 3, 64), + "inp_offset": (0, 0), + "inp_stride": (2, 2), + }, + { + "act_shape": (1, 14, 6, 3), + "wgt_shape": (5, 5, 3, 3), + "inp_offset": (0, 0), + "inp_stride": (2, 2), + }, + { + "act_shape": (1, 8, 8, 3), + "wgt_shape": (2, 2, 3, 3), + "inp_offset": (0, 0), + "inp_stride": (2, 2), + }, +] + + +def gen_id(param): + """Utility function to generate useful ids for shape_parameters""" + + dims = lambda vals: "x".join(map(str, vals)) + + act_shape = param["act_shape"] + wgt_shape = param["wgt_shape"] + inp_stride = param["inp_stride"] + return f"nhwc{dims(act_shape)}-hwio{dims(wgt_shape)}-stride{dims(inp_stride)}" + + [email protected]_hexagon [email protected]("shapes", shape_parameters, ids=map(gen_id, shape_parameters)) Review Comment: Done, thanks. -- This is an automated message from the Apache Git Service. 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