sunggg commented on code in PR #11474: URL: https://github.com/apache/tvm/pull/11474#discussion_r889062857
########## tests/python/relay/transform/test_compiler_function_utils.py: ########## @@ -0,0 +1,162 @@ +# 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 +"""Unit tests for the OutlineCompilerFunctionsWithExistingGlobalSymbols and + MarkCompilerFunctionsAsExtern external codegen helper passes.""" + +import tvm +import tvm.testing +import numpy as np + + +def make_const(dtype, shape): + return tvm.relay.const(np.random.rand(*shape).astype(dtype)) + + +def make_consts(dtype, shapes): + return [make_const(dtype, shape) for shape in shapes] + + +metatable = { + "relay.Constant": make_consts( + "float16", + [ + (2304, 768), # 0 + (2304,), # 1 + (600, 32, 64), # 2 + ], + ), + "attributes": [{"relay_attrs": None}], +} + + +def inlined_mod(): + return tvm.parser.parse( + """ + #[version = "0.0.5"] + def @main(%x0 : Tensor[(1600, 768), float16], %x3 : Tensor[(600, 32, 64), float16]) -> (Tensor[(1600, 2304), float16], Tensor[(600, 32, 32), float16]) { + %0 = fn(%y_0_i0: Tensor[(1600, 768), float16], %y_0_i1: Tensor[(2304, 768), float16], %y_0_i2: Tensor[(2304), float16], + Inline=1, Compiler="cutlass", global_symbol="tvmgen_default_cutlass_main_0", Primitive=1) -> Tensor[(1600, 2304), float16] { + %4 = fn (%FunctionVar_0_0: Tensor[(1600, 768), float16], %FunctionVar_0_1: Tensor[(2304, 768), float16], %FunctionVar_0_2: Tensor[(2304), float16], + PartitionedFromPattern="nn.dense_add_", Composite="cutlass.dense_bias") -> Tensor[(1600, 2304), float16] { + %5 = nn.dense(%FunctionVar_0_0, %FunctionVar_0_1, units=2304); + add(%5, %FunctionVar_0_2) + }; + %4(%y_0_i0, %y_0_i1, %y_0_i2) + }; + %1 = %0(%x0, meta[relay.Constant][0], meta[relay.Constant][1]); + %2 = fn(%y_3_i0: Tensor[(600, 32, 64), float16], %y_3_i1: Tensor[(600, 32, 64), float16], + Inline=1, Compiler="cublas", global_symbol="tvmgen_default_cublas_main_3", Primitive=1) -> Tensor[(600, 32, 32), float16] { + %6 = fn (%FunctionVar_0_01: Tensor[(600, 32, 64), float16], %FunctionVar_0_11: Tensor[(600, 32, 64), float16], + PartitionedFromPattern="nn.batch_matmul_", Composite="cublas.batch_matmul") -> Tensor[(600, 32, 32), float16] { + nn.batch_matmul(%FunctionVar_0_01, %FunctionVar_0_11, out_dtype="float16", transpose_b=True) + }; + %6(%y_3_i0, %y_3_i1) + }; + %3 = %2(%x3, meta[relay.Constant][2]); + (%1, %3) + } + """, + "from_string", + None, + metatable, + ) + + +def expected_outlined_mod(): + return tvm.parser.parse( + """ + #[version = "0.0.5"] + def @main(%x0 : Tensor[(1600, 768), float16], %x3 : Tensor[(600, 32, 64), float16]) -> (Tensor[(1600, 2304), float16], Tensor[(600, 32, 32), float16]) { + %1 = @tvmgen_default_cutlass_main_0(%x0, meta[relay.Constant][0], meta[relay.Constant][1]); + %2 = fn(%y_3_i0: Tensor[(600, 32, 64), float16], %y_3_i1: Tensor[(600, 32, 64), float16], + Inline=1, Compiler="cublas", global_symbol="tvmgen_default_cublas_main_3", Primitive=1) -> Tensor[(600, 32, 32), float16] { + %6 = fn (%FunctionVar_0_01: Tensor[(600, 32, 64), float16], %FunctionVar_0_11: Tensor[(600, 32, 64), float16], + PartitionedFromPattern="nn.batch_matmul_", Composite="cublas.batch_matmul") -> Tensor[(600, 32, 32), float16] { + nn.batch_matmul(%FunctionVar_0_01, %FunctionVar_0_11, out_dtype="float16", transpose_b=True) + }; + %6(%y_3_i0, %y_3_i1) + }; + %3 = %2(%x3, meta[relay.Constant][2]); + (%1, %3) + } + + def @tvmgen_default_cutlass_main_0(%y_0_i0: Tensor[(1600, 768), float16], %y_0_i1: Tensor[(2304, 768), float16], %y_0_i2: Tensor[(2304), float16], + Inline=1, Compiler="cutlass", global_symbol="tvmgen_default_cutlass_main_0", Primitive=1) -> Tensor[(1600, 2304), float16] { + %4 = fn (%FunctionVar_0_0: Tensor[(1600, 768), float16], %FunctionVar_0_1: Tensor[(2304, 768), float16], %FunctionVar_0_2: Tensor[(2304), float16], + PartitionedFromPattern="nn.dense_add_", Composite="cutlass.dense_bias") -> Tensor[(1600, 2304), float16] { + %5 = nn.dense(%FunctionVar_0_0, %FunctionVar_0_1, units=2304); + add(%5, %FunctionVar_0_2) + }; + %4(%y_0_i0, %y_0_i1, %y_0_i2) + } + """, + "from_string", + None, + metatable, + ) + + +def expected_extern_mod(): + return tvm.parser.parse( + """ + #[version = "0.0.5"] + def @main(%x0 : Tensor[(1600, 768), float16], %x3 : Tensor[(600, 32, 64), float16]) -> (Tensor[(1600, 2304), float16], Tensor[(600, 32, 32), float16]) { + %1 = call_lowered(@tvmgen_default_cutlass_main_0, (%x0, meta[relay.Constant][0], meta[relay.Constant][1]), metadata=meta[attributes][0]); + %2 = fn(%y_3_i0: Tensor[(600, 32, 64), float16], %y_3_i1: Tensor[(600, 32, 64), float16], + Inline=1, Compiler="cublas", global_symbol="tvmgen_default_cublas_main_3", Primitive=1) -> Tensor[(600, 32, 32), float16] { + %6 = fn (%FunctionVar_0_01: Tensor[(600, 32, 64), float16], %FunctionVar_0_11: Tensor[(600, 32, 64), float16], + PartitionedFromPattern="nn.batch_matmul_", Composite="cublas.batch_matmul") -> Tensor[(600, 32, 32), float16] { + nn.batch_matmul(%FunctionVar_0_01, %FunctionVar_0_11, out_dtype="float16", transpose_b=True) + }; + %6(%y_3_i0, %y_3_i1) + }; + %3 = %2(%x3, meta[relay.Constant][2]); + (%1, %3) + } + + def @tvmgen_default_cutlass_main_0(%y_0_i0: Tensor[(1600, 768), float16], %y_0_i1: Tensor[(2304, 768), float16], %y_0_i2: Tensor[(2304), float16], + Extern=1) -> Tensor[(1600, 2304), float16] { + %4 = fn (%FunctionVar_0_0: Tensor[(1600, 768), float16], %FunctionVar_0_1: Tensor[(2304, 768), float16], %FunctionVar_0_2: Tensor[(2304), float16], + PartitionedFromPattern="nn.dense_add_", Composite="cutlass.dense_bias") -> Tensor[(1600, 2304), float16] { Review Comment: I see. So if I understand correctly, `PartitionedFromPattern` is a more fine-grained way to specify lowering decision like a `Dense+Add` fusion. Meanwhile, `Primitive` can specify larger granularity of function that can contain multiple functions with different `PartitionedFromFusions`. Yeah, I agree that `Composite` seems to serve very similar role with `PartitionFromPattern` and would like to remove this potential duplication in the near future. It seemed bit tricky tho since some BYOCs seem to rely on `Composite` quite heavily. -- 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. To unsubscribe, e-mail: [email protected] For queries about this service, please contact Infrastructure at: [email protected]
