LeiWang1999 commented on code in PR #15462: URL: https://github.com/apache/tvm/pull/15462#discussion_r1282774166
########## tests/python/relax/test_codegen_cudnn.py: ########## @@ -0,0 +1,255 @@ +# 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 tvm +import tvm.testing +import tvm.topi.testing +from tvm import relax +from tvm.relax.backend.contrib.cudnn import partition_for_cudnn +from tvm.relax.testing import get_relax_matmul_module +from tvm.script import relax as R + +from tvm.script.ir_builder import IRBuilder +from tvm.script.ir_builder import relax as relax_builder + + [email protected](autouse=True) +def reset_seed(): + np.random.seed(0) + + +has_cudnn = tvm.get_global_func("relax.ext.cudnn", False) + +cudnn_enabled = pytest.mark.skipif( + not has_cudnn, + reason="cuDNN not enabled.", +) + +pytestmark = [cudnn_enabled] + + +_activation_table = { + "none": None, + "relu": R.nn.relu, + "gelu": R.nn.gelu, + "silu": R.nn.silu, +} + + +def get_relax_conv2d_module( + data_shape, + weight_shape, + dtype, + with_bias=False, + activation=None, + residual_bin_op=None, + residual_activation=None, + data_layout="NHWC", + kernel_layout="OHWI", +): + with IRBuilder() as builder: + with relax_builder.function(): + R.func_name("main") + data = R.arg("data", R.Tensor(data_shape, dtype)) + weight = R.arg("weight", R.Tensor(weight_shape, dtype)) + if with_bias: + if data_layout == "NHWC": + bias = R.arg("bias", R.Tensor((1, 1, 1, weight_shape[0]), dtype)) + elif data_layout == "NCHW": + bias = R.arg("bias", R.Tensor((1, weight_shape[0], 1, 1), dtype)) + else: + raise ValueError("Unsupported data_layout: {}".format(data_layout)) + + with R.dataflow() as frame: + output = R.emit( + R.nn.conv2d( + data, + weight, + out_dtype=dtype, + padding=(1, 1), + data_layout=data_layout, + kernel_layout=kernel_layout, + ) + ) + if with_bias: + output = R.emit(output + bias) + if activation is not None: + output = R.emit(activation(output)) + if residual_bin_op is not None: + output = R.emit(residual_bin_op(output, data)) + if residual_activation is not None: + output = R.emit(residual_activation(output)) + R.output(output) + + R.func_ret_value(frame.output_vars[0]) + + func = builder.get() + return tvm.IRModule({"main": func}) + + +def get_result_with_relax_cudnn_offload(mod, np_inputs, cuda_graph=False): + mod = partition_for_cudnn(mod) + mod = relax.transform.RunCodegen()(mod) + return build_and_run(mod, np_inputs, "cuda", cuda_graph) + + +def build_and_run(mod, inputs_np, target, legalize=False, cuda_graph=False): + if legalize: + mod = relax.transform.LegalizeOps()(mod) + + dev = tvm.device(target, 0) + with tvm.transform.PassContext(config={"relax.backend.use_cuda_graph": cuda_graph}): + ex = relax.build(mod, target) + vm = relax.VirtualMachine(ex, dev) + f = vm["main"] + inputs = [tvm.nd.array(inp, dev) for inp in inputs_np] + + # For cuda graph, run the compiled function twice to make sure that we can launch the cached + # graph on the second run. + if cuda_graph: + f(*inputs) + + return f(*inputs).numpy() + + [email protected]( + "data_shape, weight_shape, dtype, with_bias, activation", + [ + # Regular + ((16, 32, 32, 16), (32, 3, 3, 16), "float16", False, "none"), + ], +) +def test_cudnn_partition_conv2d_without_bias( + data_shape, weight_shape, dtype, with_bias, activation +): + low, high = -1, 1 + data = np.random.randint(low, high, size=data_shape).astype(dtype) + weight = np.random.randint(low, high, size=weight_shape).astype(dtype) + bias = np.random.randint(low, high, size=(1, 1, 1, weight_shape[0])).astype(dtype) + activation = _activation_table[activation] + if with_bias: + args = (data, weight, bias) + else: + args = (data, weight) + mod = get_relax_conv2d_module( + data_shape, + weight_shape, + dtype, + with_bias=with_bias, + activation=activation, + ) + mod = partition_for_cudnn(mod) + assert ( + mod["main"].body.blocks[0].bindings[0].value.op.name_hint == "fused_relax_nn_conv2d_cudnn" + ) + + [email protected]( + "data_shape, weight_shape, dtype, with_bias, activation", + [ + # Regular + ((16, 32, 32, 16), (32, 3, 3, 16), "float32", False, "none"), + # Bias + ((16, 32, 32, 16), (32, 3, 3, 16), "float32", True, "none"), + # Bias+ReLU + ((16, 32, 32, 16), (32, 3, 3, 16), "float32", True, "relu"), + # Bias+ReLU+half + ((16, 32, 32, 16), (32, 3, 3, 16), "float16", True, "relu"), + ], +) +def test_conv2d_offload(data_shape, weight_shape, dtype, with_bias, activation): + input = np.random.randn(*data_shape).astype(dtype) + weight = np.random.randn(*weight_shape).astype(dtype) + + if with_bias: + oc = weight_shape[0] + # bias = np.random.randn(oc).astype(dtype) + # bias = bias.reshape((1, 1, 1, weight_shape[0])) + bias = np.ones((1, 1, 1, weight_shape[0])).astype(dtype) + args = (input, weight, bias) + else: + bias = None + args = (input, weight) + + activation = _activation_table[activation] + + mod = get_relax_conv2d_module( + data_shape, + weight_shape, + dtype, + with_bias=with_bias, + activation=activation, + ) + + out = get_result_with_relax_cudnn_offload(mod, args) + ref = build_and_run(mod, args, "llvm", legalize=True) + if dtype == "float16": + tvm.testing.assert_allclose(out, ref, rtol=1e-1, atol=1e-1) + else: + tvm.testing.assert_allclose(out, ref, rtol=1e-2, atol=1e-2) + + [email protected]( + "data_shape, weight_shape, dtype, with_bias, activation", + [ + # Regular + ((16, 16, 32, 32), (32, 16, 3, 3), "float32", False, "none"), + # Bias + ((16, 16, 32, 32), (32, 16, 3, 3), "float32", True, "none"), + # Bias+ReLU + ((16, 16, 32, 32), (32, 16, 3, 3), "float32", True, "relu"), + # Bias+ReLU+half + ((16, 16, 32, 32), (32, 16, 3, 3), "float16", True, "relu"), + ], +) +def test_conv2d_nchw_oihw_offload(data_shape, weight_shape, dtype, with_bias, activation): + input = np.random.randn(*data_shape).astype(dtype) + weight = np.random.randn(*weight_shape).astype(dtype) Review Comment: I'll have a try, but what's the benefit? -- This is an automated message from the Apache Git Service. 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