srkreddy1238 commented on code in PR #10243: URL: https://github.com/apache/tvm/pull/10243#discussion_r890681293
########## python/tvm/relay/op/contrib/clml.py: ########## @@ -0,0 +1,310 @@ +# 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. +# pylint: disable=invalid-name, unused-argument +"""CLML Library supported operators.""" +import tvm + +from tvm import relay +from tvm._ffi import register_func +from tvm.relay import transform +from tvm.relay.build_module import bind_params_by_name + +from ...dataflow_pattern import wildcard, is_op, is_constant, is_tuple_get_item +from .register import register_pattern_table +from ..strategy.generic import is_depthwise_conv2d + + +def is_clml_runtime_enabled(): + """Check if the CLML graph runtime is present. + + Returns + ------- + ret: bool + True if present, False if not. + """ + check_enabled = tvm.get_global_func("relay.op.is_clml_runtime_enabled", True) + if check_enabled: + return check_enabled() + return False + + +def partition_for_clml(mod, params=None): + """Partition the graph greedily offloading supported + operators to CLML Library. + + Parameters + ---------- + mod : Module + The module to run passes on. + params : Optional[Dict[str, NDArray]] + Constant input parameters. + + Returns + ------- + ret : annotated and partitioned module. + """ + + if params: + mod["main"] = bind_params_by_name(mod["main"], params) + + seq = tvm.transform.Sequential( + [ + transform.InferType(), + transform.FoldConstant(), + transform.MergeComposite(clml_pattern_table()), + transform.AnnotateTarget("clml", False), + transform.MergeCompilerRegions(), + transform.PartitionGraph(), + ] + ) + + result_mod = seq(mod) + return result_mod + + +@register_func("relay.ext.clml.optimize") +def preprocess_module(mod): + """ + Pre-process a module containing functions ready for CLML codegen. For now we enforce OHWI + kernel layout and fold the transforms away. + + Parameters + ---------- + mod : Module + The module to run passes on. + + Returns + ------- + preprocessed_mod : The processed module. + """ + + def convert_layout_conv2d(conv2d_function): + def convert_conv(attrs, inputs, tinfos, desired_layouts): + new_attrs = dict(attrs) + data_info = tinfos[0] + weight_info = tinfos[1] + desired_data_layout, desired_kernel_layout = map(str, desired_layouts) + new_attrs["data_layout"] = desired_data_layout + new_attrs["kernel_layout"] = desired_kernel_layout + + if is_depthwise_conv2d( + data_info.shape, + attrs["data_layout"], + weight_info.shape, + attrs["kernel_layout"], + attrs["groups"], + ): + dkl = desired_kernel_layout + new_attrs["kernel_layout"] = dkl[1] + dkl[0] + dkl[2] + dkl[3] + return conv2d_function(*inputs, **new_attrs) + + return convert_conv + + with OpAttrContext( + "nn.conv2d", "FTVMConvertOpLayout", convert_layout_conv2d(tvm.relay.nn.conv2d) + ): + seq = tvm.transform.Sequential( + [ + transform.ConvertLayout({"nn.conv2d": ["NCHW", "OIHW"]}), + transform.FoldConstant(), + ] + ) + preprocessed_mod = seq(mod) + return preprocessed_mod + + +@register_pattern_table("clml") +def clml_pattern_table(): + """Get the CLML pattern table.""" + + def conv_pattern(): + """Create a convolution pattern. + + Returns + ------- + pattern : dataflow_pattern.AltPattern + Denotes the convolution pattern. + """ Review Comment: Done -- 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]
