srkreddy1238 commented on code in PR #13837:
URL: https://github.com/apache/tvm/pull/13837#discussion_r1086400714
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python/tvm/relay/op/contrib/clml.py:
##########
@@ -387,3 +393,769 @@ def __exit__(self, ptype, value, trace):
self.op.reset_attr(self.attr_key)
if self.older_attr:
self.op.set_attr(self.attr_key, self.older_attr)
+
+
+class CLMLGetSubModuleSrc:
+ """Generates CLML API one CLML sub module out ot global TVM module"""
+
+ def __init__(self, cmod):
+ """Initialize
+ Parameters
+ ----------
+ cmod : Module
+ The CLML sub module from TVM module
+ """
+ self.cmod = cmod
+ self.codegen = None
+ self.nodes = None
+ self.node_map = {}
+ self.input_meta = []
+ self.output_meta = []
+ self.clml_code = []
+ self.sub_module_name = None
+
+ self.MakeCLMLTensor = Template(
+ """auto $name = runner.MakeCLMLTensor
+ (std::vector<size_t>({$shape}), "$dtype", $layout);"""
+ )
+ self.MapInsert = Template("""runner.storage_map.insert({"$nid",
$tensor_desc});""")
+ self.MakeConv2D = Template(
+ """
+ // Convolution / Depthwise Convolution
+ runner.MakeConv2D($input_tensor,
+ $weight_tensor,
+ $bias_tensor,
+ $output_tensor,
+ std::vector<cl_uint>({$padding}),
+ std::vector<cl_uint>({$dilation}),
+ std::vector<cl_uint>({$strides}),
+ $groups,
+ $mode,
+ $activation,
+ $has_bias,
+ $has_act,
+ "$dtype");"""
+ )
+ self.MakeConv2DWithBN = Template(
+ """
+ // Batchnorm
+ runner.MakeConv2DWithBN($input_tensor,
+ $weight_tensor,
+ $bias_tensor,
+ $output_tensor,
+ $bn_scale_tensor,
+ $bn_bias_tensor,
+ $bn_mean_tensor,
+ $bn_var_tensor,
+ std::vector<float> ({$bn_attrs}),
+ std::vector<cl_uint> ({$padding}),
+ std::vector<cl_uint> ({$dilation}),
+ std::vector<cl_uint> ({$strides}),
+ $groups,
+ $mode,
+ $activation,
+ $has_bias,
+ $has_act,
+ "$dtype");"""
+ )
+ self.MakeRelu = Template(
+ """
+ // Relu / Relu6
+ runner.MakeRelu($input_tensor, $output_tensor, $relu_type, "$dtype");
+ """
+ )
+ self.MakeBN = Template(
+ """
+ // Batchnorm
+ runner.MakeBatchNorm($input_tensor,
+ $output_tensor,
+ $bn_scale_tensor,
+ $bn_bias_tensor,
+ $bn_mean_tensor,
+ $bn_var_tensor,
+ std::vector<float> ({$bn_attrs}), "$dtype");"""
+ )
+ self.MakePool2D = Template(
+ """
+ // Pool2D
+ runner.MakePool2D($input_tensor,
+ $output_tensor,
+ std::vector<cl_uint> ({$pool_size}),
+ std::vector<cl_uint> ({$strides}),
+ std::vector<cl_uint> ({$padding}),
+ "$pool_type", "$dtype");"""
+ )
+ self.MakeGlobalPool2D = Template(
+ """
+ // GlobalPool2D
+ runner.MakeGlobalPool2D($input_tensor,
+ $output_tensor,
+ std::vector<cl_uint> ({$in_shape}),
+ "$pool_type", "$dtype");"""
+ )
+ self.MakeReshape = Template(
+ """
+ // Reshape
+ runner.MakeReshape($input_tensor,
+ $output_tensor, "$dtype");"""
+ )
+ self.MakeConcatenate = Template(
+ """
+ // Concatinate
+ runner.MakeConcatenate(
+ std::vector<std::shared_ptr<cl_ml_tensor_memory_desc_qcom>>
({$in_list}),
+ $output_tensor,
+ $axis, "$dtype");"""
+ )
+ self.MakeDense = Template(
+ """
+ // Dense
+ runner.MakeDense($input_tensor,
+ $weight_tensor,
+ $output_tensor,
+ $bias_tensor, "$dtype");"""
+ )
+ self.MakeSoftMax = Template(
+ """
+ // Softmax
+ runner.MakeSoftMax($input_tensor,
+ $output_tensor, "$dtype");"""
+ )
+ self.MakePad = Template(
+ """
+ // Pad
+ runner.MakePad($input_tensor,
+ $output_tensor,
+ "$pad_mode",
+ std::vector<cl_uint> ({$padding}), "$dtype");"""
+ )
+ self.MakeBatchFlatten = Template(
+ """
+ // BatchFlatten
+ runner.MakeBatchFlatten($input_tensor,
+ $output_tensor, "$dtype");"""
+ )
+ self.MakeClip = Template(
+ """
+ // Clip
+ runner.MakeClip($input_tensor,
+ $output_tensor,
+ $a_max,
+ $a_min,
+ "$dtype");"""
+ )
+ self.MakeBinaryOp = Template(
+ """
+ // BinaryOp
+ runner.MakeBinaryOp($input_a,
+ $input_b,
+ $output_tensor,
+ "$op", "$dtype");"""
+ )
+
+ self.MakeHeader = Template(
+ """
+ CLMLRunner $module(std::string name,
+ ToolArgs& args,
+ cl_platform_id arg_platform_id,
+ cl_context arg_context,
+ cl_device_id arg_device_id,
+ cl_command_queue arg_queue) {
+ CLMLRunner runner = CLMLRunner(name,
+ args,
+ arg_platform_id,
+ arg_context,
+ arg_device_id,
+ arg_queue);
+ runner.MakeUnusedTensor();
+ """
+ )
+
+ self.MakeFooter = Template(
+ """
+ return runner;
+ }
+ """
+ )
+
+ self.MakeMetaInfo = Template(
+ "runner.SetMetaInfo("
+ '"Subgraph Name: $name\\n Input Count : $input_count\\n'
+ " Output Count : $output_count\\n"
+ ' Input MetaInfo\\n$input_meta\\n Output
MetaInfo\\n$output_meta");'
+ )
+
+ self.MakeInputMetaInfo = Template(
+ " Input: $in_name\\n Dtype : $dtype\\n
Shape : [$shape]"
+ )
+
+ self.MakeOutputMetaInfo = Template(
+ " Output: $out_name\\n Dtype : $dtype\\n
Shape : [$shape]"
+ )
+
+ def get_src(self):
+ """Returns pair of sub module name and the generated source"""
+
+ self.codegen = json.loads(self.cmod.get_source("json"))
+ self.sub_module_name = self.codegen["symbol"]
+ self.nodes = self.codegen["nodes"]
+
self.clml_code.append(self.MakeHeader.substitute(module=self.sub_module_name))
+
+ def get_tensor_from_map(
+ node_seq, shape=None, layout="CL_TENSOR_LAYOUT_OPTIMAL_QCOM",
dtype="float32"
+ ):
+ if node_seq in self.node_map:
+ return self.node_map[node_seq]
+ else:
+ node = self.nodes[node_seq]
+ dtype = str(node["attrs"]["dtype"][0][0])
+ if shape is None:
+ shape = str(tuple(node["attrs"]["shape"][0][0]))[1:-1]
+
+ self.clml_code.append(
+ self.MakeCLMLTensor.substitute(
+ name=node["name"], shape=shape, dtype=dtype,
layout=layout
+ )
+ )
+ self.clml_code.append(
+ self.MapInsert.substitute(nid=node["name"],
tensor_desc=node["name"])
+ )
+ if self.nodes[node_seq]["op"] == "const":
+ self.clml_code.append(
+
Template('runner.consts.push_back("$nid");').substitute(nid=node["name"])
+ )
+ self.node_map[node_seq] = node["name"]
+ return node["name"]
+
+ def make_output_tensor(
+ node, node_seq, shape=None,
layout="CL_TENSOR_LAYOUT_OPTIMAL_QCOM", dtype="float32"
+ ):
+ if dtype is None:
+ dtype = str(node["attrs"]["dtype"][0][0])
+ if shape is None:
+ shape = str(tuple(node["attrs"]["shape"][0][0]))[1:-1]
+ node_out_name = self.sub_module_name + "_" + "layer_out_" +
str(node_seq)
+ self.clml_code.append(
+ self.MakeCLMLTensor.substitute(
+ name=node_out_name,
+ shape=shape,
+ dtype=dtype,
+ layout="CL_TENSOR_LAYOUT_OPTIMAL_QCOM",
+ )
+ )
+ return node_out_name
+
+ for node_seq, node in enumerate(self.nodes):
+ if node["op"] == "input":
+ self.clml_code.append("// Input Node")
+ dtype = str(node["attrs"]["dtype"][0][0])
+ shape = str(tuple(node["attrs"]["shape"][0][0]))[1:-1]
+ node_out_name = self.sub_module_name + "_" + "input_" +
str(node_seq)
+ self.clml_code.append(
+ self.MakeCLMLTensor.substitute(
+ name=node_out_name,
+ shape=shape,
+ dtype=dtype,
+ layout="CL_TENSOR_LAYOUT_OPTIMAL_QCOM",
+ )
+ )
+ self.clml_code.append(
+ self.MapInsert.substitute(nid=node_out_name,
tensor_desc=node_out_name)
+ )
+ self.clml_code.append(
+
Template("runner.inputs.push_back($clml_input);").substitute(
+ clml_input=node_out_name
+ )
+ )
+ self.node_map[node_seq] = node_out_name
+ self.input_meta.append(
+ self.MakeInputMetaInfo.substitute(
+ in_name=node_out_name, dtype=dtype, shape=shape
+ )
+ )
+ elif node["op"] == "kernel":
+ self.clml_code.append("// Kernel Node : " + node["name"])
+ if node["name"] == "nn.conv2d" or node["name"] ==
"nn.depthwise_conv2d":
+ if "padding" in node["attrs"]:
+ padding = str(tuple(int(x) for x in
node["attrs"]["padding"][0]))[1:-1]
+ else:
+ padding = "0, 0, 0, 0"
+ dilation = str(tuple(int(x) for x in
node["attrs"]["dilation"][0]))[1:-1]
+ strides = str(tuple(int(x) for x in
node["attrs"]["strides"][0]))[1:-1]
+ groups = node["attrs"]["groups"][0][0]
+ if node["name"] == "nn.conv2d":
+ mode = "CL_CONVOLUTION_MODE_CONVOLUTION_QCOM"
+ else:
+ mode = "CL_CONVOLUTION_MODE_DEPTHWISE_QCOM"
+ activation = "CL_ACTIVATION_RELU"
+ has_act = False
+ if "activation_type" in node["attrs"]:
+ has_act = True
+ activation = node["attrs"]["activation_type"][0][0]
+ if activation == "relu":
+ activation = "CL_ACTIVATION_RELU"
+ elif activation == "relu6":
+ activation = "CL_ACTIVATION_RELU6"
+ else:
+ RuntimeError("Unknown activation:" + activation)
+ has_bias = bool((node["inputs"] == 3) or (node["inputs"]
== 7))
+ has_bn = bool((node["inputs"] == 6) or (node["inputs"] ==
7))
+ input_tensor = get_tensor_from_map(node["inputs"][0][0])
+ weight_tensor = get_tensor_from_map(node["inputs"][1][0])
+ if not has_bias:
+ bias_tensor = "runner.unusedTensor"
+ else:
+ bias_tensor = get_tensor_from_map(node["inputs"][2][0])
+
+ node_out_name = make_output_tensor(node, node_seq)
+
+ if not has_bn:
+ self.clml_code.append(
+ self.MakeConv2D.substitute(
+ input_tensor=input_tensor,
+ weight_tensor=weight_tensor,
+ bias_tensor=bias_tensor,
+ output_tensor=node_out_name,
+ padding=padding,
+ dilation=dilation,
+ strides=strides,
+ groups=groups,
+ mode=mode,
+ activation=activation,
+ has_bias="true" if has_bias else "false",
+ has_act="true" if has_act else "false",
+ dtype=node["attrs"]["dtype"][0][0],
+ )
+ )
+ else:
+ bn_index = 3 if has_bias else 2
+ bn_attrs = tuple(node["attrs"]["batchnorm"][0][0])
+ axis = bn_attrs[0]
+ bn_shape = [1, 1, 1, 1]
+ bn_node = self.nodes[node["inputs"][bn_index][0]]
+ bn_shape[axis] = bn_node["attrs"]["shape"][0][0]
+
+ bn_scale_tensor = get_tensor_from_map(
+ node["inputs"][bn_index][0],
+ shape=str(tuple(bn_shape))[1:-1],
+ dtype=dtype,
+ )
+
+ bn_bias_tensor = get_tensor_from_map(
+ node["inputs"][bn_index + 1][0],
+ shape=str(tuple(bn_shape))[1:-1],
+ dtype=dtype,
+ )
+
+ bn_mean_tensor = get_tensor_from_map(
+ node["inputs"][bn_index + 2][0],
+ shape=str(tuple(bn_shape))[1:-1],
+ dtype=dtype,
+ )
+
+ bn_var_tensor = get_tensor_from_map(
+ node["inputs"][bn_index + 3][0],
+ shape=str(tuple(bn_shape))[1:-1],
+ dtype=dtype,
+ )
+
+ self.clml_code.append(
+ self.MakeConv2DWithBN.substitute(
+ input_tensor=input_tensor,
+ weight_tensor=weight_tensor,
+ bias_tensor=bias_tensor,
+ output_tensor=node_out_name,
+ bn_scale_tensor=bn_scale_tensor,
+ bn_bias_tensor=bn_bias_tensor,
+ bn_mean_tensor=bn_mean_tensor,
+ bn_var_tensor=bn_var_tensor,
+ bn_attrs=str(bn_attrs)[1:-1],
+ padding=padding,
+ dilation=dilation,
+ strides=strides,
+ groups=groups,
+ mode=mode,
+ activation=activation,
+ has_bias="true" if has_bias else "false",
+ has_act="true" if has_act else "false",
+ dtype=node["attrs"]["dtype"][0][0],
+ )
+ )
+ elif node["name"] == "nn.relu6" or node["name"] == "nn.relu":
+ input_tensor = get_tensor_from_map(node["inputs"][0][0])
+ node_out_name = make_output_tensor(node, node_seq)
+ relu_type = (
+ "CL_ACTIVATION_RELU" if node["name"] == "nn.relu" else
"CL_ACTIVATION_RELU6"
+ )
+ self.clml_code.append(
+ self.MakeRelu.substitute(
+ input_tensor=input_tensor,
+ output_tensor=node_out_name,
+ relu_type=relu_type,
+ dtype=node["attrs"]["dtype"][0][0],
+ )
+ )
+ elif node["name"] == "nn.batch_norm":
+ bn_attrs = tuple(node["attrs"]["batchnorm"][0][0])
+ axis = bn_attrs[0]
+ bn_shape = [1, 1, 1, 1]
+ bn_node = self.nodes[node["inputs"][0][0]]
+ bn_shape[axis] = bn_node["attrs"]["shape"][0][0]
+ bn_scale_tensor = get_tensor_from_map(
+ node["inputs"][0][0],
shape=str(tuple(bn_shape))[1:-1], dtype=dtype
+ )
+ bn_bias_tensor = get_tensor_from_map(
+ node["inputs"][1][0],
shape=str(tuple(bn_shape))[1:-1], dtype=dtype
+ )
+ bn_mean_tensor = get_tensor_from_map(
+ node["inputs"][2][0],
shape=str(tuple(bn_shape))[1:-1], dtype=dtype
+ )
+ bn_var_tensor = get_tensor_from_map(
+ node["inputs"][3][0],
shape=str(tuple(bn_shape))[1:-1], dtype=dtype
+ )
+
+ input_tensor = get_tensor_from_map(node["inputs"][0][0])
+ node_out_name = make_output_tensor(node, node_seq)
+
+ self.clml_code.append(
+ self.MakeBN.substitute(
+ input_tensor=input_tensor,
+ output_tensor=node_out_name,
+ bn_scale_tensor=bn_scale_tensor,
+ bn_bias_tensor=bn_bias_tensor,
+ bn_mean_tensor=bn_mean_tensor,
+ bn_var_tensor=bn_var_tensor,
+ bn_attrs=str(bn_attrs)[1:-1],
+ dtype=node["attrs"]["dtype"][0][0],
+ )
+ )
+ elif node["name"] in ["nn.max_pool2d", "nn.avg_pool2d",
"nn.l2_pool2d"]:
+ input_tensor = get_tensor_from_map(node["inputs"][0][0])
+ node_out_name = make_output_tensor(node, node_seq)
+ pool_size = str(tuple(int(x) for x in
node["attrs"]["pool_size"][0]))[1:-1]
+ strides = str(tuple(int(x) for x in
node["attrs"]["strides"][0]))[1:-1]
+ padding = str(tuple(int(x) for x in
node["attrs"]["padding"][0]))[1:-1]
+ self.clml_code.append(
+ self.MakePool2D.substitute(
+ input_tensor=input_tensor,
+ output_tensor=node_out_name,
+ pool_size=pool_size,
+ strides=strides,
+ padding=padding,
+ pool_type=node["name"],
+ dtype=node["attrs"]["dtype"][0][0],
+ )
+ )
+ elif node["name"] in ["nn.global_max_pool2d",
"nn.global_avg_pool2d"]:
+ input_tensor = get_tensor_from_map(node["inputs"][0][0])
+ node_out_name = make_output_tensor(node, node_seq)
+ in_node = self.nodes[node["inputs"][0][0]]
+ in_shape =
str(tuple(in_node["attrs"]["shape"][0][0]))[1:-1]
+ self.clml_code.append(
+ self.MakeGlobalPool2D.substitute(
+ input_tensor=input_tensor,
+ output_tensor=node_out_name,
+ in_shape=in_shape,
+ pool_type=node["name"],
+ dtype=node["attrs"]["dtype"][0][0],
+ )
+ )
+ elif node["name"] == "reshape":
+ input_tensor = get_tensor_from_map(node["inputs"][0][0])
+ node_out_name = make_output_tensor(node, node_seq)
+ self.clml_code.append(
+ self.MakeReshape.substitute(
+ input_tensor=input_tensor,
+ output_tensor=node_out_name,
+ dtype=node["attrs"]["dtype"][0][0],
+ )
+ )
+ elif node["name"] == "concatenate":
+ input_len = len(node["inputs"])
+ in_list = str(
+ [get_tensor_from_map(node["inputs"][x][0]) for x in
range(input_len)]
+ )[1:-1]
+ node_out_name = make_output_tensor(node, node_seq)
+ axis = node["attrs"]["axis"][0][0]
+ self.clml_code.append(
+ self.MakeConcatenate.substitute(
+ in_list=in_list,
+ output_tensor=node_out_name,
+ axis=axis,
+ dtype=node["attrs"]["dtype"][0][0],
+ )
+ )
+ elif node["name"] == "nn.dense":
+ in_node = self.nodes[node["inputs"][0][0]]
+ in_shape = tuple(in_node["attrs"]["shape"][0][0])
+ wt_shape = tuple(in_node["attrs"]["shape"][0][0])
+ input_tensor = get_tensor_from_map(
+ node["inputs"][0][0], shape=str(tuple(1, in_shape[1],
1, 1))[1:-1]
+ )
+ weight_tensor = get_tensor_from_map(
+ node["inputs"][1][0],
+ shape=str(tuple(wt_shape[0], wt_shape[1], 1, 1))[1:-1],
+ )
+ if len(node["inputs"]) == 3:
+ bias_tensor = "runner.unusedTensor"
+ else:
+ bias_tensor = get_tensor_from_map(node["inputs"][2][0])
+
+ node_out_name = make_output_tensor(
+ node, node_seq, shape=str(tuple(1, wt_shape[0], 1,
1))[1:-1]
+ )
+ self.clml_code.append(
+ self.MakeDense.substitute(
+ input_tensor=input_tensor,
+ weight_tensor=weight_tensor,
+ output_tensor=node_out_name,
+ bias_tensor=bias_tensor,
+ dtype=node["attrs"]["dtype"][0][0],
+ )
+ )
+ elif node["name"] == "nn.softmax":
+ input_tensor = get_tensor_from_map(node["inputs"][0][0])
+ node_out_name = make_output_tensor(node, node_seq)
+ self.clml_code.append(
+ self.MakeSoftMax.substitute(
+ input_tensor=input_tensor,
+ output_tensor=node_out_name,
+ dtype=node["attrs"]["dtype"][0][0],
+ )
+ )
+ elif node["name"] == "nn.pad":
+ input_tensor = get_tensor_from_map(node["inputs"][0][0])
+ node_out_name = make_output_tensor(node, node_seq)
+ pad_mode = node["attrs"]["pad_mode"][0][0]
+ padding = str(tuple(int(x) for x in
node["attrs"]["pad_width"][0]))[1:-1]
+ self.clml_code.append(
+ self.MakePad.substitute(
+ input_tensor=input_tensor,
+ output_tensor=node_out_name,
+ pad_mode=pad_mode,
+ padding=padding,
+ dtype=node["attrs"]["dtype"][0][0],
+ )
+ )
+ elif node["name"] == "nn.batch_flatten":
+ input_tensor = get_tensor_from_map(node["inputs"][0][0])
+ node_out_name = make_output_tensor(node, node_seq)
+ self.clml_code.append(
+ self.MakeBatchFlatten.substitute(
+ input_tensor=input_tensor,
+ output_tensor=node_out_name,
+ dtype=node["attrs"]["dtype"][0][0],
+ )
+ )
+ elif node["name"] == "clip":
+ input_tensor = get_tensor_from_map(node["inputs"][0][0])
+ node_out_name = make_output_tensor(node, node_seq)
+ a_max = node["attrs"]["a_max"][0][0]
+ a_min = node["attrs"]["a_min"][0][0]
+ self.clml_code.append(
+ self.MakeClip.substitute(
+ input_tensor=input_tensor,
+ output_tensor=node_out_name,
+ a_max=a_max,
+ a_min=a_min,
+ dtype=node["attrs"]["dtype"][0][0],
+ )
+ )
+ elif node["name"] in [
+ "add",
+ "subtract",
+ "multiply",
+ "minimum",
+ "maximum",
+ "divide",
+ ]:
+ input_a = get_tensor_from_map(node["inputs"][0][0])
+ input_b = get_tensor_from_map(node["inputs"][1][0])
+ node_out_name = make_output_tensor(node, node_seq)
+ self.clml_code.append(
+ self.MakeBinaryOp.substitute(
+ input_a=input_a,
+ input_b=input_b,
+ output_tensor=node_out_name,
+ op=node["name"],
+ dtype=node["attrs"]["dtype"][0][0],
+ )
+ )
+ else:
+ RuntimeError("Unsupported Op:" + node["name"])
+ self.clml_code.append(
+ self.MapInsert.substitute(nid=node_out_name,
tensor_desc=node_out_name)
+ )
+ self.node_map[node_seq] = node_out_name
+
+ elif node["op"] != "const":
+ print("Unknown Node type:", node["op"])
+
+ # Populate outputs
+ out_nodes = self.codegen["heads"]
+ self.clml_code.append("// Populate outputs")
+ for nid_triple in out_nodes:
+ nid = nid_triple[0]
+ out_node = self.nodes[nid]
+ dtype = str(out_node["attrs"]["dtype"][0][0])
+ shape = str(tuple(out_node["attrs"]["shape"][0][0]))[1:-1]
+ out_name = self.sub_module_name + "_" + "layer_out_" + str(nid)
+ self.clml_code.append(
+ Template(
+ 'runner.outputs.insert({"$out_name",
runner.storage_map["$out_name"]});'
+ ).substitute(out_name=out_name)
+ )
+ self.clml_code.append(
+ Template('runner.outputs_dtypes.insert({"$out_name",
"$dtype"});').substitute(
+ out_name=out_name, dtype=dtype
+ )
+ )
+ self.clml_code.append(
+ Template(
+ "runner.outputs_shapes.insert" '({"$out_name",
std::vector<size_t>({$shape})});'
+ ).substitute(out_name=out_name, shape=shape)
+ )
+ self.output_meta.append(
+ self.MakeOutputMetaInfo.substitute(out_name=out_name,
dtype=dtype, shape=shape)
+ )
+
+ # Mem allocation & Param copy
+ self.clml_code.append("// Allocate Tensor Memory and copy params")
+ self.clml_code.append("runner.AllocateMemAndPopulateParams();")
+
+ # Meta data preparation
+ self.clml_code.append(
+ self.MakeMetaInfo.substitute(
+ name=self.sub_module_name,
+ input_count=len(self.input_meta),
+ output_count=len(self.output_meta),
+ input_meta="\n".join(self.input_meta),
+ output_meta="\n".join(self.output_meta),
+ )
+ )
+
+ self.clml_code.append(self.MakeFooter.substitute())
+ return (self.sub_module_name, self.clml_code)
+
+
+class CLMLGenSrc:
+ """Generates CLML API source given a TVM compiled mod"""
+
+ def __init__(self, libm):
+ """Initialize
+ Parameters
+ ----------
+ libm : Module
+ Compiled relay module
+ """
+ self.libm = libm
+ self.gen_src = []
+ self.clml_modules = None
+ self.clml_builds = {}
+ self.codegen = None
+ self.nodes = None
+
+ self.MakeFileHeader = Template(
+ """/*
+ * 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.
+ */
+
+ /*!
+ * \\file clml_models.cc
+ * \\brief CLML models for all subgraph in given TVM module.
+ */
+
+ // AUTO GENERATED BY TOOL (clml_codegen.py), PLEASE DO NOT CHANGE THIS
FILE!
Review Comment:
You mean ```clml_codegen.py``` ? Its correct.
```scripts/clml_codegen.py``` generates ```clml_model.cc``` (a source file
corresponding to TVM module). And this generated file is part of ```clml_run```
compilation.
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