echuraev commented on code in PR #13837:
URL: https://github.com/apache/tvm/pull/13837#discussion_r1087810934
##########
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]
Review Comment:
Thank you for the clarification.
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