S100 OpenExplorer

# #HBDK Tool API Reference

## #hbdk api introduction

General restrictions:

The maximum number of model inputs/outputs should not exceed 256.

The dimensions of all tensors in the model should not exceed 10.

The name of the input and output of the model must be unique.

The data types we support are ui8/si8/si16/ui32/si32/si64/float/bool. For specific data types supported by a certain operator, please refer to the operator constraint document.

### #Module: hbdk4.compiler.onnx

#### #`export`(proto onnx.ModelProto, *, name Optional[str] = None) -> Module

`export an onnx module to hbir mlir Args: proto (onnx.ModelProto): onnx protobuf name (Optional[str], optional): rename the onnx function. "None" means using onnx graph name Returns: Module: a helper for mlir.Module that manages hbdk operations`

#### #`statistics`(proto onnx.ModelProto)

`Print op statics of given onnx module. Args: proto (onnx.ModelProto): onnx protobuf`

### #Module: hbdk4.compiler.torch

#### #`export`( jit torch.jit.ScriptModule, example_input Any, *, name Optional[str] = None, input_names List[str] = None, output_names List[str] = None, lower_non_tensor bool = True) -> Module

`export a torch.jit.ScriptModule to hbir mlir Args: jit (torch.jit.ScriptModule): a ScriptModule created from torch.jit.trace example_input (Any): input format of the ScriptModule, used for analysis name (Optional[str], optional): rename the function. "None" means uses the name recorded in ScriptModule. input_names (List[str], optional): rename inputs. "None" means uses input names recorded in ScriptModule. output_names (List[str], optional): rename outputs. "None" means uses input names recorded in ScriptModule. lower_non_tensor (bool, optional): flatten the pytree in ScriptModule input and return or keep the tree in hbir. Returns: Module: a helper for mlir.Module that manages hbdk operations`

#### #`statistics`(jit torch.jit.ScriptModule, example_input Any)

`Print op statics of given ScriptModule module. Args: jit (torch.jit.ScriptModule): a ScriptModule created from torch.jit.trace`

### #Module: hbdk4.compiler.apis

#### #`load`(path str) -> Module

`load mlir text or bytecode to mlir.Module Args: * path (str): A filesystem path to load bytecode ended with ".bc" Raises: * ValueError: When "path" is not ended with ".bc" Returns: * Module: a helper for mlir.Module that manages hbdk operations`

#### #`save`(m Module, path str) -> None

`save mlir.Module to mlir bytecode Args: * m (Module): a helper for mlir.Module that manages hbdk operations * path (str): A filesystem path to save bytecode. Must end with ".bc" Raises: * ValueError: When "path" is not ended with ".bc"`

#### #`convert`( m Module, march Union[MarchBase, str], advice=False, advice_path="", **kwargs) -> Module

`Convert hbir to backend ir. Args: * m (Module): a helper for mlir.Module that manages hbdk operations * march (Union[MarchBase, str]): BPU march, options are "bayes", "nash-e", "nash-m", "nash-p" * advice (bool, optional): Set whether to enable op check * advice_path (str, optional): path to store op check info. Defaults to empty, print op check info directly without saving the file`

#### #`statistics`(m Module) -> list

`Print op statics of given mlir module. Args: * m (Module): a helper for mlir.Module that manages hbdk operations`

#### #`link`(hbo_list List[Hbo], output_path str, desc Optional[str] = None)

`Link hbo to hbm Args: * hbo_list (List[Hbo): A List of Hbo, which is general by compile * output_path (str, required): A filesystem path to save hbm. Must ends with ".hbm" * desc (str optional): Generate a description of hbm when linking, if this parameter is not given, the description information of hbm will not be generated`

#### #`compile`( m Module, path str, march Union[MarchBase, str], opt int = 2, jobs int = 4, max_time_per_fc float = 0.0, debug bool = False, hbdk3_compatible_mode bool = False, progress_bar bool = False, advice float = 0.0, balance int = 100, input_no_padding bool = False, output_no_padding bool = False) -> Union[Hbm, Hbo]

`Compile hbir module to hbm or hbo. If the suffix of the output is 'hbo', it will compile to generate an hbo file. Afterward, you need to call the link function to perform linking or packing operations. Args: * m (Module): a helper for mlir.Module that manages hbdk operations * march (Union[MarchBase, str]): BPU march, options are "bayes", "nash-e", "nash-m", "nash-p" * path (str, required): A filesystem path to save hbm or hbo. Must ends with ".hbm" or ".hbo" * opt (int, optional): Optimization level. Defaults to 2. * jobs (int, optional): Number of threads launched during compiler optimization. Defaults to 4. * max_time_per_fc (float, optional): Set maximum time constraint (unit:us) for per funccall. * debug (bool, optional): Set whether to contain debug info in HBM * hbdk3_compatible_mode (bool, optional): Set whether to compile in hbdk3 compatible mode, True use hbm3 and False use hbm4 * progress_bar(bool, optional): Set whether to show progress bar * advice(float, optional): Print advice on ops that take longer more than the specified time (unit:us) * balance(int, optional): Specify a integer (recommend 2) to balance cycles and DDR access. The integer should be between 0 (minimal DDR access) and 100 (minimal cycles) * input_no_padding (bool, optional): Set whether model input is native without padding * output_no_padding (bool, optional): Set whether model output is native without padding Raises: * ValueError: When "path" is not ended with ".hbm" * ValueError: When "balance" is not in range of [0,100]`

#### #`visualize`( m Module, onnx_file Optional[str] = None, use_netron Optional[bool] = False)

`Generate visualizable mlir onnx and view it in netron. Args: * m (Module): a helper for mlir.Module that manages hbdk operations * onnx_file (str): path to store onnx proto if it is None then create directory in /tmp * use_netron (bool): if True, start netron server to view onnx, otherwise just generate onnx`

### #Module: hbdk4.compiler.hbm_tools

#### #`hbm_extract_desc`(model str) -> dict

`Extract hbm desc info DEPRECATED: It will be removed in the future Args: * model (str): Hbm model file name Return: * desc_dict (dict): Hbm desc info`

#### #`hbm_update_desc`(model str, desc_dict dict)

`Update hbm desc info DEPRECATED: It will be removed in the future Args: * model (str): Hbm model file name * desc_dict (dict): Hbm desc info`

#### #`hbm_perf`(model str, output_dir str = None)

`HBM performance analysis Args: * model (str): Hbm model file name * output_dir (str): Output directory to hold the results, default to the current path Return: * 0 if no error`

### #Class: hbdk4.compiler.overlay.Argument

#### #`is_removable`(self) -> Tuple

`Check if the attached operation is removable. The operation should be single input and single output. For input argument, it should only be used by the operation. For output argument, the operation input should only be used by the operation. Returns: Tuple: The first element is bool indicating the removable flag. The second element is the diagnostic if it cannot be removed.`

#### #`get_attached_op`(self) -> List[Operation]

`Get the argument attached operations. For input argument, return operations uses the argument. For output argument, return the operation defining the argument. Returns: List[Operation]: _description_`

#### #`remove_attached_op`(self)

`Remove the only attached operation Returns: Tuple: The first element is True when the removal done. The second element is the diagnostic if it cannot be removed. Note: Quantize and Dequantize op should be removed after convert`

#### #`erase`(self)

`Remove the argument from function argument Returns: Tuple: The first element is True when the removal done. The second element is the diagnostic if it cannot be removed.`

#### #`insert_transpose`(self, permutes List[int])

`Insert transpose op. Change input/output parameter dimension order. Args: * permutes (List): Dimension transformation arrangement. Must contain all dimensions of the original input parameters, starting from 0 Returns: List of newly inserted function arguments which is also the inputs/outputs of inserted transpose op Raises: ValueError when this argument is no longer valid Note: To avoid the new insertion operator not running in some conversion passes, it is recommended to call the insert_xxx api before the convert stage Example: module = load("model.bc") func = module[0] res = func.inputs[0].insert_transpose([3, 1, 2, 0])`

#### #`insert_image_convert`(self, mode str = "nv12")

`Insert image_convert op. Change input parameter type. Args: * mode (str): Specify conversion mode, optional values are "nv12"(default) and "gray". Returns: List of newly inserted function arguments which is also the inputs of inserted image convert op Raises: ValueError when this argument is no longer valid Note: To avoid the new insertion operator not running in some conversion passes, it is recommended to call the insert_xxx api before the convert stage Example: module = load("model.bc") func = module[0] res = func.inputs[0].insert_image_convert("nv12")`

#### #`insert_image_preprocess`( self, mode str, divisor int, mean List[float], std List[float], is_signed bool = True)

`Insert image_convert op. Change input parameter type. Args: * mode (str): Specify conversion mode, optional values are "skip"(default, same as None), "yuvbt601full2rgb", "yuvbt601full2bgr", "yuvbt601video2rgb" and "yuvbt601video2bgr". Returns: List of newly inserted function arguments which is also the inputs of inserted image preprocess op Raises: ValueError when this argument is no longer valid Note: To avoid the new insertion operator not running in some conversion passes, it is recommended to call the insert_xxx api before the convert stage Example: module = load("model.bc") func = module[0] res = func.inputs[0].insert_image_preprocess("yuvbt601full2rgb", 255, [0.485, 0.456, 0.406], [0.229, 0.224, 0.225], True)`

#### #`insert_roi_resize`( self, mode str, interp_mode="bilinear", pad_mode="constant", pad_value Optional[tuple] = (0, -128))

`Insert roi_resize op. Change input parameter type. Args: * mode (str): Specify conversion mode, optional values are "nv12"(default) and "gray". * interp_mode (str): Specify interpolation mode, optional values are "bilinear"(default) and "nearest". * pad_mode (str): Specify fill mode, optional values are "constant"(default) and "border". * pad_value (tuple): Specify the padding value for Y and UV in custom pad_mode, default values are (0, -128). Returns: List of newly inserted function arguments which is also the inputs of inserted roi resize op Raises: ValueError when this argument is no longer valid Note: To avoid the new insertion operator not running in some conversion passes, it is recommended to call the insert_xxx api before the convert stage Example: module = load("model.bc") func = module[0] res = func.inputs[0].insert_roi_resize( mode = "nv12", interp_mode = "nearest", pad_mode = "constant", pad_value = (66, 77) )`

#### #`insert_split`(self, dim int)

`Insert split op. Split a single input/output parameter into multiple input/output parameters with a specified dimension of 1. Args: * dim (int): Dimension along which to split the tensor. Returns: List of newly inserted function arguments which is also the inputs/outputs of inserted concat/slice op Note: To avoid the new insertion operator not running in some conversion passes, it is recommended to call the insert_xxx api before the convert stage Raises: ValueError when this argument is no longer valid Example: module = load("model.bc") func = module[0] res = func.inputs[0].insert_split(0)`

### #Class: hbdk4.compiler.overlay.Module

#### #`functions`(self) -> List[Function]

`return all functions in module Returns: List[FunctionHelper]: function are wrapped in FunctionHelper in pair with its symbol name`

#### #`graphs`(self) -> List[Function]

`return all functions in module Returns: List[FunctionHelper]: function are wrapped in FunctionHelper in pair with its symbol name`

### #Class: hbdk4.compiler.overlay.Function

#### #`remove_io_op`(self, op_types=None, op_names=None)

`Experimental function to remove nodes from the model based on types or names Note: Quantize and Dequantize op should be removed after convert Args: op_types(list[str]|tuple[str]): a list/tuple of types to remove op_names(list[str]|tuple[str]): a list/tuple of names to remove Example: module = load("model.bc") func = module[0] func.remove_io_op(['Dequantize','Transpose','Cast'])`

## #hbdk api example usage

### #export model and view model information

#### #onnx

`import onnx onnx_model = onnx.load(onnx_path) from hbdk4.compiler.onnx import statistics # print onnx op list and quantity statistics(onnx_model) from hbdk4.compiler.onnx import export # export onnx to hbir mlir # the shape must have already been recorded in onnx proto # specify the model name by name, and if default, extract it from onnx proto exported_module = export(onnx_model, name="OnnxModel") print("export onnx to hbir mlir successfully") from hbdk4.compiler import statistics, visualize # print hbir op list and quantity statistics(exported_module) # generate visual model Onnx but do not generate view netron link visualize(exported_module) # generate a netron link to open a browser and view the model visualize(exported_module, use_netron=True)`

### #serialize model

`import os from hbdk4.compiler import save, load # serialize mlir.Module to bytecode save(exported_module, "converted.bc") if os.path.exists("converted.bc"): print("save mlir.Module to bytecode successfully") # deserialize bytecode to mlir.Module restored_module = load("converted.bc") print("load bytecode to mlir.Module successfully")`

### #fixed point model

`from hbdk4.compiler import convert, March # convert hbir to backend ir converted_module = convert(exported_module, March.nash_e, advice=False) print("convert hbir to backend ir successfully") # when advice=True, the following op check info will be output # op check info like this """ { "backend": "bpu", "loc": "loc(fused<#hbdk.track<resultMap = [(d0, d1) -> (d0, d1)], resultType = [!qnt.uniform<si8:f32, 0.0024351498577743769>]>>[unknown])", "op": "%12 = \"hbir.reshape\"(%11) <{foldable = true, shape = [12000, 64]}> : (tensor<1x12000x1x64xsi8>) -> tensor<12000x64xsi8>", "tensor_names": [ { "data_type": "si8", "tensor_name": "" } ] }, { "backend": "external_cpu", "choice_reason": "hbir.point_pillar_scatter is not currently supported on bpu.hbir.point_pillar_scatter is not currently supported on vpu.", "loc": "loc(fused<#hbdk.track<resultType = [!qnt.uniform<si8:f32, 0.0024351498577743769>]>>[unknown])", "op": "%13 = \"hbir.point_pillar_scatter\"(%12, %coords) <{outShape = [1, 432, 496, 64]}> : (tensor<12000x64xsi8>, tensor<12000x4xsi32>) -> tensor<1x432x496x64xsi8>", "tensor_names": [ { "data_type": "si8", "tensor_name": "" } ] } """`

### #compile model

#### #compile the model exported using PTQ/QAT

`from hbdk4.compiler import convert, compile, link, March converted_module = convert(exported_module, March.nash_e) # gen hbm3 # DEPRECATED: hbm3 will be removed in the future print("HBM3: compile nash_e mlir") hbm = compile( converted_module, "deploy_hbm3.hbm", March.nash_e, 0, hbdk3_compatible_mode=True ) if os.path.exists("deploy_hbm3.hbm"): print("compile nash_e mlir to deploy_hbm3.hbm successfully") # gen hbm4 print("HBM4: compile nash_e mlir") # Method 1: compile into HBO first, then link to HBM hbo = compile(converted_module, "deploy1_hbm4_.hbo", March.nash_e, 0) if os.path.exists("deploy1_hbm4_.hbo"): print("compile nash_e mlir to deploy1_hbm4_.hbo successfully") hbm = link([hbo], "deploy1_hbm4_.hbm") if os.path.exists("deploy1_hbm4_.hbm"): print("link hbo to deploy1_hbm4_.hbm successfully") # Method 2: compile hbm directly hbm = compile(converted_module, "deploy2_hbm4_.hbm", March.nash_e, 0) if os.path.exists("deploy2_hbm4_.hbm"): print("compile nash_e mlir to deploy2_hbm4_.hbm successfully")`

### #package multiple models into one HBM

`from hbdk4.compiler import compile, link, March conv_hbo_name = "conv_compiled.hbo" conv_hbo = compile( converted_module_1, conv_hbo_name, march, 0, progress_bar=True, advice=0.01, balance=2, ) linear_hbo_name = "linear_compiled.hbo" linear_hbo = compile( converted_module_2, linear_hbo_name, march, 0, progress_bar=True, advice=0.01, balance=2, ) hbm_name = "compiled.hbm" hbm = link([conv_hbo, linear_hbo], hbm_name) if os.path.exists(hbm_name): print("link two hbos successfully") # If multiple HBOs have already been generated and you want to package them into one HBM, you can use the following method from hbdk4.compiler.hbm import Hbo hbo1 = Hbo("conv_compiled.hbo") hbo2 = Hbo("linear_compiled.hbo") hbm = link([hbo1, hbo2], "pack.hbm") if os.path.exists("pack.hbm"): print("link two hbo files successfully")`

### #model static perf

`import os from hbdk4.compiler import hbm_perf # After successful execution, FPS, latency, and DDR data volume information will be printed, and detailed information can be viewed in the generated HTML file hbm_perf("deploy.hbm") if os.path.exists(f"{model_name}.html"): print("hbm perf successfully")`

### #model inference

`from hbdk4.compiler import load, Hbm import numpy as np hbir = load(bc_path) hbm = Hbm(hbm_path) # prepare for random input inputs = { v.name: np.random.rand(*v.type.shape).astype(v.type.np_dtype) for v in hbir[0].inputs } # hbir and Hbm inference hbir_outputs = hbir[0].feed(inputs) hbm_outputs = hbm[0].feed(inputs) # compare Hbir and hbm outputs for idx, v in enumerate(hbir[0].outputs): hbir_data = hbir_outputs[v.name] hbm_data = hbm_outputs[v.name] np.testing.assert_equal( hbm_data, hbir_data, "output{} -- {} is not equal".format(idx, v.name), ) print("All outputs are equal")`

### #HBIR model modify

#### #tensor name modify

`from hbdk4.compiler import load module = load(bc_path) # get the corresponding FunctionalHelper for the function func = module.functions[0] # original func: func @unet_mobilenetv1_cityscapes(tensor<1x3x1024x2048xf32> _input_0) -> tensor<1x1x256x512xsi64> _output_0 print(func) # modify inputs[0]'s name func.inputs[0].name = "modified_name_img" # modify outputs[0]'s name func.outputs[0].name = "modified_name_output" # modified func: func @unet_mobilenetv1_cityscapes(tensor<1x3x1024x2048xf32> modified_name_img) -> tensor<1x1x256x512xsi64> modified_name_output print(func)`

#### #model desc modify

`from hbdk4.compiler import Module mlir_text = """ module { func.func @model(%arg0 : tensor<64x4x3xf32>) -> tensor<64x4x3xf32> { %1 = "hbir.abs"(%arg0) : (tensor<64x4x3xf32>) -> tensor<64x4x3xf32> return %1 : tensor<64x4x3xf32> } } """ module = Module.parse(mlir_text) # get the corresponding FunctionalHelper for the function func = module.functions[0] # modify func's desc func.desc = "model description" # modify inputs[0]'s desc func.inputs[0].desc = "RGB input" # modify outputs[0]'s desc func.outputs[0].desc = "gesture" # model description print(func.desc) # RGB input print(func.inputs[0].desc) # gesture print(func.outputs[0].desc) """ modified model module { func.func @model(%arg0: tensor<64x4x3xf32> {hbir.desc = "RGB input", hbir.id = 1 : i64, hbir.name = "_input_0"}) -> (tensor<64x4x3xf32> {hbir.desc = "gesture", hbir.id = -1 : i64, hbir.name = "_output_0"}) attributes {hbir.desc = "model description"} { %0 = "hbir.abs"(%arg0) : (tensor<64x4x3xf32>) -> tensor<64x4x3xf32> return %0 : tensor<64x4x3xf32> } } """ print(module.module)`

#### #insert nodes

Note: To avoid the new insertion operator not running in some conversion passes, it is recommended to call the insert_xxx api before the convert stage

##### #insert pyramid input

`from hbdk4.compiler import Module # nv12 mlir_text = """ module { func.func @main(%arg0: tensor<1x32x32x3xf32>, %arg1: tensor<4x1x1x3xf32>) -> tensor<1x32x32x4xf32> { %0 = "hbir.conv2d"(%arg0, %arg1) <{dilation = [1, 1], groupNum = 1 : i64, pad = [0, 0, 0, 0], stride = [1, 1]}> : (tensor<1x32x32x3xf32>, tensor<4x1x1x3xf32>) -> tensor<1x32x32x4xf32> return %0 : tensor<1x32x32x4xf32> } } """ module = Module.parse(mlir_text) func = module[0] y, uv = func.inputs[0].insert_image_convert("nv12") # tensor<1x32x32x1xui8> _input_0_y print(y) # tensor<1x16x16x2xui8> _input_0_uv print(uv) """ modified model module { func.func @main(%arg0: tensor<1x32x32x1xui8> {hbir.id = 3 : i64, hbir.name = "_input_0_y"}, %arg1: tensor<1x16x16x2xui8> {hbir.id = 4 : i64, hbir.name = "_input_0_uv"}, %arg2: tensor<4x1x1x3xf32> {hbir.id = 2 : i64, hbir.name = "_input_1"}) -> (tensor<1x32x32x4xf32> {hbir.id = -1 : i64, hbir.name = "_output_0"}) { %0 = "hbir.image_convert"(%arg0, %arg1) <{mode = #hbdk<ImageConvertMode NV12>}> : (tensor<1x32x32x1xui8>, tensor<1x16x16x2xui8>) -> tensor<1x32x32x3xsi8> %1 = "hbir.cast_type"(%0) : (tensor<1x32x32x3xsi8>) -> tensor<1x32x32x3xf32> %2 = "hbir.conv2d"(%1, %arg2) <{dilation = [1, 1], groupNum = 1 : i64, pad = [0, 0, 0, 0], stride = [1, 1]}> : (tensor<1x32x32x3xf32>, tensor<4x1x1x3xf32>) -> tensor<1x32x32x4xf32> return %2 : tensor<1x32x32x4xf32> } } """ print(module.module) # gray mlir_text = """ func.func @main(%arg0: tensor<1x32x32x1xf32>, %arg1: tensor<4x1x1x1xf32>) -> tensor<1x32x32x4xf32> { %0 = "hbir.conv2d"(%arg0, %arg1) : (tensor<1x32x32x1xf32>, tensor<4x1x1x1xf32>) -> tensor<1x32x32x4xf32> return %0 : tensor<1x32x32x4xf32> } """ module = Module.parse(mlir_text) func = module[0] y = func.inputs[0].insert_image_convert("gray") # tensor<1x32x32x1xui8> _input_0_y print(y) """ modified model module { func.func @main(%arg0: tensor<1x32x32x1xui8> {hbir.id = 3 : i64, hbir.name = "_input_0_y"}, %arg1: tensor<4x1x1x1xf32> {hbir.id = 2 : i64, hbir.name = "_input_1"}) -> (tensor<1x32x32x4xf32> {hbir.id = -1 : i64, hbir.name = "_output_0"}) { %0 = "hbir.image_convert"(%arg0) <{mode = #hbdk<ImageConvertMode GRAY>}> : (tensor<1x32x32x1xui8>) -> tensor<1x32x32x1xsi8> %1 = "hbir.cast_type"(%0) : (tensor<1x32x32x1xsi8>) -> tensor<1x32x32x1xf32> %2 = "hbir.conv2d"(%1, %arg1) <{dilation = [1, 1], groupNum = 1 : i64, pad = [0, 0, 0, 0], stride = [1, 1]}> : (tensor<1x32x32x1xf32>, tensor<4x1x1x1xf32>) -> tensor<1x32x32x4xf32> return %2 : tensor<1x32x32x4xf32> } } """ print(module.module)`

##### #insert image preprocess

`from hbdk4.compiler import Module mlir_text = """ func.func @main(%arg0: tensor<1x32x32x3xf32>, %arg1: tensor<1x32x32x3xf32>) -> (tensor<1x32x32x4xf32>, tensor<1x32x32x4xf32>) { %0 = "hbir.constant"() <{values = dense<3.> : tensor<4x1x1x3xf32>}> : () -> tensor<4x1x1x3xf32> %1 = "hbir.conv2d"(%arg0, %0) : (tensor<1x32x32x3xf32>, tensor<4x1x1x3xf32>) -> tensor<1x32x32x4xf32> %2 = "hbir.constant"() <{values = dense<4.> : tensor<4x1x1x3xf32>}> : () -> tensor<4x1x1x3xf32> %3 = "hbir.conv2d"(%arg1, %2) : (tensor<1x32x32x3xf32>, tensor<4x1x1x3xf32>) -> tensor<1x32x32x4xf32> return %1, %3 : tensor<1x32x32x4xf32>, tensor<1x32x32x4xf32> } """ module = Module.parse(mlir_text) func = module[0] func.inputs[0].name = "img1" func.inputs[1].name = "img2" func.outputs[0].name = "pred1" func.outputs[1].name = "pred2" func.inputs[0].desc = "This is yuv" node_0 = func.inputs[1].insert_image_preprocess( mode=None, divisor=1, mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225] ) # tensor<1x32x32x3xsi8> img2 print(node_0) yuv = func.inputs[0].insert_image_preprocess( mode="yuvbt601full2rgb", divisor=255, mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225], ) # tensor<1x32x32x3xsi8> img1 print(yuv) """ modified model module { func.func @main(%arg0: tensor<1x32x32x3xsi8> {hbir.desc = "This is yuv", hbir.id = 4 : i64, hbir.name = "img1"}, %arg1: tensor<1x32x32x3xsi8> {hbir.id = 3 : i64, hbir.name = "img2"}) -> (tensor<1x32x32x4xf32> {hbir.id = -1 : i64, hbir.name = "pred1"}, tensor<1x32x32x4xf32> {hbir.id = -2 : i64, hbir.name = "pred2"}) { %0 = "hbir.image_preprocess"(%arg0) <{csc = #hbdk<CSCMode YUVBT601FULL2RGB>, divisor = 255 : i64, mean = [4.850000e-01, 4.560000e-01, 4.060000e-01], sd = [2.290000e-01, 2.240000e-01, 2.250000e-01]}> : (tensor<1x32x32x3xsi8>) -> tensor<1x32x32x3xf32> %1 = "hbir.image_preprocess"(%arg1) <{csc = #hbdk<CSCMode NONE>, divisor = 1 : i64, mean = [4.850000e-01, 4.560000e-01, 4.060000e-01], sd = [2.290000e-01, 2.240000e-01, 2.250000e-01]}> : (tensor<1x32x32x3xsi8>) -> tensor<1x32x32x3xf32> %2 = "hbir.constant"() <{values = dense<3.000000e+00> : tensor<4x1x1x3xf32>}> : () -> tensor<4x1x1x3xf32> %3 = "hbir.conv2d"(%0, %2) <{dilation = [1, 1], groupNum = 1 : i64, pad = [0, 0, 0, 0], stride = [1, 1]}> : (tensor<1x32x32x3xf32>, tensor<4x1x1x3xf32>) -> tensor<1x32x32x4xf32> %4 = "hbir.constant"() <{values = dense<4.000000e+00> : tensor<4x1x1x3xf32>}> : () -> tensor<4x1x1x3xf32> %5 = "hbir.conv2d"(%1, %4) <{dilation = [1, 1], groupNum = 1 : i64, pad = [0, 0, 0, 0], stride = [1, 1]}> : (tensor<1x32x32x3xf32>, tensor<4x1x1x3xf32>) -> tensor<1x32x32x4xf32> return %3, %5 : tensor<1x32x32x4xf32>, tensor<1x32x32x4xf32> } } """ print(module.module)`

##### #insert roi resize

`from hbdk4.compiler import Module mlir_text = """ func.func @main(%arg0: tensor<1x32x32x3xf32>, %arg1: tensor<4x1x1x3xf32>) -> tensor<1x32x32x4xf32> { %0 = "hbir.conv2d"(%arg0, %arg1) : (tensor<1x32x32x3xf32>, tensor<4x1x1x3xf32>) -> tensor<1x32x32x4xf32> return %0 : tensor<1x32x32x4xf32> } """ module = Module.parse(mlir_text) func = module[0] func.inputs[0].name = "img" func.inputs[0].desc = "test" func.inputs[1].name = "weight" func.outputs[0].name = "pred" y, uv, roi = func.inputs[0].insert_roi_resize("nv12") # tensor<1x8192x8192x1xui8> img_y print(y) # tensor<1x4096x4096x2xui8> img_uv print(uv) # tensor<1x4xsi32> img_roi print(roi) """ modified model module { func.func @main(%arg0: tensor<1x8192x8192x1xui8, {dynamic_dims = [-2, -3]}> {hbir.desc = "test", hbir.id = 3 : i64, hbir.name = "img_y"}, %arg1: tensor<1x4096x4096x2xui8, {dynamic_dims = [-2, -3]}> {hbir.desc = "test", hbir.id = 4 : i64, hbir.name = "img_uv"}, %arg2: tensor<1x4xsi32> {hbir.desc = "test", hbir.id = 5 : i64, hbir.name = "img_roi"}, %arg3: tensor<4x1x1x3xf32> {hbir.id = 2 : i64, hbir.name = "weight"}) -> (tensor<1x32x32x4xf32> {hbir.id = -1 : i64, hbir.name = "pred"}) { %0 = "hbir.roi_resize"(%arg0, %arg1, %arg2) <{expansionMode = #hbdk<ExpansionMode constant>, interpolateMode = #hbdk<InterpolationMode bilinear>, mode = #hbdk<ImageConvertMode NV12>, padValue = [0, -128], size = [32, 32]}> : (tensor<1x8192x8192x1xui8, {dynamic_dims = [-2, -3]}>, tensor<1x4096x4096x2xui8, {dynamic_dims = [-2, -3]}>, tensor<1x4xsi32>) -> tensor<1x32x32x3xsi8> %1 = "hbir.cast_type"(%0) : (tensor<1x32x32x3xsi8>) -> tensor<1x32x32x3xf32> %2 = "hbir.conv2d"(%1, %arg3) <{dilation = [1, 1], groupNum = 1 : i64, pad = [0, 0, 0, 0], stride = [1, 1]}> : (tensor<1x32x32x3xf32>, tensor<4x1x1x3xf32>) -> tensor<1x32x32x4xf32> return %2 : tensor<1x32x32x4xf32> } } """ print(module.module)`

##### #insert transpose

`from hbdk4.compiler import Module mlir_text = """ func.func @main(%arg0: tensor<1x32x32x3xf32>, %arg1: tensor<4x1x1x3xf32>) -> tensor<1x32x32x4xf32> { %0 = "hbir.conv2d"(%arg0, %arg1) : (tensor<1x32x32x3xf32>, tensor<4x1x1x3xf32>) -> tensor<1x32x32x4xf32> return %0 : tensor<1x32x32x4xf32> } """ module = Module.parse(mlir_text) func = module[0] func.inputs[0].name = "img" func.inputs[0].desc = "in" func.inputs[1].name = "weight" func.outputs[0].name = "pred" func.outputs[0].desc = "out" """ modified model module { func.func @main(%arg0: tensor<1x32x32x3xf32> {hbir.desc = "in", hbir.id = 1 : i64, hbir.name = "img"}, %arg1: tensor<4x1x1x3xf32> {hbir.id = 2 : i64, hbir.name = "weight"}) -> (tensor<1x32x32x4xf32> {hbir.desc = "out", hbir.id = -1 : i64, hbir.name = "pred"}) { %0 = "hbir.conv2d"(%arg0, %arg1) <{dilation = [1, 1], groupNum = 1 : i64, pad = [0, 0, 0, 0], stride = [1, 1]}> : (tensor<1x32x32x3xf32>, tensor<4x1x1x3xf32>) -> tensor<1x32x32x4xf32> return %0 : tensor<1x32x32x4xf32> } } """ print(module.module) in_node = func.inputs[0].insert_transpose([2, 3, 0, 1]) # tensor<32x3x1x32xf32> img print(in_node) out_node = func.outputs[0].insert_transpose([2, 3, 1, 0]) # tensor<32x4x32x1xf32> pred print(out_node) """ modified model module { func.func @main(%arg0: tensor<32x3x1x32xf32> {hbir.desc = "in", hbir.id = 3 : i64, hbir.name = "img"}, %arg1: tensor<4x1x1x3xf32> {hbir.id = 2 : i64, hbir.name = "weight"}) -> (tensor<32x4x32x1xf32> {hbir.desc = "out", hbir.id = -2 : i64, hbir.name = "pred"}) { %0 = "hbir.transpose"(%arg0) <{dims = [2, 3, 0, 1]}> : (tensor<32x3x1x32xf32>) -> tensor<1x32x32x3xf32> %1 = "hbir.conv2d"(%0, %arg1) <{dilation = [1, 1], groupNum = 1 : i64, pad = [0, 0, 0, 0], stride = [1, 1]}> : (tensor<1x32x32x3xf32>, tensor<4x1x1x3xf32>) -> tensor<1x32x32x4xf32> %2 = "hbir.transpose"(%1) <{dims = [2, 3, 1, 0]}> : (tensor<1x32x32x4xf32>) -> tensor<32x4x32x1xf32> return %2 : tensor<32x4x32x1xf32> } } """ print(module.module)`

##### #insert split

`from hbdk4.compiler import Module mlir_text = """ func.func @main(%arg0: tensor<1x32x32x3xf32>, %arg1: tensor<4x1x1x3xf32>) -> tensor<1x32x32x4xf32> { %0 = "hbir.conv2d"(%arg0, %arg1) : (tensor<1x32x32x3xf32>, tensor<4x1x1x3xf32>) -> tensor<1x32x32x4xf32> return %0 : tensor<1x32x32x4xf32> } """ module = Module.parse(mlir_text) func = module[0] func.inputs[0].name = "img" func.inputs[0].desc = "its img" func.inputs[1].name = "weight" func.inputs[1].desc = "its weight" func.outputs[0].name = "pred" func.outputs[0].desc = "its out" # func @main(tensor<1x32x32x3xf32> img, tensor<4x1x1x3xf32> weight) -> tensor<1x32x32x4xf32> pred print(module) # Split the dimension 0 of the input parameter into multiple input parameters res_list = func.inputs[1].insert_split(0) # tensor<1x1x1x3xf32> weight_0 print(res_list[0]) """ modified model module { func.func @main(%arg0: tensor<1x32x32x3xf32> {hbir.desc = "its img", hbir.id = 1 : i64, hbir.name = "img"}, %arg1: tensor<1x1x1x3xf32> {hbir.desc = "its weight", hbir.id = 3 : i64, hbir.name = "weight_0"}, %arg2: tensor<1x1x1x3xf32> {hbir.desc = "its weight", hbir.id = 4 : i64, hbir.name = "weight_1"}, %arg3: tensor<1x1x1x3xf32> {hbir.desc = "its weight", hbir.id = 5 : i64, hbir.name = "weight_2"}, %arg4: tensor<1x1x1x3xf32> {hbir.desc = "its weight", hbir.id = 6 : i64, hbir.name = "weight_3"}) -> (tensor<1x32x32x4xf32> {hbir.desc = "its out", hbir.id = -1 : i64, hbir.name = "pred"}) { %0 = "hbir.concat"(%arg1, %arg2, %arg3, %arg4) <{dim = 0 : i64}> : (tensor<1x1x1x3xf32>, tensor<1x1x1x3xf32>, tensor<1x1x1x3xf32>, tensor<1x1x1x3xf32>) -> tensor<4x1x1x3xf32> %1 = "hbir.conv2d"(%arg0, %0) <{dilation = [1, 1], groupNum = 1 : i64, pad = [0, 0, 0, 0], stride = [1, 1]}> : (tensor<1x32x32x3xf32>, tensor<4x1x1x3xf32>) -> tensor<1x32x32x4xf32> return %1 : tensor<1x32x32x4xf32> } } """ print(module.module) res_list_1 = func.outputs[0].insert_split(3) # tensor<1x32x32x1xf32> pred_0 print(res_list_1[0]) """ modified model module { func.func @main(%arg0: tensor<1x32x32x3xf32> {hbir.desc = "its img", hbir.id = 1 : i64, hbir.name = "img"}, %arg1: tensor<1x1x1x3xf32> {hbir.desc = "its weight", hbir.id = 3 : i64, hbir.name = "weight_0"}, %arg2: tensor<1x1x1x3xf32> {hbir.desc = "its weight", hbir.id = 4 : i64, hbir.name = "weight_1"}, %arg3: tensor<1x1x1x3xf32> {hbir.desc = "its weight", hbir.id = 5 : i64, hbir.name = "weight_2"}, %arg4: tensor<1x1x1x3xf32> {hbir.desc = "its weight", hbir.id = 6 : i64, hbir.name = "weight_3"}) -> (tensor<1x32x32x1xf32> {hbir.desc = "its out", hbir.id = -2 : i64, hbir.name = "pred_0"}, tensor<1x32x32x1xf32> {hbir.desc = "its out", hbir.id = -3 : i64, hbir.name = "pred_1"}, tensor<1x32x32x1xf32> {hbir.desc = "its out", hbir.id = -4 : i64, hbir.name = "pred_2"}, tensor<1x32x32x1xf32> {hbir.desc = "its out", hbir.id = -5 : i64, hbir.name = "pred_3"}) { %0 = "hbir.concat"(%arg1, %arg2, %arg3, %arg4) <{dim = 0 : i64}> : (tensor<1x1x1x3xf32>, tensor<1x1x1x3xf32>, tensor<1x1x1x3xf32>, tensor<1x1x1x3xf32>) -> tensor<4x1x1x3xf32> %1 = "hbir.conv2d"(%arg0, %0) <{dilation = [1, 1], groupNum = 1 : i64, pad = [0, 0, 0, 0], stride = [1, 1]}> : (tensor<1x32x32x3xf32>, tensor<4x1x1x3xf32>) -> tensor<1x32x32x4xf32> %2 = "hbir.slice"(%1) <{begin = [0, 0, 0, 0], end = [1, 32, 32, 1], foldable = false, step = [1, 1, 1, 1]}> : (tensor<1x32x32x4xf32>) -> tensor<1x32x32x1xf32> %3 = "hbir.slice"(%1) <{begin = [0, 0, 0, 1], end = [1, 32, 32, 2], foldable = false, step = [1, 1, 1, 1]}> : (tensor<1x32x32x4xf32>) -> tensor<1x32x32x1xf32> %4 = "hbir.slice"(%1) <{begin = [0, 0, 0, 2], end = [1, 32, 32, 3], foldable = false, step = [1, 1, 1, 1]}> : (tensor<1x32x32x4xf32>) -> tensor<1x32x32x1xf32> %5 = "hbir.slice"(%1) <{begin = [0, 0, 0, 3], end = [1, 32, 32, 4], foldable = false, step = [1, 1, 1, 1]}> : (tensor<1x32x32x4xf32>) -> tensor<1x32x32x1xf32> return %2, %3, %4, %5 : tensor<1x32x32x1xf32>, tensor<1x32x32x1xf32>, tensor<1x32x32x1xf32>, tensor<1x32x32x1xf32> } } """ print(module.module)`

#### #remove adjacent nodes in the input and output of the model

`from hbdk4.compiler import Module mlir_text = """ module { func.func @model(%arg0 : tensor<64x4x3xf32>, %arg1 : tensor<64x4x3xf32>) -> tensor<64x4x3xf32> { %0 = "hbir.add"(%arg0, %arg1) : (tensor<64x4x3xf32>, tensor<64x4x3xf32>) -> tensor<64x4x3xf32> %1 = "hbir.mul"(%arg0, %0) : (tensor<64x4x3xf32>, tensor<64x4x3xf32>) -> tensor<64x4x3xf32> %2 = "hbir.abs"(%1) : (tensor<64x4x3xf32>) -> tensor<64x4x3xf32> return %2 : tensor<64x4x3xf32> } } """ module = Module.parse(mlir_text) removable, reason = module[0].inputs[0].is_removable # False print(removable) # '_input_0 has multiple uses. cannot remove.' print(reason) removable, reason = module[0].inputs[1].is_removable # False print(removable) # '%0 = "hbir.add"(%arg0, %arg1) : (tensor<64x4x3xf32>, tensor<64x4x3xf32>) -> tensor<64x4x3xf32> loc("-":4:10) is not a single input and single output op' print(reason) removable, reason = module[0].outputs[0].is_removable # True print(removable) # '' print(reason) res = module[0].outputs[0].remove_attached_op() # (True, '') print(res) """ modified model module { func.func @model(%arg0: tensor<64x4x3xf32> {hbir.id = 1 : i64, hbir.name = "_input_0"}, %arg1: tensor<64x4x3xf32> {hbir.id = 2 : i64, hbir.name = "_input_1"}) -> (tensor<64x4x3xf32> {hbir.id = -2 : i64, hbir.name = "_output_0"}) { %0 = "hbir.add"(%arg0, %arg1) : (tensor<64x4x3xf32>, tensor<64x4x3xf32>) -> tensor<64x4x3xf32> %1 = "hbir.mul"(%arg0, %0) : (tensor<64x4x3xf32>, tensor<64x4x3xf32>) -> tensor<64x4x3xf32> return %1 : tensor<64x4x3xf32> } } """ print(module.module)`

### #HBM modify

`import json from hbdk4.compiler import hbm_extract_desc, hbm_update_desc hbm4_model_name = "hbm4_model.hbm" # step1: get hbm desc dict hbm4_desc_dict = hbm_extract_desc(hbm4_model_name) """ original hbm desc { "march": "NashE", "models": { "hbm4_model": { "desc": "", "desc_type": "string", "input_features": { "input_0": { "desc": "", "desc_type": "string" }, "input_1": { "desc": "", "desc_type": "string" } }, "output_features": { "output_0": { "desc": "", "desc_type": "string" } } } } } """ print(json.dumps(hbm4_desc_dict, indent=2)) # step2: modify hbm desc dict # adjust name and description information # graph desc/name modification hbm4_desc_dict["models"]["hbm4_model"]["desc"] = "set_new_graph_desc" hbm4_desc_dict["models"]["hbm4_model"]["new_name"] = "set_new_graph_name" # input desc/name modification hbm4_desc_dict["models"]["hbm4_model"]["input_features"]["input_0"][ "desc" ] = "set_new_input_desc" hbm4_desc_dict["models"]["hbm4_model"]["input_features"]["input_0"][ "desc_type" ] = "string" hbm4_desc_dict["models"]["hbm4_model"]["input_features"]["input_0"][ "new_name" ] = "set_new_input_name" # output desc/name modification hbm4_desc_dict["models"]["hbm4_model"]["output_features"]["output_0"][ "desc" ] = "set_new_output_desc" hbm4_desc_dict["models"]["hbm4_model"]["output_features"]["output_0"][ "desc_type" ] = "string" hbm4_desc_dict["models"]["hbm4_model"]["output_features"]["output_0"][ "new_name" ] = "set_new_output_name" # step3: update model file hbm_update_desc(hbm4_model_name, hbm4_desc_dict) new_hbm4_desc_dict = hbm_extract_desc(hbm4_model_name) """ new hbm desc { "march": "NashE", "models": { "set_new_graph_name": { "desc": "set_new_graph_desc", "desc_type": "string", "input_features": { "input_1": { "desc": "", "desc_type": "string" }, "set_new_input_name": { "desc": "set_new_input_desc", "desc_type": "string" } }, "output_features": { "set_new_output_name": { "desc": "set_new_output_desc", "desc_type": "string" } } } } } """ print(json.dumps(new_hbm4_desc_dict, indent=2))`
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