import warnings from .basic_utils import basicOperator, deep_copy_dicts, isweightstr, overwrite_dicts from .conv2d import Conv2d __all__ = ["Conv2d"] # USE_FP_REQ = True default_params = { # op related "op": "CONV_2D", "is_SEBlock": False, "stride_h": None, "stride_w": None, "input_idx": None, "output_idx": None, # tensor related "input_dim": None, "input_h": None, "input_w": None, "input_c": None, "output_dim": None, "output_h": None, "output_w": None, "output_c": None, "kernel_h": None, "kernel_w": None, "input_dtype": "int8", "output_dtype": "int8", "norm_buffer": None, # quantization related "weight_value": None, "weight_name": None, "bias": None, "bias_name": None, "effective_scale": None, "input_zero_point": None, "output_zero_point": None, "input_scale": None, "output_scale": None, "weight_scale": None, "multiplier": None, "shift": None, # for fp implementation "float_max": "FLT_MAX", "float_min": "-FLT_MAX", "padding": None, "padding_h": None, "padding_w": None, "dilation_h": None, "dilation_w": None, "groups": 1, # for inplace SGD "inplace_weight_name": None, "inplace_int8_input": False, "QAS": None, # for int8/fp implementation "float32_input2": None, } class groupConv2d(basicOperator): conv_params_declared = False def __init__(self, params: dict) -> None: self.params = deep_copy_dicts(default_params) overwrite_dicts(self.params, params) super().__init__() # handle input/output tensors in HWC format self._add_input( self.params["input_idx"], self.params["input_dtype"], self.params["input_c"], self.params["input_w"], self.params["input_h"], ) self._add_output( self.params["output_idx"], self.params["output_dtype"], self.params["output_c"], self.params["output_w"], self.params["output_h"], ) if None in default_params: warnings.warn(f"parameters are not all set for op {self.params['op']}") def _op_hparam_info(self) -> str: return ( f" k{self.params['kernel_h']}x{self.params['kernel_w']}_r{self.params['input_h']}x" + f"{self.params['input_w']}x{self.params['input_c']}_{self.params['output_h']}x" + f"{self.params['output_w']}x{self.params['output_c']}" ) def set_input_zero_point(self, zero_x): p = self.params p["input_zero_point"] = zero_x def set_output_zero_point(self, zero_y): p = self.params p["input_zero_point"] = zero_y def get_macs(self): p = self.params return ( p["output_h"] * p["output_w"] * p["kernel_h"] * p["kernel_w"] * p["input_c"] * p["output_c"] / p["groups"] ) def get_weights_size(self) -> int: p = self.params if p["input_dtype"] == "float32" or "fp32": size = 4 else: size = 1 return p["kernel_h"] * p["kernel_w"] * p["input_c"] * p["output_c"] * size / (p["groups"] * p["groups"]) def get_bias_size(self) -> int: p = self.params return 4 * p["output_c"] def get_scale_size(self) -> int: p = self.params return 4 * p["output_c"] def get_sbuf_size(self) -> int: p = self.params if p["output_c"] == p["input_c"] and p["output_c"] == p["groups"]: return int((p["input_h"] + p["padding_h"] * 2) * (p["input_w"] + p["padding_w"] * 2) * 4) + int( p["kernel_h"] * p["kernel_w"] * p["input_c"] * 4 ) # Same like depthwise conv; brutally transform filter_data from HWC -> CHW elif (p["output_c"] / p["groups"]) % 16 == 0: return int(p["kernel_h"] * p["kernel_w"] * 4 * 4) + int( p["kernel_h"] * p["kernel_w"] * 16 * 4 ) # group_conv 4row16col, im2col for both input and weight, and floating point elif (p["output_c"] / p["groups"]) % 8 == 0: return int(p["kernel_h"] * p["kernel_w"] * 4 * 4) + int( p["kernel_h"] * p["kernel_w"] * 8 * 4 ) # group_conv 4row8col, im2col for both input and weight, and floating point else: return 0 def get_kbuf_size(self) -> int: return 0 def add_int32_buffer_tensor(self): params = self.params if (params["output_c"] / params["groups"]) % 16 == 0: self._add_input( params["output_idx"] + "_buffer", "int32", 16, params["groups"], 1, ) elif (params["output_c"] / params["groups"]) % 8 == 0: self._add_input( params["output_idx"] + "_buffer", "int32", 8, params["groups"], 1, ) else: raise NotImplementedError params["norm_buffer"] = len(self.input_tensors) - 1 def generate_inference_str( self, tflite_op: bool = False, dummy_address: bool = False, ): string = "" params = self.params # floating point implmenetation if tflite_op: string += f"conv_params.stride_height = {params['stride_h']};\n" string += f"conv_params.stride_width = {params['stride_w']};\n" string += f"conv_params.dilation_width_factor = {params['dilation_w']};\n" string += f"conv_params.dilation_height_factor = {params['dilation_h']};\n" string += f"conv_params.input_offset = {params['input_zero_point']};\n" string += f"conv_params.output_offset = {params['output_zero_point']};\n" string += f"conv_params.padding_values.width = {params['padding_w']};\n" string += f"conv_params.padding_values.height = {params['padding_h']};\n" string += "conv_params.quantized_activation_min = -128;\n" string += "conv_params.quantized_activation_max = 127;\n" string += f"conv_params.float_activation_min = {params['float_min']};\n" string += f"conv_params.float_activation_max = {params['float_max']};\n" if isinstance(params["weight_name"], str) and isweightstr(params["weight_name"]): weight_string = params["weight_name"] else: weight_string = f"weight_fp{params['parsed_trainable']}" function_name = "group_conv" if params["input_dtype"] == "int8": function_name += "_int8input" if not params["float32_input2"]: function_name += "_int8weight" if params["inplace_weight_name"] is not None: if self.params["QAS"] is not None: QAS_cnt = int(self.params["output_c"] / self.params["input_c"]) if QAS_cnt == 1: QAS_cnt = len(self.params["QAS"].flatten()) string += f"const float {self.params['inplace_weight_name']}_QAS[{QAS_cnt}] = " + "{" QAS = self.params["QAS"].flatten() for i in 1 / QAS: string += str(i) + "," string += "};\n" if dummy_address: string += ( f"{function_name}_inplace(conv_params,{params['groups']},&buffer0[0]," + f"{params['input_h']},{params['input_w']},{params['input_c']}," + f"{weight_string},{params['kernel_h']},{params['kernel_w']},NULL," + f"{params['inplace_weight_name']}," + f"{str(params['output_h'])},{str(params['output_w'])}," + f"{str(params['output_c'])},(float*)sbuf,1, " + f"{self.params['inplace_weight_name']}_QAS, lr);\n" ) else: string += ( f"{function_name}_inplace(conv_params,{params['groups']}," + f"{self._getBufferstrCast(params['input_buf_add'], params['input_buf_add_offset'])}," + f"{params['input_h']},{params['input_w']},{params['input_c']}," + f"{weight_string},{params['kernel_h']},{params['kernel_w']},NULL," + f"{params['inplace_weight_name']}," + f"{str(params['output_h'])},{str(params['output_w'])},{str(params['output_c'])}," + f"(float*)sbuf,1, {self.params['inplace_weight_name']}_QAS, lr);\n" ) else: string += ( f"{function_name}(conv_params,{params['groups']}," + f"{self._getBufferstrCast(params['input_buf_add'], params['input_buf_add_offset'])}," + f"{params['input_h']},{params['input_w']},{params['input_c']}," + f"{weight_string},{params['kernel_h']},{params['kernel_w']},NULL," + f"{self._getBufferstrCast(params['output_buf_add'], params['output_buf_add_offset'])}," + f"{str(params['output_h'])},{str(params['output_w'])}," + f"{str(params['output_c'])},(float*)sbuf,1);\n" ) elif not tflite_op: # function name if ( params["kernel_h"] == 1 and params["kernel_w"] == 1 and params["input_h"] == 1 and params["input_w"] == 1 and params["output_h"] == 1 and params["output_w"] == 1 and params["output_c"] / params["input_c"] == 10 ): # group pointwise conv function_name = ( "group_pointwise_conv_in1x1_out1x1_1row10col_uniweight" if not params["float32_input2"] else "group_pointwise_conv_fp_in1x1_out1x1_1row10col_uniweight" ) elif ( params["output_c"] == params["input_c"] and params["output_c"] == params["groups"] ): # Same like depthwise conv function_name = "depthwise_conv_kernel" if not params["float32_input2"] else "depthwise_conv_fp_kernel" function_name += ( f"{str(params['kernel_h'])}_stride{str(params['stride_h'])}_pad{str(params['padding_h'])}" + f"_in{str(params['input_h'])}x{str(params['input_w'])}_out{str(params['output_h'])}x" + f"{str(params['output_w'])}_uniweight_1row1col" ) elif (params["output_c"] / params["groups"]) % 16 == 0: function_name = "group_conv_kernel" if not params["float32_input2"] else "group_conv_fp_kernel" function_name += ( f"{str(params['kernel_h'])}_stride{str(params['stride_h'])}_pad{str(params['padding_h'])}_in" + f"{str(params['input_h'])}x{str(params['input_w'])}_out{str(params['output_h'])}x" + f"{str(params['output_w'])}_uniweight_4row16col" ) elif (params["output_c"] / params["groups"]) % 8 == 0: function_name = "group_conv_kernel" if not params["float32_input2"] else "group_conv_fp_kernel" function_name += ( f"{str(params['kernel_h'])}_stride{str(params['stride_h'])}_pad{str(params['padding_h'])}_in" + f"{str(params['input_h'])}x{str(params['input_w'])}_out{str(params['output_h'])}x" + f"{str(params['output_w'])}_uniweight_4row8col" ) else: raise NotImplementedError # int8 input for inplace cast if params["input_dtype"] == "int8": function_name += "_int8input" if not params["float32_input2"]: function_name += "_int8weight" if ( (params["output_c"] / params["groups"]) % 16 == 0 or (params["output_c"] / params["groups"]) % 8 == 0 or params["output_c"] / params["input_c"] == 10 ): function_name += "_inplace" else: raise NotImplementedError # weight name if isinstance(params["weight_name"], str) and isweightstr(params["weight_name"]): weight_string = params["weight_name"] else: weight_string = f"weight_fp{params['parsed_trainable']}" # require int32 output buffer norm_buffer_add = None if params["norm_buffer"]: norm_tensor = self.input_tensors[params["norm_buffer"]] norm_buffer_add = f"&{norm_tensor.buffer_name}[{norm_tensor.buffer_address}]" if params["inplace_weight_name"] is not None: if self.params["QAS"] is not None: QAS_cnt = int(self.params["output_c"] / self.params["input_c"]) if QAS_cnt == 1: QAS_cnt = len(self.params["QAS"].flatten()) string += f"const float {self.params['inplace_weight_name']}_QAS[{QAS_cnt}] = " + "{" QAS = self.params["QAS"].flatten() for i in 1 / QAS: string += str(i) + "," string += "};\n" string += ( f"{function_name}" + f"({self._getBufferstrCast(params['input_buf_add'], params['input_buf_add_offset'])}," + f"{params['input_h']},{params['input_w']},{params['input_c']}," + f"{weight_string},NULL," + f"{params['inplace_weight_name']}," + f"{str(params['output_h'])},{str(params['output_w'])},{str(params['output_c'])}," + f"{params['float_min']},{params['float_max']}," ) if not params["float32_input2"]: string += ( ( f"(float*)sbuf, NULL, 1,{params['groups']}, " + f"{self.params['inplace_weight_name']}_QAS, lr);\n" ) if not norm_buffer_add else ( f"(float*)sbuf, {norm_buffer_add}, 1, {params['groups']}, " + f"{self.params['inplace_weight_name']}_QAS, lr);\n" ) ) else: string += f"(float*)sbuf,1,{params['groups']}, {self.params['inplace_weight_name']}_QAS, lr);\n" else: string += ( f"{function_name}" + f"({self._getBufferstrCast(params['input_buf_add'], params['input_buf_add_offset'])}," + f"{params['input_h']},{params['input_w']},{params['input_c']}," + f"{weight_string},NULL," + f"{params['inplace_weight_name']}," + f"{str(params['output_h'])},{str(params['output_w'])},{str(params['output_c'])}," + f"{params['float_min']},{params['float_max']}," ) if not params["float32_input2"]: string += ( f"(float*)sbuf, NULL, 1,{params['groups']});\n" if not norm_buffer_add else f"(float*)sbuf, {norm_buffer_add}, 1, {params['groups']});\n" ) else: string += f"(float*)sbuf,1,{params['groups']});\n" else: string += ( f"{function_name}" + f"({self._getBufferstrCast(params['input_buf_add'], params['input_buf_add_offset'])}," + f"{params['input_h']},{params['input_w']},{params['input_c']}," + f"{weight_string},NULL," + f"{self._getBufferstrCast(params['output_buf_add'], params['output_buf_add_offset'])}," + f"{str(params['output_h'])},{str(params['output_w'])},{str(params['output_c'])}," + f"{params['float_min']},{params['float_max']}," ) if not params["float32_input2"]: string += ( f"(float*)sbuf,NULL,1,{params['groups']});\n" if not norm_buffer_add else f"(float*)sbuf, {norm_buffer_add}, 1, {params['groups']});\n" ) else: string += f"(float*)sbuf,1,{params['groups']});\n" return string