[RTL Thresh] Move weight file generation for runtime writeable weight…

…s in separate function

src/finn/custom_op/fpgadataflow/rtl/thresholding_rtl.py

@@ -232,33 +232,7 @@ def prepare_codegen_rtl_values(self, model):
 
         if self.get_nodeattr("runtime_writeable_weights") == 1:
             thresh_file_name = f"{t_path}/memblock.dat"
-            width_padded = roundup_to_integer_multiple(thresholds.shape[1], 2**o_bitwidth)
-            thresh_padded = np.zeros((thresholds.shape[0], width_padded))
-            thresh_padded[: thresholds.shape[0], :n_thres_steps] = thresholds
-            thresh_stream = []
-            bw_hexdigit = roundup_to_integer_multiple(wdt.bitwidth(), 32)
-            padding = np.zeros(width_padded, dtype=np.int32)
-
-            chan_ind = 0
-            cf = ch // pe
-            for fold in range(cf):
-                for c in range(2 ** (pe - 1).bit_length()):
-                    if (c == 0 or c % pe != 0) and c < pe:
-                        for t in thresh_padded[chan_ind]:
-                            t_packed = pack_innermost_dim_as_hex_string(
-                                [t], wdt, bw_hexdigit, prefix=""
-                            ).item()
-                            thresh_stream.append(t_packed)
-                        chan_ind += 1
-                    else:
-                        for z in padding:
-                            t_packed = pack_innermost_dim_as_hex_string(
-                                [z], wdt, bw_hexdigit, prefix=""
-                            ).item()
-                            thresh_stream.append(t_packed)
-            with open(thresh_file_name, "w") as f:
-                for val in thresh_stream:
-                    f.write(val + "\n")
+            self.make_weight_file(thresholds, "decoupled", thresh_file_name)
 
         # Identify the module name
         code_gen_dict["$MODULE_NAME_AXI_WRAPPER$"] = [
@@ -532,3 +506,49 @@ def get_verilog_top_module_intf_names(self):
             intf_names["axilite"] = ["s_axilite"]
 
         return intf_names
+
+    def make_weight_file(self, weights, weight_file_mode, weight_file_name):
+        """Produce a file containing given weights (thresholds) in appropriate
+        format for this layer. This file can be used for either synthesis or
+        run-time reconfig of weights.
+
+        Arguments:
+
+        * weights : numpy array with weights to be put into the file
+        * weight_file_name : filename for the weight file to be generated
+
+        """
+        thresholds = weights
+        pe = self.get_nodeattr("PE")
+        ch = self.get_nodeattr("NumChannels")
+        output_data_type = self.get_nodeattr("outputDataType")  # output precision
+        o_bitwidth = DataType[output_data_type].bitwidth()
+        n_thres_steps = 2**o_bitwidth - 1
+        width_padded = roundup_to_integer_multiple(thresholds.shape[1], 2**o_bitwidth)
+        thresh_padded = np.zeros((thresholds.shape[0], width_padded))
+        thresh_padded[: thresholds.shape[0], :n_thres_steps] = thresholds
+        thresh_stream = []
+        wdt = self.get_weight_datatype()
+        bw_hexdigit = roundup_to_integer_multiple(wdt.bitwidth(), 32)
+        padding = np.zeros(width_padded, dtype=np.int32)
+
+        chan_ind = 0
+        cf = ch // pe
+        for fold in range(cf):
+            for c in range(2 ** (pe - 1).bit_length()):
+                if (c == 0 or c % pe != 0) and c < pe:
+                    for t in thresh_padded[chan_ind]:
+                        t_packed = pack_innermost_dim_as_hex_string(
+                            [t], wdt, bw_hexdigit, prefix=""
+                        ).item()
+                        thresh_stream.append(t_packed)
+                    chan_ind += 1
+                else:
+                    for z in padding:
+                        t_packed = pack_innermost_dim_as_hex_string(
+                            [z], wdt, bw_hexdigit, prefix=""
+                        ).item()
+                        thresh_stream.append(t_packed)
+        with open(weight_file_name, "w") as f:
+            for val in thresh_stream:
+                f.write(val + "\n")