From 9b1c84828b2b75c9467f1dd1f900aaf05f5574c5 Mon Sep 17 00:00:00 2001
From: auphelia <jakobapk@web.de>
Date: Mon, 9 Dec 2019 10:41:58 +0000
Subject: [PATCH] [notebook - code gen] Finished code generation part of the
notebook
---
.../FINN-CodeGenerationAndCompilation.ipynb | 131 +++++++++++++++++-
1 file changed, 128 insertions(+), 3 deletions(-)
diff --git a/notebooks/FINN-CodeGenerationAndCompilation.ipynb b/notebooks/FINN-CodeGenerationAndCompilation.ipynb
index d911d9dec..e3f0f03e8 100644
--- a/notebooks/FINN-CodeGenerationAndCompilation.ipynb
+++ b/notebooks/FINN-CodeGenerationAndCompilation.ipynb
@@ -30,7 +30,8 @@
"## Outline\n",
"-------------\n",
"* <font size=\"3\">Example model</font>\n",
- "* <font size=\"3\">Code generation</font>"
+ "* <font size=\"3\">Code generation</font>\n",
+ "* <font size=\"3\">Compilation</font>"
]
},
{
@@ -329,7 +330,7 @@
},
{
"cell_type": "code",
- "execution_count": 11,
+ "execution_count": 13,
"metadata": {},
"outputs": [
{
@@ -385,7 +386,7 @@
},
{
"cell_type": "code",
- "execution_count": 21,
+ "execution_count": 14,
"metadata": {},
"outputs": [
{
@@ -422,6 +423,130 @@
"showSrc(StreamingFCLayer_Batch.code_generation)"
]
},
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "<font size=\"3\">Except for the function `generate_params(model)` all functions needed to fill the template correspond to the `$ $` variable names, i.e. function `defines()` returns the part of the c++ code that replaces `$DEFINES$` in the template. The individual functions are member functions of the class HLSCustomOp and are defined in each CustomOp. The code for a StreamingFCLayer_Batch node can be looked up in the [code](https://github.com/Xilinx/finn/blob/dev/src/finn/custom_op/fpgadataflow/streamingfclayer_batch.py).</font> "
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "<font size=\"3\">A special function for code generation for the StreamingFCLayer_Batch node is the `generate_params(model)` function. Besides the normal input tensor, a fc layer has weight values as input and can get additional thresholds for activation. This function reads the values for the weights and thresholds via the `get_initializer` function of the ModelWrapper and writes them c++ conform in .h files, which are added to the includes. \n",
+ "\n",
+ "The `generate_params` function of the StreamingFCLayer_Batch is shown below.</font>"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 15,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ " def generate_params(self, model):\n",
+ " # weights\n",
+ " weights = model.get_initializer(self.onnx_node.input[1])\n",
+ " # convert weights into hlslib-compatible format\n",
+ " weight_tensor = self.get_hls_compatible_weight_tensor(weights)\n",
+ " export_wdt = self.get_weight_datatype()\n",
+ " # we have converted bipolar weights to binary for export,\n",
+ " # so use it as such for weight generation\n",
+ " if self.get_weight_datatype() == DataType.BIPOLAR:\n",
+ " export_wdt = DataType.BINARY\n",
+ " weight_hls_code = numpy_to_hls_code(\n",
+ " weight_tensor, export_wdt, \"weights\", True, True\n",
+ " )\n",
+ " # write weights into params.h\n",
+ " code_gen_dir = self.get_nodeattr(\"code_gen_dir\")\n",
+ " f_weights = open(\"{}/params.h\".format(code_gen_dir), \"w\")\n",
+ "\n",
+ " if export_wdt.bitwidth() != 1:\n",
+ " f_weights.write(\n",
+ " \"static FixedPointWeights<{},{},{},{}> weights = \".format(\n",
+ " self.get_nodeattr(\"SIMD\"),\n",
+ " export_wdt.get_hls_datatype_str(),\n",
+ " self.get_nodeattr(\"PE\"),\n",
+ " self.calc_wmem(),\n",
+ " )\n",
+ " )\n",
+ " else:\n",
+ " f_weights.write(\n",
+ " \"static BinaryWeights<{},{},{}> weights = \".format(\n",
+ " self.get_nodeattr(\"SIMD\"), self.get_nodeattr(\"PE\"), self.calc_wmem()\n",
+ " )\n",
+ " )\n",
+ " f_weights.write(weight_hls_code)\n",
+ " f_weights.close()\n",
+ " # thresholds\n",
+ " if len(self.onnx_node.input) > 2:\n",
+ " thresholds = model.get_initializer(self.onnx_node.input[2])\n",
+ " if thresholds is not None:\n",
+ " threshold_tensor = self.get_hls_compatible_threshold_tensor(thresholds)\n",
+ " tdt = DataType.INT32\n",
+ " # use UINT32 threshold export for bipolar times bipolar\n",
+ " inp_is_bipolar = self.get_input_datatype() == DataType.BIPOLAR\n",
+ " wt_is_bipolar = self.get_weight_datatype() == DataType.BIPOLAR\n",
+ " # reinterpret inp/wt as bipolar if bin_xnor_mode is iset\n",
+ " inp_is_binary = self.get_input_datatype() == DataType.BINARY\n",
+ " wt_is_binary = self.get_weight_datatype() == DataType.BINARY\n",
+ " bin_xnor_mode = self.get_nodeattr(\"binaryXnorMode\") == 1\n",
+ " inp_is_bipolar = inp_is_bipolar or (inp_is_binary and bin_xnor_mode)\n",
+ " wt_is_bipolar = wt_is_bipolar or (wt_is_binary and bin_xnor_mode)\n",
+ " if inp_is_bipolar and wt_is_bipolar:\n",
+ " tdt = DataType.UINT32\n",
+ " thresholds_hls_code = numpy_to_hls_code(\n",
+ " threshold_tensor, tdt, \"thresholds\", False, True\n",
+ " )\n",
+ " # write thresholds into thresh.h\n",
+ " code_gen_dir = self.get_nodeattr(\"code_gen_dir\")\n",
+ " f_thresh = open(\"{}/thresh.h\".format(code_gen_dir), \"w\")\n",
+ " tdt_hls = tdt.get_hls_datatype_str()\n",
+ " # use binary to export bipolar activations\n",
+ " export_odt = self.get_output_datatype()\n",
+ " if self.get_output_datatype() == DataType.BIPOLAR:\n",
+ " export_odt = DataType.BINARY\n",
+ " odt_hls = export_odt.get_hls_datatype_str()\n",
+ " f_thresh.write(\n",
+ " \"static ThresholdsActivation<{},{},{},{},{},{},{}> threshs \\\n",
+ " = \".format(\n",
+ " self.calc_tmem(),\n",
+ " self.get_nodeattr(\"PE\"),\n",
+ " threshold_tensor.shape[-1],\n",
+ " tdt_hls,\n",
+ " odt_hls,\n",
+ " self.get_nodeattr(\"ActVal\"),\n",
+ " \"std::less_equal<%s>\" % tdt_hls,\n",
+ " )\n",
+ " )\n",
+ " f_thresh.write(thresholds_hls_code)\n",
+ " f_thresh.close()\n",
+ "\n"
+ ]
+ }
+ ],
+ "source": [
+ "showSrc(StreamingFCLayer_Batch.generate_params)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "<font size=\"3\">The generated code is written to the previously created temporary directory and the node attribute `code_gen_dir` is set. This completes the code generation for executing a single CustomOp and the next step is compilation. </font>"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Compilation"
+ ]
+ },
{
"cell_type": "code",
"execution_count": null,
--
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