From dd00db030f147bde64b05cc56c3d034e7bcac9e9 Mon Sep 17 00:00:00 2001
From: auphelia <jakobapk@web.de>
Date: Wed, 8 Jul 2020 17:02:12 +0100
Subject: [PATCH] [CustomOp] Add output activation support to VVAU node
---
.../vector_vector_activate_batch.py | 84 ++++++++++++++-----
1 file changed, 62 insertions(+), 22 deletions(-)
diff --git a/src/finn/custom_op/fpgadataflow/vector_vector_activate_batch.py b/src/finn/custom_op/fpgadataflow/vector_vector_activate_batch.py
index 5a7db03c1..d7339f761 100644
--- a/src/finn/custom_op/fpgadataflow/vector_vector_activate_batch.py
+++ b/src/finn/custom_op/fpgadataflow/vector_vector_activate_batch.py
@@ -11,13 +11,6 @@ from finn.util.data_packing import (
rtlsim_output_to_npy,
)
-# ONNX i/o tensor shape assumptions for Vector_Vector_Activate_Batch:
-# input 0 is the input tensor, shape (.., i_size) = (..., MW)
-# input 1 is the weight tensor, shape (i_size, o_size) = (MW, MH)
-# (optional) input 2 is the thresholds tensor, shape (o_size, n_thres)
-# output 0 is the output tensor, shape (.., o_size) = (..., MH)
-# the ... here can be any shape (representing groups of vectors)
-
class Vector_Vector_Activate_Batch(HLSCustomOp):
"""Class that corresponds to finn-hlslib Vector_Vector_Activate_Batch function"""
@@ -158,22 +151,13 @@ class Vector_Vector_Activate_Batch(HLSCustomOp):
ret = dict()
inp_hls_str = self.get_input_datatype().get_hls_datatype_str()
out_hls_str = self.get_output_datatype().get_hls_datatype_str()
- # inp_is_binary = self.get_input_datatype() == DataType.BINARY
- # wt_is_binary = self.get_weight_datatype() == DataType.BINARY
inp_is_bipolar = self.get_input_datatype() == DataType.BIPOLAR
wt_is_bipolar = self.get_weight_datatype() == DataType.BIPOLAR
# fill in TSrcI and TWeightI
# TODO handle non-bipolar binary inputs
- if inp_is_bipolar and wt_is_bipolar:
- ret["TSrcI"] = "Recast<XnorMul>"
- ret["TWeightI"] = "Identity"
- elif (not inp_is_bipolar) and wt_is_bipolar:
- ret["TSrcI"] = "Slice<%s>" % inp_hls_str
- ret["TWeightI"] = "Recast<Binary>"
- elif inp_is_bipolar and (not wt_is_bipolar):
- ret["TSrcI"] = "Recast<Binary>"
- ret["TWeightI"] = "Identity"
- elif (not inp_is_bipolar) and (not wt_is_bipolar):
+ if inp_is_bipolar or wt_is_bipolar:
+ raise Exception("VVAU node doesn't support bipolar values yet.")
+ else:
ret["TSrcI"] = "Slice<%s>" % inp_hls_str
ret["TWeightI"] = "Identity"
@@ -202,6 +186,33 @@ class Vector_Vector_Activate_Batch(HLSCustomOp):
ret = ret.reshape(1, pe, wmem, 1)
return ret
+ def get_hls_compatible_threshold_tensor(self, orig_thres_matrix):
+ ch = self.get_nodeattr("Channels")
+ pe = self.get_nodeattr("PE")
+ tmem = self.calc_tmem()
+ assert ch % pe == 0, "Requirement Channels divisable by PE is violated."
+ assert (
+ orig_thres_matrix.ndim == 2
+ ), """Threshold matrix dimension is
+ not as expected (2)."""
+ n_thres_steps = orig_thres_matrix.shape[1]
+ ret = orig_thres_matrix
+ # distribute rows between PEs
+ ret = interleave_matrix_outer_dim_from_partitions(ret, pe)
+ assert (
+ ret.shape[0] == pe
+ ), """First dimension after distribution of the
+ rows between PEs is not as expected (pe)"""
+ assert (
+ ret.shape[1] == tmem
+ ), """Second dimension after distribution of the
+ rows between PEs is not as expected (tmem)"""
+ assert (
+ ret.shape[2] == n_thres_steps
+ ), """Third dimension after distribution of the
+ rows between PEs is not as expected (n_thres_steps)"""
+ return ret.reshape(1, pe, tmem, n_thres_steps)
+
def generate_params(self, model, path):
# weights
weights = model.get_initializer(self.onnx_node.input[1])
@@ -238,6 +249,36 @@ class Vector_Vector_Activate_Batch(HLSCustomOp):
f_weights.write(weight_hls_code)
f_weights.close()
+ # save thresholds in thresh.h
+ if len(self.onnx_node.input) > 2:
+ thresholds = model.get_initializer(self.onnx_node.input[2])
+ if thresholds is not None:
+ threshold_tensor = self.get_hls_compatible_threshold_tensor(thresholds)
+ tdt = DataType.INT32
+ thresholds_hls_code = numpy_to_hls_code(
+ threshold_tensor, tdt, "thresholds", False, True
+ )
+ # write thresholds into thresh.h
+ f_thresh = open("{}/thresh.h".format(code_gen_dir), "w")
+ tdt_hls = tdt.get_hls_datatype_str()
+ # use binary to export bipolar activations
+ odt = self.get_output_datatype()
+ odt_hls = odt.get_hls_datatype_str()
+ f_thresh.write(
+ "static ThresholdsActivation<{},{},{},{},{},{},{}> threshs \
+ = ".format(
+ self.calc_tmem(),
+ self.get_nodeattr("PE"),
+ threshold_tensor.shape[-1],
+ tdt_hls,
+ odt_hls,
+ self.get_nodeattr("ActVal"),
+ "std::less_equal<%s>" % tdt_hls,
+ )
+ )
+ f_thresh.write(thresholds_hls_code)
+ f_thresh.close()
+
def execute_node(self, context, graph):
mode = self.get_nodeattr("exec_mode")
node = self.onnx_node
@@ -336,9 +377,8 @@ class Vector_Vector_Activate_Batch(HLSCustomOp):
def global_includes(self):
self.code_gen_dict["$GLOBALS$"] = ['#include "weights.hpp"']
self.code_gen_dict["$GLOBALS$"] += ['#include "activations.hpp"']
- # if self.calc_tmem() != 0:
- # # TODO find a better way of checking for no pregenerated thresholds
- # self.code_gen_dict["$GLOBALS$"] += ['#include "thresh.h"']
+ if self.calc_tmem() != 0:
+ self.code_gen_dict["$GLOBALS$"] += ['#include "thresh.h"']
def defines(self, var):
dim = self.get_nodeattr("Dim")
--
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