diff --git a/src/finn/custom_op/fpgadataflow/streamingfclayer_batch.py b/src/finn/custom_op/fpgadataflow/streamingfclayer_batch.py
index a7ebff68749120868cae9ce5ac18d2856fe2cb8a..9c3bd3ac87b94f3e0ff11a2937bf5083aae614f6 100644
--- a/src/finn/custom_op/fpgadataflow/streamingfclayer_batch.py
+++ b/src/finn/custom_op/fpgadataflow/streamingfclayer_batch.py
@@ -87,7 +87,8 @@ class StreamingFCLayer_Batch(HLSCustomOp):
"numInputVectors": ("ints", False, [1]),
# memory mode for the FC weights
# const -- embedded weights, default, long compile/synth times
- # decoupled -- streaming weights
+ # decoupled -- streaming weights with weight streamer packaged inside IP
+ # external -- streaming weights with external streamer
"mem_mode": ("s", False, "const"),
# FPGA resource type for memories in decoupled mode
# auto -- let Vivado decide
@@ -105,14 +106,14 @@ class StreamingFCLayer_Batch(HLSCustomOp):
node = self.onnx_node
# set top name depending on mem_mode
mem_mode = self.get_nodeattr("mem_mode")
- if mem_mode == "const":
+ if mem_mode == "const" or mem_mode == "external":
prefixed_top_name = "%s_%s" % (node.name, node.name)
elif mem_mode == "decoupled":
prefixed_top_name = "%s_memstream" % (node.name)
else:
raise Exception(
- """Please set mem_mode to "const" or "decoupled", currently no other
- parameter value is supported!"""
+ """Please set mem_mode to "const", "decoupled", or "external",
+ currently no other parameter value is supported!"""
)
return prefixed_top_name
@@ -301,7 +302,10 @@ class StreamingFCLayer_Batch(HLSCustomOp):
def get_weightstream_width(self):
"""Returns weight stream width. Used only in decoupled mode."""
- if self.get_nodeattr("mem_mode") == "decoupled":
+ if (
+ self.get_nodeattr("mem_mode") == "decoupled"
+ or self.get_nodeattr("mem_mode") == "external"
+ ):
pe = self.get_nodeattr("PE")
simd = self.get_nodeattr("SIMD")
wp = self.get_weight_datatype().bitwidth()
@@ -484,7 +488,8 @@ class StreamingFCLayer_Batch(HLSCustomOp):
def generate_params(self, model, path):
mem_mode = self.get_nodeattr("mem_mode")
- # weights
+ code_gen_dir = path
+ # weights, if not external
weights = model.get_initializer(self.onnx_node.input[1])
# convert weights into hlslib-compatible format
weight_tensor = self.get_hls_compatible_weight_tensor(weights)
@@ -493,7 +498,6 @@ class StreamingFCLayer_Batch(HLSCustomOp):
# so use it as such for weight generation
if self.get_weight_datatype() == DataType.BIPOLAR:
export_wdt = DataType.BINARY
- code_gen_dir = path
if mem_mode == "const":
"""Saves weights into params.h"""
@@ -523,7 +527,7 @@ class StreamingFCLayer_Batch(HLSCustomOp):
f_weights.write(weight_hls_code)
f_weights.close()
- elif mem_mode == "decoupled":
+ elif mem_mode == "decoupled" or mem_mode == "external":
"""Saves weights in corresponding file format for cppsim or rtlsim"""
# transpose weight tensor from (1, PE, WMEM, SIMD) to (1, WMEM, PE, SIMD)
weight_tensor_unflipped = np.transpose(weight_tensor, (0, 2, 1, 3))
@@ -552,37 +556,37 @@ class StreamingFCLayer_Batch(HLSCustomOp):
os.path.join(code_gen_dir, "weights.npy"), weight_tensor_simd_flipped
)
- """Saves weights into .dat file"""
- # convert weight values into hexstring
- weight_width = self.get_weightstream_width()
- # pad to nearest 4 bits to get hex strings
- weight_width_padded = roundup_to_integer_multiple(weight_width, 4)
- weight_tensor_pe_flipped = pack_innermost_dim_as_hex_string(
- weight_tensor_pe_flipped, export_wdt, weight_width_padded, prefix=""
- )
- weight_stream_len = np.prod(weight_tensor_pe_flipped.shape)
- factor = math.ceil(weight_stream_len / 1024)
- # add zeroes to pad out file to 1024 entries
- weight_stream = weight_tensor_pe_flipped.flatten()
- pad_amt = (factor * 1024) - weight_stream_len
- weight_stream = np.pad(
- weight_stream, (0, pad_amt), mode="constant", constant_values="0"
- )
- weight_stream = weight_stream.copy()
- i = 0
- j = 0
- for val in weight_stream:
- if i == 1024:
- i = 0
- j += 1
- with open("{}/memblock_{}.dat".format(code_gen_dir, j), "a+") as f:
- f.write(val + "\n")
- i += 1
-
+ if mem_mode == "decoupled":
+ """Saves weights into .dat file"""
+ # convert weight values into hexstring
+ weight_width = self.get_weightstream_width()
+ # pad to nearest 4 bits to get hex strings
+ weight_width_padded = roundup_to_integer_multiple(weight_width, 4)
+ weight_tensor_pe_flipped = pack_innermost_dim_as_hex_string(
+ weight_tensor_pe_flipped, export_wdt, weight_width_padded, prefix=""
+ )
+ weight_stream_len = np.prod(weight_tensor_pe_flipped.shape)
+ factor = math.ceil(weight_stream_len / 1024)
+ # add zeroes to pad out file to 1024 entries
+ weight_stream = weight_tensor_pe_flipped.flatten()
+ pad_amt = (factor * 1024) - weight_stream_len
+ weight_stream = np.pad(
+ weight_stream, (0, pad_amt), mode="constant", constant_values="0"
+ )
+ weight_stream = weight_stream.copy()
+ i = 0
+ j = 0
+ for val in weight_stream:
+ if i == 1024:
+ i = 0
+ j += 1
+ with open("{}/memblock_{}.dat".format(code_gen_dir, j), "a+") as f:
+ f.write(val + "\n")
+ i += 1
else:
raise Exception(
- """Please set mem_mode to "const"i or "decoupled", currently no other
- parameter value is supported!"""
+ """Please set mem_mode to "const", "decoupled", or "external",
+ currently no other parameter value is supported!"""
)
# save thresholds in thresh.h
@@ -630,6 +634,7 @@ class StreamingFCLayer_Batch(HLSCustomOp):
def execute_node(self, context, graph):
mode = self.get_nodeattr("exec_mode")
+ mem_mode = self.get_nodeattr("mem_mode")
node = self.onnx_node
# TODO ensure codegen dir exists
@@ -698,7 +703,24 @@ class StreamingFCLayer_Batch(HLSCustomOp):
)
super().reset_rtlsim(sim)
super().toggle_clk(sim)
- output = self.rtlsim(sim, inp)
+ if mem_mode == "external":
+ wnbits = self.get_weightstream_width()
+ export_wdt = self.get_weight_datatype()
+ # we have converted bipolar weights to binary for export,
+ # so use it as such for weight generation
+ if self.get_weight_datatype() == DataType.BIPOLAR:
+ export_wdt = DataType.BINARY
+ wei = npy_to_rtlsim_input(
+ "{}/weights.npy".format(code_gen_dir), export_wdt, wnbits
+ )
+ io_dict = {
+ "inputs": {"in0": inp, "weights": wei},
+ "outputs": {"out": []},
+ }
+ self.rtlsim_multi_io(sim, io_dict)
+ output = io_dict["outputs"]["out"]
+ else:
+ output = self.rtlsim(sim, inp)
odt = self.get_output_datatype()
target_bits = odt.bitwidth()
packed_bits = self.get_outstream_width()
@@ -729,12 +751,12 @@ class StreamingFCLayer_Batch(HLSCustomOp):
if mem_mode == "const":
# self.code_gen_dict["$GLOBALS$"] += ['#include "params.h"']
pass
- elif mem_mode == "decoupled":
+ elif mem_mode == "decoupled" or mem_mode == "external":
self.code_gen_dict["$GLOBALS$"] += ['#include "mvau.hpp"']
else:
raise Exception(
- """Please set mem_mode to "const" or "decoupled", currently no other
- parameter value is supported!"""
+ """Please set mem_mode to "const", "decoupled", or "external",
+ currently no other parameter value is supported!"""
)
if self.calc_tmem() != 0:
# TODO find a better way of checking for no pregenerated thresholds
@@ -757,7 +779,7 @@ class StreamingFCLayer_Batch(HLSCustomOp):
numReps,
)
]
- if mem_mode == "decoupled":
+ if mem_mode == "decoupled" or mem_mode == "external":
wdt = self.get_weight_datatype()
self.code_gen_dict["$DEFINES$"].append(
"#define WP1 {}\n".format(wdt.bitwidth())
@@ -783,7 +805,7 @@ class StreamingFCLayer_Batch(HLSCustomOp):
)
mem_mode = self.get_nodeattr("mem_mode")
- if mem_mode == "decoupled":
+ if mem_mode == "decoupled" or mem_mode == "external":
wdt = self.get_weight_datatype()
elem_bits = wdt.bitwidth()
packed_bits = self.get_weightstream_width()
@@ -807,7 +829,7 @@ class StreamingFCLayer_Batch(HLSCustomOp):
'hls::stream<ap_uint<{}>> out ("out");'.format(self.get_outstream_width())
)
- if mem_mode == "decoupled":
+ if mem_mode == "decoupled" or mem_mode == "external":
self.code_gen_dict["$STREAMDECLARATIONS$"].append(
'hls::stream<ap_uint<{}>> weights ("weights");'.format(
self.get_weightstream_width()
@@ -835,7 +857,7 @@ class StreamingFCLayer_Batch(HLSCustomOp):
self.get_nodeattr("resType"),
)
]
- elif mem_mode == "decoupled":
+ elif mem_mode == "decoupled" or mem_mode == "external":
wdt = self.get_weight_datatype()
if wdt == DataType.BIPOLAR:
export_wdt = DataType.BINARY
@@ -856,8 +878,8 @@ class StreamingFCLayer_Batch(HLSCustomOp):
else:
raise Exception(
- """Please set mem_mode to "const" or "decoupled", currently no other
- parameter value is supported!"""
+ """Please set mem_mode to "const", "decoupled", or "external",
+ currently no other parameter value is supported!"""
)
def dataoutstrm(self):
@@ -903,7 +925,7 @@ class StreamingFCLayer_Batch(HLSCustomOp):
self.get_outstream_width(),
)
]
- elif mem_mode == "decoupled":
+ elif mem_mode == "decoupled" or mem_mode == "external":
self.code_gen_dict["$BLACKBOXFUNCTION$"] = [
"""void {}(
hls::stream<ap_uint<{}>> &in0,
@@ -952,7 +974,7 @@ class StreamingFCLayer_Batch(HLSCustomOp):
"complete dim=1"
)
)
- elif mem_mode == "decoupled":
+ elif mem_mode == "decoupled" or mem_mode == "external":
self.code_gen_dict["$PRAGMAS$"].append(
"#pragma HLS INTERFACE axis port=weights"
)
@@ -962,8 +984,8 @@ class StreamingFCLayer_Batch(HLSCustomOp):
else:
raise Exception(
- """Please set mem_mode to "const", currently no other
- parameter value is supported!"""
+ """Please set mem_mode to "const", "decoupled", or external,
+ currently no other parameter value is supported!"""
)
# the threshold tensor is acc_type [PE][TMEM][N_THRES]
@@ -1092,3 +1114,10 @@ class StreamingFCLayer_Batch(HLSCustomOp):
)
self.set_nodeattr("ip_vlnv", vlnv)
self.code_gen_dict.clear()
+
+ def get_verilog_top_module_intf_names(self):
+ intf_names = super().get_verilog_top_module_intf_names()
+ mem_mode = self.get_nodeattr("mem_mode")
+ if mem_mode == "external":
+ intf_names["s_axis"] = ["in0_V_V", "weights_V_V"]
+ return intf_names
diff --git a/tests/fpgadataflow/test_fpgadataflow_fclayer.py b/tests/fpgadataflow/test_fpgadataflow_fclayer.py
index fc5cdb7745945bee99564ba9ab19423a66d8e035..952d994076fc4da7e7f763d9f0fe303d8da0ff11 100644
--- a/tests/fpgadataflow/test_fpgadataflow_fclayer.py
+++ b/tests/fpgadataflow/test_fpgadataflow_fclayer.py
@@ -134,7 +134,7 @@ def prepare_inputs(input_tensor, idt, wdt):
# mem_mode: const or decoupled
-@pytest.mark.parametrize("mem_mode", ["const", "decoupled"])
+@pytest.mark.parametrize("mem_mode", ["const", "decoupled", "external"])
# activation: None or DataType
@pytest.mark.parametrize("act", [None, DataType.BIPOLAR, DataType.INT4])
# weight datatype
@@ -221,7 +221,7 @@ def test_fpgadataflow_fclayer_cppsim(mem_mode, idt, wdt, act, nf, sf, mw, mh):
# mem_mode: const or decoupled
-@pytest.mark.parametrize("mem_mode", ["const", "decoupled"])
+@pytest.mark.parametrize("mem_mode", ["const", "decoupled", "external"])
# activation: None or DataType
@pytest.mark.parametrize("act", [None, DataType.BIPOLAR, DataType.INT4])
# weight datatype