diff --git a/src/finn/qnn-data/templates/driver/driver_base.py b/src/finn/qnn-data/templates/driver/driver_base.py
index 58ef94bfd7a03bb0d5fb8e5a7bf5695f6b961527..01f332b3b353ff89c1b70b2cd7e749374fd392e1 100644
--- a/src/finn/qnn-data/templates/driver/driver_base.py
+++ b/src/finn/qnn-data/templates/driver/driver_base.py
@@ -36,6 +36,7 @@ from finn.util.data_packing import (
finnpy_to_packed_bytearray,
packed_bytearray_to_finnpy,
)
+from warnings import warn
# Driver base class for FINN-generated dataflow accelerators.
# The particulars of the generated accelerator are specified via the
@@ -84,11 +85,17 @@ class FINNExampleOverlay(Overlay):
self.batch_size = batch_size
self.fclk_mhz = fclk_mhz
if self.platform == "alveo":
- self.idma = self.idma0
+ if "input_dma_name" in io_shape_dict.keys():
+ self.idma = getattr(self,io_shape_dict["input_dma_name"])
+ else:
+ self.idma = self.idma0
self.odma = self.odma0
self.odma_handle = None
elif self.platform == "zynq-iodma":
- self.idma = self.idma0
+ if "input_dma_name" in io_shape_dict.keys():
+ self.idma = getattr(self,io_shape_dict["input_dma_name"])
+ else:
+ self.idma = self.idma0
self.odma = self.odma0
# set the clock frequency as specified by user during transformations
if self.fclk_mhz > 0:
@@ -96,8 +103,67 @@ class FINNExampleOverlay(Overlay):
else:
raise ValueError("Supported platforms are zynq-iodma alveo")
# load any runtime weights
+ self.external_weights = []
+ self.load_external_weights()
self.load_runtime_weights()
+ def load_external_weights(self):
+ """Load any existing runtime weights from the specified dir into the
+ appropriate layer of the accelerator. Note that this must be enabled
+ during the accelerator build process. The runtime weights directory
+ is specified as the class member ``runtime_weight_dir``.
+
+ Parameters
+ ----------
+ flush_accel: bool
+ Run the accelerator with dummy input after weights are written to
+ flush any stale weight data in the weight streamer FIFOs.
+ verify: bool
+ Whether the written weights will be re-read and verified.
+ """
+
+
+ w_filenames = []
+ if not os.path.isdir(self.runtime_weight_dir):
+ return
+ for (dirpath, dirnames, filenames) in os.walk(self.runtime_weight_dir):
+ w_filenames.extend(filenames)
+
+ tmp_weight_dict = {}
+
+ for w_filename in w_filenames:
+ if w_filename.endswith(".npy"):
+ weight_tensor = np.load(self.runtime_weight_dir + "/" + w_filename)
+ else:
+ continue
+
+ idma_name = w_filename.split(".")[0]
+ tmp_weight_dict[idma_name] = weight_tensor
+
+
+ if self.platform != "alveo" and len(tmp_weight_dict)>0:
+ #Todo: add zynq support pynq API is different
+ warn("external_weights are not yet supported for non-Alveo builds")
+ return
+
+ for idma_name in tmp_weight_dict.keys():
+ if idma_name in self.ip_dict.keys():
+ iwdma = getattr(self, idma_name)
+ weight_tensor = tmp_weight_dict[idma_name]
+ weight_buf = allocate(weight_tensor.shape, dtype=np.uint8)
+ weight_buf[:] = weight_tensor
+ weight_buf.sync_to_device()
+
+ self.external_weights +=[(iwdma,weight_buf)]
+
+ if "number_of_external_weights" in self._io_shape_dict:
+ hw_ext_weights = self._io_shape_dict["number_of_external_weights"]
+ assert len(self.external_weights) == hw_ext_weights, (
+ "Number of hardware external weights and number of external " +
+ "weight tensors available do not match. \n"+
+ "Is runtime_weight_dir pointing to the correct folder?")
+
+
def load_runtime_weights(self, flush_accel=True, verify=True):
"""Load any existing runtime weights from the specified dir into the
appropriate layer of the accelerator. Note that this must be enabled
@@ -122,6 +188,8 @@ class FINNExampleOverlay(Overlay):
if w_filename.endswith(".dat"):
with open(self.runtime_weight_dir + "/" + w_filename, "r") as f:
dat = f.read()
+ else:
+ continue
layer_w = np.fromiter(
[int(x, 16) for x in dat.strip().split()], dtype=np.uint32
)
@@ -288,6 +356,8 @@ class FINNExampleOverlay(Overlay):
elif self.platform == "alveo":
assert self.odma_handle is None, "Output DMA is already running"
self.idma.start(self.ibuf_packed_device, batch_size)
+ for iwdma, iwbuf in self.external_weights:
+ iwdma.start(iwbuf,batch_size)
self.odma_handle = self.odma.start(self.obuf_packed_device, batch_size)
else:
raise Exception("Unrecognized platform: %s" % self.platform)
diff --git a/src/finn/transformation/fpgadataflow/make_pynq_driver.py b/src/finn/transformation/fpgadataflow/make_pynq_driver.py
index ac5cdb4f5389cd2f30f180773741f140b7145822..de5e180f59b1d731e71e523b342ccd8c0282c464 100644
--- a/src/finn/transformation/fpgadataflow/make_pynq_driver.py
+++ b/src/finn/transformation/fpgadataflow/make_pynq_driver.py
@@ -38,6 +38,24 @@ import warnings
import pkg_resources as pk
from . import template_driver
from finn.core.modelwrapper import ModelWrapper
+import numpy as np
+
+from finn.util.data_packing import pack_innermost_dim_as_hex_string, hexstring2npbytearray
+from finn.util.basic import (
+ roundup_to_integer_multiple,
+)
+
+
+def to_external_tensor(init,w_dtype):
+ weight_width = init.shape[1]*w_dtype.bitwidth()
+ weight_width_padded = roundup_to_integer_multiple(weight_width, 4)
+ hex_init = pack_innermost_dim_as_hex_string(init,w_dtype,weight_width_padded, prefix="0x")
+ ext_weight=np.array([], dtype=np.uint8)
+ for line in hex_init:
+ array_line = [x for x in reversed(hexstring2npbytearray(line, remove_prefix="0x"))]
+ ext_weight = np.append( ext_weight, array_line)
+
+ return ext_weight
class MakePYNQDriver(Transformation):
"""Create PYNQ Python code to correctly interface the generated
@@ -98,7 +116,38 @@ class MakePYNQDriver(Transformation):
)
i_tensor_shape_packed = i_tensor_dummy_packed.shape
o_tensor_shape_packed = o_tensor_dummy_packed.shape
-
+
+
+ #generate external weights npy files
+ weights_dir = pynq_driver_dir + "/runtime_weights"
+
+ os.makedirs(weights_dir)
+ idma_idx = 0
+ ext_weight_dma_cnt = 0
+
+ for node in model.graph.node:
+ assert node.op_type == "StreamingDataflowPartition", (
+ "CreateDataflowPartition needs to be applied before driver generation")
+
+ producer = model.find_producer(node.input[0])
+ init_tensor = model.get_initializer(node.input[0])
+
+ if producer is None : # input dma?
+ idma_name = "idma" + str(idma_idx)
+ if init_tensor is not None: # input weights dma?
+ ext_weight_dma_cnt += 1
+ w_dtype = model.get_tensor_datatype(node.input[0])
+ init_external_tensor = to_external_tensor(init_tensor,w_dtype)
+ np.save(weights_dir+"/"+ idma_name+".npy",init_external_tensor)
+ if self.platform != "alveo":
+ #Todo: add support in driver_base.py
+ warn("external_weights are not yet supported for non-Alveo builds")
+ else:
+ net_input_name = idma_name
+
+ idma_idx += 1
+
+
# fill in the driver template
driver_py = pynq_driver_dir + "/driver.py"
driver = template_driver.pynq_driver_template
@@ -122,6 +171,8 @@ class MakePYNQDriver(Transformation):
driver = driver.replace("$OUTPUT_SHAPE_NORMAL$", mss(o_tensor_shape_normal))
driver = driver.replace("$OUTPUT_SHAPE_FOLDED$", mss(o_tensor_shape_folded))
driver = driver.replace("$OUTPUT_SHAPE_PACKED$", mss(o_tensor_shape_packed))
+ driver = driver.replace("$INPUT_DMA_NAME$", "'%s'" % net_input_name)
+ driver = driver.replace("$EXT_WEIGHT_NUM$", str(ext_weight_dma_cnt) )
with open(driver_py, "w") as f:
f.write(driver)
@@ -148,9 +199,7 @@ class MakePYNQDriver(Transformation):
shutil.copytree(dtp_root, pynq_driver_dir + "/finn/core")
# generate weight files for runtime-writable layers
- weights_dir = pynq_driver_dir + "/runtime_weights"
- os.makedirs(weights_dir)
for sdp_ind, sdp_node in enumerate(model.graph.node):
assert sdp_node.op_type == "StreamingDataflowPartition"
# get dataflow model
@@ -174,4 +223,7 @@ class MakePYNQDriver(Transformation):
)
else:
continue
+
+
+
return (model, False)
\ No newline at end of file
diff --git a/src/finn/transformation/fpgadataflow/template_driver.py b/src/finn/transformation/fpgadataflow/template_driver.py
index b595205714d8cb630816d2b42fe96640e49e506e..5265835dd2530a5c93ceefbef629a43d6f33de52 100644
--- a/src/finn/transformation/fpgadataflow/template_driver.py
+++ b/src/finn/transformation/fpgadataflow/template_driver.py
@@ -77,7 +77,9 @@ io_shape_dict = {
"ishape_folded" : $INPUT_SHAPE_FOLDED$,
"oshape_folded" : $OUTPUT_SHAPE_FOLDED$,
"ishape_packed" : $INPUT_SHAPE_PACKED$,
- "oshape_packed" : $OUTPUT_SHAPE_PACKED$
+ "oshape_packed" : $OUTPUT_SHAPE_PACKED$,
+ "input_dma_name" : $INPUT_DMA_NAME$,
+ "number_of_external_weights": $EXT_WEIGHT_NUM$
}
if __name__ == "__main__":