From 6c49cda900c35490e3e513fd75107a61e4b82033 Mon Sep 17 00:00:00 2001
From: Yaman Umuroglu <maltanar@gmail.com>
Date: Mon, 19 Apr 2021 11:17:22 +0100
Subject: [PATCH] [InsertIODMA] refactoring: use existing util functions
---
.../fpgadataflow/insert_iodma.py | 25 +++++++++++--------
1 file changed, 15 insertions(+), 10 deletions(-)
diff --git a/src/finn/transformation/fpgadataflow/insert_iodma.py b/src/finn/transformation/fpgadataflow/insert_iodma.py
index ebd7cbe02..27055a4fd 100644
--- a/src/finn/transformation/fpgadataflow/insert_iodma.py
+++ b/src/finn/transformation/fpgadataflow/insert_iodma.py
@@ -33,7 +33,6 @@ from finn.util.basic import get_by_name
from finn.custom_op.registry import getCustomOp
from finn.transformation.base import Transformation
from finn.transformation.general import SortGraph
-import finn.core.data_layout as DataLayout
import math
import numpy as np
@@ -59,6 +58,10 @@ class InsertIODMA(Transformation):
addr = 1: [(pe-1,simd*2-1),.......(0,simd+1),(0,simd)]
.
"""
+
+ # TODO: refactor this into streamingfclayer_batch.py, could go into
+ # make_weight_file except it doesn't write a file but returns a npy
+ # array instead
w_shape = weights.shape
assert len(w_shape) == 2, "weights withincorrect number of dims"
inp_w, out_w = w_shape
@@ -70,13 +73,15 @@ class InsertIODMA(Transformation):
addr = 0
for fr in range(out_w // pe):
for fc in range(inp_w // simd):
- tile = weights[
- (fc * simd) : ((fc + 1) * simd), (fr * pe) : ((fr + 1) * pe)
- ]
+ w0_lower = fc * simd
+ w0_upper = (fc + 1) * simd
+ w1_lower = fr * pe
+ w1_upper = (fr + 1) * pe
+ tile = weights[w0_lower:w0_upper, w1_lower:w1_upper]
for p in range(pe):
- reshaped_w[addr, (p * simd) : ((p + 1) * simd)] = tile[
- :, p
- ].transpose()
+ rw0_lower = p * simd
+ rw0_upper = (p + 1) * simd
+ reshaped_w[addr, rw0_lower:rw0_upper] = tile[:, p].transpose()
addr += 1
reshaped_w = np.flip(reshaped_w, axis=-1)
return reshaped_w
@@ -92,8 +97,7 @@ class InsertIODMA(Transformation):
fc_extw_nodes = list(
filter(
lambda x: x.op_type == "StreamingFCLayer_Batch"
- and get_by_name(x.attribute, "mem_mode") is not None
- and get_by_name(x.attribute, "mem_mode").s.decode("UTF-8") == "external"
+ and getCustomOp(x).get_nodeattr("mem_mode") == "external"
and model.find_producer(x.input[1]) is None,
all_nodes,
)
@@ -191,6 +195,7 @@ class InsertIODMA(Transformation):
)
model.graph.node.insert(0, dma_node)
for fc_node in fc_extw_nodes:
+ fc_inst = getCustomOp(fc_node)
fc_w_name = fc_node.input[1]
w_shape = model.get_tensor_shape(fc_w_name)
w_dtype = model.get_tensor_datatype(fc_w_name)
@@ -203,7 +208,7 @@ class InsertIODMA(Transformation):
# calculate width of stream output from DMA
pe = get_by_name(fc_node.attribute, "PE").i
simd = get_by_name(fc_node.attribute, "SIMD").i
- streamWidth = simd * pe * w_dtype.bitwidth()
+ streamWidth = fc_inst.get_weightstream_width_padded()
# make new buffer
W = model.get_initializer(fc_w_name)
iodma_mem = self.get_mem_init(W, pe, simd)
--
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