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new weight reorder for pw convolution

This commit is contained in:
Erjing (Matthew) Luo 2023-01-18 21:56:36 +08:00 committed by GitHub
parent 333ac9a8c9
commit ed6b0ec2fa
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1 changed files with 361 additions and 343 deletions
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58
dacsdc/export_hls_skynet.py
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@ -173,7 +173,7 @@ def process_batchnorm(model_param):
incbit = len(bit(inc)); biasbit = len(bit(bias))
larger lshift is better, but MBIT+incbit<48
'''
lshift = 16
lshift = 6
for conv in model_param[:-1]:
print(f'Process bn_{conv.n}, shape {conv.bn_w.shape},', end = ' ')
@ -206,23 +206,36 @@ def process_batchnorm(model_param):
#conv_last.biasbit = bitlength(conv_last.bias)
#print(f'conv_last biasbit {conv_last.biasbit}, div {conv_last.div}')
def reorder_weight(model_param, layers_simd, layers_pe):
def reorder_weight(model_param, layers_simd, layers_pe, layers_actp, layers_pep):
'''reorder_weight(model_param)
Reorder array for hlscode.
'''
for conv, simd, pe in zip(model_param, layers_simd, layers_pe):
for conv, simd, pe, actp, pep in zip(model_param, layers_simd, layers_pe, layers_actp, layers_pep):
print(f'Reorder conv_{conv.n}, w {conv.w.shape}', end='')
conv.simd = simd
conv.pe = pe
conv.actp = actp
conv.pep = pep
# process batchnorm
if conv.inc is not None:
conv.inc = conv.inc.reshape(conv.och//conv.pe, conv.pe).T
conv.inc = conv.inc.reshape(conv.och//conv.actp, conv.actp).T
if hasattr(conv, 'bias') and conv.bias is not None:
conv.bias = conv.bias.reshape(conv.och//conv.pe, conv.pe).T
conv.bias = conv.bias.reshape(conv.och//conv.actp, conv.actp).T
# process conv weight
if conv.k == 1:
w = conv.w # [och, ich, kr, kc]
g_ich = w.shape[1]
assert conv.och%(conv.pe * conv.pep) == 0, f"conv_{conv.n}, och {conv.och}, pe {conv.pe}, pep {conv.pep}"
assert g_ich%simd == 0, f"conv_{conv.n}, ich {g_ich}, simd {conv.simd}"
w = w.reshape(conv.och//(conv.pe * conv.pep), conv.pe, conv.pep, g_ich//simd, simd) # [och / (pe * pep), pe, pep, ich / simd, simd]
w = w.transpose(1,0,3,4,2) #[pe, och / (pe * pep), ich / simd, simd, pep]
w = w.reshape(conv.pe, -1, g_ich//simd, simd*conv.pep) # [pe, och / (pe * pep), ich / simd, simd * pep]
w = w.reshape(conv.pe, -1, simd*conv.pep) # hls format [pe, och/(pe * pep) * ich/simd, simd * pep]
else:
w = conv.w # [och, ich, kr, kc]
g_ich = w.shape[1]
assert conv.och%conv.pe == 0, f"conv_{conv.n}, och {conv.och}, pe {conv.pe}"
@ -237,9 +250,6 @@ def reorder_weight(model_param, layers_simd, layers_pe):
w = w.transpose(1,2,0,3,4) # [pe, k, och/pe, k*ich/simd, simd]
w = w.reshape(conv.pe, conv.k, -1, simd) # hls format [pe, k, och/pe*k*ich/simd, simd]
if conv.k == 1: # kernel size=1
w = w.reshape(conv.pe, -1, simd)
print(' ->', w.shape)
conv.w = w
@ -275,15 +285,6 @@ def write_hls_weights(model_param, path):
for conv in model_param:
n = conv.n
print(f"Write conv_{n} weight, pe {conv.pe}, simd {conv.simd}, wbit {conv.wbit}")
print(f"// layer: {n}, PE: {conv.pe}, SIMD: {conv.simd}, wbit: {conv.wbit}", file=f)
# print conv weight, merge [SIMD] value into one ap_uint
if conv.k>1:
print(f"const ap_uint<{conv.wbit * conv.simd}> conv_{n}_w[{conv.pe}][{conv.k}][{conv.w.shape[2]}]=", file=f)
else:
print(f"const ap_uint<{conv.wbit * conv.simd}> conv_{n}_w[{conv.pe}][{conv.w.shape[1]}]=", file=f)
hex_str = lambda x: '"' + hex(x) + '"'
def pack1d_str(arr): # x: 1d-array
x = 0
for v in arr[::-1]: # [!] reverse simd pack, it is related to hls implemention
@ -291,16 +292,33 @@ def write_hls_weights(model_param, path):
assert -1<<conv.wbit-1 <= v < 1<<conv.wbit-1, f'got v={v} while wbit={conv.wbit}'
x=(x<<conv.wbit) + (v&(2**conv.wbit-1))
return hex_str(x)
if conv.k == 1:
print(f"Write conv_{n} weight, pe {conv.pe}, pep{conv.pep}, simd {conv.simd}, actp {conv.actp}, wbit {conv.wbit}")
print(f"// layer: {n}, PE: {conv.pe}, PEP: {conv.pep}, SIMD: {conv.simd}, ACTP: {conv.actp}, wbit: {conv.wbit}", file=f)
# print conv weight, merge [SIMD] value into one ap_uint
print(f"const ap_uint<{conv.wbit * conv.pep * conv.simd}> conv_{n}_w[{conv.pe}][{conv.w.shape[1]}]=", file=f)
hex_str = lambda x: '"' + hex(x) + '"'
print_ndarray_recursion(conv.w, pack1d_str, f, stop=1)
print(';', file=f)
else:
print(f"Write conv_{n} weight, pe {conv.pe}, simd {conv.simd}, actp {conv.actp}, wbit {conv.wbit}")
print(f"// layer: {n}, PE: {conv.pe}, SIMD: {conv.simd}, ACTP: {conv.actp}, wbit: {conv.wbit}", file=f)
# print conv weight, merge [SIMD] value into one ap_uint
print(f"const ap_uint<{conv.wbit * conv.simd}> conv_{n}_w[{conv.pe}][{conv.k}][{conv.w.shape[2]}]=", file=f)
hex_str = lambda x: '"' + hex(x) + '"'
print_ndarray_recursion(conv.w, pack1d_str, f, stop=1)
print(';', file=f)
# print inc, bias
if conv.inc is not None:
print(f"const ap_int<{conv.incbit}> conv_{n}_inc[{conv.pe}][{conv.och//conv.pe}]=", file=f)
print(f"const ap_int<{conv.incbit}> conv_{n}_inc[{conv.actp}][{conv.och//conv.actp}]=", file=f)
print_ndarray_recursion(conv.inc, hex_str, f)
print(';', file=f)
if hasattr(conv, 'bias') and conv.bias is not None:
print(f"const ap_int<{conv.biasbit}> conv_{n}_bias[{conv.pe}][{conv.och//conv.pe}]=", file=f)
print(f"const ap_int<{conv.biasbit}> conv_{n}_bias[{conv.actp}][{conv.och//conv.actp}]=", file=f)
print_ndarray_recursion(conv.bias, hex_str, f)
print(';', file=f)
@ -338,6 +356,6 @@ if __name__=='__main__':
process_batchnorm(model_param) # get bn param before write hls config
torch.save(model_param, dir_output + 'model_param.pkl')
reorder_weight(model_param, simd_pe[:,0], simd_pe[:,1]) # get pe, simd param before write hls config
reorder_weight(model_param, simd_pe[:,0], simd_pe[:,1], simd_pe[:,2], simd_pe[:,3]) # get pe, simd, actp, pep param before write hls config
write_hls_config(model_param, dir_output)
write_hls_weights(model_param, dir_output)