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replace_decoder_attn.py
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import onnx
import onnx_graphsurgeon as gs
import numpy as np
import sys
@gs.Graph.register()
def replace_attn(self, inputs, outputs, name, attrs):
# Disconnect output nodes of all input tensors
# for inp in inputs:
# inp.outputs.clear()
# Disconnet input nodes of all output tensors
for out in outputs:
out.inputs.clear()
# Insert the new node.
return self.layer(op="MultiHeadAttn",
inputs=inputs,
outputs=outputs,
name=name,
attrs=attrs,
)
@gs.Graph.register()
def replace_layer_norm(self, inputs, outputs, name):
# Disconnect output nodes of all input tensors
# for inp in inputs:
# inp.outputs.clear()
# Disconnet input nodes of all output tensors
for out in outputs:
out.inputs.clear()
# Insert the new node.
return self.layer(op="LayerNormPlugin",
inputs=inputs,
outputs=outputs,
name=name,
)
def parent(node, num=0):
if node.inputs[num].inputs[0].op in ["QuantizeLinear", "DequantizeLinear"]:
return node.inputs[num].inputs[0].inputs[0].inputs[0].inputs[0].inputs[0]
else:
return node.inputs[num].inputs[0]
def find_masked_softmax_nodes(graph):
out_nodes = []
for node in graph.nodes:
if node.op == "Softmax":
if node.inputs[0].inputs[0].op == "Where" and \
node.inputs[0].inputs[0].inputs[2].inputs[0].op == "Div" and \
node.outputs[0].outputs[0].op == "Where":
div_node = node.inputs[0].inputs[0].inputs[2].inputs[0]
qk_mm_node = parent(div_node, 0)
q_mm_node = parent(parent(parent(parent(qk_mm_node, 0)), 0), 1)
k_mm_node = parent(parent(parent(parent(qk_mm_node, 1)), 0), 1)
attn_mask = "self_attn_mask"
if q_mm_node.inputs[0].name != k_mm_node.inputs[0].name:
attn_mask = "cross_attn_mask"
out_nodes += [{
"inps":[node.inputs[0].inputs[0].inputs[2].inputs[0].inputs[0].name,
attn_mask,],
"outs" : [node.outputs[0].outputs[0].outputs[0].name],
}]
return out_nodes
@gs.Graph.register()
def replace_masked_softmax(self, inputs, outputs, name):
# Disconnect output nodes of all input tensors
# for inp in inputs:
# inp.outputs.clear()
# Disconnet input nodes of all output tensors
for out in outputs:
out.inputs.clear()
# Insert the new node.
return self.layer(op="AttnMaskedSoftmax",
inputs=inputs,
outputs=outputs,
name=name,
)
cross_attn_nodes = [
{"inps" : ["476", # q
"214", # enc_in
"cross_attn_mask", # mask
"1793", "decoder.decoders.0.src_attn.linear_q.bias",
"1797", "decoder.decoders.0.src_attn.linear_k.bias",
"1801", "decoder.decoders.0.src_attn.linear_v.bias",
"1807", "decoder.decoders.0.src_attn.linear_out.bias",
"decoder.decoders.0.norm2.weight",
"decoder.decoders.0.norm2.bias",
],
"outs" : ["575"]},
{"inps" : ["693", # q
"214", # enc_in
"cross_attn_mask", # mask
"1825", "decoder.decoders.1.src_attn.linear_q.bias",
"1829", "decoder.decoders.1.src_attn.linear_k.bias",
"1833", "decoder.decoders.1.src_attn.linear_v.bias",
"1839", "decoder.decoders.1.src_attn.linear_out.bias",
"decoder.decoders.1.norm2.weight",
"decoder.decoders.1.norm2.bias",
],
"outs" : ["792"]},
{"inps" : ["910", # q
"214", # enc_in
"cross_attn_mask", # mask
"1857", "decoder.decoders.2.src_attn.linear_q.bias",
"1861", "decoder.decoders.2.src_attn.linear_k.bias",
"1865", "decoder.decoders.2.src_attn.linear_v.bias",
"1871", "decoder.decoders.2.src_attn.linear_out.bias",
"decoder.decoders.2.norm2.weight",
"decoder.decoders.2.norm2.bias",
],
"outs" : ["1009"]},
{"inps" : ["1127", # q
"214", # enc_in
"cross_attn_mask", # mask
"1889", "decoder.decoders.3.src_attn.linear_q.bias",
"1893", "decoder.decoders.3.src_attn.linear_k.bias",
"1897", "decoder.decoders.3.src_attn.linear_v.bias",
"1903", "decoder.decoders.3.src_attn.linear_out.bias",
"decoder.decoders.3.norm2.weight",
"decoder.decoders.3.norm2.bias",
],
"outs" : ["1226"]},
{"inps" : ["1344", # q
"214", # enc_in
"cross_attn_mask", # mask
"1921", "decoder.decoders.4.src_attn.linear_q.bias",
"1925", "decoder.decoders.4.src_attn.linear_k.bias",
"1929", "decoder.decoders.4.src_attn.linear_v.bias",
"1935", "decoder.decoders.4.src_attn.linear_out.bias",
"decoder.decoders.4.norm2.weight",
"decoder.decoders.4.norm2.bias",
],
"outs" : ["1443"]},
{"inps" : ["1561", # q
"214", # enc_in
"cross_attn_mask", # mask
"1953", "decoder.decoders.5.src_attn.linear_q.bias",
"1957", "decoder.decoders.5.src_attn.linear_k.bias",
"1961", "decoder.decoders.5.src_attn.linear_v.bias",
"1967", "decoder.decoders.5.src_attn.linear_out.bias",
"decoder.decoders.5.norm2.weight",
"decoder.decoders.5.norm2.bias",
],
"outs" : ["1660"]},
]
self_attn_nodes = [
{"inps" : ["377", # q
"377", # enc_in
"self_attn_mask", # mask
"1778", "decoder.decoders.0.self_attn.linear_q.bias",
"1782", "decoder.decoders.0.self_attn.linear_k.bias",
"1786", "decoder.decoders.0.self_attn.linear_v.bias",
"1792", "decoder.decoders.0.self_attn.linear_out.bias",
"decoder.decoders.0.norm1.weight",
"decoder.decoders.0.norm1.bias",
],
"outs" : ["476"]},
{"inps" : ["594", # q
"594", # enc_in
"self_attn_mask", # mask
"1810", "decoder.decoders.1.self_attn.linear_q.bias",
"1814", "decoder.decoders.1.self_attn.linear_k.bias",
"1818", "decoder.decoders.1.self_attn.linear_v.bias",
"1824", "decoder.decoders.1.self_attn.linear_out.bias",
"decoder.decoders.1.norm1.weight",
"decoder.decoders.1.norm1.bias",
],
"outs" : ["693"]},
{"inps" : ["811", # q
"811", # enc_in
"self_attn_mask", # mask
"1842", "decoder.decoders.2.self_attn.linear_q.bias",
"1846", "decoder.decoders.2.self_attn.linear_k.bias",
"1850", "decoder.decoders.2.self_attn.linear_v.bias",
"1856", "decoder.decoders.2.self_attn.linear_out.bias",
"decoder.decoders.2.norm1.weight",
"decoder.decoders.2.norm1.bias",
],
"outs" : ["910"]},
{"inps" : ["1028", # q
"1028", # enc_in
"self_attn_mask", # mask
"1874", "decoder.decoders.3.self_attn.linear_q.bias",
"1878", "decoder.decoders.3.self_attn.linear_k.bias",
"1882", "decoder.decoders.3.self_attn.linear_v.bias",
"1888", "decoder.decoders.3.self_attn.linear_out.bias",
"decoder.decoders.3.norm1.weight",
"decoder.decoders.3.norm1.bias",
],
"outs" : ["1127"]},
{"inps" : ["1245", # q
"1245", # enc_in
"self_attn_mask", # mask
"1906", "decoder.decoders.4.self_attn.linear_q.bias",
"1910", "decoder.decoders.4.self_attn.linear_k.bias",
"1914", "decoder.decoders.4.self_attn.linear_v.bias",
"1920", "decoder.decoders.4.self_attn.linear_out.bias",
"decoder.decoders.4.norm1.weight",
"decoder.decoders.4.norm1.bias",
],
"outs" : ["1344"]},
{"inps" : ["1462", # q
"1462", # enc_in
"self_attn_mask", # mask
"1938", "decoder.decoders.5.self_attn.linear_q.bias",
"1942", "decoder.decoders.5.self_attn.linear_k.bias",
"1946", "decoder.decoders.5.self_attn.linear_v.bias",
"1952", "decoder.decoders.5.self_attn.linear_out.bias",
"decoder.decoders.5.norm1.weight",
"decoder.decoders.5.norm1.bias",
],
"outs" : ["1561"]},
]
layer_norm_nodes = [
{"inps" : ["575", # q
"decoder.decoders.0.norm3.weight",
"decoder.decoders.0.norm3.bias",
],
"outs" : ["586"]},
{"inps" : ["792", # q
"decoder.decoders.1.norm3.weight",
"decoder.decoders.1.norm3.bias",
],
"outs" : ["803"]},
{"inps" : ["1009", # q
"decoder.decoders.2.norm3.weight",
"decoder.decoders.2.norm3.bias",
],
"outs" : ["1020"]},
{"inps" : ["1226", # q
"decoder.decoders.3.norm3.weight",
"decoder.decoders.3.norm3.bias",
],
"outs" : ["1237"]},
{"inps" : ["1443", # q
"decoder.decoders.4.norm3.weight",
"decoder.decoders.4.norm3.bias",
],
"outs" : ["1454"]},
{"inps" : ["1660", # q
"decoder.decoders.5.norm3.weight",
"decoder.decoders.5.norm3.bias",
],
"outs" : ["1671"]},
{"inps" : ["1679", # q
"decoder.after_norm.weight",
"decoder.after_norm.bias",
],
"outs" : ["1690"]},
]
if __name__ == "__main__":
input_mdl = sys.argv[1]
output_mdl = sys.argv[2]
graph = gs.import_onnx(onnx.load(input_mdl))
self_attn_mask = gs.Variable(name="self_attn_mask", shape=["B_Attn", 63, 63], dtype=np.float32)
cross_attn_mask = gs.Variable(name="cross_attn_mask", shape=["B_Attn", 63, "T"], dtype=np.float32)
graph.inputs.extend([self_attn_mask])
graph.inputs.extend([cross_attn_mask])
tmap = graph.tensors()
# You can figure out the input and output tensors using Netron. In our case:
# Inputs: [inp, MIN_VAL, MAX_VAL]
# Outputs: [max_out]
out_nodes = find_masked_softmax_nodes(graph)
for i,itn in enumerate(out_nodes):
inputs = [tmap[i] for i in itn["inps"]]
outputs = [tmap[i] for i in itn["outs"]]
name = "masked_softmax_{}".format(i)
graph.replace_masked_softmax(inputs, outputs, name)
# for i,itn in enumerate(cross_attn_nodes):
# inputs = [tmap[i] for i in itn["inps"]]
# outputs = [tmap[i] for i in itn["outs"]]
# name = "cross_attn_{}".format(i)
# attrs = {"AttentionType":"cross"}
# graph.replace_attn(inputs, outputs, name, attrs)
# for i,itn in enumerate(self_attn_nodes):
# inputs = [tmap[i] for i in itn["inps"]]
# outputs = [tmap[i] for i in itn["outs"]]
# name = "self_attn_{}".format(i)
# attrs = {"AttentionType":"self"}
# graph.replace_attn(inputs, outputs, name, attrs)
for i,itn in enumerate(layer_norm_nodes):
inputs = [tmap[i] for i in itn["inps"]]
outputs = [tmap[i] for i in itn["outs"]]
name = "layer_norm_{}".format(i)
graph.replace_layer_norm(inputs, outputs, name)
# Remove the now-dangling subgraph.
graph.cleanup().toposort()
graph.inputs[2].shape = ["B",10, 64]
# That's it!
onnx.save(gs.export_onnx(graph), output_mdl)