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pi 2026-06-16 17:09:34 +00:00
parent 189d56026b
commit 7333a22bcd
10774 changed files with 634644 additions and 933308 deletions
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25
venv/lib/python3.12/site-packages/onnxruntime/transformers/fusion_attention.py
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@ -892,6 +892,13 @@ class FusionAttention(Fusion):
add_before_layernorm = self.model.match_parent(normalize_node, "Add", 0)
if add_before_layernorm is not None:
start_node = add_before_layernorm
elif self.model.find_graph_input(normalize_node.input[0]) is not None:
# Pre-LN first block: LN fed directly by graph input. QKV matching will
# still fail from this (first) LN anchor because its inputs are weights, not
# the QKV projection path. The real fusion happens when fuse() is called
# again from the second LN/SkipLN anchor after the residual Add, where the
# other_inputs and root_input changes (#2-#4) take effect.
start_node = normalize_node
else:
return
@ -917,7 +924,8 @@ class FusionAttention(Fusion):
other_inputs = []
for _i, node_input in enumerate(start_node.input):
if node_input not in output_name_to_node:
continue
if self.model.find_graph_input(node_input) is None:
continue
if node_input == qkv_nodes[0].output[0]:
continue
@ -946,7 +954,7 @@ class FusionAttention(Fusion):
root_input = mul_before_layernorm.output[0]
else:
return
elif normalize_node.op_type == "LayerNormalization":
elif normalize_node.op_type in ("LayerNormalization", "SkipLayerNormalization"):
children = input_name_to_nodes[root_input]
for child in children:
if child.op_type == "LayerNormalization":
@ -961,9 +969,10 @@ class FusionAttention(Fusion):
# | |
# | |
# +---------------------------------------------------------------------+
parent_node = output_name_to_node[root_input]
if parent_node.op_type == "SkipLayerNormalization" and len(parent_node.output) == 4:
root_input = parent_node.output[0]
if root_input in output_name_to_node:
parent_node = output_name_to_node[root_input]
if parent_node.op_type == "SkipLayerNormalization" and len(parent_node.output) == 4:
root_input = parent_node.output[0]
children = input_name_to_nodes[root_input]
children_types = [child.op_type for child in children]
@ -1112,11 +1121,11 @@ class FusionAttention(Fusion):
if (
(mul_val is None)
or not (isinstance(mul_val, np.ndarray) and mul_val.size == 1)
or (float(mul_val) >= 0)
or (mul_val.item() >= 0)
):
return
if float(mul_val) != -10000:
self.mask_filter_value = float(mul_val)
if mul_val.item() != -10000:
self.mask_filter_value = mul_val.item()
if matmul_v.input[0] == root_input and matmul_q.input[0] == root_input and matmul_k.input[0] == root_input:
mask_index = self.attention_mask.process_mask(mask_nodes[-1].input[0]) if not is_no_mask_attention else None