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[bugfix] fix qwen3.5 gpt_bridge lora #28

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Merged
Jintao-Huang merged 5 commits into from
Apr 14, 2026
Merged

[bugfix] fix qwen3.5 gpt_bridge lora #28

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933aaf0
fix qwen3.5 gpt_bridge lora
Jintao-Huang Apr 14, 2026
b45c0b2
fix
Jintao-Huang Apr 14, 2026
8df23b7
update
Jintao-Huang Apr 14, 2026
6fb2fb7
fix
Jintao-Huang Apr 14, 2026
2585124
fix
Jintao-Huang Apr 14, 2026
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91 changes: 38 additions & 53 deletions src/mcore_bridge/bridge/gpt_bridge.py
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Original file line number Diff line number Diff line change
Expand Up @@ -1238,52 +1238,41 @@ def _set_linear_attn_state(self, mg_attn, hf_state_dict, hf_prefix: str, layer_i
hf_state_dict = {}
config = self.config
num_key_heads = config.linear_num_key_heads
key_dim = config.linear_key_head_dim * num_key_heads
value_dim = config.linear_value_head_dim * config.linear_num_value_heads
key_dim = config.linear_key_head_dim
value_dim = config.linear_value_head_dim * config.linear_num_value_heads // num_key_heads
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if to_mcore:
if isinstance(mg_attn.in_proj, LoraParallelLinear):
lora_A = hf_state_dict['in_proj_qkv.lora_A.weight'].load()
assert (lora_A == hf_state_dict['in_proj_z.lora_A.weight'].load()).all() and \
(lora_A == hf_state_dict['in_proj_b.lora_A.weight'].load()).all() and \
(lora_A == hf_state_dict['in_proj_a.lora_A.weight'].load()).all(), \
'Need to ensure QKVZBA\'s lora_A are consistent'
qkv_lora_B = hf_state_dict['in_proj_qkv.lora_B.weight'].load()
q_lora_B, k_lora_B, v_lora_B = torch.split(
qkv_lora_B, [key_dim * num_key_heads, key_dim * num_key_heads, value_dim * num_key_heads], dim=0)
lora_B = torch.cat([
hf_state_dict['in_proj_qkv.lora_B.weight'].load(),
hf_state_dict['in_proj_z.lora_B.weight'].load(),
hf_state_dict['in_proj_b.lora_B.weight'].load(),
hf_state_dict['in_proj_a.lora_B.weight'].load(),
*(x.reshape(num_key_heads, -1, qkv_lora_B.shape[-1]) for x in [q_lora_B, k_lora_B, v_lora_B]),
*(hf_state_dict[f'{key}.lora_B.weight'].load().reshape(num_key_heads, -1, qkv_lora_B.shape[-1])
for key in ['in_proj_z', 'in_proj_b', 'in_proj_a'])
],
dim=0)
dim=1).reshape(-1, qkv_lora_B.shape[-1])
self._set_weight(mg_attn.in_proj.lora_A[self._adapter_name].weight, lora_A, 'in_proj.lora_A.weight')
self._set_weight(mg_attn.in_proj.lora_B[self._adapter_name].weight, lora_B, 'in_proj.lora_B.weight')
elif not self._peft_format:
qkv = hf_state_dict['in_proj_qkv.weight'].load()
q, k, v = torch.split(qkv, [key_dim, key_dim, value_dim], dim=0)
q, k, v = torch.split(
qkv, [key_dim * num_key_heads, key_dim * num_key_heads, value_dim * num_key_heads], dim=0)
in_proj_weight = torch.cat([
*(x.reshape(num_key_heads, -1, config.hidden_size) for x in [q, k, v]),
*(hf_state_dict[key].load().reshape(num_key_heads, -1, config.hidden_size)
for key in ['in_proj_z.weight', 'in_proj_b.weight', 'in_proj_a.weight']),
*(hf_state_dict[f'{key}.weight'].load().reshape(num_key_heads, -1, config.hidden_size)
for key in ['in_proj_z', 'in_proj_b', 'in_proj_a']),
],
dim=1).reshape((-1, config.hidden_size))
in_scale_inv = None
if 'in_proj_qkv.weight_scale_inv' in hf_state_dict:
in_scale_inv = torch.cat([
hf_state_dict['in_proj_qkv.weight_scale_inv'].load(),
hf_state_dict['in_proj_z.weight_scale_inv'].load(),
hf_state_dict['in_proj_b.weight_scale_inv'].load(),
hf_state_dict['in_proj_a.weight_scale_inv'].load(),
],
dim=0)
self._set_weight(mg_attn.in_proj.weight, in_proj_weight, 'in_proj.weight', hf_scale_inv=in_scale_inv)
self._set_weight(mg_attn.in_proj.weight, in_proj_weight, 'in_proj.weight')
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else:
key_dim = self.config.linear_key_head_dim * self.config.linear_num_key_heads
value_dim = self.config.linear_value_head_dim * self.config.linear_num_value_heads
qkv_dim = key_dim * 2 + value_dim
z_dim = value_dim
a_dim = config.linear_num_value_heads
qkv_block = qkv_dim // self.fp8_block_size
z_block = z_dim // self.fp8_block_size
a_block = a_dim // self.fp8_block_size
a_dim = config.linear_num_value_heads // num_key_heads
is_lora = False if mg_attn is None else isinstance(mg_attn.in_proj,
LoraParallelLinear) and self._peft_format
is_lora = torch.tensor([is_lora], dtype=torch.bool, device='cuda')
Expand All @@ -1297,42 +1286,39 @@ def _set_linear_attn_state(self, mg_attn, hf_state_dict, hf_prefix: str, layer_i
None if mg_attn is None else mg_attn.in_proj.lora_B[self._adapter_name].weight.data,
f'in_proj.lora_B.{self._adapter_name}.weight')
if lora_A is not None:
lora_B = lora_B.reshape(num_key_heads, -1, lora_B.shape[-1])
self._peft_target_modules.update({'in_proj_qkv', 'in_proj_z', 'in_proj_b', 'in_proj_a'})
for key in ['in_proj_qkv', 'in_proj_z', 'in_proj_b', 'in_proj_a']:
hf_state_dict[f'{key}.lora_A.weight'] = lora_A.clone()
hf_state_dict['in_proj_qkv.lora_B.weight'] = lora_B[:qkv_dim].clone()
hf_state_dict['in_proj_z.lora_B.weight'] = lora_B[qkv_dim:qkv_dim + z_dim].clone()
hf_state_dict['in_proj_b.lora_B.weight'] = lora_B[qkv_dim + z_dim:-a_dim].clone()
hf_state_dict['in_proj_a.lora_B.weight'] = lora_B[-a_dim:].clone()
q_lora_B = lora_B[:, :key_dim].reshape(-1, lora_B.shape[-1])
k_lora_B = lora_B[:, key_dim:2 * key_dim].reshape(-1, lora_B.shape[-1])
v_lora_B = lora_B[:, 2 * key_dim:qkv_dim].reshape(-1, lora_B.shape[-1])
hf_state_dict['in_proj_qkv.lora_B.weight'] = torch.concat([q_lora_B, k_lora_B, v_lora_B], dim=0)
hf_state_dict['in_proj_z.lora_B.weight'] = lora_B[:, qkv_dim:qkv_dim + z_dim].reshape(
-1, lora_B.shape[-1]).clone()
hf_state_dict['in_proj_b.lora_B.weight'] = lora_B[:, qkv_dim + z_dim:-a_dim].reshape(
-1, lora_B.shape[-1]).clone()
hf_state_dict['in_proj_a.lora_B.weight'] = lora_B[:, -a_dim:].reshape(-1, lora_B.shape[-1]).clone()
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elif not self._peft_format:
in_proj_weight, scale_inv = self._get_weight(None if mg_attn is None else mg_attn.in_proj.weight.data,
'in_proj.weight')
in_proj_weight, _ = self._get_weight(None if mg_attn is None else mg_attn.in_proj.weight.data,
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'in_proj.weight')
if in_proj_weight is not None:
in_proj_weight = in_proj_weight.reshape(num_key_heads, -1, config.hidden_size)
q = in_proj_weight[:, :key_dim // num_key_heads].reshape(-1, config.hidden_size)
k = in_proj_weight[:, key_dim // num_key_heads:2 * key_dim // num_key_heads].reshape(
-1, config.hidden_size)
v = in_proj_weight[:, 2 * key_dim // num_key_heads:qkv_dim // num_key_heads].reshape(
-1, config.hidden_size)
q = in_proj_weight[:, :key_dim].reshape(-1, config.hidden_size)
k = in_proj_weight[:, key_dim:2 * key_dim].reshape(-1, config.hidden_size)
v = in_proj_weight[:, 2 * key_dim:qkv_dim].reshape(-1, config.hidden_size)
hf_state_dict['in_proj_qkv.weight'] = torch.concat([q, k, v], dim=0)
hf_state_dict['in_proj_z.weight'] = in_proj_weight[:, qkv_dim // num_key_heads:(qkv_dim + z_dim)
// num_key_heads].reshape(
-1, config.hidden_size).clone()
hf_state_dict['in_proj_b.weight'] = in_proj_weight[:, (qkv_dim + z_dim) // num_key_heads:-a_dim
// num_key_heads].reshape(
-1, config.hidden_size).clone()
hf_state_dict['in_proj_a.weight'] = in_proj_weight[:, -a_dim // num_key_heads:].reshape(
hf_state_dict['in_proj_z.weight'] = in_proj_weight[:, qkv_dim:(qkv_dim + z_dim)].reshape(
-1, config.hidden_size).clone()
if scale_inv is not None:
hf_state_dict['in_proj_qkv.weight_scale_inv'] = scale_inv[:qkv_block].clone()
hf_state_dict['in_proj_z.weight_scale_inv'] = scale_inv[qkv_block:qkv_block + z_block].clone()
hf_state_dict['in_proj_b.weight_scale_inv'] = scale_inv[qkv_block + z_block:-a_block].clone()
hf_state_dict['in_proj_a.weight_scale_inv'] = scale_inv[-a_block:].clone()
del in_proj_weight
hf_state_dict['in_proj_b.weight'] = in_proj_weight[:, (qkv_dim + z_dim):-a_dim].reshape(
-1, config.hidden_size).clone()
hf_state_dict['in_proj_a.weight'] = in_proj_weight[:, -a_dim:].reshape(-1,
config.hidden_size).clone()
if not self._peft_format:
if to_mcore:
conv1d = hf_state_dict['conv1d.weight'].load()
q_c, k_c, v_c = torch.split(conv1d, [key_dim, key_dim, value_dim], dim=0)
q_c, k_c, v_c = torch.split(
conv1d, [key_dim * num_key_heads, key_dim * num_key_heads, value_dim * num_key_heads], dim=0)
conv1d = torch.cat([
*(x.reshape(num_key_heads, -1, *conv1d.shape[-2:]) for x in [q_c, k_c, v_c]),
],
Expand All @@ -1342,8 +1328,7 @@ def _set_linear_attn_state(self, mg_attn, hf_state_dict, hf_prefix: str, layer_i
conv1d, _ = self._get_weight(None if mg_attn is None else mg_attn.conv1d.weight, 'conv1d.weight')
if conv1d is not None:
conv1d = conv1d.reshape(num_key_heads, -1, *conv1d.shape[-2:])
q_c, k_c, v_c = torch.split(
conv1d, [key_dim // num_key_heads, key_dim // num_key_heads, value_dim // num_key_heads], dim=1)
q_c, k_c, v_c = torch.split(conv1d, [key_dim, key_dim, value_dim], dim=1)
q_c = q_c.reshape(-1, *q_c.shape[-2:])
k_c = k_c.reshape(-1, *k_c.shape[-2:])
v_c = v_c.reshape(-1, *v_c.shape[-2:])
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