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[Model] tp+ep support v1_loader #5600

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Merged
Jiang-Jia-Jun merged 12 commits into from
Dec 18, 2025
Merged

[Model] tp+ep support v1_loader #5600

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36dbbe2
[Model] tp+ep support v1_loader
Wanglongzhi2001 Dec 9, 2025
6f6256f
fix
Wanglongzhi2001 Dec 15, 2025
aee0907
fix mtp_linear
Wanglongzhi2001 Dec 15, 2025
e380ec4
fix mtp_linear
Wanglongzhi2001 Dec 15, 2025
f0ee4d0
fix
Wanglongzhi2001 Dec 15, 2025
45b0351
fix
Wanglongzhi2001 Dec 15, 2025
3923b6d
fix v0 loader
Wanglongzhi2001 Dec 16, 2025
2bb4bf6
fix
Wanglongzhi2001 Dec 16, 2025
adbb24b
Add get_tensor for EP
Wanglongzhi2001 Dec 17, 2025
f9df709
fix linear weight_loader
Wanglongzhi2001 Dec 17, 2025
65efe2d
fix typo
Wanglongzhi2001 Dec 17, 2025
f52d7d8
fix
Wanglongzhi2001 Dec 18, 2025
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5 changes: 5 additions & 0 deletions fastdeploy/model_executor/layers/attention/attention.py
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Original file line number Diff line number Diff line change
Expand Up @@ -229,6 +229,11 @@ def load_state_dict(self, state_dict: Dict[str, paddle.Tensor | np.ndarray]):
self.sinks.set_value(sinks_tensor)

def weight_loader(self, param, loaded_weight, loaded_shard_id: Optional[str] = None):
if self.use_qk_norm and ("q_norm" in param.name or "k_norm" in param.name):
loaded_weight = get_tensor(loaded_weight).astype("float32")
param.copy_(loaded_weight, False)
return

loaded_weight = get_tensor(loaded_weight).cast(paddle.get_default_dtype())
if self.quant_method.cache_quant_config.has_zero_point: # cache_int4_zp
loaded_weight = 1.0 / loaded_weight
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6 changes: 4 additions & 2 deletions fastdeploy/model_executor/layers/embeddings.py
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Original file line number Diff line number Diff line change
Expand Up @@ -283,10 +283,12 @@ def weight_loader(self, param, loaded_weight, shard_id=None):
if output_dim == 0:
h2d_copy(param[: shard_weight.shape[0]], shard_weight)
if not current_platform.is_maca():
param[shard_weight.shape[0] :].fill_(0)
if param.shape[0] != shard_weight.shape[0]:
param[shard_weight.shape[0] :].fill_(0)
else:
h2d_copy(param[:, : shard_weight.shape[1]], shard_weight)
param[:, shard_weight.shape[1] :].fill_(0)
if param.shape[1] != shard_weight.shape[1]:
param[:, shard_weight.shape[1] :].fill_(0)

def forward(self, ids_remove_padding=None) -> paddle.Tensor:
"""
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36 changes: 21 additions & 15 deletions fastdeploy/model_executor/layers/linear.py
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Original file line number Diff line number Diff line change
Expand Up @@ -356,25 +356,31 @@ def __init__(
self.output_sizes = output_sizes

def weight_loader(self, param, loaded_weight, loaded_shard_id: Optional[str] = None):
assert loaded_shard_id in ["q_a", "kv_a"]
if not param._is_initialized():
param.initialize()
if loaded_shard_id is None:
axis = -1 if (self.fd_config.model_config.model_format == "torch") ^ True else 0
if hasattr(param, "tensor_track"):
param.tensor_track.mark(start=0, end=loaded_weight.shape[axis])

if loaded_shard_id == "q_a":
param_shard_offset = 0
param_shard_size = self.output_sizes[0]
else:
# loaded_shard_id == "kv_a"
param_shard_offset = self.output_sizes[0]
param_shard_size = self.output_sizes[1]
if hasattr(param, "tensor_track"):
param.tensor_track.mark(start=param_shard_offset, end=param_shard_offset + param_shard_size)
param = slice_fn(
param,
(self.fd_config.model_config.model_format == "torch") ^ True,
start=param_shard_offset,
end=param_shard_offset + param_shard_size,
)
assert loaded_shard_id in ["q_a", "kv_a", "gate", "up"]

if loaded_shard_id in ["q_a", "gate"]:
param_shard_offset = 0
param_shard_size = self.output_sizes[0]
elif loaded_shard_id in ["kv_a", "up"]:
param_shard_offset = self.output_sizes[0]
param_shard_size = self.output_sizes[1]

if hasattr(param, "tensor_track"):
param.tensor_track.mark(start=param_shard_offset, end=param_shard_offset + param_shard_size)
param = slice_fn(
param,
(self.fd_config.model_config.model_format == "torch") ^ True,
start=param_shard_offset,
end=param_shard_offset + param_shard_size,
)
assert param.shape == loaded_weight.shape, (
f" Attempted to load weight ({loaded_weight.shape}) " f"into parameter ({param.shape})"
)
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4 changes: 4 additions & 0 deletions fastdeploy/model_executor/layers/lm_head.py
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Original file line number Diff line number Diff line change
Expand Up @@ -102,6 +102,10 @@ def __init__(
},
)
set_weight_attrs(self.linear.weight, {"output_dim": True})
if self.tp_size > 1:
if with_bias:
set_weight_attrs(self.linear.bias, {"output_dim": True})

else:
self.linear = RowParallelLinear(
embedding_dim,
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6 changes: 4 additions & 2 deletions fastdeploy/model_executor/layers/moe/moe.py
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Original file line number Diff line number Diff line change
Expand Up @@ -274,10 +274,13 @@ def weight_loader(
if not param._is_initialized():
param.initialize()
weight_need_transpose = getattr(param, "weight_need_transpose", False)

if self.ep_size > 1 or weight_need_transpose:
loaded_weight = get_tensor(loaded_weight)

if shard_id is None:
# 1.gate up fused in disk
if weight_need_transpose:
loaded_weight = get_tensor(loaded_weight)
loaded_weight = loaded_weight.transpose([1, 0])
output_size = param[expert_id - self.expert_id_offset].shape[SHARD_ID_TO_SHARDED_DIM["gate"]]
shard_offsets = [
Expand All @@ -293,7 +296,6 @@ def weight_loader(
self.weight_loader(param, loaded_weight_shard, expert_id, shard_id, "fused")
else:
if weight_need_transpose and source != "fused":
loaded_weight = get_tensor(loaded_weight)
loaded_weight = loaded_weight.transpose([1, 0])
# 2.gate up splited in disk
assert shard_id in ["gate", "down", "up"]
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3 changes: 3 additions & 0 deletions fastdeploy/model_executor/layers/mtp_linear.py
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Original file line number Diff line number Diff line change
Expand Up @@ -86,6 +86,9 @@ def __init__(
)
if self.tp_size > 1:
set_weight_attrs(self.linear.weight, {"output_dim": True})
if self.bias_key is not None:
set_weight_attrs(self.linear.bias, {"output_dim": True})

else:
self.linear = RowParallelLinear(
embedding_dim,
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4 changes: 4 additions & 0 deletions fastdeploy/model_executor/layers/normalization.py
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Original file line number Diff line number Diff line change
Expand Up @@ -130,6 +130,10 @@ def init_weight(self):
dtype=self._norm_weight_dtype,
)

def weight_loader(self, param, loaded_weight, loaded_shard_id: Optional[str] = None):
loaded_weight = get_tensor(loaded_weight).astype(self._norm_weight_dtype)
param.copy_(loaded_weight, False)

def load_state_dict(self, state_dict: Dict[str, paddle.Tensor | np.ndarray]):
"""
Load the checkpoint state dictionary into the layer.
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4 changes: 3 additions & 1 deletion fastdeploy/model_executor/layers/quantization/block_wise_fp8.py
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Original file line number Diff line number Diff line change
Expand Up @@ -138,7 +138,9 @@ def create_weights(self, layer, **extra_weight_attrs):
weight_shape = layer.weight_shape
weight_scale_inv_shape = weight_scale_inv_shape
extra_weight_attrs["output_dim"] = (
not extra_weight_attrs["output_dim"] if extra_weight_attrs["output_dim"] is not None else None
not extra_weight_attrs["output_dim"]
if extra_weight_attrs.get("output_dim", None) is not None
else None
)

layer.weight_dtype = "float8_e4m3fn"
Expand Down
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