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[feat] support fa3 backend for pd disaggregated (#2695)
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* support fa3 backend run in pd disaggregated

* support fa3 backend run in pd disaggregated

* support fa3 backend run in pd disaggregated

* support fa3 backend run in pd disaggregated

* delete use_fast_ffn
This commit is contained in:
Yuanle Liu
2025-07-03 22:33:27 +08:00
committed by GitHub
parent 00863c43fd
commit 240bdac2a4
26 changed files with 455 additions and 139 deletions
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21
fastdeploy/model_executor/layers/linear.py
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@@ -329,7 +329,6 @@ class MergedColumnParallelLinear(ColumnParallelLinear):
with_bias: bool = False,
add_bias: bool = False,
activation: str = "gelu",
use_fast_ffn: bool = False,
skip_quant: bool = False,
):
"""
@@ -344,11 +343,8 @@ class MergedColumnParallelLinear(ColumnParallelLinear):
with_bias (bool): Whether to include bias or not. Defaults to False.
add_bias (bool): Whether to add bias in the current layer or in the pre/post layer. Defaults to False.
activation (str): Activation function to use. Defaults to "gelu".
use_fast_ffn (bool): Whether to use a faster FFN implementation.
Defaults to False.
skip_quant (bool): Whether to skip quantization. Defaults to False.
"""
self.use_fast_ffn = use_fast_ffn
self.activation = activation
self.hidden_size = fd_config.model_config.hidden_size
self.nranks = fd_config.parallel_config.tensor_parallel_degree
@@ -385,23 +381,10 @@ class MergedColumnParallelLinear(ColumnParallelLinear):
"gate_proj")
bias_tensor = get_tensor(state_dict.pop(gate_bias_key)).astype(
paddle.get_default_dtype())
converted_bias_tensor = paddle.zeros(shape=list(
bias_tensor.shape),
dtype=bias_tensor.dtype)
if not self.use_fast_ffn:
converted_bias_tensor = paddle.concat(
[bias_tensor[::2], bias_tensor[1::2]], axis=0)
else:
converted_bias_tensor = bias_tensor
state_dict[self.bias_key] = converted_bias_tensor
if not self.use_fast_ffn:
converted_weight_tensor = paddle.concat(
[weight_tensor[:, ::2], weight_tensor[:, 1::2]], axis=1)
else:
converted_weight_tensor = weight_tensor
state_dict[self.bias_key] = bias_tensor
state_dict[self.weight_key] = converted_weight_tensor
state_dict[self.weight_key] = weight_tensor
super().load_state_dict(state_dict)