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[GCU] Support gcu platform (#2702)

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baseline: e7fa57ebae

Co-authored-by: yongqiangma <xing.wo@163.com>
This commit is contained in:
EnflameGCU
2025-07-08 13:00:52 +08:00
committed by GitHub
parent 26d5d737dd
commit d0f4d6ba3a
33 changed files with 2988 additions and 85 deletions
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97
fastdeploy/model_executor/layers/normalization.py
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@@ -19,9 +19,14 @@ from typing import Callable, Dict, Optional
import numpy as np
import paddle
from paddle import nn
from paddle.incubate.nn.functional import fused_layer_norm, fused_rms_norm
from fastdeploy.platforms import current_platform
if current_platform.is_gcu():
from fastdeploy.model_executor.ops.gcu import fused_add_rms_norm, rms_norm
else:
from paddle.incubate.nn.functional import fused_layer_norm, fused_rms_norm
from fastdeploy.config import FDConfig
from .utils import get_tensor
@@ -69,7 +74,10 @@ class RMSNorm(nn.Layer):
self.weight_key: Optional[str] = f"{prefix}.weight"
self.with_weight: bool = self.weight_key is not None
self.eps: float = eps
self.norm_func: Callable = fused_rms_norm
if current_platform.is_gcu():
self.norm_func: Callable = fused_add_rms_norm
else:
self.norm_func: Callable = fused_rms_norm
self.linear_bias: Optional[paddle.Tensor] = linear_bias
self.quant_scale: Optional[float] = quant_scale
self._dtype: str = self._helper.get_default_dtype()
@@ -129,19 +137,26 @@ class RMSNorm(nn.Layer):
The `residual_output` is the result of applying the normalization and possibly other
operations (like linear transformation) on the `residual_input`.
"""
norm_out = self.norm_func(
x,
norm_weight=self.ln_weight,
norm_bias=None,
epsilon=self.eps,
begin_norm_axis=self.begin_norm_axis,
bias=self.linear_bias,
residual=residual_input,
quant_scale=-1 if self.quant_scale is None else self.quant_scale,
quant_round_type=self.quant_round_type,
quant_max_bound=self.quant_max_bound,
quant_min_bound=self.quant_min_bound,
)
if current_platform.is_gcu():
if residual_input is None:
return rms_norm(x, self.ln_weight, self.eps)
norm_out = self.norm_func(
x, residual_input, self.ln_weight, self.eps
)
else:
norm_out = self.norm_func(
x,
norm_weight=self.ln_weight,
norm_bias=None,
epsilon=self.eps,
begin_norm_axis=self.begin_norm_axis,
bias=self.linear_bias,
residual=residual_input,
quant_scale=-1 if self.quant_scale is None else self.quant_scale,
quant_round_type=self.quant_round_type,
quant_max_bound=self.quant_max_bound,
quant_min_bound=self.quant_min_bound,
)
if residual_input is not None:
return norm_out[0], norm_out[1]
else:
@@ -193,7 +208,10 @@ class LayerNorm(nn.Layer):
self.with_bias: bool = with_bias
self.eps: float = eps
self.quant_scale: float = quant_scale
self.norm_func: Callable = fused_layer_norm
if current_platform.is_gcu():
self.norm_func: Callable = paddle.nn.functional.layer_norm
else:
self.norm_func: Callable = fused_layer_norm
self.linear_bias: Optional[paddle.Tensor] = linear_bias
self._dtype: str = self._helper.get_default_dtype()
self._norm_weight_dtype: str = "float32"
@@ -279,19 +297,40 @@ class LayerNorm(nn.Layer):
else:
raise NotImplementedError("Iluvatar does not support yet!")
norm_out = self.norm_func(
x,
norm_weight=self.ln_weight,
norm_bias=self.ln_bias,
epsilon=self.eps,
begin_norm_axis=1,
bias=self.linear_bias,
residual=residual_input,
quant_scale=-1 if self.quant_scale is None else self.quant_scale,
quant_round_type=self.quant_round_type,
quant_max_bound=self.quant_max_bound,
quant_min_bound=self.quant_min_bound,
)
if current_platform.is_gcu():
if residual_input is not None:
y = x + residual_input
out = self.norm_func(
x=y,
normalized_shape=y.shape[1:],
weight=self.ln_weight,
bias=self.linear_bias,
epsilon=self.eps,
)
return out, y
else:
out = self.norm_func(
x=x,
normalized_shape=x.shape[1:],
weight=self.ln_weight,
bias=self.linear_bias,
epsilon=self.eps,
)
return out
else:
norm_out = self.norm_func(
x,
norm_weight=self.ln_weight,
norm_bias=self.ln_bias,
epsilon=self.eps,
begin_norm_axis=1,
bias=self.linear_bias,
residual=residual_input,
quant_scale=-1 if self.quant_scale is None else self.quant_scale,
quant_round_type=self.quant_round_type,
quant_max_bound=self.quant_max_bound,
quant_min_bound=self.quant_min_bound,
)
if residual_input is not None:
return norm_out[0], norm_out[1]
else: