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chore: removed repeating code.

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Nilabhra 2024-05-14 11:58:12 +04:00
parent e45ede2cde
commit 8ce8265966
1 changed files with 5 additions and 166 deletions
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171
server/text_generation_server/models/custom_modeling/flash_rw_modeling.py
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@ -6,21 +6,16 @@ from torch import nn
from transformers.configuration_utils import PretrainedConfig
from transformers.modeling_utils import PreTrainedModel
from text_generation_server.utils import paged_attention, flash_attn
from text_generation_server.utils.flash_attn import attention
from text_generation_server.layers import (
TensorParallelRowLinear,
SpeculativeHead,
TensorParallelColumnLinear,
TensorParallelEmbedding,
SpeculativeHead,
TensorParallelRowLinear,
get_linear,
)
from text_generation_server.layers.layernorm import (
FastLayerNorm,
)
from text_generation_server.layers.rotary import (
PositionRotaryEmbedding,
)
from text_generation_server.layers.layernorm import FastLayerNorm
from text_generation_server.layers.rotary import PositionRotaryEmbedding
from text_generation_server.utils import flash_attn, paged_attention
def load_row(config, prefix: str, weights, bias: bool):
@ -520,162 +515,6 @@ class FlashRWLayerNorm(nn.Module):
return ln_attn, ln_mlp, residual
class FlashRWLayerNorm(nn.Module):
def __init__(self, config, prefix, weights):
super().__init__()
self.num_ln = config.num_ln_in_parallel_attn
if self.num_ln == 1:
self.input_ln = FastLayerNorm.load(
prefix=f"{prefix}.input_layernorm",
weights=weights,
eps=config.layer_norm_epsilon,
)
elif self.num_ln == 2:
self.ln_attn = FastLayerNorm.load(
prefix=f"{prefix}.ln_attn",
weights=weights,
eps=config.layer_norm_epsilon,
)
self.ln_mlp = FastLayerNorm.load(
prefix=f"{prefix}.ln_mlp",
weights=weights,
eps=config.layer_norm_epsilon,
)
else:
raise ValueError("Number of layer norms can either be 1 or 2.")
def forward(
self,
hidden_states,
residual,
):
if self.num_ln == 1:
ln_hidden_states, residual = self.input_ln(hidden_states, residual)
return ln_hidden_states, ln_hidden_states, residual
elif self.num_ln == 2:
ln_attn, residual = self.ln_attn(hidden_states, residual)
ln_mlp, _ = self.ln_mlp(residual)
return ln_attn, ln_mlp, residual
class FlashRWLayerNorm(nn.Module):
def __init__(self, config, prefix, weights):
super().__init__()
self.num_ln = config.num_ln_in_parallel_attn
if self.num_ln == 1:
self.input_ln = FastLayerNorm.load(
prefix=f"{prefix}.input_layernorm",
weights=weights,
eps=config.layer_norm_epsilon,
)
elif self.num_ln == 2:
self.ln_attn = FastLayerNorm.load(
prefix=f"{prefix}.ln_attn",
weights=weights,
eps=config.layer_norm_epsilon,
)
self.ln_mlp = FastLayerNorm.load(
prefix=f"{prefix}.ln_mlp",
weights=weights,
eps=config.layer_norm_epsilon,
)
else:
raise ValueError("Number of layer norms can either be 1 or 2.")
def forward(
self,
hidden_states,
residual,
):
if self.num_ln == 1:
ln_hidden_states, residual = self.input_ln(hidden_states, residual)
return ln_hidden_states, ln_hidden_states, residual
elif self.num_ln == 2:
ln_attn, residual = self.ln_attn(hidden_states, residual)
ln_mlp, _ = self.ln_mlp(residual)
return ln_attn, ln_mlp, residual
class FlashRWLayerNorm(nn.Module):
def __init__(self, config, prefix, weights):
super().__init__()
self.num_ln = config.num_ln_in_parallel_attn
if self.num_ln == 1:
self.input_ln = FastLayerNorm.load(
prefix=f"{prefix}.input_layernorm",
weights=weights,
eps=config.layer_norm_epsilon,
)
elif self.num_ln == 2:
self.ln_attn = FastLayerNorm.load(
prefix=f"{prefix}.ln_attn",
weights=weights,
eps=config.layer_norm_epsilon,
)
self.ln_mlp = FastLayerNorm.load(
prefix=f"{prefix}.ln_mlp",
weights=weights,
eps=config.layer_norm_epsilon,
)
else:
raise ValueError("Number of layer norms can either be 1 or 2.")
def forward(
self,
hidden_states,
residual,
):
if self.num_ln == 1:
ln_hidden_states, residual = self.input_ln(hidden_states, residual)
return ln_hidden_states, ln_hidden_states, residual
elif self.num_ln == 2:
ln_attn, residual = self.ln_attn(hidden_states, residual)
ln_mlp, _ = self.ln_mlp(residual)
return ln_attn, ln_mlp, residual
class FlashRWLayerNorm(nn.Module):
def __init__(self, config, prefix, weights):
super().__init__()
self.num_ln = config.num_ln_in_parallel_attn
if self.num_ln == 1:
self.input_ln = FastLayerNorm.load(
prefix=f"{prefix}.input_layernorm",
weights=weights,
eps=config.layer_norm_epsilon,
)
elif self.num_ln == 2:
self.ln_attn = FastLayerNorm.load(
prefix=f"{prefix}.ln_attn",
weights=weights,
eps=config.layer_norm_epsilon,
)
self.ln_mlp = FastLayerNorm.load(
prefix=f"{prefix}.ln_mlp",
weights=weights,
eps=config.layer_norm_epsilon,
)
else:
raise ValueError("Number of layer norms can either be 1 or 2.")
def forward(
self,
hidden_states,
residual,
):
if self.num_ln == 1:
ln_hidden_states, residual = self.input_ln(hidden_states, residual)
return ln_hidden_states, ln_hidden_states, residual
elif self.num_ln == 2:
ln_attn, residual = self.ln_attn(hidden_states, residual)
ln_mlp, _ = self.ln_mlp(residual)
return ln_attn, ln_mlp, residual
class FlashRWLargeLayer(nn.Module):
def __init__(self, layer_id, config, weights):
super().__init__()