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Using Triton-based layer-norm #1

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Using Triton-based layer-norm #1

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19 changes: 9 additions & 10 deletions src/models/sequence/long_conv_lm.py
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Original file line number Diff line number Diff line change
Expand Up @@ -28,9 +28,9 @@
ColumnParallelLinear = None

try:
from flash_attn.ops.layer_norm import dropout_add_layer_norm
from flash_attn.ops.triton.layer_norm import layer_norm_fn
except ImportError:
dropout_add_layer_norm = None
layer_norm_fn = None

from src.utils import instantiate
import src.utils.registry as registry
Expand Down Expand Up @@ -301,8 +301,8 @@ def __init__(
# nn.Dropout probabilities are changed.
# This is for performance reason: we can fuse dropout + add + layer_norm.
self.fused_dropout_add_ln = fused_dropout_add_ln
if self.fused_dropout_add_ln and dropout_add_layer_norm is None:
raise ImportError("dropout_add_layer_norm is not installed")
if self.fused_dropout_add_ln and layer_norm_fn is None:
raise ImportError("Triton is not installed")

self.layers = nn.ModuleList(
[
Expand Down Expand Up @@ -384,15 +384,14 @@ def forward(self, input_ids, position_ids=None, inference_params=None):
hidden_states = self.ln_f(residual.to(dtype=self.ln_f.weight.dtype))
else:
# Set prenorm=False here since we don't need the residual
hidden_states = dropout_add_layer_norm(
hidden_states = layer_norm_fn(
hidden_states,
residual,
self.ln_f.weight,
self.ln_f.bias,
self.drop_f.p if self.training else 0.0,
self.ln_f.eps,
residual=residual,
eps=self.ln_f.eps,
dropout_p=self.drop_f.p if self.training else 0.0,
prenorm=False,
residual_in_fp32=self.residual_in_fp32,
)
return hidden_states

Expand Down Expand Up @@ -687,4 +686,4 @@ def shard_qkv_headdim(state_dict, key):
for name in ["kernel.kernel.C", "ssm_k_kernel.kernel.C"]:
if f"backbone.layers.{i}.mixer.{name}" in state_dict:
shard_dim(state_dict, f"backbone.layers.{i}.mixer.{name}", 1)
return state_dict
return state_dict