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Add: Support for the Falcon2 11B architecture (#1886) 

# What does this PR do?

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Add's support for the Falcon2 11B model architecture.


## Before submitting
- [ ] This PR fixes a typo or improves the docs (you can dismiss the
other checks if that's the case).
- [x] Did you read the [contributor
guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests),
      Pull Request section?
- [ ] Was this discussed/approved via a Github issue or the
[forum](https://discuss.huggingface.co/)? Please add a link
      to it if that's the case.
- [ ] Did you make sure to update the documentation with your changes?
Here are the
[documentation
guidelines](https://github.com/huggingface/transformers/tree/main/docs),
and
[here are tips on formatting
docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation).
- [ ] Did you write any new necessary tests?


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---------

Signed-off-by: Raphael Glon <oOraph@users.noreply.github.com>
Signed-off-by: Wang, Yi A <yi.a.wang@intel.com>
Co-authored-by: OlivierDehaene <olivier@huggingface.co>
Co-authored-by: Nicolas Patry <patry.nicolas@protonmail.com>
Co-authored-by: oOraph <13552058+oOraph@users.noreply.github.com>
Co-authored-by: Raphael Glon <oOraph@users.noreply.github.com>
Co-authored-by: Julien Chaumond <julien@huggingface.co>
Co-authored-by: OlivierDehaene <23298448+OlivierDehaene@users.noreply.github.com>
Co-authored-by: abhishek thakur <1183441+abhishekkrthakur@users.noreply.github.com>
Co-authored-by: Dong Shin <d0104.shin@gmail.com>
Co-authored-by: Christof Weickhardt <christof@weickhardt.ch>
Co-authored-by: Ikko Eltociear Ashimine <eltociear@gmail.com>
Co-authored-by: drbh <david.richard.holtz@gmail.com>
Co-authored-by: Lucain <lucain@huggingface.co>
Co-authored-by: fxmarty <9808326+fxmarty@users.noreply.github.com>
Co-authored-by: Moritz Laurer <41862082+MoritzLaurer@users.noreply.github.com>
Co-authored-by: dr3s <dr3s@users.noreply.github.com>
Co-authored-by: Wang, Yi <yi.a.wang@intel.com>
Co-authored-by: Morgan Funtowicz <funtowiczmo@gmail.com>
Co-authored-by: Maziyar Panahi <maziyar.panahi@iscpif.fr>
Co-authored-by: Brandon Royal <2762697+brandonroyal@users.noreply.github.com>
Co-authored-by: Mishig <mishig.davaadorj@coloradocollege.edu>
Co-authored-by: Martin Iglesias Goyanes <martinigoyanes@hotmail.com>
Co-authored-by: martini <martin.iglesiasgoyanes@adyen.com>
This commit is contained in:
Nilabhra Roy Chowdhury 2024-05-14 10:06:02 +02:00 committed by GitHub
parent d348d2b28f
commit 3136f27f36
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2 changed files with 72 additions and 32 deletions
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3
server/text_generation_server/models/custom_modeling/flash_llama_modeling.py
View File

@ -18,9 +18,10 @@
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import List, Optional, Tuple
import torch
import torch.distributed
from torch import nn
from transformers.activations import ACT2FN
from typing import Optional, List, Tuple

101
server/text_generation_server/models/custom_modeling/flash_rw_modeling.py
View File

@ -1,26 +1,21 @@
from typing import List, Optional, Tuple
import torch
import torch.distributed
from torch import nn
from transformers.modeling_utils import PreTrainedModel
from transformers.configuration_utils import PretrainedConfig
from typing import Optional, List, Tuple
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):
@ -52,6 +47,7 @@ class RWConfig(PretrainedConfig):
hidden_size=64,
num_hidden_layers=None,
num_attention_heads=None,
num_ln_in_prallel_attention=None,
layer_norm_epsilon=1e-5,
initializer_range=0.02,
use_cache=True,
@ -65,6 +61,7 @@ class RWConfig(PretrainedConfig):
new_decoder_architecture=None,
bias=False,
parallel_attn=False,
rope_theta=10_000.0,
**kwargs,
):
if alibi:
@ -75,6 +72,7 @@ class RWConfig(PretrainedConfig):
self.model_type = model_type
self.alibi = False
self.rotary = True
self.rope_theta = rope_theta
self.vocab_size = vocab_size
# Backward compatibility with n_embed kwarg
@ -91,6 +89,7 @@ class RWConfig(PretrainedConfig):
else kwargs.pop("n_head", 8)
)
self.layer_norm_epsilon = layer_norm_epsilon
self.num_ln_in_parallel_attention = num_ln_in_prallel_attention
self.initializer_range = initializer_range
self.use_cache = use_cache
self.hidden_dropout = hidden_dropout
@ -132,9 +131,13 @@ class FlashRWAttention(torch.nn.Module):
self.num_heads_kv = config.n_head_kv
self.hidden_size = config.hidden_size
self.head_size = self.hidden_size // self.num_heads
self.rope_theta = config.rope_theta
self.rotary_emb = PositionRotaryEmbedding.static(
config=config, dim=self.head_size, base=10000.0, device=weights.device
config=config,
dim=self.head_size,
base=self.rope_theta,
device=weights.device,
)
self.softmax_scale = self.head_size ** (-0.5)
@ -244,9 +247,13 @@ class FlashRWLargeAttention(torch.nn.Module):
self.hidden_size = hidden_size
self.head_size = hidden_size // num_heads
self.num_groups = num_groups
self.rope_theta = config.rope_theta
self.rotary_emb = PositionRotaryEmbedding.static(
config=config, dim=self.head_size, base=10000.0, device=weights.device
config=config,
dim=self.head_size,
base=self.rope_theta,
device=weights.device,
)
self.softmax_scale = self.head_size ** (-0.5)
@ -257,7 +264,7 @@ class FlashRWLargeAttention(torch.nn.Module):
if process_group.size() > self.num_groups:
raise NotImplementedError(
f"Tensor Parallelism is not implemented for world_size > n groups"
"Tensor Parallelism is not implemented for world_size > n groups"
)
if self.num_groups % process_group.size() != 0:
raise NotImplementedError(
@ -459,29 +466,61 @@ class FlashRWLayer(nn.Module):
max_s,
)
hidden_states, residual = self.post_attention_layernorm(
hidden_states, residual
)
if self.post_attention_layernorm is not None:
hidden_states, residual = self.post_attention_layernorm(
hidden_states, residual
)
mlp_output = self.mlp(hidden_states)
return mlp_output, 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__()
prefix = f"transformer.h.{layer_id}"
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,
)
self.ln_layer = FlashRWLayerNorm(config, prefix, weights)
self.self_attention = FlashRWLargeAttention(
config,
@ -507,8 +546,8 @@ class FlashRWLargeLayer(nn.Module):
input_lengths,
max_s,
):
ln_attn, residual = self.ln_attn(hidden_states, residual)
ln_mlp, _ = self.ln_mlp(residual)
# Layer norm.
ln_attn, ln_mlp, residual = self.ln_layer(hidden_states, residual)
# Self attention.
attn_output = self.self_attention(