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Fixing gemma2. (#2135) 

* Fixing gemma2.

* Adding new model.
This commit is contained in:
Nicolas Patry 2024-06-27 16:04:20 +02:00 committed by GitHub
parent 0e4ab6d31c
commit 3ea8259af1
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7 changed files with 622 additions and 9 deletions
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1
docs/source/supported_models.md
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@ -10,6 +10,7 @@ Text Generation Inference enables serving optimized models on specific hardware
- [Llama](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct)
- [Phi 3](https://huggingface.co/microsoft/Phi-3-mini-4k-instruct)
- [Gemma](https://huggingface.co/google/gemma-7b)
- [Gemma2](https://huggingface.co/google/gemma2-9b)
- [Cohere](https://huggingface.co/CohereForAI/c4ai-command-r-plus)
- [Dbrx](https://huggingface.co/databricks/dbrx-instruct)
- [Mamba](https://huggingface.co/state-spaces/mamba-2.8b-slimpj)

30
server/text_generation_server/models/__init__.py
View File

@ -68,6 +68,9 @@ try:
from text_generation_server.models.flash_gemma import (
FlashGemma,
)
from text_generation_server.models.flash_gemma2 import (
FlashGemma2,
)
from text_generation_server.models.pali_gemma import (
PaliGemma,
)
@ -102,6 +105,7 @@ if FLASH_ATTENTION:
__all__.append(FlashQwen2)
__all__.append(FlashStarcoder2)
__all__.append(FlashGemma)
__all__.append(FlashGemma2)
__all__.append(FlashCohere)
MAMBA_AVAILABLE = True
@ -143,6 +147,11 @@ class ModelType(enum.Enum):
"name": "Gemma",
"url": "https://huggingface.co/google/gemma-7b",
}
GEMMA2 = {
"type": "gemma2",
"name": "Gemma2",
"url": "https://huggingface.co/google/gemma2-9b",
}
COHERE = {
"type": "cohere",
"name": "Cohere",
@ -630,6 +639,27 @@ def get_model(
dtype=dtype,
trust_remote_code=trust_remote_code,
)
elif model_type == GEMMA2:
if FLASH_ATTENTION:
return FlashGemma2(
model_id,
revision,
quantize=quantize,
speculator=speculator,
dtype=dtype,
trust_remote_code=trust_remote_code,
)
elif sharded:
raise NotImplementedError(FLASH_ATT_ERROR_MESSAGE.format("Sharded Gemma2"))
else:
return CausalLM(
model_id,
revision,
quantize=quantize,
speculator=speculator,
dtype=dtype,
trust_remote_code=trust_remote_code,
)
if model_type == COHERE:
if FLASH_ATTENTION:

500
server/text_generation_server/models/custom_modeling/flash_gemma2_modeling.py Normal file
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@ -0,0 +1,500 @@
# coding=utf-8
# Copyright 2022 EleutherAI and the HuggingFace Inc. team. All rights reserved.
#
# This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX
# and OPT implementations in this library. It has been modified from its
# original forms to accommodate minor architectural differences compared
# to GPT-NeoX and OPT used by the Meta AI team that trained the model.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import torch
import torch.distributed
from torch import nn
from transformers.activations import ACT2FN
from transformers.configuration_utils import PretrainedConfig
from typing import Optional, List, Tuple
from text_generation_server.layers.attention import (
paged_attention,
attention,
reshape_and_cache,
)
from text_generation_server.layers import (
TensorParallelRowLinear,
TensorParallelColumnLinear,
TensorParallelEmbedding,
SpeculativeHead,
get_linear,
)
from text_generation_server.layers.rotary import PositionRotaryEmbedding
from text_generation_server.layers.layernorm import (
FastRMSNorm,
)
class Gemma2Config(PretrainedConfig):
def __init__(
self,
vocab_size=256128,
hidden_size=3072,
intermediate_size=24576,
num_hidden_layers=28,
num_attention_heads=16,
num_key_value_heads=16,
head_dim=256,
hidden_act="gelu_pytorch_tanh",
max_position_embeddings=8192,
initializer_range=0.02,
rms_norm_eps=1e-6,
use_cache=True,
pad_token_id=None,
bos_token_id=1,
eos_token_id=2,
tie_word_embeddings=True,
rope_theta=10000.0,
rope_scaling=None,
attention_bias=False,
attention_dropout=0.0,
**kwargs,
):
self.vocab_size = vocab_size
self.max_position_embeddings = max_position_embeddings
self.hidden_size = hidden_size
self.head_dim = head_dim
self.intermediate_size = intermediate_size
self.num_hidden_layers = num_hidden_layers
self.num_attention_heads = num_attention_heads
# for backward compatibility
if num_key_value_heads is None:
num_key_value_heads = num_attention_heads
self.num_key_value_heads = num_key_value_heads
self.hidden_act = hidden_act
self.initializer_range = initializer_range
self.rms_norm_eps = rms_norm_eps
self.use_cache = use_cache
self.rope_theta = rope_theta
self.rope_scaling = rope_scaling
self.attention_bias = attention_bias
self.attention_dropout = attention_dropout
super().__init__(
pad_token_id=pad_token_id,
bos_token_id=bos_token_id,
eos_token_id=eos_token_id,
tie_word_embeddings=tie_word_embeddings,
**kwargs,
)
class Gemma2FastRMSNorm(FastRMSNorm):
@classmethod
def load(cls, prefix, weights, eps=1e-6):
dtype = weights.dtype
weights.dtype = torch.float32
weight = weights.get_tensor(f"{prefix}.weight") + 1
weights.dtype = dtype
new = cls(weight, eps)
new.dtype = dtype
return new
# perform the multiplication in full precision and downcast after
def forward(self, hidden_states, residual=None):
if residual is not None:
hidden_states += residual
residual = hidden_states
hidden_states = hidden_states.to(torch.float32)
variance = hidden_states.pow(2).mean(-1, keepdim=True)
hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon)
hidden_states = hidden_states * self.weight
return hidden_states.to(self.dtype), residual
def load_attention(config, prefix, weights):
if config.num_attention_heads != config.num_key_value_heads:
return _load_gqa(config, prefix, weights)
else:
return TensorParallelColumnLinear.load_multi(
config,
prefixes=[f"{prefix}.q_proj", f"{prefix}.k_proj", f"{prefix}.v_proj"],
dim=0,
weights=weights,
bias=False,
)
def _load_gqa(config, prefix: str, weights):
assert config.num_attention_heads % weights.process_group.size() == 0
weight = weights.get_multi_weights_col(
prefixes=[f"{prefix}.q_proj", f"{prefix}.k_proj", f"{prefix}.v_proj"],
quantize=config.quantize,
dim=0,
)
if config.quantize not in ["gptq", "awq", "marlin"]:
weight = weight.to(dtype=weights.dtype).to(device=weights.device)
head_size = config.head_dim
num_heads = config.num_attention_heads // weights.process_group.size()
num_key_value_heads = config.num_key_value_heads // weights.process_group.size()
assert list(weight.shape) == [
(num_heads + 2 * num_key_value_heads) * head_size,
config.hidden_size,
], f"{list(weight.shape)} != {[(num_heads + 2 * config.num_key_value_heads) * head_size, config.hidden_size]}"
return TensorParallelColumnLinear(
get_linear(weight, bias=None, quantize=config.quantize)
)
class FlashGemma2Attention(torch.nn.Module):
def __init__(self, prefix: str, config, weights, causal: bool, is_sliding: bool):
super().__init__()
self.num_heads = config.num_attention_heads
self.head_size = config.head_dim
self.causal = causal
if is_sliding:
self.window_size = config.sliding_window
else:
self.window_size = -1
self.rotary_emb = PositionRotaryEmbedding.static(
config=config,
dim=self.head_size,
base=config.rope_theta,
device=weights.device,
)
# self.softmax_scale = self.head_size**-0.5
self.softmax_scale = config.query_pre_attn_scalar**-0.5
if self.num_heads % weights.process_group.size() != 0:
raise ValueError(
f"`num_heads` must be divisible by `num_shards` (got `num_heads`: {self.num_heads} "
f"and `num_shards`: {weights.process_group.size()}"
)
self.num_heads = self.num_heads // weights.process_group.size()
self.num_key_value_heads = (
config.num_key_value_heads // weights.process_group.size()
)
self.query_key_value = load_attention(config, prefix, weights)
self.o_proj = TensorParallelRowLinear.load(
config,
prefix=f"{prefix}.o_proj",
weights=weights,
bias=False,
)
self.num_groups = self.num_heads // self.num_key_value_heads
self.kv_head_mapping = torch.arange(
0, self.num_key_value_heads, dtype=torch.int32, device=weights.device
).repeat_interleave(self.num_groups)
def forward(
self,
hidden_states,
cos,
sin,
cu_seqlen_prefill,
kv_cache,
block_tables,
slots,
input_lengths,
max_s,
):
qkv = self.query_key_value(hidden_states)
query, kv = qkv.split(
[
self.head_size * self.num_heads,
2 * self.head_size * self.num_key_value_heads,
],
dim=1,
)
query = query.view(-1, self.num_heads, self.head_size)
kv = kv.view(-1, 2, self.num_key_value_heads, self.head_size)
self.rotary_emb(query, torch.select(kv, dim=1, index=0), cos, sin)
reshape_and_cache(kv[:, 0], kv[:, 1], kv_cache[0], kv_cache[1], slots)
# output tensor
attn_output = torch.empty_like(query)
# Prefill
if cu_seqlen_prefill is not None:
# flash attention
attention(
query,
torch.select(kv, dim=1, index=0),
torch.select(kv, dim=1, index=1),
attn_output,
cu_seqlen_prefill,
max_s,
self.softmax_scale,
causal=self.causal,
window_size_left=self.window_size,
)
# Decode
else:
paged_attention(
attn_output,
query,
kv_cache[0],
kv_cache[1],
self.kv_head_mapping,
self.softmax_scale,
block_tables,
input_lengths,
max_s,
)
return self.o_proj(attn_output.view(-1, self.num_heads * self.head_size))
class Gemma2MLP(nn.Module):
def __init__(self, prefix, config, weights):
super().__init__()
act = config.hidden_act
self.act = (
ACT2FN[act]
if "gelu" not in act
else lambda x: torch.nn.functional.gelu(
x,
approximate=(
"tanh" if act in ["gelu_fast", "gelu_pytorch_tanh"] else "none"
),
)
)
# Fuse gate and up proj
self.gate_up_proj = TensorParallelColumnLinear.load_multi(
config,
prefixes=[f"{prefix}.gate_proj", f"{prefix}.up_proj"],
weights=weights,
dim=0,
bias=False,
)
self.down_proj = TensorParallelRowLinear.load(
config,
prefix=f"{prefix}.down_proj",
weights=weights,
bias=False,
)
self.intermediate_size = (
config.intermediate_size // weights.process_group.size()
)
def forward(self, hidden_states):
gate_up_states = self.gate_up_proj(hidden_states)
gate_up_states = gate_up_states.view(-1, 2, self.intermediate_size)
return self.down_proj(self.act(gate_up_states[:, 0]) * gate_up_states[:, 1])
class FlashGemma2Layer(nn.Module):
def __init__(self, prefix, config, weights, causal: bool, is_sliding: bool):
super().__init__()
self.self_attn = FlashGemma2Attention(
prefix=f"{prefix}.self_attn",
config=config,
weights=weights,
causal=causal,
is_sliding=is_sliding,
)
self.mlp = Gemma2MLP(prefix=f"{prefix}.mlp", config=config, weights=weights)
self.input_layernorm = Gemma2FastRMSNorm.load(
prefix=f"{prefix}.input_layernorm", weights=weights, eps=config.rms_norm_eps
)
self.post_attention_layernorm = Gemma2FastRMSNorm.load(
prefix=f"{prefix}.post_attention_layernorm",
weights=weights,
eps=config.rms_norm_eps,
)
self.pre_feedforward_layernorm = Gemma2FastRMSNorm.load(
prefix=f"{prefix}.pre_feedforward_layernorm",
weights=weights,
eps=config.rms_norm_eps,
)
self.post_feedforward_layernorm = Gemma2FastRMSNorm.load(
prefix=f"{prefix}.post_feedforward_layernorm",
weights=weights,
eps=config.rms_norm_eps,
)
def forward(
self,
hidden_states,
residual,
cos,
sin,
cu_seqlen_prefill,
kv_cache,
block_tables,
slots,
input_lengths,
max_s,
):
normed_hidden_states, res = self.input_layernorm(hidden_states, residual)
# Self Attention
attn_output = self.self_attn(
normed_hidden_states,
cos,
sin,
cu_seqlen_prefill,
kv_cache,
block_tables,
slots,
input_lengths,
max_s,
)
# faster post attention rms norm
normed_attn_res_output, _ = self.post_attention_layernorm(attn_output)
normed_attn_res_output = normed_attn_res_output + res
res = normed_attn_res_output
pre_normed, _ = self.pre_feedforward_layernorm(normed_attn_res_output)
mlp_output = self.mlp(pre_normed)
post_hidden_states, _ = self.post_feedforward_layernorm(mlp_output)
return post_hidden_states, normed_attn_res_output
class FlashGemma2Model(torch.nn.Module):
def __init__(self, prefix, config, weights, causal: bool):
super().__init__()
process_group = weights.process_group
self.tp_rank = process_group.rank()
self.tp_world_size = process_group.size()
self.layers = nn.ModuleList(
[
FlashGemma2Layer(
prefix=f"{prefix}.layers.{layer_id}",
config=config,
weights=weights,
causal=causal,
is_sliding=layer_id % 2 == 0,
)
for layer_id in range(config.num_hidden_layers)
]
)
self.norm = Gemma2FastRMSNorm.load(
prefix=f"{prefix}.norm", weights=weights, eps=config.rms_norm_eps
)
self.head_size = self.layers[0].self_attn.head_size
self.num_heads = self.layers[0].self_attn.num_heads
self.num_key_value_heads = self.layers[0].self_attn.num_key_value_heads
def forward(
self,
inputs_embeds: torch.Tensor,
position_ids: torch.Tensor,
cu_seqlen_prefill: Optional[torch.Tensor],
kv_cache: List[Tuple[torch.Tensor, torch.Tensor]],
block_tables: torch.Tensor,
slots: torch.Tensor,
input_lengths: torch.Tensor,
max_s: int,
) -> torch.Tensor:
hidden_states = inputs_embeds
# Get rotary cos and sin for this forward
# Avoid to index in each layer
cos, sin = self.layers[0].self_attn.rotary_emb.get_cos_sin(
position_ids, max_s, hidden_states.dtype
)
residual = None
for i, layer in enumerate(self.layers):
hidden_states, residual = layer(
hidden_states,
residual,
cos,
sin,
cu_seqlen_prefill,
kv_cache[i],
block_tables,
slots,
input_lengths,
max_s,
)
hidden_states, _ = self.norm(hidden_states, residual)
return hidden_states
class FlashGemma2ForCausalLM(torch.nn.Module):
def __init__(self, prefix, config, weights, causal: bool):
super().__init__()
embed_norm = config.hidden_size**0.5
if not prefix:
prefix = "model"
else:
prefix = f"{prefix}.model"
self.embed_tokens = TensorParallelEmbedding(
prefix=f"{prefix}.embed_tokens", weights=weights
)
self.embed_tokens.weight *= embed_norm
self.model = FlashGemma2Model(
prefix=prefix, config=config, weights=weights, causal=causal
)
self.lm_head = SpeculativeHead.load(
prefix=(
f"{prefix}.embed_tokens"
if config.tie_word_embeddings
else f"{prefix}.lm_head"
),
config=config,
weights=weights,
)
def forward(
self,
input_ids: torch.Tensor,
position_ids: torch.Tensor,
cu_seqlen_prefill: Optional[torch.Tensor],
kv_cache: List[Tuple[torch.Tensor, torch.Tensor]],
block_tables: torch.Tensor,
slots: torch.Tensor,
input_lengths: torch.Tensor,
max_s: int,
prefill_cache_indices: Optional[torch.Tensor],
lm_head_indices: Optional[torch.Tensor] = None,
adapter_data: Optional[torch.Tensor] = None,
) -> Tuple[torch.Tensor, Optional[torch.Tensor]]:
input_embeds = self.embed_tokens(input_ids)
hidden_states = self.model(
input_embeds,
position_ids,
cu_seqlen_prefill,
kv_cache,
block_tables,
slots,
input_lengths,
max_s,
)
if lm_head_indices is not None:
hidden_states = hidden_states[lm_head_indices]
logits, speculative_logits = self.lm_head(hidden_states)
return logits, speculative_logits

2
server/text_generation_server/models/custom_modeling/flash_gemma_modeling.py
View File

@ -375,8 +375,6 @@ class FlashGemmaModel(torch.nn.Module):
prefix=f"{prefix}.norm", weights=weights, eps=config.rms_norm_eps
)
self.gradient_checkpointing = False
self.head_size = self.layers[0].self_attn.head_size
self.num_heads = self.layers[0].self_attn.num_heads
self.num_key_value_heads = self.layers[0].self_attn.num_key_value_heads

18
server/text_generation_server/models/flash_causal_lm.py
View File

@ -28,8 +28,12 @@ from text_generation_server.models.types import (
GeneratedText,
)
from text_generation_server.pb import generate_pb2
from text_generation_server.models.globals import MEM_POOL, CUDA_GRAPHS
import text_generation_server.models.globals as tgi_globals
from text_generation_server.models.globals import (
MEM_POOL,
CUDA_GRAPHS,
get_adapter_to_index,
MODEL_ID,
)
from text_generation_server.utils import StoppingCriteria, HeterogeneousNextTokenChooser
from text_generation_server.utils.dist import MEMORY_FRACTION
from text_generation_server.utils.segments import SegmentConcatBuilder, find_segments
@ -233,7 +237,8 @@ class FlashCausalLMBatch(Batch):
stopping_criterias.append(stopping_criteria)
top_n_tokens.append(r.top_n_tokens)
adapter_index = tgi_globals.ADAPTER_TO_INDEX.get(r.adapter_id, 0)
ADAPTER_TO_INDEX = get_adapter_to_index()
adapter_index = ADAPTER_TO_INDEX.get(r.adapter_id, 0)
adapter_indices_list.append(torch.full((input_length,), adapter_index))
adapter_set.add(adapter_index)
@ -499,9 +504,8 @@ class FlashCausalLMBatch(Batch):
top_n_tokens.append(self.top_n_tokens[idx])
adapter_index = tgi_globals.ADAPTER_TO_INDEX.get(
self.requests[idx].adapter_id, 0
)
ADAPTER_TO_INDEX = get_adapter_to_index()
adapter_index = ADAPTER_TO_INDEX.get(self.requests[idx].adapter_id, 0)
adapter_set.add(adapter_index)
remaining_tokens = (
@ -1017,7 +1021,7 @@ class FlashCausalLM(Model):
tunableop_filepath = os.path.join(
HUGGINGFACE_HUB_CACHE,
f"tunableop_{tgi_globals.MODEL_ID.replace('/', '-')}_tp{self.world_size}_rank{self.rank}.csv",
f"tunableop_{MODEL_ID.replace('/', '-')}_tp{self.world_size}_rank{self.rank}.csv",
)
logger.info(

75
server/text_generation_server/models/flash_gemma2.py Normal file
View File

@ -0,0 +1,75 @@
import torch
import torch.distributed
from opentelemetry import trace
from typing import Optional
from transformers import PretrainedConfig, AutoTokenizer
from text_generation_server.models import FlashCausalLM
from text_generation_server.models.custom_modeling.flash_gemma2_modeling import (
FlashGemma2ForCausalLM,
)
from text_generation_server.utils import (
initialize_torch_distributed,
weight_files,
Weights,
)
tracer = trace.get_tracer(__name__)
class FlashGemma2(FlashCausalLM):
def __init__(
self,
model_id: str,
revision: Optional[str] = None,
quantize: Optional[str] = None,
speculator: Optional[str] = None,
dtype: Optional[torch.dtype] = None,
trust_remote_code: bool = False,
):
self.process_group, rank, world_size = initialize_torch_distributed()
if torch.cuda.is_available():
device = torch.device(f"cuda:{rank}")
dtype = torch.bfloat16 if dtype is None else dtype
else:
raise NotImplementedError("FlashGemma2 is only available on GPU")
tokenizer = AutoTokenizer.from_pretrained(
model_id,
revision=revision,
padding_side="left",
truncation_side="left",
trust_remote_code=trust_remote_code,
)
config = PretrainedConfig.from_pretrained(
model_id, revision=revision, trust_remote_code=trust_remote_code
)
config.quantize = quantize
config.speculator = speculator
torch.distributed.barrier(group=self.process_group)
filenames = weight_files(model_id, revision=revision, extension=".safetensors")
weights = Weights(filenames, device, dtype, process_group=self.process_group)
if config.quantize in ["gptq", "awq", "marlin"]:
weights._set_gptq_params(model_id, revision)
# TODO hardcoded
prefix = ""
model = FlashGemma2ForCausalLM(prefix, config, weights, causal=True)
torch.distributed.barrier(group=self.process_group)
super(FlashGemma2, self).__init__(
model_id=model_id,
model=model,
tokenizer=tokenizer,
num_layers=len(model.model.layers),
num_kv_heads=model.model.num_key_value_heads,
head_size=model.model.head_size,
dtype=dtype,
device=device,
rank=rank,
world_size=world_size,
)

5
server/text_generation_server/models/globals.py
View File

@ -44,3 +44,8 @@ ADAPTER_TO_INDEX: Dict[str, int] = None
def set_adapter_to_index(adapter_to_index: Dict[str, int]):
global ADAPTER_TO_INDEX
ADAPTER_TO_INDEX = adapter_to_index
def get_adapter_to_index():
global ADAPTER_TO_INDEX
return ADAPTER_TO_INDEX