* add gptj modeling Signed-off-by: Wang, Yi A <yi.a.wang@intel.com> * fix: update docs for model addition * fix: adjust syntax typo * fix: adjust syntax typo again --------- Signed-off-by: Wang, Yi A <yi.a.wang@intel.com> Co-authored-by: Wang, Yi A <yi.a.wang@intel.com>
This commit is contained in:
drbh
GitHub
parent
8094ecfc9e
commit
21267f3ca3
|
|
@ -32,6 +32,7 @@ Text Generation Inference enables serving optimized models on specific hardware
|
|||
- [Mpt](https://huggingface.co/mosaicml/mpt-7b-instruct)
|
||||
- [Gpt2](https://huggingface.co/openai-community/gpt2)
|
||||
- [Gpt Neox](https://huggingface.co/EleutherAI/gpt-neox-20b)
|
||||
- [Gptj](https://huggingface.co/EleutherAI/gpt-j-6b)
|
||||
- [Idefics](https://huggingface.co/HuggingFaceM4/idefics-9b) (Multimodal)
|
||||
|
||||
|
||||
|
|
|
|||
|
|
@ -153,6 +153,7 @@ pub enum Config {
|
|||
Bloom,
|
||||
Mpt,
|
||||
Gpt2,
|
||||
Gptj,
|
||||
GptNeox,
|
||||
Phi,
|
||||
#[serde(rename = "phi-msft")]
|
||||
|
|
|
|||
|
|
@ -132,6 +132,9 @@ try:
|
|||
from text_generation_server.models.custom_modeling.flash_gpt2_modeling import (
|
||||
FlashGPT2ForCausalLM,
|
||||
)
|
||||
from text_generation_server.models.custom_modeling.flash_gptj_modeling import (
|
||||
FlashGPTJForCausalLM,
|
||||
)
|
||||
from text_generation_server.models.custom_modeling.idefics2 import (
|
||||
Idefics2ForConditionalGeneration,
|
||||
)
|
||||
|
|
@ -294,6 +297,11 @@ class ModelType(enum.Enum):
|
|||
"name": "Gpt Neox",
|
||||
"url": "https://huggingface.co/EleutherAI/gpt-neox-20b",
|
||||
}
|
||||
GPTJ = {
|
||||
"type": "gptj",
|
||||
"name": "Gptj",
|
||||
"url": "https://huggingface.co/EleutherAI/gpt-j-6b",
|
||||
}
|
||||
IDEFICS = {
|
||||
"type": "idefics",
|
||||
"name": "Idefics",
|
||||
|
|
@ -641,6 +649,41 @@ def get_model(
|
|||
dtype=dtype,
|
||||
trust_remote_code=trust_remote_code,
|
||||
)
|
||||
elif model_type == GPTJ:
|
||||
if FLASH_ATTENTION:
|
||||
try:
|
||||
return FlashCausalLM(
|
||||
model_id=model_id,
|
||||
model_class=FlashGPTJForCausalLM,
|
||||
revision=revision,
|
||||
quantize=quantize,
|
||||
speculator=speculator,
|
||||
dtype=dtype,
|
||||
trust_remote_code=trust_remote_code,
|
||||
lora_adapter_ids=lora_adapter_ids,
|
||||
)
|
||||
except RuntimeError as e:
|
||||
# Lots of legacy models with various weight names.
|
||||
log_master(logger.warning, f"Couldn't load flash gptj variant: {e}")
|
||||
return CausalLM.fallback(
|
||||
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 GPT-J"))
|
||||
else:
|
||||
return CausalLM.fallback(
|
||||
model_id,
|
||||
revision,
|
||||
quantize=quantize,
|
||||
speculator=speculator,
|
||||
dtype=dtype,
|
||||
trust_remote_code=trust_remote_code,
|
||||
)
|
||||
elif model_type == GPT_NEOX:
|
||||
if FLASH_ATTENTION:
|
||||
from text_generation_server.models.custom_modeling.flash_neox_modeling import (
|
||||
|
|
|
|||
|
|
@ -0,0 +1,405 @@
|
|||
# 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 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 (
|
||||
FastLayerNorm,
|
||||
)
|
||||
from text_generation_server.utils.import_utils import SYSTEM
|
||||
|
||||
|
||||
def load_attention(config, prefix: str, weights):
|
||||
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_row(config, prefix: str, weights, bias: bool):
|
||||
weight = weights.get_weights_row(prefix)
|
||||
|
||||
if bias and weights.process_group.rank() == 0:
|
||||
# Rank is only on the first rank process
|
||||
bias = weights.get_tensor(f"{prefix}.bias")
|
||||
else:
|
||||
bias = None
|
||||
|
||||
linear = get_linear(weight, bias)
|
||||
return TensorParallelRowLinear(linear, process_group=weights.process_group)
|
||||
|
||||
|
||||
class GPTJRotary(PositionRotaryEmbedding):
|
||||
def forward(
|
||||
self,
|
||||
query: torch.Tensor,
|
||||
key: torch.Tensor,
|
||||
cos: torch.Tensor,
|
||||
sin: torch.Tensor,
|
||||
):
|
||||
# Such controlflows may add some overhead.
|
||||
if SYSTEM == "cuda":
|
||||
import rotary_emb
|
||||
|
||||
q1 = query[..., ::2]
|
||||
q2 = query[..., 1::2]
|
||||
|
||||
rotary_emb.apply_rotary(q1, q2, cos, sin, q1, q2, False)
|
||||
|
||||
k1 = key[..., ::2]
|
||||
k2 = key[..., 1::2]
|
||||
|
||||
rotary_emb.apply_rotary(k1, k2, cos, sin, k1, k2, False)
|
||||
elif SYSTEM == "rocm":
|
||||
from vllm._C import ops
|
||||
|
||||
# NOTE: On RoCm systems, we use a ROPE implementatation adapted from VLLM which launches a single kernel for both query/key, contrary to flash-attn implementation used on NVIDIA systems.
|
||||
# Compiling flash-attn rotary on RoCm, it appears hipcc is unable to unroll loops, resulting in an even slower inference compared to eager: https://github.com/pytorch/pytorch/issues/113773
|
||||
|
||||
head_size = query.shape[-1]
|
||||
|
||||
# Inplace operation, updating query and key.
|
||||
ops.rotary_embedding(query, key, head_size, cos, sin, False)
|
||||
elif SYSTEM == "ipex":
|
||||
import intel_extension_for_pytorch as ipex
|
||||
|
||||
ipex.llm.functional.rotary_embedding(
|
||||
query, key, sin, cos, query.size(-1), False
|
||||
)
|
||||
else:
|
||||
raise ValueError(
|
||||
"Your system seem to be not supported. Please check your install or open an issue at https://github.com/huggingface/text-generation-inference/issues with a clear reproduction."
|
||||
)
|
||||
|
||||
|
||||
class FlashGPTJAttention(torch.nn.Module):
|
||||
def __init__(
|
||||
self,
|
||||
prefix: str,
|
||||
config,
|
||||
weights,
|
||||
):
|
||||
super().__init__()
|
||||
self.num_heads = config.num_attention_heads
|
||||
self.hidden_size = config.hidden_size
|
||||
|
||||
self.head_size = self.hidden_size // self.num_heads
|
||||
self.softmax_scale = self.head_size**-0.5
|
||||
self.rotary_dim = config.rotary_dim
|
||||
|
||||
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.query_key_value = load_attention(
|
||||
config,
|
||||
prefix=prefix,
|
||||
weights=weights,
|
||||
)
|
||||
|
||||
self.o_proj = load_row(
|
||||
config,
|
||||
prefix=f"{prefix}.out_proj",
|
||||
weights=weights,
|
||||
bias=False,
|
||||
)
|
||||
|
||||
self.kv_head_mapping = torch.arange(
|
||||
0, self.num_heads, dtype=torch.int32, device=weights.device
|
||||
)
|
||||
|
||||
self.rotary_emb = GPTJRotary.static(
|
||||
config=config,
|
||||
dim=self.rotary_dim,
|
||||
base=10000,
|
||||
device=weights.device,
|
||||
)
|
||||
|
||||
def forward(
|
||||
self,
|
||||
hidden_states,
|
||||
cos,
|
||||
sin,
|
||||
cu_seqlen_prefill,
|
||||
kv_cache,
|
||||
block_tables,
|
||||
slots,
|
||||
input_lengths,
|
||||
max_s,
|
||||
):
|
||||
query, key, value = self.query_key_value(hidden_states).split(
|
||||
self.head_size * self.num_heads, dim=1
|
||||
)
|
||||
query = query.view(-1, self.num_heads, self.head_size)
|
||||
key = key.view(-1, self.num_heads, self.head_size)
|
||||
value = value.view(-1, self.num_heads, self.head_size)
|
||||
|
||||
# Compute rotary embeddings on rotary_ndims
|
||||
if self.rotary_dim is not None:
|
||||
self.rotary_emb(
|
||||
query[..., : self.rotary_dim], key[..., : self.rotary_dim], cos, sin
|
||||
)
|
||||
else:
|
||||
self.rotary_emb(query, key, cos, sin)
|
||||
|
||||
reshape_and_cache(key, value, kv_cache[0], kv_cache[1], slots)
|
||||
|
||||
# Prefill
|
||||
if cu_seqlen_prefill is not None:
|
||||
# flash attention
|
||||
attn_output = attention(
|
||||
query,
|
||||
key,
|
||||
value,
|
||||
cu_seqlen_prefill,
|
||||
max_s,
|
||||
self.softmax_scale,
|
||||
)
|
||||
# Decode
|
||||
else:
|
||||
attn_output = paged_attention(
|
||||
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 GPTJMLP(nn.Module):
|
||||
def __init__(self, prefix: str, config, weights):
|
||||
super().__init__()
|
||||
act = config.activation_function
|
||||
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"
|
||||
),
|
||||
)
|
||||
)
|
||||
|
||||
self.fc_in = TensorParallelColumnLinear.load(
|
||||
config, prefix=f"{prefix}.fc_in", weights=weights, bias=True
|
||||
)
|
||||
|
||||
self.fc_out = load_row(
|
||||
config,
|
||||
prefix=f"{prefix}.fc_out",
|
||||
weights=weights,
|
||||
bias=True,
|
||||
)
|
||||
|
||||
def forward(self, hidden_states):
|
||||
hidden_states = self.fc_in(hidden_states)
|
||||
hidden_states = self.act(hidden_states)
|
||||
return self.fc_out(hidden_states)
|
||||
|
||||
|
||||
class FlashGPTJLayer(nn.Module):
|
||||
def __init__(self, prefix: str, config, weights):
|
||||
super().__init__()
|
||||
self.self_attn = FlashGPTJAttention(
|
||||
prefix=f"{prefix}.attn", config=config, weights=weights
|
||||
)
|
||||
self.mlp = GPTJMLP(prefix=f"{prefix}.mlp", config=config, weights=weights)
|
||||
|
||||
self.input_layernorm = FastLayerNorm.load(
|
||||
prefix=f"{prefix}.ln_1", weights=weights, eps=config.layer_norm_epsilon
|
||||
)
|
||||
|
||||
def forward(
|
||||
self,
|
||||
hidden_states,
|
||||
residual,
|
||||
cos,
|
||||
sin,
|
||||
cu_seqlen_prefill,
|
||||
kv_cache,
|
||||
block_tables,
|
||||
slots,
|
||||
input_lengths,
|
||||
max_s,
|
||||
):
|
||||
hidden_states, residual = self.input_layernorm(hidden_states, residual)
|
||||
# Self Attention
|
||||
attn_output = self.self_attn(
|
||||
hidden_states,
|
||||
cos,
|
||||
sin,
|
||||
cu_seqlen_prefill,
|
||||
kv_cache,
|
||||
block_tables,
|
||||
slots,
|
||||
input_lengths,
|
||||
max_s,
|
||||
)
|
||||
|
||||
feed_forward_hidden_states = self.mlp(hidden_states)
|
||||
|
||||
return attn_output + feed_forward_hidden_states, residual
|
||||
|
||||
|
||||
class FlashGPTJModel(torch.nn.Module):
|
||||
def __init__(self, prefix: str, config, weights):
|
||||
super().__init__()
|
||||
self.config = config
|
||||
|
||||
self.wte = TensorParallelEmbedding(prefix=f"{prefix}.wte", weights=weights)
|
||||
self.layers = nn.ModuleList(
|
||||
[
|
||||
FlashGPTJLayer(
|
||||
prefix=(
|
||||
f"h.{layer_id}" if not prefix else f"{prefix}.h.{layer_id}"
|
||||
),
|
||||
config=config,
|
||||
weights=weights,
|
||||
)
|
||||
for layer_id in range(config.num_hidden_layers)
|
||||
]
|
||||
)
|
||||
|
||||
self.ln_f = FastLayerNorm.load(
|
||||
prefix="ln_f" if not prefix else f"{prefix}.ln_f",
|
||||
weights=weights,
|
||||
eps=config.layer_norm_epsilon,
|
||||
)
|
||||
|
||||
self.gradient_checkpointing = False
|
||||
|
||||
self.head_size = self.layers[0].self_attn.head_size
|
||||
self.num_heads = self.layers[0].self_attn.num_heads
|
||||
|
||||
def forward(
|
||||
self,
|
||||
input_ids: Optional[torch.LongTensor],
|
||||
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],
|
||||
) -> torch.Tensor:
|
||||
hidden_states = self.wte(input_ids)
|
||||
|
||||
# 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.ln_f(hidden_states, residual)
|
||||
|
||||
return hidden_states
|
||||
|
||||
|
||||
class FlashGPTJForCausalLM(torch.nn.Module):
|
||||
def __init__(self, prefix: str, config, weights):
|
||||
super().__init__()
|
||||
if not prefix:
|
||||
prefix = "transformer"
|
||||
else:
|
||||
prefix = f"{prefix}.transformer"
|
||||
self.model = FlashGPTJModel(prefix, config, weights)
|
||||
self.lm_head = SpeculativeHead.load(
|
||||
config,
|
||||
prefix="lm_head",
|
||||
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] = None,
|
||||
lm_head_indices: Optional[torch.Tensor] = None,
|
||||
adapter_data: Optional[torch.Tensor] = None,
|
||||
) -> Tuple[torch.Tensor, Optional[torch.Tensor]]:
|
||||
hidden_states = self.model(
|
||||
input_ids,
|
||||
position_ids,
|
||||
cu_seqlen_prefill,
|
||||
kv_cache,
|
||||
block_tables,
|
||||
slots,
|
||||
input_lengths,
|
||||
max_s,
|
||||
prefill_cache_indices=prefill_cache_indices,
|
||||
)
|
||||
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
|
||||
Loading…
Reference in New Issue