add gptj modeling
Signed-off-by: Wang, Yi A <yi.a.wang@intel.com>
This commit is contained in:
Wang, Yi A
parent
133015f408
commit
3e41ec28c7
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@ -153,6 +153,7 @@ pub enum Config {
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Bloom,
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Mpt,
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Gpt2,
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Gptj,
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GptNeox,
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Phi,
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#[serde(rename = "phi-msft")]
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@ -132,6 +132,9 @@ try:
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from text_generation_server.models.custom_modeling.flash_gpt2_modeling import (
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FlashGPT2ForCausalLM,
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)
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from text_generation_server.models.custom_modeling.flash_gptj_modeling import (
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FlashGPTJForCausalLM,
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)
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from text_generation_server.models.custom_modeling.idefics2 import (
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Idefics2ForConditionalGeneration,
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)
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@ -294,6 +297,11 @@ class ModelType(enum.Enum):
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"name": "Gpt Neox",
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"url": "https://huggingface.co/EleutherAI/gpt-neox-20b",
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}
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GPTJ = {
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"type": "gptj",
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"name": "Gptj",
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"url": "https://huggingface.co/EleutherAI/gpt-j-6b",
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}
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IDEFICS = {
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"type": "idefics",
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"name": "Idefics",
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@ -641,6 +649,41 @@ def get_model(
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dtype=dtype,
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trust_remote_code=trust_remote_code,
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)
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elif model_type == GPTJ:
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if FLASH_ATTENTION:
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try:
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return FlashCausalLM(
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model_id=model_id,
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model_class=FlashGPTJForCausalLM,
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revision=revision,
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quantize=quantize,
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speculator=speculator,
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dtype=dtype,
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trust_remote_code=trust_remote_code,
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lora_adapter_ids=lora_adapter_ids,
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)
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except RuntimeError as e:
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# Lots of legacy models with various weight names.
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log_master(logger.warning, f"Couldn't load flash gptj variant: {e}")
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return CausalLM.fallback(
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model_id,
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revision,
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quantize=quantize,
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speculator=speculator,
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dtype=dtype,
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trust_remote_code=trust_remote_code,
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)
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elif sharded:
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raise NotImplementedError(FLASH_ATT_ERROR_MESSAGE.format("Sharded GPT-J"))
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else:
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return CausalLM.fallback(
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model_id,
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revision,
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quantize=quantize,
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speculator=speculator,
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dtype=dtype,
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trust_remote_code=trust_remote_code,
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)
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elif model_type == GPT_NEOX:
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if FLASH_ATTENTION:
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from text_generation_server.models.custom_modeling.flash_neox_modeling import (
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@ -0,0 +1,405 @@
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# coding=utf-8
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# Copyright 2022 EleutherAI and the HuggingFace Inc. team. All rights reserved.
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#
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# This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX
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# and OPT implementations in this library. It has been modified from its
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# original forms to accommodate minor architectural differences compared
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# to GPT-NeoX and OPT used by the Meta AI team that trained the model.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import torch
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import torch.distributed
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from torch import nn
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from transformers.activations import ACT2FN
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from typing import Optional, List, Tuple
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from text_generation_server.layers.attention import (
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paged_attention,
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attention,
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reshape_and_cache,
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)
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from text_generation_server.layers import (
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TensorParallelRowLinear,
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TensorParallelColumnLinear,
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TensorParallelEmbedding,
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SpeculativeHead,
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get_linear,
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)
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from text_generation_server.layers.rotary import (
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PositionRotaryEmbedding,
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)
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from text_generation_server.layers.layernorm import (
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FastLayerNorm,
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)
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from text_generation_server.utils.import_utils import SYSTEM
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def load_attention(config, prefix: str, weights):
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return TensorParallelColumnLinear.load_multi(
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config,
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prefixes=[f"{prefix}.q_proj", f"{prefix}.k_proj", f"{prefix}.v_proj"],
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dim=0,
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weights=weights,
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bias=False,
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)
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def load_row(config, prefix: str, weights, bias: bool):
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weight = weights.get_weights_row(prefix)
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if bias and weights.process_group.rank() == 0:
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# Rank is only on the first rank process
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bias = weights.get_tensor(f"{prefix}.bias")
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else:
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bias = None
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linear = get_linear(weight, bias)
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return TensorParallelRowLinear(linear, process_group=weights.process_group)
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class GPTJRotary(PositionRotaryEmbedding):
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def forward(
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self,
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query: torch.Tensor,
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key: torch.Tensor,
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cos: torch.Tensor,
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sin: torch.Tensor,
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):
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# Such controlflows may add some overhead.
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if SYSTEM == "cuda":
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import rotary_emb
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q1 = query[..., ::2]
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q2 = query[..., 1::2]
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rotary_emb.apply_rotary(q1, q2, cos, sin, q1, q2, False)
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k1 = key[..., ::2]
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k2 = key[..., 1::2]
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rotary_emb.apply_rotary(k1, k2, cos, sin, k1, k2, False)
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elif SYSTEM == "rocm":
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from vllm._C import ops
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# 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.
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# 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
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head_size = query.shape[-1]
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# Inplace operation, updating query and key.
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ops.rotary_embedding(query, key, head_size, cos, sin, False)
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elif SYSTEM == "ipex":
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import intel_extension_for_pytorch as ipex
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ipex.llm.functional.rotary_embedding(
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query, key, sin, cos, query.size(-1), False
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)
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else:
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raise ValueError(
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"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."
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)
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class FlashGPTJAttention(torch.nn.Module):
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def __init__(
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self,
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prefix: str,
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config,
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weights,
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):
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super().__init__()
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self.num_heads = config.num_attention_heads
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self.hidden_size = config.hidden_size
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self.head_size = self.hidden_size // self.num_heads
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self.softmax_scale = self.head_size**-0.5
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self.rotary_dim = config.rotary_dim
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if self.num_heads % weights.process_group.size() != 0:
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raise ValueError(
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f"`num_heads` must be divisible by `num_shards` (got `num_heads`: {self.num_heads} "
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f"and `num_shards`: {weights.process_group.size()}"
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)
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self.num_heads = self.num_heads // weights.process_group.size()
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self.query_key_value = load_attention(
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config,
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prefix=prefix,
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weights=weights,
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)
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self.o_proj = load_row(
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config,
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prefix=f"{prefix}.out_proj",
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weights=weights,
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bias=False,
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)
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self.kv_head_mapping = torch.arange(
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0, self.num_heads, dtype=torch.int32, device=weights.device
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)
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self.rotary_emb = GPTJRotary.static(
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config=config,
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dim=self.rotary_dim,
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base=10000,
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device=weights.device,
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)
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def forward(
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self,
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hidden_states,
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cos,
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sin,
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cu_seqlen_prefill,
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kv_cache,
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block_tables,
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slots,
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input_lengths,
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max_s,
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):
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query, key, value = self.query_key_value(hidden_states).split(
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self.head_size * self.num_heads, dim=1
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)
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query = query.view(-1, self.num_heads, self.head_size)
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key = key.view(-1, self.num_heads, self.head_size)
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value = value.view(-1, self.num_heads, self.head_size)
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# Compute rotary embeddings on rotary_ndims
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if self.rotary_dim is not None:
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self.rotary_emb(
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query[..., : self.rotary_dim], key[..., : self.rotary_dim], cos, sin
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)
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else:
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self.rotary_emb(query, key, cos, sin)
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reshape_and_cache(key, value, kv_cache[0], kv_cache[1], slots)
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# Prefill
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if cu_seqlen_prefill is not None:
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# flash attention
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attn_output = attention(
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query,
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key,
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value,
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cu_seqlen_prefill,
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max_s,
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self.softmax_scale,
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)
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# Decode
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else:
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attn_output = paged_attention(
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query,
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kv_cache[0],
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kv_cache[1],
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self.kv_head_mapping,
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self.softmax_scale,
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block_tables,
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input_lengths,
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max_s,
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)
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return self.o_proj(attn_output.view(-1, self.num_heads * self.head_size))
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class GPTJMLP(nn.Module):
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def __init__(self, prefix: str, config, weights):
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super().__init__()
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act = config.activation_function
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self.act = (
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ACT2FN[act]
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if "gelu" not in act
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else lambda x: torch.nn.functional.gelu(
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x,
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approximate=(
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"tanh" if act in ["gelu_fast", "gelu_pytorch_tanh"] else "none"
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),
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)
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)
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self.fc_in = TensorParallelColumnLinear.load(
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config, prefix=f"{prefix}.fc_in", weights=weights, bias=True
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)
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self.fc_out = load_row(
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config,
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prefix=f"{prefix}.fc_out",
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weights=weights,
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bias=True,
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)
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def forward(self, hidden_states):
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hidden_states = self.fc_in(hidden_states)
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hidden_states = self.act(hidden_states)
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return self.fc_out(hidden_states)
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class FlashGPTJLayer(nn.Module):
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def __init__(self, prefix: str, config, weights):
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super().__init__()
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self.self_attn = FlashGPTJAttention(
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prefix=f"{prefix}.attn", config=config, weights=weights
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)
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self.mlp = GPTJMLP(prefix=f"{prefix}.mlp", config=config, weights=weights)
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self.input_layernorm = FastLayerNorm.load(
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prefix=f"{prefix}.ln_1", weights=weights, eps=config.layer_norm_epsilon
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)
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def forward(
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self,
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hidden_states,
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residual,
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cos,
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sin,
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cu_seqlen_prefill,
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kv_cache,
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block_tables,
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slots,
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input_lengths,
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max_s,
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):
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hidden_states, residual = self.input_layernorm(hidden_states, residual)
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# Self Attention
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attn_output = self.self_attn(
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hidden_states,
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cos,
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sin,
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cu_seqlen_prefill,
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kv_cache,
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block_tables,
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slots,
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input_lengths,
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max_s,
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)
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feed_forward_hidden_states = self.mlp(hidden_states)
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return attn_output + feed_forward_hidden_states, residual
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class FlashGPTJModel(torch.nn.Module):
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def __init__(self, prefix: str, config, weights):
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super().__init__()
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self.config = config
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self.wte = TensorParallelEmbedding(prefix=f"{prefix}.wte", weights=weights)
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self.layers = nn.ModuleList(
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[
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FlashGPTJLayer(
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prefix=(
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f"h.{layer_id}" if not prefix else f"{prefix}.h.{layer_id}"
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),
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config=config,
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weights=weights,
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)
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for layer_id in range(config.num_hidden_layers)
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]
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)
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self.ln_f = FastLayerNorm.load(
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prefix="ln_f" if not prefix else f"{prefix}.ln_f",
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weights=weights,
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eps=config.layer_norm_epsilon,
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)
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self.gradient_checkpointing = False
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self.head_size = self.layers[0].self_attn.head_size
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self.num_heads = self.layers[0].self_attn.num_heads
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def forward(
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self,
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input_ids: Optional[torch.LongTensor],
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position_ids: torch.Tensor,
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cu_seqlen_prefill: Optional[torch.Tensor],
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kv_cache: List[Tuple[torch.Tensor, torch.Tensor]],
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block_tables: torch.Tensor,
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slots: torch.Tensor,
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input_lengths: torch.Tensor,
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max_s: int,
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prefill_cache_indices: Optional[torch.Tensor],
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) -> torch.Tensor:
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hidden_states = self.wte(input_ids)
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# Get rotary cos and sin for this forward
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# Avoid to index in each layer
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cos, sin = self.layers[0].self_attn.rotary_emb.get_cos_sin(
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position_ids, max_s, hidden_states.dtype
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)
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residual = None
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for i, layer in enumerate(self.layers):
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hidden_states, residual = layer(
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hidden_states,
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residual,
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cos,
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sin,
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cu_seqlen_prefill,
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kv_cache[i],
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block_tables,
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slots,
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input_lengths,
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max_s,
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)
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hidden_states, _ = self.ln_f(hidden_states, residual)
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return hidden_states
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class FlashGPTJForCausalLM(torch.nn.Module):
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def __init__(self, prefix: str, config, weights):
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super().__init__()
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if not prefix:
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prefix = "transformer"
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else:
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prefix = f"{prefix}.transformer"
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self.model = FlashGPTJModel(prefix, config, weights)
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self.lm_head = SpeculativeHead.load(
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config,
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prefix="lm_head",
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weights=weights,
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)
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def forward(
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self,
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input_ids: torch.Tensor,
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position_ids: torch.Tensor,
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cu_seqlen_prefill: Optional[torch.Tensor],
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kv_cache: List[Tuple[torch.Tensor, torch.Tensor]],
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block_tables: torch.Tensor,
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slots: torch.Tensor,
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input_lengths: torch.Tensor,
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max_s: int,
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prefill_cache_indices: Optional[torch.Tensor] = None,
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lm_head_indices: Optional[torch.Tensor] = None,
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adapter_data: Optional[torch.Tensor] = None,
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) -> Tuple[torch.Tensor, Optional[torch.Tensor]]:
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hidden_states = self.model(
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input_ids,
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position_ids,
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cu_seqlen_prefill,
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kv_cache,
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block_tables,
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slots,
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input_lengths,
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max_s,
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prefill_cache_indices=prefill_cache_indices,
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)
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if lm_head_indices is not None:
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hidden_states = hidden_states[lm_head_indices]
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logits, speculative_logits = self.lm_head(hidden_states)
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return logits, speculative_logits
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