From 21267f3ca3f121302b86c1702cc2da6091164c55 Mon Sep 17 00:00:00 2001 From: drbh Date: Wed, 7 Aug 2024 21:32:37 -0400 Subject: [PATCH] add gptj modeling in TGI #2366 (CI RUN) (#2372) * add gptj modeling Signed-off-by: Wang, Yi A * fix: update docs for model addition * fix: adjust syntax typo * fix: adjust syntax typo again --------- Signed-off-by: Wang, Yi A Co-authored-by: Wang, Yi A --- docs/source/supported_models.md | 1 + router/src/config.rs | 1 + .../text_generation_server/models/__init__.py | 43 ++ .../custom_modeling/flash_gptj_modeling.py | 405 ++++++++++++++++++ 4 files changed, 450 insertions(+) create mode 100644 server/text_generation_server/models/custom_modeling/flash_gptj_modeling.py diff --git a/docs/source/supported_models.md b/docs/source/supported_models.md index bc124f31..b78104df 100644 --- a/docs/source/supported_models.md +++ b/docs/source/supported_models.md @@ -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) diff --git a/router/src/config.rs b/router/src/config.rs index 7737165e..5d0be9c8 100644 --- a/router/src/config.rs +++ b/router/src/config.rs @@ -153,6 +153,7 @@ pub enum Config { Bloom, Mpt, Gpt2, + Gptj, GptNeox, Phi, #[serde(rename = "phi-msft")] diff --git a/server/text_generation_server/models/__init__.py b/server/text_generation_server/models/__init__.py index ae791ef8..1f9c7526 100644 --- a/server/text_generation_server/models/__init__.py +++ b/server/text_generation_server/models/__init__.py @@ -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 ( diff --git a/server/text_generation_server/models/custom_modeling/flash_gptj_modeling.py b/server/text_generation_server/models/custom_modeling/flash_gptj_modeling.py new file mode 100644 index 00000000..eb667384 --- /dev/null +++ b/server/text_generation_server/models/custom_modeling/flash_gptj_modeling.py @@ -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