From 0dcf31a749d51d449311fe41f49e56aa2d836aad Mon Sep 17 00:00:00 2001 From: Nicolas Patry Date: Wed, 26 Jun 2024 13:02:56 +0000 Subject: [PATCH] Fixing gemma2. --- .../text_generation_server/models/__init__.py | 30 ++ .../custom_modeling/flash_gemma2_modeling.py | 500 ++++++++++++++++++ .../custom_modeling/flash_gemma_modeling.py | 2 - .../models/flash_causal_lm.py | 18 +- .../models/flash_gemma2.py | 75 +++ .../text_generation_server/models/globals.py | 5 + 6 files changed, 621 insertions(+), 9 deletions(-) create mode 100644 server/text_generation_server/models/custom_modeling/flash_gemma2_modeling.py create mode 100644 server/text_generation_server/models/flash_gemma2.py diff --git a/server/text_generation_server/models/__init__.py b/server/text_generation_server/models/__init__.py index 648fcee9..f2f0f457 100644 --- a/server/text_generation_server/models/__init__.py +++ b/server/text_generation_server/models/__init__.py @@ -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: diff --git a/server/text_generation_server/models/custom_modeling/flash_gemma2_modeling.py b/server/text_generation_server/models/custom_modeling/flash_gemma2_modeling.py new file mode 100644 index 00000000..a71de61f --- /dev/null +++ b/server/text_generation_server/models/custom_modeling/flash_gemma2_modeling.py @@ -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 diff --git a/server/text_generation_server/models/custom_modeling/flash_gemma_modeling.py b/server/text_generation_server/models/custom_modeling/flash_gemma_modeling.py index a4fd4740..82891823 100644 --- a/server/text_generation_server/models/custom_modeling/flash_gemma_modeling.py +++ b/server/text_generation_server/models/custom_modeling/flash_gemma_modeling.py @@ -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 diff --git a/server/text_generation_server/models/flash_causal_lm.py b/server/text_generation_server/models/flash_causal_lm.py index f7678762..a0a78b33 100644 --- a/server/text_generation_server/models/flash_causal_lm.py +++ b/server/text_generation_server/models/flash_causal_lm.py @@ -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( diff --git a/server/text_generation_server/models/flash_gemma2.py b/server/text_generation_server/models/flash_gemma2.py new file mode 100644 index 00000000..9608113b --- /dev/null +++ b/server/text_generation_server/models/flash_gemma2.py @@ -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, + ) diff --git a/server/text_generation_server/models/globals.py b/server/text_generation_server/models/globals.py index cc2f172a..bde86e6e 100644 --- a/server/text_generation_server/models/globals.py +++ b/server/text_generation_server/models/globals.py @@ -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