diff --git a/.buildkite/lm-eval-harness/configs/Minitron-4B-Base.yaml b/.buildkite/lm-eval-harness/configs/Minitron-4B-Base.yaml new file mode 100644 index 0000000000000..a0466748ea71e --- /dev/null +++ b/.buildkite/lm-eval-harness/configs/Minitron-4B-Base.yaml @@ -0,0 +1,11 @@ +# bash .buildkite/lm-eval-harness/run-lm-eval-gsm-vllm-baseline.sh -m nvidia/Minitron-4B-Base -b auto -l 1000 -f 5 -t 1 +model_name: "nvidia/Minitron-4B-Base" +tasks: +- name: "gsm8k" + metrics: + - name: "exact_match,strict-match" + value: 0.252 + - name: "exact_match,flexible-extract" + value: 0.252 +limit: 1000 +num_fewshot: 5 diff --git a/.buildkite/lm-eval-harness/configs/models-small.txt b/.buildkite/lm-eval-harness/configs/models-small.txt index 109692395acf6..e4df4b547aa5e 100644 --- a/.buildkite/lm-eval-harness/configs/models-small.txt +++ b/.buildkite/lm-eval-harness/configs/models-small.txt @@ -4,5 +4,6 @@ Meta-Llama-3-8B-Instruct-FP8-compressed-tensors.yaml Meta-Llama-3-8B-Instruct-INT8-compressed-tensors.yaml Meta-Llama-3-8B-Instruct-nonuniform-compressed-tensors.yaml Meta-Llama-3-8B-Instruct-Channelwise-compressed-tensors.yaml +Minitron-4B-Base.yaml Qwen2-1.5B-Instruct-INT8-compressed-tensors.yaml Qwen2-1.5B-Instruct-FP8W8.yaml diff --git a/vllm/model_executor/layers/activation.py b/vllm/model_executor/layers/activation.py index 5bfdba67b443d..6578193a31597 100644 --- a/vllm/model_executor/layers/activation.py +++ b/vllm/model_executor/layers/activation.py @@ -159,6 +159,21 @@ def forward_cuda(self, x: torch.Tensor) -> torch.Tensor: # def forward_xpu(self, x: torch.Tensor) -> torch.Tensor: +class ReLUSquaredActivation(CustomOp): + """ + Applies the relu^2 activation introduced in https://arxiv.org/abs/2109.08668v2 + """ + + def forward_native(self, x: torch.Tensor) -> torch.Tensor: + """PyTorch-native implementation equivalent to forward().""" + relu_applied = nn.functional.relu(x) + squared = torch.square(relu_applied) + return squared + + def forward_cuda(self, x: torch.Tensor) -> torch.Tensor: + return self.forward_native(x) + + class ScaledActivation(nn.Module): """An activation function with post-scale parameters. @@ -207,6 +222,7 @@ def weight_loader(self, param: nn.Parameter, loaded_weight: torch.Tensor): "gelu_new": NewGELU(), "gelu_pytorch_tanh": nn.GELU(approximate="tanh"), "relu": nn.ReLU(), + "relu2": ReLUSquaredActivation(), "quick_gelu": QuickGELU(), } diff --git a/vllm/model_executor/layers/rotary_embedding.py b/vllm/model_executor/layers/rotary_embedding.py index 60ba4623edc38..aecba0ae74911 100644 --- a/vllm/model_executor/layers/rotary_embedding.py +++ b/vllm/model_executor/layers/rotary_embedding.py @@ -774,6 +774,7 @@ def get_rope( is_neox_style: bool = True, rope_scaling: Optional[Dict[str, Any]] = None, dtype: Optional[torch.dtype] = None, + rotary_percent: float = 1.0, ) -> RotaryEmbedding: if dtype is None: dtype = torch.get_default_dtype() @@ -786,6 +787,8 @@ def get_rope( rope_scaling_args = tuple(rope_scaling_tuple.items()) else: rope_scaling_args = None + if rotary_percent < 1.0: + rotary_dim = int(rotary_dim * rotary_percent) key = (head_size, rotary_dim, max_position, base, is_neox_style, rope_scaling_args, dtype) if key in _ROPE_DICT: diff --git a/vllm/model_executor/models/__init__.py b/vllm/model_executor/models/__init__.py index 7df5b8fa64710..ead64c0e92553 100644 --- a/vllm/model_executor/models/__init__.py +++ b/vllm/model_executor/models/__init__.py @@ -51,6 +51,7 @@ "MPTForCausalLM": ("mpt", "MPTForCausalLM"), "MiniCPMForCausalLM": ("minicpm", "MiniCPMForCausalLM"), "MiniCPMV": ("minicpmv", "MiniCPMV"), + "NemotronForCausalLM": ("nemotron", "NemotronForCausalLM"), "OlmoForCausalLM": ("olmo", "OlmoForCausalLM"), "OPTForCausalLM": ("opt", "OPTForCausalLM"), "OrionForCausalLM": ("orion", "OrionForCausalLM"), diff --git a/vllm/model_executor/models/nemotron.py b/vllm/model_executor/models/nemotron.py new file mode 100644 index 0000000000000..bb85f20ab9802 --- /dev/null +++ b/vllm/model_executor/models/nemotron.py @@ -0,0 +1,531 @@ +# coding=utf-8 +# Adapted from +# https://github.com/huggingface/transformers/blob/v4.28.0/src/transformers/models/llama/modeling_llama.py +# Copyright 2023 The vLLM team. +# 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. +"""Inference-only Nemotron model compatible with HuggingFace weights.""" +from typing import Any, Dict, Iterable, List, Optional, Tuple, Union + +import torch +from torch import nn + +from vllm.attention import Attention, AttentionMetadata +from vllm.config import CacheConfig, LoRAConfig +from vllm.distributed import get_pp_group, get_tensor_model_parallel_world_size +from vllm.model_executor.layers.activation import get_act_fn +from vllm.model_executor.layers.linear import (ColumnParallelLinear, + QKVParallelLinear, + RowParallelLinear) +from vllm.model_executor.layers.logits_processor import LogitsProcessor +from vllm.model_executor.layers.quantization.base_config import ( + QuantizationConfig) +from vllm.model_executor.layers.rotary_embedding import get_rope +from vllm.model_executor.layers.sampler import Sampler +from vllm.model_executor.layers.vocab_parallel_embedding import ( + DEFAULT_VOCAB_PADDING_SIZE, ParallelLMHead, VocabParallelEmbedding) +from vllm.model_executor.model_loader.weight_utils import ( + default_weight_loader, maybe_remap_kv_scale_name) +from vllm.model_executor.sampling_metadata import SamplingMetadata +from vllm.sequence import IntermediateTensors, SamplerOutput +from vllm.transformers_utils.configs import NemotronConfig + +from .interfaces import SupportsLoRA +from .utils import PPMissingLayer, is_pp_missing_parameter, make_layers + +# The architecture is pretty similar to Llama, with these changes: +# - There is no gate_proj, just up_proj +# - Normal LayerNorm (with a +1 to the weights) instead of RMSNorm +# - Squared ReLU instead of SwiGLU +# - Adds a rotary_percent to RoPE + + +def _cast_if_autocast_enabled(*args): + if not torch.is_autocast_enabled(): + return args + else: + return torch.cuda.amp.autocast_mode._cast( + args, torch.get_autocast_gpu_dtype()) + + +class NemotronLayerNorm1P(nn.LayerNorm): + + def __init__(self, + normalized_shape: Union[int, List[int], torch.Size], + eps: float = 1e-5, + elementwise_affine: bool = True, + bias: bool = True, + device=None, + dtype=None): + super().__init__(normalized_shape, eps, elementwise_affine, bias, + device, dtype) + + def forward( + self, + x: torch.Tensor, + residual: Optional[torch.Tensor] = None, + ) -> torch.Tensor: + if residual is not None: + x = x + residual + residual = x + args = _cast_if_autocast_enabled(x, self.normalized_shape, + self.weight + 1, self.bias, self.eps) + with torch.cuda.amp.autocast(enabled=False): + x = torch.nn.functional.layer_norm(*args) + return x if residual is None else (x, residual) + + +class NemotronMLP(nn.Module): + + def __init__( + self, + hidden_size: int, + intermediate_size: int, + hidden_act: str, + quant_config: Optional[QuantizationConfig] = None, + bias: bool = False, + prefix: str = "", + ) -> None: + super().__init__() + self.up_proj = ColumnParallelLinear(input_size=hidden_size, + output_size=intermediate_size, + bias=bias, + quant_config=quant_config, + prefix=f"{prefix}.up_proj") + self.down_proj = RowParallelLinear(input_size=intermediate_size, + output_size=hidden_size, + bias=bias, + quant_config=quant_config, + prefix=f"{prefix}.down_proj") + self.act_fn = get_act_fn(hidden_act) + + def forward(self, x): + up, _ = self.up_proj(x) + x = self.act_fn(up) + x, _ = self.down_proj(x) + return x + + +class NemotronAttention(nn.Module): + + def __init__( + self, + config: NemotronConfig, + hidden_size: int, + num_heads: int, + num_kv_heads: int, + rope_theta: float = 10000, + rope_scaling: Optional[Dict[str, Any]] = None, + max_position_embeddings: int = 8192, + quant_config: Optional[QuantizationConfig] = None, + bias: bool = False, + cache_config: Optional[CacheConfig] = None, + prefix: str = "", + ) -> None: + super().__init__() + self.hidden_size = hidden_size + tp_size = get_tensor_model_parallel_world_size() + self.total_num_heads = num_heads + assert self.total_num_heads % tp_size == 0 + self.num_heads = self.total_num_heads // tp_size + self.total_num_kv_heads = num_kv_heads + if self.total_num_kv_heads >= tp_size: + # Number of KV heads is greater than TP size, so we partition + # the KV heads across multiple tensor parallel GPUs. + assert self.total_num_kv_heads % tp_size == 0 + else: + # Number of KV heads is less than TP size, so we replicate + # the KV heads across multiple tensor parallel GPUs. + assert tp_size % self.total_num_kv_heads == 0 + self.num_kv_heads = max(1, self.total_num_kv_heads // tp_size) + # MistralConfig has an optional head_dim introduced by Mistral-Nemo + self.head_dim = getattr(config, "head_dim", + self.hidden_size // self.total_num_heads) + self.q_size = self.num_heads * self.head_dim + self.kv_size = self.num_kv_heads * self.head_dim + self.scaling = self.head_dim**-0.5 + self.rope_theta = rope_theta + self.rotary_percent = config.rope_percent + self.max_position_embeddings = max_position_embeddings + + self.qkv_proj = QKVParallelLinear( + hidden_size=hidden_size, + head_size=self.head_dim, + total_num_heads=self.total_num_heads, + total_num_kv_heads=self.total_num_kv_heads, + bias=bias, + quant_config=quant_config, + prefix=f"{prefix}.qkv_proj", + ) + self.o_proj = RowParallelLinear( + input_size=self.total_num_heads * self.head_dim, + output_size=hidden_size, + bias=bias, + quant_config=quant_config, + prefix=f"{prefix}.o_proj", + ) + + self.rotary_emb = get_rope( + self.head_dim, + rotary_dim=self.head_dim, + max_position=max_position_embeddings, + base=rope_theta, + rope_scaling=rope_scaling, + rotary_percent=self.rotary_percent, + ) + self.attn = Attention(self.num_heads, + self.head_dim, + self.scaling, + num_kv_heads=self.num_kv_heads, + cache_config=cache_config, + quant_config=quant_config) + + def forward( + self, + positions: torch.Tensor, + hidden_states: torch.Tensor, + kv_cache: torch.Tensor, + attn_metadata: AttentionMetadata, + ) -> torch.Tensor: + qkv, _ = self.qkv_proj(hidden_states) + q, k, v = qkv.split([self.q_size, self.kv_size, self.kv_size], dim=-1) + q, k = self.rotary_emb(positions, q, k) + attn_output = self.attn(q, k, v, kv_cache, attn_metadata) + output, _ = self.o_proj(attn_output) + return output + + +class NemotronDecoderLayer(nn.Module): + + def __init__( + self, + config: NemotronConfig, + cache_config: Optional[CacheConfig] = None, + quant_config: Optional[QuantizationConfig] = None, + prefix: str = "", + ) -> None: + super().__init__() + self.hidden_size = config.hidden_size + rope_theta = getattr(config, "rope_theta", 10000) + rope_scaling = getattr(config, "rope_scaling", None) + if rope_scaling is not None and getattr( + config, "original_max_position_embeddings", None): + rope_scaling["original_max_position_embeddings"] = ( + config.original_max_position_embeddings) + max_position_embeddings = getattr(config, "max_position_embeddings", + 8192) + # Support abacusai/Smaug-72B-v0.1 with attention_bias + # Support internlm/internlm-7b with bias + attention_bias = getattr(config, "attention_bias", False) or getattr( + config, "bias", False) + self.self_attn = NemotronAttention( + config=config, + hidden_size=self.hidden_size, + num_heads=config.num_attention_heads, + num_kv_heads=getattr(config, "num_key_value_heads", + config.num_attention_heads), + rope_theta=rope_theta, + rope_scaling=rope_scaling, + max_position_embeddings=max_position_embeddings, + quant_config=quant_config, + bias=attention_bias, + cache_config=cache_config, + prefix=f"{prefix}.self_attn", + ) + self.mlp = NemotronMLP( + hidden_size=self.hidden_size, + intermediate_size=config.intermediate_size, + hidden_act=config.hidden_act, + quant_config=quant_config, + bias=getattr(config, "mlp_bias", False), + prefix=f"{prefix}.mlp", + ) + self.input_layernorm = NemotronLayerNorm1P(config.hidden_size, + eps=config.norm_eps) + self.post_attention_layernorm = NemotronLayerNorm1P( + config.hidden_size, eps=config.norm_eps) + + def forward( + self, + positions: torch.Tensor, + hidden_states: torch.Tensor, + kv_cache: torch.Tensor, + attn_metadata: AttentionMetadata, + residual: Optional[torch.Tensor], + ) -> Tuple[torch.Tensor, torch.Tensor]: + # Self Attention + if residual is None: + residual = hidden_states + hidden_states = self.input_layernorm(hidden_states) + else: + hidden_states, residual = self.input_layernorm( + hidden_states, residual) + hidden_states = self.self_attn( + positions=positions, + hidden_states=hidden_states, + kv_cache=kv_cache, + attn_metadata=attn_metadata, + ) + + # Fully Connected + hidden_states, residual = self.post_attention_layernorm( + hidden_states, residual) + hidden_states = self.mlp(hidden_states) + return hidden_states, residual + + +class NemotronModel(nn.Module): + + def __init__( + self, + config: NemotronConfig, + cache_config: Optional[CacheConfig] = None, + quant_config: Optional[QuantizationConfig] = None, + lora_config: Optional[LoRAConfig] = None, + prefix: str = "", + ) -> None: + super().__init__() + self.config = config + self.padding_idx = config.pad_token_id + lora_vocab = (lora_config.lora_extra_vocab_size * + (lora_config.max_loras or 1)) if lora_config else 0 + self.vocab_size = config.vocab_size + lora_vocab + self.org_vocab_size = config.vocab_size + if get_pp_group().is_first_rank or (config.tie_word_embeddings + and get_pp_group().is_last_rank): + self.embed_tokens = VocabParallelEmbedding( + self.vocab_size, + config.hidden_size, + org_num_embeddings=config.vocab_size, + ) + else: + self.embed_tokens = PPMissingLayer() + self.start_layer, self.end_layer, self.layers = make_layers( + config.num_hidden_layers, + lambda prefix: NemotronDecoderLayer(config=config, + cache_config=cache_config, + quant_config=quant_config, + prefix=prefix), + prefix=f"{prefix}.layers") + if get_pp_group().is_last_rank: + self.norm = NemotronLayerNorm1P(config.hidden_size, + eps=config.norm_eps) + else: + self.norm = PPMissingLayer() + + def get_input_embeddings(self, input_ids: torch.Tensor) -> torch.Tensor: + return self.embed_tokens(input_ids) + + def forward( + self, + input_ids: Optional[torch.Tensor], + positions: torch.Tensor, + kv_caches: List[torch.Tensor], + attn_metadata: AttentionMetadata, + intermediate_tensors: Optional[IntermediateTensors], + inputs_embeds: Optional[torch.Tensor] = None, + ) -> Union[torch.Tensor, IntermediateTensors]: + if get_pp_group().is_first_rank: + if inputs_embeds is not None: + hidden_states = inputs_embeds + else: + hidden_states = self.get_input_embeddings(input_ids) + residual = None + else: + assert intermediate_tensors is not None + hidden_states = intermediate_tensors["hidden_states"] + residual = intermediate_tensors["residual"] + + for i in range(self.start_layer, self.end_layer): + layer = self.layers[i] + hidden_states, residual = layer( + positions, + hidden_states, + kv_caches[i - self.start_layer], + attn_metadata, + residual, + ) + + if not get_pp_group().is_last_rank: + return IntermediateTensors({ + "hidden_states": hidden_states, + "residual": residual + }) + + hidden_states, _ = self.norm(hidden_states, residual) + return hidden_states + + +class NemotronForCausalLM(nn.Module, SupportsLoRA): + packed_modules_mapping = { + "qkv_proj": [ + "q_proj", + "k_proj", + "v_proj", + ], + } + + # LoRA specific attributes + supported_lora_modules = [ + "qkv_proj", "o_proj", "up_proj", "down_proj", "embed_tokens", "lm_head" + ] + embedding_modules = { + "embed_tokens": "input_embeddings", + "lm_head": "output_embeddings", + } + embedding_padding_modules = ["lm_head"] + bitsandbytes_stacked_params_mapping = { + # shard_name, weight_name, index + "q_proj": ("qkv_proj", 0), + "k_proj": ("qkv_proj", 1), + "v_proj": ("qkv_proj", 2), + } + + def __init__( + self, + config: NemotronConfig, + cache_config: Optional[CacheConfig] = None, + quant_config: Optional[QuantizationConfig] = None, + lora_config: Optional[LoRAConfig] = None, + ) -> None: + super().__init__() + + assert isinstance(config, NemotronConfig) + + self.config = config + self.lora_config = lora_config + + self.model = NemotronModel(config, + cache_config, + quant_config, + lora_config=lora_config, + prefix="model") + if get_pp_group().is_last_rank: + self.unpadded_vocab_size = config.vocab_size + if lora_config: + self.unpadded_vocab_size += lora_config.lora_extra_vocab_size + self.lm_head = ParallelLMHead( + self.unpadded_vocab_size, + config.hidden_size, + org_num_embeddings=config.vocab_size, + padding_size=DEFAULT_VOCAB_PADDING_SIZE + # We need bigger padding if using lora for kernel + # compatibility + if not lora_config else lora_config.lora_vocab_padding_size, + quant_config=quant_config, + ) + if config.tie_word_embeddings: + self.lm_head.weight = self.model.embed_tokens.weight + + logit_scale = getattr(config, "logit_scale", 1.0) + self.logits_processor = LogitsProcessor(self.unpadded_vocab_size, + config.vocab_size, + logit_scale) + self.sampler = Sampler() + else: + self.lm_head = PPMissingLayer() + + def forward( + self, + input_ids: torch.Tensor, + positions: torch.Tensor, + kv_caches: List[torch.Tensor], + attn_metadata: AttentionMetadata, + intermediate_tensors: Optional[IntermediateTensors] = None, + ) -> Union[torch.Tensor, IntermediateTensors]: + model_output = self.model(input_ids, positions, kv_caches, + attn_metadata, intermediate_tensors) + return model_output + + def compute_logits(self, hidden_states: torch.Tensor, + sampling_metadata: SamplingMetadata) -> torch.Tensor: + logits = self.logits_processor(self.lm_head, hidden_states, + sampling_metadata) + return logits + + def sample( + self, + logits: torch.Tensor, + sampling_metadata: SamplingMetadata, + ) -> Optional[SamplerOutput]: + next_tokens = self.sampler(logits, sampling_metadata) + return next_tokens + + def make_empty_intermediate_tensors( + self, batch_size: int, dtype: torch.dtype, + device: torch.device) -> IntermediateTensors: + return IntermediateTensors({ + "hidden_states": + torch.zeros((batch_size, self.config.hidden_size), + dtype=dtype, + device=device), + "residual": + torch.zeros((batch_size, self.config.hidden_size), + dtype=dtype, + device=device), + }) + + def load_weights(self, weights: Iterable[Tuple[str, torch.Tensor]]): + stacked_params_mapping = [ + # (param_name, shard_name, shard_id) + (".qkv_proj", ".q_proj", "q"), + (".qkv_proj", ".k_proj", "k"), + (".qkv_proj", ".v_proj", "v"), + ] + params_dict = dict(self.named_parameters()) + for name, loaded_weight in weights: + if "rotary_emb.inv_freq" in name: + continue + if ("rotary_emb.cos_cached" in name + or "rotary_emb.sin_cached" in name): + # Models trained using ColossalAI may include these tensors in + # the checkpoint. Skip them. + continue + for (param_name, weight_name, shard_id) in stacked_params_mapping: + if weight_name not in name: + continue + name = name.replace(weight_name, param_name) + # Skip loading extra bias for GPTQ models. + if name.endswith(".bias") and name not in params_dict: + continue + + if is_pp_missing_parameter(name, self): + continue + + param = params_dict[name] + weight_loader = param.weight_loader + weight_loader(param, loaded_weight, shard_id) + + break + else: + # Skip loading extra bias for GPTQ models. + if name.endswith(".bias") and name not in params_dict: + continue + # Remapping the name of FP8 kv-scale. + name = maybe_remap_kv_scale_name(name, params_dict) + if name is None: + continue + + if is_pp_missing_parameter(name, self): + continue + + param = params_dict[name] + weight_loader = getattr(param, "weight_loader", + default_weight_loader) + weight_loader(param, loaded_weight) diff --git a/vllm/transformers_utils/config.py b/vllm/transformers_utils/config.py index 652505a892142..3ba2e01985598 100644 --- a/vllm/transformers_utils/config.py +++ b/vllm/transformers_utils/config.py @@ -8,7 +8,7 @@ from vllm.transformers_utils.configs import (ChatGLMConfig, DbrxConfig, JAISConfig, MedusaConfig, MLPSpeculatorConfig, MPTConfig, - RWConfig) + NemotronConfig, RWConfig) if VLLM_USE_MODELSCOPE: from modelscope import AutoConfig @@ -26,6 +26,7 @@ "jais": JAISConfig, "mlp_speculator": MLPSpeculatorConfig, "medusa": MedusaConfig, + "nemotron": NemotronConfig, } for name, cls in _CONFIG_REGISTRY.items(): diff --git a/vllm/transformers_utils/configs/__init__.py b/vllm/transformers_utils/configs/__init__.py index 51de11ca3e42a..1750950b3c38b 100644 --- a/vllm/transformers_utils/configs/__init__.py +++ b/vllm/transformers_utils/configs/__init__.py @@ -8,6 +8,7 @@ from vllm.transformers_utils.configs.medusa import MedusaConfig from vllm.transformers_utils.configs.mlp_speculator import MLPSpeculatorConfig from vllm.transformers_utils.configs.mpt import MPTConfig +from vllm.transformers_utils.configs.nemotron import NemotronConfig __all__ = [ "ChatGLMConfig", @@ -17,4 +18,5 @@ "JAISConfig", "MedusaConfig", "MLPSpeculatorConfig", + "NemotronConfig", ] diff --git a/vllm/transformers_utils/configs/nemotron.py b/vllm/transformers_utils/configs/nemotron.py new file mode 100644 index 0000000000000..a22a9f475dda9 --- /dev/null +++ b/vllm/transformers_utils/configs/nemotron.py @@ -0,0 +1,209 @@ +# coding=utf-8 +# Copyright 2024 HuggingFace Inc. team. All rights reserved. +# Copyright (c) 2024, NVIDIA CORPORATION. All rights reserved. +# +# 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. +"""Nemotron model configuration""" + +from transformers import PretrainedConfig +from transformers.utils import logging + +logger = logging.get_logger(__name__) + + +class NemotronConfig(PretrainedConfig): + r""" + This is the configuration class to store the configuration of a + [`NemotronModel`]. It is used to instantiate an Nemotron model + according to the specified arguments, defining the model architecture. + Instantiating a configuration with the defaults will yield a similar + configuration to that of the Nemotron-8B. + + Configuration objects inherit from [`PretrainedConfig`] and can be + used to control the model outputs. Read the documentation from + [`PretrainedConfig`] for more information. + + + Args: + vocab_size (`int`, *optional*, defaults to 32000): + Vocabulary size of the Nemotron model. Defines the number of + different tokens that can be represented by the + `inputs_ids` passed when calling [`NemotronModel`] + hidden_size (`int`, *optional*, defaults to 4096): + Dimension of the hidden representations. + intermediate_size (`int`, *optional*, defaults to 11008): + Dimension of the MLP representations. + num_hidden_layers (`int`, *optional*, defaults to 32): + Number of hidden layers in the Transformer decoder. + num_attention_heads (`int`, *optional*, defaults to 32): + Number of attention heads for each attention layer in the + Transformer decoder. + head_dim (`int`, *optional*, defaults to None): + Projection weights dimension in multi-head attention. Set to + hidden_size // num_attention_heads if None + num_key_value_heads (`int`, *optional*): + This is the number of key_value heads that should be used to + implement Grouped Query Attention. If + `num_key_value_heads=num_attention_heads`, the model will use + Multi Head Attention (MHA), if + `num_key_value_heads=1 the model will use Multi Query Attention + (MQA) otherwise GQA is used. When converting a multi-head + checkpoint to a GQA checkpoint, each group key and value + head should be constructed by meanpooling all the original + heads within that group. For more details checkout + [this paper](https://arxiv.org/pdf/2305.13245.pdf). If it + is not specified, will default to `num_attention_heads`. + hidden_act (`str` or `function`, *optional*, defaults to `"silu"`): + The non-linear activation function (function or string) in the + decoder. + max_position_embeddings (`int`, *optional*, defaults to 2048): + The maximum sequence length that this model might ever be used + with. + initializer_range (`float`, *optional*, defaults to 0.02): + The standard deviation of the truncated_normal_initializer for + initializing all weight matrices. + norm_eps (`float`, *optional*, defaults to 1e-06): + The epsilon used by the normalization layers. + use_cache (`bool`, *optional*, defaults to `True`): + Whether or not the model should return the last key/values + attentions (not used by all models). Only relevant if + `config.is_decoder=True`. + pad_token_id (`int`, *optional*): + Padding token id. + bos_token_id (`int`, *optional*, defaults to 1): + Beginning of stream token id. + eos_token_id (`int`, *optional*, defaults to 2): + End of stream token id. + tie_word_embeddings (`bool`, *optional*, defaults to `False`): + Whether to tie weight embeddings + rope_theta (`float`, *optional*, defaults to 10000.0): + The base period of the RoPE embeddings. + rope_scaling (`Dict`, *optional*): + Dictionary containing the scaling configuration for the RoPE + embeddings. Currently supports two scaling strategies: linear + and dynamic. Their scaling factor must be a float greater than 1. + The expected format is `{"type": strategy name, + "factor": scaling factor}`. When using this flag, don't update + `max_position_embeddings` to the expected new maximum. + attention_bias (`bool`, *optional*, defaults to `False`): + Whether to use a bias in the query, key, value and output + projection layers during self-attention. + attention_dropout (`float`, *optional*, defaults to 0.0): + The dropout ratio for the attention probabilities. + mlp_bias (`bool`, *optional*, defaults to `False`): + Whether to use a bias in up_proj and down_proj layers in the MLP + layers. + + ```python + >>> from transformers import NemotronModel, NemotronConfig + + >>> # Initializing a Nemotron nemotron-15b style configuration + >>> configuration = NemotronConfig() + + >>> # Initializing a model from the nemotron-15b style configuration + >>> model = NemotronModel(configuration) + + >>> # Accessing the model configuration + >>> configuration = model.config + ```""" + + model_type = "nemotron" + keys_to_ignore_at_inference = ["past_key_values"] + + def __init__( + self, + vocab_size=256000, + hidden_size=6144, + intermediate_size=24576, + num_hidden_layers=32, + num_attention_heads=48, + head_dim=None, + num_key_value_heads=None, + hidden_act="relu2", + max_position_embeddings=4096, + initializer_range=0.0134, + norm_eps=1e-5, + use_cache=True, + pad_token_id=None, + bos_token_id=2, + eos_token_id=3, + tie_word_embeddings=False, + rope_theta=10000.0, + rope_scaling=None, + rope_percent=0.5, + attention_bias=False, + attention_dropout=0.0, + mlp_bias=False, + **kwargs, + ): + self.vocab_size = vocab_size + self.max_position_embeddings = max_position_embeddings + self.hidden_size = hidden_size + self.intermediate_size = intermediate_size + self.num_hidden_layers = num_hidden_layers + self.num_attention_heads = num_attention_heads + head_dim = head_dim or kwargs.get("kv_channels", None) + self.head_dim = head_dim if head_dim is not None else ( + hidden_size // 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.norm_eps = norm_eps + self.use_cache = use_cache + self.rope_theta = rope_theta + self.rope_scaling = rope_scaling + rope_percent = rope_percent or kwargs.get("rope_percentage", None) + self.rope_percent = rope_percent + self._rope_scaling_validation() + self.attention_bias = attention_bias + self.attention_dropout = attention_dropout + self.mlp_bias = mlp_bias + + 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, + ) + + def _rope_scaling_validation(self): + """ + Validate the `rope_scaling` configuration. + """ + if self.rope_scaling is None: + return + + if not isinstance(self.rope_scaling, + dict) or len(self.rope_scaling) != 2: + raise ValueError( + "`rope_scaling` must be a dictionary with two fields, " + f"`type` and `factor`, got {self.rope_scaling}") + rope_scaling_type = self.rope_scaling.get("type", None) + rope_scaling_factor = self.rope_scaling.get("factor", None) + if rope_scaling_type is None or rope_scaling_type not in [ + "linear", "dynamic" + ]: + raise ValueError( + "`rope_scaling`'s type field must be one of ['linear', " + f"'dynamic'], got {rope_scaling_type}") + if rope_scaling_factor is None or not isinstance( + rope_scaling_factor, float) or rope_scaling_factor <= 1.0: + raise ValueError( + "`rope_scaling`'s factor field must be a float > 1, got " + f"{rope_scaling_factor}")