diff --git a/README.md b/README.md
index 72df3a6a55..dc4636d308 100644
--- a/README.md
+++ b/README.md
@@ -280,6 +280,7 @@ The following model architectures, tasks and device distributions have been vali
| MiniCPM3 | | Single card | [text generation](https://github.com/huggingface/optimum-habana/tree/main/examples/text-generation) |
| Baichuan2 | DeepSpeed | Single card | [language modeling](https://github.com/huggingface/optimum-habana/tree/main/examples/language-modeling)[text generation](https://github.com/huggingface/optimum-habana/tree/main/examples/text-generation) |
| DeepSeek-V2 | :heavy_check_mark: | :heavy_check_mark: | [text generation](https://github.com/huggingface/optimum-habana/tree/main/examples/text-generation) |
+| DeepSeek-V3 | | :heavy_check_mark: | [text generation](https://github.com/huggingface/optimum-habana/tree/main/examples/text-generation) |
| ChatGLM | DeepSpeed | Single card | [language modeling](https://github.com/huggingface/optimum-habana/tree/main/examples/language-modeling)[text generation](https://github.com/huggingface/optimum-habana/tree/main/examples/text-generation) |
| Qwen2-VL | | Single card | [image to text](https://github.com/huggingface/optimum-habana/tree/main/examples/image-to-text) |
| VideoLLaVA | | Single card | [Video comprehension](https://github.com/huggingface/optimum-habana/tree/main/examples/video-comprehension) |
diff --git a/docs/source/index.mdx b/docs/source/index.mdx
index de2c9b9892..a7cb1f1e92 100644
--- a/docs/source/index.mdx
+++ b/docs/source/index.mdx
@@ -109,6 +109,7 @@ In the tables below, ✅ means single-card, multi-card and DeepSpeed have all be
| MiniCPM3 | | Single card | [text generation](https://github.com/huggingface/optimum-habana/tree/main/examples/text-generation) |
| Baichuan2 | DeepSpeed | Single card | [language modeling](https://github.com/huggingface/optimum-habana/tree/main/examples/language-modeling)[text generation](https://github.com/huggingface/optimum-habana/tree/main/examples/text-generation) |
| DeepSeek-V2 | ✅ | ✅ | [text generation](https://github.com/huggingface/optimum-habana/tree/main/examples/text-generation) |
+| DeepSeek-V3 | | ✅ | [text generation](https://github.com/huggingface/optimum-habana/tree/main/examples/text-generation) |
| ChatGLM | DeepSpeed | Single card | [language modeling](https://github.com/huggingface/optimum-habana/tree/main/examples/language-modeling)[text generation](https://github.com/huggingface/optimum-habana/tree/main/examples/text-generation) |
| Qwen2-VL | | Single card | [image to text](https://github.com/huggingface/optimum-habana/tree/main/examples/image-to-text) |
diff --git a/examples/text-generation/README.md b/examples/text-generation/README.md
index 78dcd44c30..5adf348217 100755
--- a/examples/text-generation/README.md
+++ b/examples/text-generation/README.md
@@ -202,6 +202,20 @@ python ../gaudi_spawn.py --use_deepspeed --world_size 8 run_generation.py \
--flash_attention_causal_mask
```
+To run Deepseek-R1-BF16 inference on 16 Gaudi3 cards (2 nodes) use the following command. Ensure you replace the hostfile parameter with the appropriate file. Sample hostfile reference [here](https://github.com/huggingface/optimum-habana/blob/main/examples/multi-node-training/hostfile)
+```bash
+python3 ../gaudi_spawn.py --hostfile= --use_deepspeed \
+--world_size 16 ./run_generation.py \
+--model_name_or_path opensourcerelease/DeepSeek-R1-bf16 \
+--bf16 \
+--trim_logits \
+--batch_size 1 \
+--use_hpu_graphs \
+--use_kv_cache \
+--parallel_strategy "ep" \
+--prompt "DeepSpeed is a machine learning framework"
+```
+
> To be able to run gated models like [StarCoder](https://huggingface.co/bigcode/starcoder), you should:
> - have a HF account
> - agree to the terms of use of the model in its model card on the HF Hub
diff --git a/optimum/habana/transformers/generation/utils.py b/optimum/habana/transformers/generation/utils.py
index bcb4d74e5b..73347a0008 100755
--- a/optimum/habana/transformers/generation/utils.py
+++ b/optimum/habana/transformers/generation/utils.py
@@ -116,6 +116,7 @@
"minicpm3",
"baichuan",
"deepseek_v2",
+ "deepseek_v3",
"chatglm",
"qwen2_vl",
]
@@ -1095,6 +1096,7 @@ def generate(
"baichuan",
"chatglm",
"deepseek_v2",
+ "deepseek_v3",
], (
"reuse_cache only supported by llama, mistral, falcon, mixtral, phi, qwen2, qwen2_moe, gemma, gemma2, starcoder2, baichuan, chatglm and deepseek_v2 at the moment"
)
diff --git a/optimum/habana/transformers/modeling_utils.py b/optimum/habana/transformers/modeling_utils.py
index b11c049e3b..53ab91433b 100644
--- a/optimum/habana/transformers/modeling_utils.py
+++ b/optimum/habana/transformers/modeling_utils.py
@@ -12,6 +12,7 @@
# 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 accelerate
import transformers
import transformers.utils.fx
@@ -40,6 +41,7 @@
gaudi_awq_quantizer_process_model_before_weight_loading,
gaudi_awq_quantizer_validate_environment,
)
+from .modeling_utils_transformers import load_state_dict
from .models import (
GAUDI_WHISPER_ATTENTION_CLASSES,
BaichuanConfig,
@@ -54,6 +56,8 @@
DeepseekTokenizerFast,
DeepseekV2Config,
DeepseekV2ForCausalLM,
+ DeepseekV3Config,
+ DeepseekV3ForCausalLM,
Gaudi2Idefics2ImageProcessor,
GaudiBloomForCausalLM,
GaudiBloomMLP,
@@ -313,6 +317,9 @@ def adapt_transformers_to_gaudi():
# optimize Conv1D
transformers.pytorch_utils.Conv1D.forward = gaudi_conv1d_forward
+ # override of load_state_dict for deepseekv3. Delete on upgrade to transformers v4.48
+ transformers.modeling_utils.load_state_dict = load_state_dict
+
# Optimization tweak for ViT
transformers.models.vit.modeling_vit.ViTSelfAttention.forward = gaudi_vit_self_attention_forward
@@ -743,9 +750,12 @@ def adapt_transformers_to_gaudi():
transformers.AutoConfig.register("deci", DeciLMConfig)
transformers.AutoModelForCausalLM.register(DeciLMConfig, DeciLMForCausalLM)
+ # Optimization for deepseek on Gaudi
transformers.AutoConfig.register("deepseek_v2", DeepseekV2Config)
transformers.AutoModelForCausalLM.register(DeepseekV2Config, DeepseekV2ForCausalLM)
transformers.AutoTokenizer.register(DeepseekV2Config, fast_tokenizer_class=DeepseekTokenizerFast)
+ transformers.AutoConfig.register("deepseek_v3", DeepseekV3Config)
+ transformers.AutoModelForCausalLM.register(DeepseekV3Config, DeepseekV3ForCausalLM)
# Optimization for cohere on Gaudi
transformers.models.cohere.modeling_cohere.CohereDecoderLayer = GaudiCohereDecoderLayer
diff --git a/optimum/habana/transformers/modeling_utils_transformers.py b/optimum/habana/transformers/modeling_utils_transformers.py
new file mode 100644
index 0000000000..532b69189b
--- /dev/null
+++ b/optimum/habana/transformers/modeling_utils_transformers.py
@@ -0,0 +1,89 @@
+import os
+from typing import Optional, Union
+from zipfile import is_zipfile
+
+import torch
+from packaging import version
+from transformers.integrations import is_deepspeed_zero3_enabled
+from transformers.modeling_utils import is_fsdp_enabled, is_local_dist_rank_0
+from transformers.utils import (
+ is_safetensors_available,
+)
+
+
+if is_safetensors_available():
+ from safetensors import safe_open
+ from safetensors.torch import load_file as safe_load_file
+
+
+def load_state_dict(
+ checkpoint_file: Union[str, os.PathLike],
+ is_quantized: bool = False,
+ map_location: Optional[Union[str, torch.device]] = None,
+ weights_only: bool = True,
+):
+ """
+ Reads a PyTorch checkpoint file, returning properly formatted errors if they arise.
+
+ Copied from transformers v4.48.2 for DeepSeek-R1 support https://github.com/huggingface/transformers/blob/b673c16cad81c71f70903a9a63f5b5f06014aa9e/src/transformers/modeling_utils.py#L493
+ Delete after upgrade transformers v4.45.2 to v4.48
+ """
+ if checkpoint_file.endswith(".safetensors") and is_safetensors_available():
+ # Check format of the archive
+ with safe_open(checkpoint_file, framework="pt") as f:
+ metadata = f.metadata()
+ if metadata is not None and metadata.get("format") not in ["pt", "tf", "flax", "mlx"]:
+ raise OSError(
+ f"The safetensors archive passed at {checkpoint_file} does not contain the valid metadata. Make sure "
+ "you save your model with the `save_pretrained` method."
+ )
+ return safe_load_file(checkpoint_file)
+ try:
+ if map_location is None:
+ if (
+ (
+ is_deepspeed_zero3_enabled()
+ and torch.distributed.is_initialized()
+ and torch.distributed.get_rank() > 0
+ )
+ or (is_fsdp_enabled() and not is_local_dist_rank_0())
+ ) and not is_quantized:
+ map_location = "meta"
+ else:
+ map_location = "cpu"
+ extra_args = {}
+ # mmap can only be used with files serialized with zipfile-based format.
+ if (
+ isinstance(checkpoint_file, str)
+ and map_location != "meta"
+ and version.parse(torch.__version__) >= version.parse("2.1.0")
+ and is_zipfile(checkpoint_file)
+ ):
+ extra_args = {"mmap": True}
+ weights_only_kwarg = {"weights_only": weights_only}
+ return torch.load(
+ checkpoint_file,
+ map_location=map_location,
+ **weights_only_kwarg,
+ **extra_args,
+ )
+ except Exception as e:
+ try:
+ with open(checkpoint_file) as f:
+ if f.read(7) == "version":
+ raise OSError(
+ "You seem to have cloned a repository without having git-lfs installed. Please install "
+ "git-lfs and run `git lfs install` followed by `git lfs pull` in the folder "
+ "you cloned."
+ )
+ else:
+ raise ValueError(
+ f"Unable to locate the file {checkpoint_file} which is necessary to load this pretrained "
+ "model. Make sure you have saved the model properly."
+ ) from e
+ except (UnicodeDecodeError, ValueError):
+ raise OSError(
+ f"Unable to load weights from pytorch checkpoint file for '{checkpoint_file}' "
+ f"at '{checkpoint_file}'. "
+ "If you tried to load a PyTorch model from a TF 2.0 checkpoint, please set from_tf=True."
+ )
diff --git a/optimum/habana/transformers/models/__init__.py b/optimum/habana/transformers/models/__init__.py
index 57c23844da..b784c1c895 100644
--- a/optimum/habana/transformers/models/__init__.py
+++ b/optimum/habana/transformers/models/__init__.py
@@ -71,6 +71,10 @@
DeepseekV2Config,
DeepseekV2ForCausalLM,
)
+from .deepseek_v3 import (
+ DeepseekV3Config,
+ DeepseekV3ForCausalLM,
+)
from .detr import (
gaudi_DetrConvModel_forward,
gaudi_DetrHungarianMatcher_forward,
diff --git a/optimum/habana/transformers/models/deepseek_v3/__init__.py b/optimum/habana/transformers/models/deepseek_v3/__init__.py
new file mode 100644
index 0000000000..f76872762e
--- /dev/null
+++ b/optimum/habana/transformers/models/deepseek_v3/__init__.py
@@ -0,0 +1,2 @@
+from .configuration_deepseek_v3 import DeepseekV3Config
+from .modeling_deepseek_v3 import DeepseekV3ForCausalLM
diff --git a/optimum/habana/transformers/models/deepseek_v3/configuration_deepseek_v3.py b/optimum/habana/transformers/models/deepseek_v3/configuration_deepseek_v3.py
new file mode 100644
index 0000000000..af5eb623b1
--- /dev/null
+++ b/optimum/habana/transformers/models/deepseek_v3/configuration_deepseek_v3.py
@@ -0,0 +1,217 @@
+"""
+DeepSeekV3 model configuration. Copied from https://huggingface.co/deepseek-ai/DeepSeek-R1/resolve/main/configuration_deepseek.py
+"""
+
+from transformers.configuration_utils import PretrainedConfig
+from transformers.utils import logging
+
+
+logger = logging.get_logger(__name__)
+
+DEEPSEEK_PRETRAINED_CONFIG_ARCHIVE_MAP = {}
+
+
+class DeepseekV3Config(PretrainedConfig):
+ r"""
+ This is the configuration class to store the configuration of a [`DeepseekV3Model`]. It is used to instantiate an DeepSeek
+ 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 DeepSeek-V3.
+
+ 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 129280):
+ Vocabulary size of the Deep model. Defines the number of different tokens that can be represented by the
+ `inputs_ids` passed when calling [`DeepseekV3Model`]
+ hidden_size (`int`, *optional*, defaults to 4096):
+ Dimension of the hidden representations.
+ intermediate_size (`int`, *optional*, defaults to 11008):
+ Dimension of the MLP representations.
+ moe_intermediate_size (`int`, *optional*, defaults to 1407):
+ Dimension of the MoE representations.
+ num_hidden_layers (`int`, *optional*, defaults to 32):
+ Number of hidden layers in the Transformer decoder.
+ num_nextn_predict_layers (`int`, *optional*, defaults to 1):
+ Number of nextn predict layers in the DeepSeekV3 Model.
+ num_attention_heads (`int`, *optional*, defaults to 32):
+ Number of attention heads for each attention layer in the Transformer decoder.
+ n_shared_experts (`int`, *optional*, defaults to None):
+ Number of shared experts, None means dense model.
+ n_routed_experts (`int`, *optional*, defaults to None):
+ Number of routed experts, None means dense model.
+ routed_scaling_factor (`float`, *optional*, defaults to 1.0):
+ Scaling factor or routed experts.
+ topk_method (`str`, *optional*, defaults to `gready`):
+ Topk method used in routed gate.
+ n_group (`int`, *optional*, defaults to None):
+ Number of groups for routed experts.
+ topk_group (`int`, *optional*, defaults to None):
+ Number of selected groups for each token(for each token, ensuring the selected experts is only within `topk_group` groups).
+ num_experts_per_tok (`int`, *optional*, defaults to None):
+ Number of selected experts, None means dense model.
+ moe_layer_freq (`int`, *optional*, defaults to 1):
+ The frequency of the MoE layer: one expert layer for every `moe_layer_freq - 1` dense layers.
+ first_k_dense_replace (`int`, *optional*, defaults to 0):
+ Number of dense layers in shallow layers(embed->dense->dense->...->dense->moe->moe...->lm_head).
+ \--k dense layers--/
+ norm_topk_prob (`bool`, *optional*, defaults to False):
+ Whether to normalize the weights of the routed experts.
+ scoring_func (`str`, *optional*, defaults to 'softmax'):
+ Method of computing expert weights.
+ aux_loss_alpha (`float`, *optional*, defaults to 0.001):
+ Auxiliary loss weight coefficient.
+ seq_aux = (`bool`, *optional*, defaults to True):
+ Whether to compute the auxiliary loss for each individual sample.
+ 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.
+ rms_norm_eps (`float`, *optional*, defaults to 1e-06):
+ The epsilon used by the rms 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.
+ pretraining_tp (`int`, *optional*, defaults to 1):
+ Experimental feature. Tensor parallelism rank used during pretraining. Please refer to [this
+ document](https://huggingface.co/docs/transformers/parallelism) to understand more about it. This value is
+ necessary to ensure exact reproducibility of the pretraining results. Please refer to [this
+ issue](https://github.com/pytorch/pytorch/issues/76232).
+ 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`, defaults to `False`, *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.
+
+ ```python
+ >>> from transformers import DeepseekV3Model, DeepseekV3Config
+
+ >>> # Initializing a Deepseek-V3 style configuration
+ >>> configuration = DeepseekV3Config()
+
+ >>> # Accessing the model configuration
+ >>> configuration = model.config
+ ```"""
+
+ model_type = "deepseek_v3"
+ keys_to_ignore_at_inference = ["past_key_values"]
+
+ def __init__(
+ self,
+ vocab_size=129280,
+ hidden_size=7168,
+ intermediate_size=18432,
+ moe_intermediate_size=2048,
+ num_hidden_layers=61,
+ num_nextn_predict_layers=1,
+ num_attention_heads=128,
+ num_key_value_heads=128,
+ n_shared_experts=1,
+ n_routed_experts=256,
+ ep_size=1,
+ routed_scaling_factor=2.5,
+ kv_lora_rank=512,
+ q_lora_rank=1536,
+ qk_rope_head_dim=64,
+ v_head_dim=128,
+ qk_nope_head_dim=128,
+ topk_method="noaux_tc",
+ n_group=8,
+ topk_group=4,
+ num_experts_per_tok=8,
+ moe_layer_freq=1,
+ first_k_dense_replace=3,
+ norm_topk_prob=True,
+ scoring_func="sigmoid",
+ aux_loss_alpha=0.001,
+ seq_aux=True,
+ hidden_act="silu",
+ max_position_embeddings=4096,
+ initializer_range=0.02,
+ rms_norm_eps=1e-6,
+ use_cache=True,
+ pad_token_id=None,
+ bos_token_id=0,
+ eos_token_id=1,
+ pretraining_tp=1,
+ tie_word_embeddings=False,
+ 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.intermediate_size = intermediate_size
+ self.moe_intermediate_size = moe_intermediate_size
+ self.num_hidden_layers = num_hidden_layers
+ self.num_nextn_predict_layers = num_nextn_predict_layers
+ self.num_attention_heads = num_attention_heads
+ self.n_shared_experts = n_shared_experts
+ self.n_routed_experts = n_routed_experts
+ self.ep_size = ep_size
+ self.routed_scaling_factor = routed_scaling_factor
+ self.kv_lora_rank = kv_lora_rank
+ self.q_lora_rank = q_lora_rank
+ self.qk_rope_head_dim = qk_rope_head_dim
+ self.v_head_dim = v_head_dim
+ self.qk_nope_head_dim = qk_nope_head_dim
+ self.topk_method = topk_method
+ self.n_group = n_group
+ self.topk_group = topk_group
+ self.num_experts_per_tok = num_experts_per_tok
+ self.moe_layer_freq = moe_layer_freq
+ self.first_k_dense_replace = first_k_dense_replace
+ self.norm_topk_prob = norm_topk_prob
+ self.scoring_func = scoring_func
+ self.aux_loss_alpha = aux_loss_alpha
+ self.seq_aux = seq_aux
+ # 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.pretraining_tp = pretraining_tp
+ 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,
+ )
diff --git a/optimum/habana/transformers/models/deepseek_v3/modeling_deepseek_v3.py b/optimum/habana/transformers/models/deepseek_v3/modeling_deepseek_v3.py
new file mode 100644
index 0000000000..fb7751545a
--- /dev/null
+++ b/optimum/habana/transformers/models/deepseek_v3/modeling_deepseek_v3.py
@@ -0,0 +1,1913 @@
+# coding=utf-8
+# Copyright 2023 DeepSeek-AI 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.
+"""
+PyTorch DeepSeekV3 model. Adapted from https://huggingface.co/deepseek-ai/DeepSeek-R1/resolve/main/modeling_deepseek.py
+
+The main differences are:
+- Use Gaudi Flash Attention
+- Optimized KV cache with support for static shapes
+- Use fused Gaudi MoE, RoPE, and RMSNorm operators
+- Enable expert parallelism
+"""
+
+import math
+import warnings
+from typing import List, Optional, Tuple, Union
+
+import habana_frameworks.torch.core as htcore
+import torch
+import torch.distributed as dist
+import torch.nn.functional as F
+import torch.utils.checkpoint
+from torch import nn
+from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
+from transformers.activations import ACT2FN
+from transformers.cache_utils import Cache
+from transformers.generation import GenerationMixin
+from transformers.integrations.deepspeed import is_deepspeed_available
+from transformers.modeling_outputs import (
+ BaseModelOutputWithPast,
+ CausalLMOutputWithPast,
+ SequenceClassifierOutputWithPast,
+)
+from transformers.modeling_utils import PreTrainedModel
+from transformers.pytorch_utils import (
+ ALL_LAYERNORM_LAYERS,
+)
+from transformers.utils import (
+ add_start_docstrings,
+ add_start_docstrings_to_model_forward,
+ logging,
+ replace_return_docstrings,
+)
+
+from ....distributed.tensorparallel import _all_reduce
+from ...modeling_attn_mask_utils import _gaudi_prepare_4d_causal_attention_mask
+from ..modeling_all_models import apply_customized_rope_module
+from .configuration_deepseek_v3 import DeepseekV3Config
+
+
+logger = logging.get_logger(__name__)
+
+_CONFIG_FOR_DOC = "DeepseekV3Config"
+
+# Maximum number of experts supported by dynamic MoE op (mixture_of_experts)
+SLICE_MAX_EXPERT = 80
+
+# import hpu fused ops
+try:
+ from habana_frameworks.torch.hpex.kernels import RotaryPosEmbeddingHelperV2 as FusedRoPE
+
+ print("Using HPU fused kernel for apply_rotary_pos_emb")
+except ImportError:
+ print("Not using HPU fused kernel for apply_rotary_pos_emb")
+ FusedRoPE = None
+
+try:
+ from habana_frameworks.torch.hpex.normalization import FusedRMSNorm
+
+ print("Using HPU fused kernel for RMSNorm")
+except ImportError:
+ print("Not using HPU fused kernel for RMSNorm")
+ FusedRMSNorm = None
+
+try:
+ from habana_frameworks.torch.hpex.kernels import FusedSDPA
+except ImportError:
+ print("Not using HPU fused scaled dot-product attention kernel.")
+ FusedSDPA = None
+
+
+def _get_unpad_data(attention_mask):
+ seqlens_in_batch = attention_mask.sum(dim=-1, dtype=torch.int32)
+ indices = torch.nonzero(attention_mask.flatten(), as_tuple=False).flatten()
+ max_seqlen_in_batch = seqlens_in_batch.max().item()
+ cu_seqlens = F.pad(torch.cumsum(seqlens_in_batch, dim=0, dtype=torch.torch.int32), (1, 0))
+ return (
+ indices,
+ cu_seqlens,
+ max_seqlen_in_batch,
+ )
+
+
+class DeepseekV3RMSNorm(nn.Module):
+ def __init__(self, hidden_size, eps=1e-6):
+ """
+ DeepseekV3RMSNorm is equivalent to T5LayerNorm
+ """
+ super().__init__()
+ self.weight = nn.Parameter(torch.ones(hidden_size))
+ self.variance_epsilon = eps
+
+ def forward(self, hidden_states):
+ if hidden_states.device.type == "hpu" and FusedRMSNorm:
+ # use hpu fused rmsnorm
+ # mixed dtypes are not good for FusedRMSNorm, both inputs need to have same dtype
+ if hidden_states.dtype != self.weight.dtype:
+ orig_dtype = hidden_states.dtype
+ hidden_states = FusedRMSNorm.apply(
+ hidden_states.to(self.weight.dtype), self.weight, self.variance_epsilon
+ )
+ return hidden_states.to(orig_dtype)
+ else:
+ hidden_states = FusedRMSNorm.apply(hidden_states, self.weight, self.variance_epsilon)
+ return hidden_states
+ else:
+ input_dtype = hidden_states.dtype
+ 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)
+ return self.weight * hidden_states.to(input_dtype)
+
+
+ALL_LAYERNORM_LAYERS.append(DeepseekV3RMSNorm)
+
+
+class DeepseekV3RotaryEmbedding(nn.Module):
+ def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None):
+ super().__init__()
+
+ self.dim = dim
+ self.max_position_embeddings = max_position_embeddings
+ self.base = base
+ inv_freq = 1.0 / (self.base ** (torch.arange(0, self.dim, 2).float().to(device) / self.dim))
+ self.register_buffer("inv_freq", inv_freq, persistent=False)
+
+ # Build here to make `torch.jit.trace` work.
+
+ # make it static (max_position_embeddings) instead of updating depending on
+ # longest seq_len seen till now: seq_len > self.max_seq_len_cached
+ self.max_seq_len_cached = max_position_embeddings
+ self._set_cos_sin_cache(
+ seq_len=self.max_seq_len_cached,
+ device=self.inv_freq.device,
+ dtype=torch.get_default_dtype(),
+ )
+
+ def _set_cos_sin_cache(self, seq_len, device, dtype):
+ self.max_seq_len_cached = seq_len
+ t = torch.arange(self.max_seq_len_cached, device=device, dtype=self.inv_freq.dtype)
+
+ freqs = torch.outer(t, self.inv_freq.to(t.device))
+ # Different from paper, but it uses a different permutation in order to obtain the same calculation
+ emb = torch.cat((freqs, freqs), dim=-1)
+ self.register_buffer("cos_cached", emb.cos().to(dtype), persistent=False)
+ self.register_buffer("sin_cached", emb.sin().to(dtype), persistent=False)
+
+ def forward(self, x, seq_len=None):
+ # x: [bs, num_attention_heads, seq_len, head_size]
+ if seq_len is not None and seq_len > self.max_seq_len_cached:
+ self._set_cos_sin_cache(seq_len=seq_len, device=x.device, dtype=x.dtype)
+
+ return (
+ self.cos_cached[:seq_len].to(dtype=x.dtype),
+ self.sin_cached[:seq_len].to(dtype=x.dtype),
+ )
+
+
+# Copied from transformers.models.llama.modeling_llama.LlamaLinearScalingRotaryEmbedding with Llama->DeepseekV3
+class DeepseekV3LinearScalingRotaryEmbedding(DeepseekV3RotaryEmbedding):
+ """DeepseekV3RotaryEmbedding extended with linear scaling. Credits to the Reddit user /u/kaiokendev"""
+
+ def __init__(
+ self,
+ dim,
+ max_position_embeddings=2048,
+ base=10000,
+ device=None,
+ scaling_factor=1.0,
+ ):
+ self.scaling_factor = scaling_factor
+ super().__init__(dim, max_position_embeddings, base, device)
+
+ def _set_cos_sin_cache(self, seq_len, device, dtype):
+ self.max_seq_len_cached = seq_len
+ t = torch.arange(self.max_seq_len_cached, device=device, dtype=self.inv_freq.dtype)
+ t = t / self.scaling_factor
+
+ freqs = torch.outer(t, self.inv_freq)
+ # Different from paper, but it uses a different permutation in order to obtain the same calculation
+ emb = torch.cat((freqs, freqs), dim=-1)
+ self.register_buffer("cos_cached", emb.cos().to(dtype), persistent=False)
+ self.register_buffer("sin_cached", emb.sin().to(dtype), persistent=False)
+
+
+# Copied from transformers.models.llama.modeling_llama.LlamaDynamicNTKScalingRotaryEmbedding with Llama->DeepseekV3
+class DeepseekV3DynamicNTKScalingRotaryEmbedding(DeepseekV3RotaryEmbedding):
+ """DeepseekV3RotaryEmbedding extended with Dynamic NTK scaling. Credits to the Reddit users /u/bloc97 and /u/emozilla"""
+
+ def __init__(
+ self,
+ dim,
+ max_position_embeddings=2048,
+ base=10000,
+ device=None,
+ scaling_factor=1.0,
+ ):
+ self.scaling_factor = scaling_factor
+ super().__init__(dim, max_position_embeddings, base, device)
+
+ def _set_cos_sin_cache(self, seq_len, device, dtype):
+ self.max_seq_len_cached = seq_len
+
+ if seq_len > self.max_position_embeddings:
+ base = self.base * (
+ (self.scaling_factor * seq_len / self.max_position_embeddings) - (self.scaling_factor - 1)
+ ) ** (self.dim / (self.dim - 2))
+ inv_freq = 1.0 / (base ** (torch.arange(0, self.dim, 2).float().to(device) / self.dim))
+ self.register_buffer("inv_freq", inv_freq, persistent=False)
+
+ t = torch.arange(self.max_seq_len_cached, device=device, dtype=self.inv_freq.dtype)
+
+ freqs = torch.outer(t, self.inv_freq)
+ # Different from paper, but it uses a different permutation in order to obtain the same calculation
+ emb = torch.cat((freqs, freqs), dim=-1)
+ self.register_buffer("cos_cached", emb.cos().to(dtype), persistent=False)
+ self.register_buffer("sin_cached", emb.sin().to(dtype), persistent=False)
+
+
+# Inverse dim formula to find dim based on number of rotations
+def yarn_find_correction_dim(num_rotations, dim, base=10000, max_position_embeddings=2048):
+ return (dim * math.log(max_position_embeddings / (num_rotations * 2 * math.pi))) / (2 * math.log(base))
+
+
+# Find dim range bounds based on rotations
+def yarn_find_correction_range(low_rot, high_rot, dim, base=10000, max_position_embeddings=2048):
+ low = math.floor(yarn_find_correction_dim(low_rot, dim, base, max_position_embeddings))
+ high = math.ceil(yarn_find_correction_dim(high_rot, dim, base, max_position_embeddings))
+ return max(low, 0), min(high, dim - 1) # Clamp values just in case
+
+
+def yarn_get_mscale(scale=1, mscale=1):
+ if scale <= 1:
+ return 1.0
+ return 0.1 * mscale * math.log(scale) + 1.0
+
+
+def yarn_linear_ramp_mask(min, max, dim):
+ if min == max:
+ max += 0.001 # Prevent singularity
+
+ linear_func = (torch.arange(dim, dtype=torch.float32) - min) / (max - min)
+ ramp_func = torch.clamp(linear_func, 0, 1)
+ return ramp_func
+
+
+class DeepseekV3YarnRotaryEmbedding(DeepseekV3RotaryEmbedding):
+ def __init__(
+ self,
+ dim,
+ max_position_embeddings=2048,
+ base=10000,
+ device=None,
+ scaling_factor=1.0,
+ original_max_position_embeddings=4096,
+ beta_fast=32,
+ beta_slow=1,
+ mscale=1,
+ mscale_all_dim=0,
+ ):
+ self.scaling_factor = scaling_factor
+ self.original_max_position_embeddings = original_max_position_embeddings
+ self.beta_fast = beta_fast
+ self.beta_slow = beta_slow
+ self.mscale = mscale
+ self.mscale_all_dim = mscale_all_dim
+ super().__init__(dim, max_position_embeddings, base, device)
+
+ def _set_cos_sin_cache(self, seq_len, device, dtype):
+ self.max_seq_len_cached = seq_len
+ dim = self.dim
+
+ freq_extra = 1.0 / (self.base ** (torch.arange(0, dim, 2, dtype=torch.float32, device=device) / dim))
+ freq_inter = 1.0 / (
+ self.scaling_factor * self.base ** (torch.arange(0, dim, 2, dtype=torch.float32, device=device) / dim)
+ )
+
+ low, high = yarn_find_correction_range(
+ self.beta_fast,
+ self.beta_slow,
+ dim,
+ self.base,
+ self.original_max_position_embeddings,
+ )
+ inv_freq_mask = 1.0 - yarn_linear_ramp_mask(low, high, dim // 2).to(device=device, dtype=torch.float32)
+ inv_freq = freq_inter * (1 - inv_freq_mask) + freq_extra * inv_freq_mask
+ self.register_buffer("inv_freq", inv_freq, persistent=False)
+
+ t = torch.arange(seq_len, device=device, dtype=torch.float32)
+
+ freqs = torch.outer(t, inv_freq)
+
+ _mscale = float(
+ yarn_get_mscale(self.scaling_factor, self.mscale)
+ / yarn_get_mscale(self.scaling_factor, self.mscale_all_dim)
+ )
+
+ emb = torch.cat((freqs, freqs), dim=-1)
+ self.register_buffer("cos_cached", (emb.cos() * _mscale).to(dtype), persistent=False)
+ self.register_buffer("sin_cached", (emb.sin() * _mscale).to(dtype), persistent=False)
+
+
+def apply_customized_rope(q, k, cos, sin, position_ids, training=True):
+ if q.device.type == "hpu" and FusedRoPE: # use fused hpu op
+ return apply_customized_rope_module(q, k, cos, sin, position_ids, training)
+ else:
+ return apply_rotary_pos_emb(q, k, cos, sin, position_ids)
+
+
+# Copied from transformers.models.llama.modeling_llama.rotate_half
+def rotate_half(x):
+ """Rotates half the hidden dims of the input."""
+ x1 = x[..., : x.shape[-1] // 2]
+ x2 = x[..., x.shape[-1] // 2 :]
+ return torch.cat((-x2, x1), dim=-1)
+
+
+# Copied from transformers.models.llama.modeling_llama.apply_rotary_pos_emb
+def apply_rotary_pos_emb(q: torch.Tensor, cos, sin, position_ids, unsqueeze_dim=1):
+ """Applies Rotary Position Embedding to the query and key tensors.
+ Args:
+ q (`torch.Tensor`): The query tensor.
+ cos (`torch.Tensor`): The cosine part of the rotary embedding.
+ sin (`torch.Tensor`): The sine part of the rotary embedding.
+ position_ids (`torch.Tensor`):
+ The position indices of the tokens corresponding to the query and key tensors. For example, this can be
+ used to pass offsetted position ids when working with a KV-cache.
+ unsqueeze_dim (`int`, *optional*, defaults to 1):
+ The 'unsqueeze_dim' argument specifies the dimension along which to unsqueeze cos[position_ids] and
+ sin[position_ids] so that they can be properly broadcasted to the dimensions of q and k. For example, note
+ that cos[position_ids] and sin[position_ids] have the shape [batch_size, seq_len, head_dim]. Then, if q and
+ k have the shape [batch_size, heads, seq_len, head_dim], then setting unsqueeze_dim=1 makes
+ cos[position_ids] and sin[position_ids] broadcastable to the shapes of q and k. Similarly, if q and k have
+ the shape [batch_size, seq_len, heads, head_dim], then set unsqueeze_dim=2.
+ Returns:
+ `tuple(torch.Tensor)` comprising of the query and key tensors rotated using the Rotary Position Embedding.
+ """
+
+ b, h, s, d = q.shape
+ q = q.view(b, h, s, d // 2, 2).transpose(4, 3).reshape(b, h, s, d)
+
+ if q.device.type == "hpu" and FusedRoPE:
+ return FusedRoPE.apply(
+ q, cos.unsqueeze(0).unsqueeze(0).clone(), sin.unsqueeze(0).unsqueeze(0).clone(), position_ids
+ )
+ else:
+ cos = cos[position_ids].unsqueeze(unsqueeze_dim)
+ sin = sin[position_ids].unsqueeze(unsqueeze_dim)
+ q_embed = (q * cos) + (rotate_half(q) * sin)
+ return q_embed
+
+
+class DeepseekV3MLP(nn.Module):
+ def __init__(self, config, hidden_size=None, intermediate_size=None):
+ super().__init__()
+ self.config = config
+ self.hidden_size = config.hidden_size if hidden_size is None else hidden_size
+ self.intermediate_size = config.intermediate_size if intermediate_size is None else intermediate_size
+
+ self.gate_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False)
+ self.up_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False)
+ self.down_proj = nn.Linear(self.intermediate_size, self.hidden_size, bias=False)
+ self.act_fn = ACT2FN[config.hidden_act]
+
+ def forward(self, x):
+ down_proj = self.down_proj(self.act_fn(self.gate_proj(x)) * self.up_proj(x))
+ return down_proj
+
+
+class MoEGate(nn.Module):
+ def __init__(self, config):
+ super().__init__()
+ self.config = config
+ self.top_k = config.num_experts_per_tok
+ self.n_routed_experts = config.n_routed_experts
+ self.routed_scaling_factor = config.routed_scaling_factor
+ self.scoring_func = config.scoring_func
+ self.seq_aux = config.seq_aux
+ self.topk_method = config.topk_method
+ self.n_group = config.n_group
+ self.topk_group = config.topk_group
+
+ # topk selection algorithm
+ self.norm_topk_prob = config.norm_topk_prob
+ self.gating_dim = config.hidden_size
+ self.weight = nn.Parameter(torch.empty((self.n_routed_experts, self.gating_dim)))
+ if self.topk_method == "noaux_tc":
+ self.e_score_correction_bias = nn.Parameter(torch.empty((self.n_routed_experts)))
+ self.reset_parameters()
+
+ def reset_parameters(self) -> None:
+ import torch.nn.init as init
+
+ init.kaiming_uniform_(self.weight, a=math.sqrt(5))
+
+ def forward(self, hidden_states):
+ bsz, seq_len, h = hidden_states.shape
+ ### compute gating score
+ hidden_states = hidden_states.view(-1, h)
+ logits = F.linear(hidden_states.type(torch.float32), self.weight.type(torch.float32), None)
+ if self.scoring_func == "sigmoid":
+ scores = logits.sigmoid()
+ else:
+ raise NotImplementedError(f"insupportable scoring function for MoE gating: {self.scoring_func}")
+
+ ### select top-k experts
+ if self.topk_method == "noaux_tc":
+ assert not self.training
+ scores_for_choice = scores.view(bsz * seq_len, -1) + self.e_score_correction_bias.unsqueeze(0)
+ group_scores = (
+ scores_for_choice.view(bsz * seq_len, self.n_group, -1).topk(2, dim=-1)[0].sum(dim=-1)
+ ) # [n, n_group]
+ group_idx = torch.topk(group_scores, k=self.topk_group, dim=-1, sorted=False)[1] # [n, top_k_group]
+ group_mask = torch.zeros_like(group_scores) # [n, n_group]
+ group_mask.scatter_(1, group_idx, 1) # [n, n_group]
+ score_mask = (
+ group_mask.unsqueeze(-1)
+ .expand(bsz * seq_len, self.n_group, self.n_routed_experts // self.n_group)
+ .reshape(bsz * seq_len, -1)
+ ) # [n, e]
+ tmp_scores = scores_for_choice.masked_fill(~score_mask.bool(), 0.0) # [n, e]
+ _, topk_idx = torch.topk(tmp_scores, k=self.top_k, dim=-1, sorted=False)
+ topk_weight = scores.gather(1, topk_idx)
+ else:
+ raise NotImplementedError(f"insupportable TopK function for MoE gating: {self.topk_method}")
+
+ ### norm gate to sum 1
+ if self.top_k > 1 and self.norm_topk_prob:
+ denominator = topk_weight.sum(dim=-1, keepdim=True) + 1e-20
+ topk_weight = topk_weight / denominator
+ topk_weight = topk_weight * self.routed_scaling_factor # must multiply the scaling factor
+
+ return topk_idx, topk_weight
+
+
+class DeepseekV3MoE(nn.Module):
+ """
+ A mixed expert module containing shared experts.
+ """
+
+ def __init__(self, config):
+ super().__init__()
+ self.config = config
+ self.num_experts_per_tok = config.num_experts_per_tok
+
+ if hasattr(config, "ep_size") and config.ep_size > 1:
+ assert config.ep_size == dist.get_world_size()
+ self.ep_size = config.ep_size
+ self.experts_per_rank = config.n_routed_experts // config.ep_size
+ self.ep_rank = dist.get_rank()
+ self.experts = nn.ModuleList(
+ [
+ (
+ DeepseekV3MLP(config, intermediate_size=config.moe_intermediate_size)
+ if i >= self.ep_rank * self.experts_per_rank and i < (self.ep_rank + 1) * self.experts_per_rank
+ else None
+ )
+ for i in range(config.n_routed_experts)
+ ]
+ )
+ else:
+ self.ep_size = 1
+ self.experts_per_rank = config.n_routed_experts
+ self.ep_rank = 0
+ self.experts = nn.ModuleList(
+ [
+ DeepseekV3MLP(config, intermediate_size=config.moe_intermediate_size)
+ for i in range(config.n_routed_experts)
+ ]
+ )
+ self.gate = MoEGate(config)
+ if config.n_shared_experts is not None:
+ intermediate_size = config.moe_intermediate_size * config.n_shared_experts
+ self.shared_experts = DeepseekV3MLP(config=config, intermediate_size=intermediate_size)
+
+ # Slice experts for max experts supported by fused dynamic mixture_of_experts op
+ self.expert_slice = math.ceil(self.experts_per_rank / SLICE_MAX_EXPERT)
+ self.expert_chunk = math.ceil(self.experts_per_rank / self.expert_slice)
+
+ def forward(self, hidden_states):
+ identity = hidden_states
+ orig_shape = hidden_states.shape
+ topk_idx, topk_weight = self.gate(hidden_states)
+ hidden_states = hidden_states.view(-1, hidden_states.shape[-1])
+ # we cast back to the input dtype
+ topk_weight = topk_weight.to(hidden_states.dtype)
+ batch = orig_shape[0]
+ sequence_length = orig_shape[1]
+ hidden_dim = orig_shape[2]
+ # changes for expert parallelism -- replacement for moe_infer()
+ if self.training:
+ padded_weights = torch.zeros(
+ (batch * sequence_length, self.config.n_routed_experts),
+ dtype=topk_weight.dtype,
+ device=topk_weight.device,
+ )
+ padded_weights.scatter_(-1, topk_idx, topk_weight)
+ padded_weights = padded_weights.reshape(-1, sequence_length, self.config.n_routed_experts)
+ padded_weights = padded_weights.permute(2, 0, 1).unsqueeze(-1)
+
+ final_hidden_states = torch.zeros(
+ (batch, sequence_length, hidden_dim), dtype=hidden_states.dtype, device=hidden_states.device
+ )
+ for i, expert in enumerate(self.experts):
+ current_hidden_state = expert(hidden_states)
+ current_padded_weight = padded_weights[i]
+ final_hidden_states = (
+ final_hidden_states
+ + current_hidden_state.reshape(-1, sequence_length, hidden_dim) * current_padded_weight
+ )
+ final_hidden_states = final_hidden_states.type(hidden_states.dtype)
+ final_hidden_states = final_hidden_states.view(*orig_shape)
+ # final_hidden_states = AddAuxiliaryLoss.apply(final_hidden_states, aux_loss)
+ else:
+ final_hidden_states = torch.zeros(
+ (batch * sequence_length, hidden_dim), dtype=hidden_states.dtype, device=hidden_states.device
+ )
+ # changes to support hpu fused dynamic MoE op -- replacement for moe_infer()
+ # loop through expert slices due to limits on max. experts supported by mixture_of_experts op
+ for idx in range(self.expert_slice):
+ experts_min = (self.ep_rank * self.experts_per_rank) + (self.expert_chunk * idx)
+ experts_max = min((experts_min + self.expert_chunk), (self.ep_rank + 1) * self.experts_per_rank)
+ experts_range = range(experts_min, experts_max)
+ gate_proj_list = [self.experts[i].gate_proj.weight.squeeze() for i in experts_range]
+ down_proj_list = [self.experts[i].down_proj.weight.squeeze() for i in experts_range]
+ up_proj_list = [self.experts[i].up_proj.weight.squeeze() for i in experts_range]
+
+ hidden_states_slice = torch.ops.hpu.mixture_of_experts(
+ hidden_states=hidden_states,
+ expert_routing_table=topk_idx,
+ router_weights=topk_weight,
+ w1=gate_proj_list,
+ w2=up_proj_list,
+ w3=down_proj_list,
+ permuted_weights=True,
+ activation="silu",
+ experts_min=experts_min,
+ experts_max=experts_max - 1,
+ )
+ final_hidden_states = final_hidden_states + hidden_states_slice
+ htcore.mark_step()
+
+ if self.ep_size > 1:
+ final_hidden_states = _all_reduce(final_hidden_states)
+ elif is_deepspeed_available():
+ from deepspeed import comm as dist
+
+ if dist.is_initialized():
+ dist.all_reduce(final_hidden_states, op=dist.ReduceOp.SUM)
+
+ final_hidden_states = final_hidden_states.type(hidden_states.dtype)
+ final_hidden_states = final_hidden_states.reshape(-1, sequence_length, hidden_dim)
+
+ if self.config.n_shared_experts is not None:
+ final_hidden_states = final_hidden_states + self.shared_experts(identity)
+
+ return final_hidden_states
+
+
+# Functional apis need to be wrapped in classes for quantization on hpu
+class Matmul(torch.nn.Module):
+ def __init__(self):
+ super().__init__()
+
+ def forward(self, x, y):
+ return torch.matmul(x, y)
+
+
+def gaudi_deepseekv3_repeat_kv(
+ query_states: torch.Tensor,
+ key_states: torch.Tensor,
+ value_states: torch.Tensor,
+ attention_mask: torch.Tensor,
+ n_rep: int,
+):
+ """
+ Copied from repeat_kv: https://github.com/huggingface/transformers/blob/v4.37.0/src/transformers/models/mixtral/modeling_mixtral.py
+ The only differences are:
+ - Append num_key_value_heads == 1 check as kv states can be broadcasted during matmuls so need to expand and reshape them.
+ - Add new args query_states, key_states, value_states and attention_mask and update the logic for expansion.
+ The query states go from (batch, num_heads, seqlen, head_dim) to (batch, num_key_value_heads, n_rep, seqlen, head_dim)
+ The key/value states go from (batch, num_key_value_heads, seqlen, head_dim) to (batch, num_key_value_heads, 1, seqlen, head_dim)
+ """
+ batch, num_key_value_heads, kv_len, head_dim = key_states.shape
+ if n_rep == 1 or num_key_value_heads == 1:
+ return query_states, key_states, value_states, attention_mask
+
+ new_kv_shape = (batch, num_key_value_heads, 1, kv_len, head_dim)
+ key_states = key_states.reshape(new_kv_shape)
+ value_states = value_states.reshape(new_kv_shape)
+
+ batch, q_heads, q_len, head_dim = query_states.shape
+ new_q_shape = (batch, num_key_value_heads, n_rep, q_len, head_dim)
+ query_states = query_states.reshape(new_q_shape)
+
+ if attention_mask is not None:
+ # Add groups dim and set to 1
+ attention_mask = attention_mask.unsqueeze(1)
+
+ return query_states, key_states, value_states, attention_mask
+
+
+# hpu specific. kv cache handling. similar to optimum-habana deepseek_v2
+class KVCache(torch.nn.Module):
+ def __init__(self):
+ super(KVCache, self).__init__()
+ self.cache = None
+ self.inp_seq_len = -1
+
+ def allocate(self, inp_seq_len, dtype, device, shape):
+ if self.cache is None or self.cache.shape != shape:
+ self.inp_seq_len = inp_seq_len
+ self.cache = torch.zeros(shape, dtype=dtype, device=device)
+ else:
+ assert self.inp_seq_len == inp_seq_len, (
+ f"inp_seq_len must be the same. self.inp_seq_len:{self.inp_seq_len} inp_seq_len:{inp_seq_len}"
+ )
+ self.cache.fill_(0)
+
+ def update(self, prev, cur, dim, idx, inp_seq_len):
+ orig_cur = cur
+ if prev.shape == cur.shape:
+ prev.copy_(cur)
+ return orig_cur
+ if cur.shape[1] > 1 and cur.shape[1] <= prev.shape[1]:
+ # Initialize
+ prev[:, :inp_seq_len, :].copy_(cur)
+ return orig_cur
+ assert cur.shape[1] == 1, f"Cannot update kv-cache. Unsupported shapes. prev:{prev.shape} cur:{cur.shape}"
+
+ if idx is not None:
+ prev.index_copy_(dim, idx - 1, cur)
+ return prev
+ else:
+ return torch.cat((prev, cur), dim=dim)
+
+ def get_shape(self):
+ if self.cache is None:
+ return None
+ return self.cache.shape
+
+ def forward(self, cur, dim, idx):
+ return self.update(self.cache, cur, dim, idx, self.inp_seq_len)
+
+
+# hpu specific fused op. wrapped in a class as functional apis not supported for quantization
+class ModuleFusedSDPA(torch.nn.Module):
+ def __init__(self, fusedSDPA, scale, attention_dropout, enable_recompute, flash_attention_fp8):
+ super().__init__()
+ self._hpu_kernel_fsdpa = fusedSDPA
+ self.scale = scale
+ self.attention_dropout = attention_dropout
+ self.enable_recompute = enable_recompute
+ self.flash_attention_fp8 = flash_attention_fp8
+
+ def forward(
+ self,
+ query,
+ key,
+ value,
+ attn_mask,
+ dropout_p,
+ is_casual,
+ scale,
+ softmax_mode,
+ recompute_mode,
+ valid_sequence_lengths,
+ padding_side="left",
+ ):
+ return self._hpu_kernel_fsdpa.apply(
+ query,
+ key,
+ value,
+ attn_mask,
+ dropout_p,
+ is_casual,
+ scale,
+ softmax_mode,
+ recompute_mode,
+ valid_sequence_lengths,
+ padding_side,
+ )
+
+
+# Copied from transformers.models.llama.modeling_llama.LlamaAttention with Llama->DeepseekV3
+class DeepseekV3Attention(nn.Module):
+ """Multi-headed attention from 'Attention Is All You Need' paper"""
+
+ def __init__(self, config: DeepseekV3Config, layer_idx: Optional[int] = None):
+ super().__init__()
+ self.config = config
+ self.layer_idx = layer_idx
+ if layer_idx is None:
+ logger.warning_once(
+ f"Instantiating {self.__class__.__name__} without passing `layer_idx` is not recommended and will "
+ "to errors during the forward call, if caching is used. Please make sure to provide a `layer_idx` "
+ "when creating this class."
+ )
+
+ self.attention_dropout = config.attention_dropout
+ self.hidden_size = config.hidden_size
+ self.num_heads = config.num_attention_heads
+
+ self.max_position_embeddings = config.max_position_embeddings
+ self.rope_theta = config.rope_theta
+ self.q_lora_rank = config.q_lora_rank
+ self.qk_rope_head_dim = config.qk_rope_head_dim
+ self.kv_lora_rank = config.kv_lora_rank
+ self.v_head_dim = config.v_head_dim
+ self.qk_nope_head_dim = config.qk_nope_head_dim
+ self.q_head_dim = config.qk_nope_head_dim + config.qk_rope_head_dim
+
+ self.is_causal = True
+
+ if self.q_lora_rank is None:
+ self.q_proj = nn.Linear(self.hidden_size, self.num_heads * self.q_head_dim, bias=False)
+ else:
+ self.q_a_proj = nn.Linear(self.hidden_size, config.q_lora_rank, bias=config.attention_bias)
+ self.q_a_layernorm = DeepseekV3RMSNorm(config.q_lora_rank)
+ self.q_b_proj = nn.Linear(config.q_lora_rank, self.num_heads * self.q_head_dim, bias=False)
+
+ self.kv_a_proj_with_mqa = nn.Linear(
+ self.hidden_size,
+ config.kv_lora_rank + config.qk_rope_head_dim,
+ bias=config.attention_bias,
+ )
+ self.kv_a_layernorm = DeepseekV3RMSNorm(config.kv_lora_rank)
+ self.kv_b_proj = nn.Linear(
+ config.kv_lora_rank,
+ self.num_heads * (self.q_head_dim - self.qk_rope_head_dim + self.v_head_dim),
+ bias=False,
+ )
+
+ self.o_proj = nn.Linear(
+ self.num_heads * self.v_head_dim,
+ self.hidden_size,
+ bias=config.attention_bias,
+ )
+ self._init_rope()
+
+ self.num_key_value_groups = self.num_heads // config.num_key_value_heads
+ # hpu specific wrapping functional api into nn.module classes for quantization
+ self.matmul_qk = Matmul()
+ self.matmul_av = Matmul()
+ self.k_cache = KVCache()
+ self.v_cache = KVCache()
+ self.inp_seq_len = -1
+
+ self.softmax_scale = self.q_head_dim ** (-0.5)
+ if self.config.rope_scaling is not None:
+ mscale_all_dim = self.config.rope_scaling.get("mscale_all_dim", 0)
+ scaling_factor = self.config.rope_scaling["factor"]
+ if mscale_all_dim:
+ mscale = yarn_get_mscale(scaling_factor, mscale_all_dim)
+ self.softmax_scale = self.softmax_scale * mscale * mscale
+
+ self.norm_factor = self.softmax_scale
+ # hpu specific warpping functional api into nn.module classes for quantization
+ self.fused_scaled_dot_product_attention = (
+ ModuleFusedSDPA(
+ FusedSDPA,
+ scale=self.norm_factor,
+ attention_dropout=self.attention_dropout,
+ enable_recompute=False,
+ flash_attention_fp8=getattr(config, "flash_attention_fp8", False),
+ )
+ if FusedSDPA
+ else None
+ )
+
+ def _init_rope(self):
+ if self.config.rope_scaling is None:
+ self.rotary_emb = DeepseekV3RotaryEmbedding(
+ self.qk_rope_head_dim,
+ max_position_embeddings=self.max_position_embeddings,
+ base=self.rope_theta,
+ )
+ else:
+ scaling_type = self.config.rope_scaling["type"]
+ scaling_factor = self.config.rope_scaling["factor"]
+ if scaling_type == "linear":
+ self.rotary_emb = DeepseekV3LinearScalingRotaryEmbedding(
+ self.qk_rope_head_dim,
+ max_position_embeddings=self.max_position_embeddings,
+ scaling_factor=scaling_factor,
+ base=self.rope_theta,
+ )
+ elif scaling_type == "dynamic":
+ self.rotary_emb = DeepseekV3DynamicNTKScalingRotaryEmbedding(
+ self.qk_rope_head_dim,
+ max_position_embeddings=self.max_position_embeddings,
+ scaling_factor=scaling_factor,
+ base=self.rope_theta,
+ )
+ elif scaling_type == "yarn":
+ kwargs = {
+ key: self.config.rope_scaling[key]
+ for key in [
+ "original_max_position_embeddings",
+ "beta_fast",
+ "beta_slow",
+ "mscale",
+ "mscale_all_dim",
+ ]
+ if key in self.config.rope_scaling
+ }
+ self.rotary_emb = DeepseekV3YarnRotaryEmbedding(
+ self.qk_rope_head_dim,
+ max_position_embeddings=self.max_position_embeddings,
+ scaling_factor=scaling_factor,
+ base=self.rope_theta,
+ **kwargs,
+ )
+ else:
+ raise ValueError(f"Unknown RoPE scaling type {scaling_type}")
+
+ # hpu-specific, similar to other model files in OH
+ def allocate_kv_cache(self, batch_size, max_seq_len, inp_seq_len):
+ compressed_kv_cache_shape = (batch_size, max_seq_len, self.kv_lora_rank)
+ k_pe_cache_shape = (batch_size, max_seq_len, self.qk_rope_head_dim)
+ device = self.kv_a_proj_with_mqa.weight.device
+ dtype = self.config.torch_dtype
+
+ self.k_cache.allocate(inp_seq_len, dtype, device, compressed_kv_cache_shape)
+ self.v_cache.allocate(inp_seq_len, dtype, device, k_pe_cache_shape)
+
+ def update_sincos_cache(self, seq_len):
+ # Call rotary emb forward() to update cos/sin cache when infering more than self.max_position_embeddings
+ # This helps in avoiding creation of these caches during actual model forward pass and
+ # reduce memory consumption and improve performance.
+ if seq_len > self.max_position_embeddings:
+ self.max_position_embeddings = seq_len
+ _, _ = self.rotary_emb(self.k_b_proj.weight, seq_len=seq_len)
+
+ def reorder(self, tensor, beam_idx, dim_a, dim_b):
+ updated = tensor.index_select(0, beam_idx)
+ tensor.copy_(updated)
+
+ def reorder_kv_cache(self, beam_idx: torch.LongTensor):
+ if self.k_cache.cache is None:
+ return (None, None)
+
+ head_dim = self.k_cache.cache.size(-1)
+ seq_length = self.k_cache.cache.size(-2)
+ self.reorder(self.k_cache.cache, beam_idx, seq_length, head_dim)
+ self.reorder(self.v_cache.cache, beam_idx, seq_length, head_dim)
+ return (self.k_cache.cache.shape, self.v_cache.cache.shape)
+
+ def _shape(self, tensor: torch.Tensor, seq_len: int, bsz: int):
+ return tensor.view(bsz, seq_len, self.num_heads, self.v_head_dim).transpose(1, 2).contiguous()
+
+ def split_kv_b_proj(self):
+ kv_b_proj_weight = self.kv_b_proj.weight.view(self.num_heads, -1, self.kv_lora_rank)
+ self.q_absorb = kv_b_proj_weight[:, : self.qk_nope_head_dim, :].unsqueeze(0).transpose(0, 1)
+ self.out_absorb = kv_b_proj_weight[:, self.qk_nope_head_dim :, :].unsqueeze(0)
+
+ def compress_kv(
+ self,
+ hidden_states_kv: torch.Tensor,
+ kv_position_ids: torch.LongTensor,
+ past_key_value: Optional[Cache] = None,
+ ) -> torch.Tensor:
+ # return the RoPE'ed & compressed kv
+ bsz, kv_seq_len, _ = hidden_states_kv.size()
+ compressed_kv = self.kv_a_proj_with_mqa(hidden_states_kv)
+ compressed_kv, k_pe = torch.split(compressed_kv, [self.kv_lora_rank, self.qk_rope_head_dim], dim=-1)
+ compressed_kv = self.kv_a_layernorm(compressed_kv)
+ k_pe = k_pe.view(bsz, kv_seq_len, 1, self.qk_rope_head_dim).transpose(1, 2)
+ cos, sin = self.rotary_emb.cos_cached, self.rotary_emb.sin_cached
+ k_pe = apply_rotary_pos_emb(k_pe, cos, sin, kv_position_ids).view(bsz, kv_seq_len, self.qk_rope_head_dim)
+ return compressed_kv, k_pe
+
+ def forward(
+ self,
+ hidden_states: torch.Tensor,
+ attention_mask: Optional[torch.Tensor] = None,
+ position_ids: Optional[torch.LongTensor] = None,
+ past_key_value: Optional[Cache] = None,
+ output_attentions: bool = False,
+ use_cache: bool = False,
+ token_idx: Optional[torch.Tensor] = None,
+ reuse_cache: Optional[bool] = False,
+ cache_idx: int = None,
+ cache_position: Optional[torch.LongTensor] = None,
+ attn_softmax_bf16: Optional[bool] = False,
+ use_flash_attention: Optional[bool] = False,
+ flash_attention_recompute: Optional[bool] = False,
+ flash_attention_causal_mask: Optional[bool] = False,
+ flash_attention_fast_softmax: Optional[bool] = False,
+ valid_sequence_lengths: Optional[torch.Tensor] = None,
+ num_virtual_tokens: int = None,
+ **kwargs,
+ ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
+ """
+ Attention masks and past cache are removed.
+ Input:
+ - hidden_states: [bsz, q_len, hidden_size]
+ - position_ids: [bsz, q_len]
+ """
+
+ if "padding_mask" in kwargs:
+ warnings.warn(
+ "Passing `padding_mask` is deprecated and will be removed in v4.37. Please make sure use `attention_mask` instead.`"
+ )
+
+ if self.training:
+ if "padding_mask" in kwargs:
+ warnings.warn(
+ "Passing `padding_mask` is deprecated and will be removed in v4.37. Please make sure use `attention_mask` instead.`"
+ )
+ bsz, q_len, _ = hidden_states.size()
+ if self.q_lora_rank is None:
+ q = self.q_proj(hidden_states)
+ else:
+ q = self.q_b_proj(self.q_a_layernorm(self.q_a_proj(hidden_states)))
+ q = q.view(bsz, q_len, self.num_heads, self.q_head_dim).transpose(1, 2)
+ q_nope, q_pe = torch.split(q, [self.qk_nope_head_dim, self.qk_rope_head_dim], dim=-1)
+
+ compressed_kv = self.kv_a_proj_with_mqa(hidden_states)
+ compressed_kv, k_pe = torch.split(compressed_kv, [self.kv_lora_rank, self.qk_rope_head_dim], dim=-1)
+ k_pe = k_pe.view(bsz, q_len, 1, self.qk_rope_head_dim).transpose(1, 2)
+ kv = (
+ self.kv_b_proj(self.kv_a_layernorm(compressed_kv))
+ .view(bsz, q_len, self.num_heads, self.qk_nope_head_dim + self.v_head_dim)
+ .transpose(1, 2)
+ )
+
+ k_nope, value_states = torch.split(kv, [self.qk_nope_head_dim, self.v_head_dim], dim=-1)
+ kv_seq_len = value_states.shape[-2]
+ if past_key_value is not None:
+ if self.layer_idx is None:
+ raise ValueError(
+ f"The cache structure has changed since version v4.36. If you are using {self.__class__.__name__} "
+ "for auto-regressive decoding with k/v caching, please make sure to initialize the attention class "
+ "with a layer index."
+ )
+
+ if token_idx is None:
+ if hasattr(past_key_value, "get_usable_length"):
+ kv_seq_len += past_key_value.get_usable_length(kv_seq_len, self.layer_idx)
+ else:
+ kv_seq_len += past_key_value[0].shape[-2]
+ else:
+ if num_virtual_tokens is not None and num_virtual_tokens == past_key_value[0].shape[-2]:
+ kv_seq_len = past_key_value[0].shape[-2] + kv_seq_len
+ else:
+ kv_seq_len = past_key_value[0].shape[-2]
+
+ cos, sin = self.rotary_emb(value_states, seq_len=kv_seq_len)
+ q_pe, k_pe = apply_customized_rope(q_pe, k_pe, cos, sin, position_ids, self.training)
+
+ query_states = k_pe.new_empty(bsz, self.num_heads, q_len, self.q_head_dim)
+ query_states[:, :, :, : self.qk_nope_head_dim] = q_nope
+ query_states[:, :, :, self.qk_nope_head_dim :] = q_pe
+
+ key_states = k_pe.new_empty(bsz, self.num_heads, q_len, self.q_head_dim)
+ key_states[:, :, :, : self.qk_nope_head_dim] = k_nope
+ key_states[:, :, :, self.qk_nope_head_dim :] = k_pe
+
+ if past_key_value is not None:
+ cache_kwargs = {"sin": sin, "cos": cos} # Specific to RoPE models
+ key_states, value_states = past_key_value.update(
+ key_states, value_states, self.layer_idx, cache_kwargs
+ )
+ # hpu specific optimization, similar to other modeling files in optimum-habana
+ if use_flash_attention and FusedSDPA is not None:
+ if q_len == 1:
+ # next token
+ attn_output = self.fused_scaled_dot_product_attention(
+ query_states,
+ key_states,
+ value_states,
+ attention_mask,
+ 0.0,
+ False,
+ None,
+ "None",
+ False,
+ None,
+ "None",
+ )
+ else:
+ # first token
+ softmax_mode = "fast" if flash_attention_fast_softmax else "None"
+ if flash_attention_causal_mask:
+ attn_output = self.fused_scaled_dot_product_attention(
+ query_states,
+ key_states,
+ value_states,
+ None,
+ 0.0,
+ True,
+ None,
+ softmax_mode,
+ flash_attention_recompute,
+ valid_sequence_lengths,
+ "left",
+ )
+ else:
+ attn_output = self.fused_scaled_dot_product_attention(
+ query_states,
+ key_states,
+ value_states,
+ attention_mask,
+ 0.0,
+ False,
+ None,
+ softmax_mode,
+ flash_attention_recompute,
+ None,
+ "None",
+ )
+
+ else:
+ query_states, key_states, value_states, attention_mask = gaudi_deepseekv3_repeat_kv(
+ query_states, key_states, value_states, attention_mask, self.num_key_value_groups
+ )
+
+ attn_weights = self.matmul_qk(query_states, key_states.transpose(-2, -1)) * self.softmax_scale
+ htcore.mark_step()
+
+ if attention_mask is not None: # no matter the length, we just slice it
+ causal_mask = attention_mask
+ if cache_position is not None:
+ causal_mask = attention_mask[:, :, cache_position, : key_states.shape[-2]]
+ attn_weights = attn_weights + causal_mask.float()
+
+ if attn_softmax_bf16:
+ attn_weights = torch.nn.functional.softmax(attn_weights, dim=-1, dtype=query_states.dtype)
+ else:
+ # upcast attention to fp32
+ attn_weights = torch.nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(
+ query_states.dtype
+ )
+ attn_weights = torch.nn.functional.dropout(
+ attn_weights, p=self.attention_dropout, training=self.training
+ )
+ attn_output = self.matmul_av(attn_weights, value_states)
+ else:
+ # inference
+ hidden_states_q = hidden_states
+ hidden_states_kv = hidden_states
+ self.split_kv_b_proj()
+ q_position_ids = position_ids
+ kv_position_ids = position_ids
+ bsz, q_len, _ = hidden_states_q.size()
+
+ if self.q_lora_rank is None:
+ q = self.q_proj(hidden_states_q)
+ else:
+ q = self.q_b_proj(self.q_a_layernorm(self.q_a_proj(hidden_states_q)))
+
+ q = q.view(bsz, q_len, self.num_heads, self.q_head_dim).transpose(1, 2)
+
+ q_nope, q_pe = torch.split(q, [self.qk_nope_head_dim, self.qk_rope_head_dim], dim=-1)
+
+ kv_seq_len = q_pe.shape[-2]
+
+ if past_key_value is not None:
+ if self.layer_idx is None:
+ raise ValueError(
+ f"The cache structure has changed since version v4.36. If you are using {self.__class__.__name__} "
+ "for auto-regressive decoding with k/v caching, please make sure to initialize the attention class "
+ "with a layer index."
+ )
+ if token_idx is None:
+ if hasattr(past_key_value, "get_usable_length"):
+ kv_seq_len += past_key_value.get_usable_length(kv_seq_len, self.layer_idx)
+ else:
+ kv_seq_len += past_key_value[0].shape[-2]
+ else:
+ if reuse_cache:
+ kv_seq_len = past_key_value[0][-2]
+ else:
+ kv_seq_len = past_key_value[0].shape[-2]
+
+ cos, sin = self.rotary_emb(q_pe, seq_len=kv_seq_len)
+ q_pe = apply_rotary_pos_emb(q_pe, cos, sin, q_position_ids)
+ q_nope = torch.matmul(q_nope.transpose(0, 1), self.q_absorb).transpose(0, 1)
+ compressed_kv, k_pe = self.compress_kv(hidden_states_kv, kv_position_ids)
+
+ # update & get all compressed_kv, k_pe
+ if use_cache:
+ if reuse_cache:
+ if past_key_value is not None and isinstance(past_key_value[0], torch.Tensor):
+ # prefix tuning case. attach past_key_value to generate first token.
+ compressed_kv = torch.cat((past_key_value[0], compressed_kv), -2)
+ k_pe = torch.cat((past_key_value[1], k_pe), -2)
+
+ compressed_kv = self.k_cache(compressed_kv, 1, token_idx)
+
+ k_pe = self.v_cache(k_pe, 1, token_idx)
+ past_key_value = (self.k_cache.get_shape(), self.v_cache.get_shape())
+
+ else:
+ if past_key_value is None:
+ dtype_1 = hidden_states.dtype
+ device_1 = hidden_states.device
+ past_key = torch.zeros(compressed_kv.shape, dtype=dtype_1, device=device_1)
+ past_value = torch.zeros(k_pe.shape, dtype=dtype_1, device=device_1)
+ past_key_value = (past_key, past_value)
+ compressed_kv = self.k_cache.update(
+ past_key_value[0], compressed_kv, 1, token_idx, self.inp_seq_len
+ )
+ k_pe = self.v_cache.update(past_key_value[1], k_pe, 1, token_idx, self.inp_seq_len)
+
+ if token_idx is None:
+ past_key_value = (compressed_kv, k_pe)
+
+ if cache_idx is not None and q_len == 1:
+ compressed_kv = compressed_kv[:, :cache_idx, :]
+
+ k_pe = k_pe[:, :cache_idx, :]
+ if attention_mask is not None:
+ attention_mask = attention_mask[:, :, :, :cache_idx]
+
+ kv_seq_len = compressed_kv.shape[-2]
+ else:
+ past_key_value = None
+
+ kv_seq_len = compressed_kv.size(1)
+
+ k_pe = k_pe.view(bsz, 1, kv_seq_len, self.qk_rope_head_dim)
+
+ attn_weights = (
+ torch.matmul(q_pe, k_pe.mT) + torch.matmul(q_nope, compressed_kv.unsqueeze(-3).mT)
+ ) * self.softmax_scale
+
+ if attn_weights.size() != (bsz, self.num_heads, q_len, kv_seq_len):
+ raise ValueError(
+ f"Attention weights should be of size {(bsz, self.num_heads, q_len, kv_seq_len)}, but is"
+ f" {attn_weights.size()}"
+ )
+ # Commenting below line as MMLU tasks are failing with this assertion
+ # assert attention_mask is not None
+ if attention_mask is not None:
+ attn_weights = attn_weights + attention_mask
+
+ # upcast attention to fp32
+ attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(q_nope.dtype)
+
+ attn_weights = nn.functional.dropout(attn_weights, p=self.attention_dropout, training=self.training)
+ attn_output = torch.einsum("bhql,blc->bhqc", attn_weights, compressed_kv)
+
+ attn_output = torch.matmul(attn_output.permute(2, 1, 0, 3), self.out_absorb.mT).permute(2, 1, 0, 3)
+
+ if attn_output.size() != (bsz, self.num_heads, q_len, self.v_head_dim):
+ raise ValueError(
+ f"`attn_output` should be of size {(bsz, self.num_heads, q_len, self.v_head_dim)}, but is"
+ f" {attn_output.size()}"
+ )
+
+ attn_output = attn_output.transpose(1, 2).contiguous()
+
+ attn_output = attn_output.reshape(bsz, q_len, self.num_heads * self.v_head_dim)
+
+ attn_output = self.o_proj(attn_output)
+
+ if not output_attentions:
+ attn_weights = None
+
+ return attn_output, attn_weights, past_key_value
+
+
+class DeepseekV3DecoderLayer(nn.Module):
+ def __init__(self, config: DeepseekV3Config, layer_idx: int):
+ super().__init__()
+ self.hidden_size = config.hidden_size
+
+ self.self_attn = DeepseekV3Attention(config=config, layer_idx=layer_idx)
+
+ self.mlp = (
+ DeepseekV3MoE(config)
+ if (
+ config.n_routed_experts is not None
+ and layer_idx >= config.first_k_dense_replace
+ and layer_idx % config.moe_layer_freq == 0
+ )
+ else DeepseekV3MLP(config)
+ )
+ self.input_layernorm = DeepseekV3RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+ self.post_attention_layernorm = DeepseekV3RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+
+ def allocate_kv_cache(self, batch_size, max_seq_len, inp_seq_len):
+ self.self_attn.allocate_kv_cache(batch_size, max_seq_len, inp_seq_len)
+
+ def reorder_kv_cache(self, beam_idx: torch.LongTensor):
+ return self.self_attn.reorder_kv_cache(beam_idx)
+
+ def update_sincos_cache(self, seq_len):
+ self.self_attn.update_sincos_cache(seq_len)
+
+ def forward(
+ self,
+ hidden_states: torch.Tensor,
+ attention_mask: Optional[torch.Tensor] = None,
+ position_ids: Optional[torch.LongTensor] = None,
+ past_key_value: Optional[Tuple[torch.Tensor]] = None,
+ output_attentions: Optional[bool] = False,
+ use_cache: Optional[bool] = False,
+ token_idx: Optional[torch.Tensor] = None,
+ reuse_cache: Optional[bool] = False,
+ cache_idx: int = None,
+ cache_position: Optional[torch.LongTensor] = None,
+ attn_softmax_bf16: Optional[bool] = False,
+ use_flash_attention: Optional[bool] = False,
+ flash_attention_recompute: Optional[bool] = False,
+ flash_attention_causal_mask: Optional[bool] = False,
+ flash_attention_fast_softmax: Optional[bool] = False,
+ valid_sequence_lengths: Optional[torch.Tensor] = None,
+ num_virtual_tokens: int = None,
+ **kwargs,
+ ) -> Tuple[torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]]]:
+ """
+ Args:
+ hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)`
+ attention_mask (`torch.FloatTensor`, *optional*):
+ attention mask of size `(batch_size, sequence_length)` if flash attention is used or `(batch_size, 1,
+ query_sequence_length, key_sequence_length)` if default attention is used.
+ output_attentions (`bool`, *optional*):
+ Whether or not to return the attentions tensors of all attention layers. See `attentions` under
+ returned tensors for more detail.
+ use_cache (`bool`, *optional*):
+ If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding
+ (see `past_key_values`).
+ past_key_value (`Tuple(torch.FloatTensor)`, *optional*): cached past key and value projection states
+ """
+ if "padding_mask" in kwargs:
+ warnings.warn(
+ "Passing `padding_mask` is deprecated and will be removed in v4.37. Please make sure use `attention_mask` instead.`"
+ )
+ residual = hidden_states
+
+ hidden_states = self.input_layernorm(hidden_states)
+
+ # Self Attention
+ hidden_states, self_attn_weights, present_key_value = self.self_attn(
+ hidden_states=hidden_states,
+ attention_mask=attention_mask,
+ position_ids=position_ids,
+ past_key_value=past_key_value,
+ output_attentions=output_attentions,
+ use_cache=use_cache,
+ token_idx=token_idx,
+ reuse_cache=reuse_cache,
+ cache_idx=cache_idx,
+ cache_position=cache_position,
+ attn_softmax_bf16=attn_softmax_bf16,
+ use_flash_attention=use_flash_attention,
+ flash_attention_recompute=flash_attention_recompute,
+ flash_attention_causal_mask=flash_attention_causal_mask,
+ flash_attention_fast_softmax=flash_attention_fast_softmax,
+ valid_sequence_lengths=valid_sequence_lengths,
+ num_virtual_tokens=num_virtual_tokens,
+ **kwargs,
+ )
+ hidden_states = residual + hidden_states
+
+ # Fully Connected
+ residual = hidden_states
+ hidden_states = self.post_attention_layernorm(hidden_states)
+ hidden_states = self.mlp(hidden_states)
+ hidden_states = residual + hidden_states
+
+ outputs = (hidden_states,)
+
+ if output_attentions:
+ outputs += (self_attn_weights,)
+
+ if use_cache:
+ outputs += (present_key_value,)
+
+ return outputs
+
+
+DeepseekV3_START_DOCSTRING = r"""
+ This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
+ library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
+ etc.)
+
+ This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass.
+ Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
+ and behavior.
+
+ Parameters:
+ config ([`DeepseekV3Config`]):
+ Model configuration class with all the parameters of the model. Initializing with a config file does not
+ load the weights associated with the model, only the configuration. Check out the
+ [`~PreTrainedModel.from_pretrained`] method to load the model weights.
+"""
+
+
+@add_start_docstrings(
+ "The bare DeepseekV3 Model outputting raw hidden-states without any specific head on top.",
+ DeepseekV3_START_DOCSTRING,
+)
+class DeepseekV3PreTrainedModel(PreTrainedModel):
+ config_class = DeepseekV3Config
+ base_model_prefix = "model"
+ supports_gradient_checkpointing = True
+ _no_split_modules = ["DeepseekV3DecoderLayer"]
+ _skip_keys_device_placement = "past_key_values"
+ _supports_flash_attn_2 = False
+ _supports_cache_class = True
+
+ def _init_weights(self, module):
+ std = self.config.initializer_range
+ if isinstance(module, nn.Linear):
+ module.weight.data.normal_(mean=0.0, std=std)
+ if module.bias is not None:
+ module.bias.data.zero_()
+ elif isinstance(module, nn.Embedding):
+ module.weight.data.normal_(mean=0.0, std=std)
+ if module.padding_idx is not None:
+ module.weight.data[module.padding_idx].zero_()
+
+
+DeepseekV3_INPUTS_DOCSTRING = r"""
+ Args:
+ input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
+ Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide
+ it.
+
+ Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
+ [`PreTrainedTokenizer.__call__`] for details.
+
+ [What are input IDs?](../glossary#input-ids)
+ attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
+ Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
+
+ - 1 for tokens that are **not masked**,
+ - 0 for tokens that are **masked**.
+
+ [What are attention masks?](../glossary#attention-mask)
+
+ Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
+ [`PreTrainedTokenizer.__call__`] for details.
+
+ If `past_key_values` is used, optionally only the last `input_ids` have to be input (see
+ `past_key_values`).
+
+ If you want to change padding behavior, you should read [`modeling_opt._prepare_decoder_attention_mask`]
+ and modify to your needs. See diagram 1 in [the paper](https://arxiv.org/abs/1910.13461) for more
+ information on the default strategy.
+
+ - 1 indicates the head is **not masked**,
+ - 0 indicates the head is **masked**.
+ position_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
+ Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
+ config.n_positions - 1]`.
+
+ [What are position IDs?](../glossary#position-ids)
+ past_key_values (`Cache` or `tuple(tuple(torch.FloatTensor))`, *optional*):
+ Pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention
+ blocks) that can be used to speed up sequential decoding. This typically consists in the `past_key_values`
+ returned by the model at a previous stage of decoding, when `use_cache=True` or `config.use_cache=True`.
+
+ Two formats are allowed:
+ - a [`~cache_utils.Cache`] instance;
+ - Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of
+ shape `(batch_size, num_heads, sequence_length, embed_size_per_head)`). This is also known as the legacy
+ cache format.
+
+ The model will output the same cache format that is fed as input. If no `past_key_values` are passed, the
+ legacy cache format will be returned.
+
+ If `past_key_values` are used, the user can optionally input only the last `input_ids` (those that don't
+ have their past key value states given to this model) of shape `(batch_size, 1)` instead of all `input_ids`
+ of shape `(batch_size, sequence_length)`.
+ inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
+ Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This
+ is useful if you want more control over how to convert `input_ids` indices into associated vectors than the
+ model's internal embedding lookup matrix.
+ use_cache (`bool`, *optional*):
+ If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see
+ `past_key_values`).
+ output_attentions (`bool`, *optional*):
+ Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
+ tensors for more detail.
+ output_hidden_states (`bool`, *optional*):
+ Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
+ more detail.
+ return_dict (`bool`, *optional*):
+ Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
+"""
+
+
+@add_start_docstrings(
+ "The bare DeepseekV3 Model outputting raw hidden-states without any specific head on top.",
+ DeepseekV3_START_DOCSTRING,
+)
+class DeepseekV3Model(DeepseekV3PreTrainedModel):
+ """
+ Transformer decoder consisting of *config.num_hidden_layers* layers. Each layer is a [`DeepseekV3DecoderLayer`]
+
+ Args:
+ config: DeepseekV3Config
+ """
+
+ def __init__(self, config: DeepseekV3Config):
+ super().__init__(config)
+ self.padding_idx = config.pad_token_id
+ self.vocab_size = config.vocab_size
+
+ self.embed_tokens = nn.Embedding(config.vocab_size, config.hidden_size, self.padding_idx)
+ self.layers = nn.ModuleList(
+ [DeepseekV3DecoderLayer(config, layer_idx) for layer_idx in range(config.num_hidden_layers)]
+ )
+ self._attn_implementation = "eager"
+ self._use_flash_attention_2 = config._attn_implementation == "flash_attention_2"
+ self.norm = DeepseekV3RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+
+ self.gradient_checkpointing = False
+ # Initialize weights and apply final processing
+ self.post_init()
+
+ def allocate_kv_cache(self, batch_size, max_seq_len, inp_seq_len):
+ for layer in self.layers:
+ layer.allocate_kv_cache(batch_size, max_seq_len, inp_seq_len)
+
+ def reorder_kv_cache(self, beam_idx: torch.LongTensor):
+ return tuple(layer.reorder_kv_cache(beam_idx) for layer in self.layers)
+
+ def update_sincos_cache(self, seq_len):
+ for layer in self.layers:
+ layer.update_sincos_cache(seq_len)
+
+ def get_input_embeddings(self):
+ return self.embed_tokens
+
+ def set_input_embeddings(self, value):
+ self.embed_tokens = value
+
+ @add_start_docstrings_to_model_forward(DeepseekV3_INPUTS_DOCSTRING)
+ def forward(
+ self,
+ input_ids: torch.LongTensor = None,
+ attention_mask: Optional[torch.Tensor] = None,
+ position_ids: Optional[torch.LongTensor] = None,
+ past_key_values: Optional[List[torch.FloatTensor]] = None,
+ inputs_embeds: Optional[torch.FloatTensor] = None,
+ use_cache: Optional[bool] = None,
+ output_attentions: Optional[bool] = None,
+ output_hidden_states: Optional[bool] = None,
+ return_dict: Optional[bool] = None,
+ cache_position: Optional[torch.LongTensor] = None,
+ token_idx: Optional[torch.Tensor] = None,
+ attn_softmax_bf16: Optional[bool] = False,
+ reuse_cache: Optional[bool] = False,
+ use_flash_attention: Optional[bool] = False,
+ flash_attention_recompute: Optional[bool] = False,
+ flash_attention_causal_mask: Optional[bool] = False,
+ flash_attention_fast_softmax: Optional[bool] = False,
+ cache_idx: int = None,
+ lazy_mode: Optional[bool] = True,
+ valid_sequence_lengths: Optional[torch.Tensor] = None,
+ num_virtual_tokens: int = None,
+ ) -> Union[Tuple, BaseModelOutputWithPast]:
+ output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
+ output_hidden_states = (
+ output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+ )
+ use_cache = use_cache if use_cache is not None else self.config.use_cache
+
+ return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+ # retrieve input_ids and inputs_embeds
+ if input_ids is not None and inputs_embeds is not None:
+ raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time")
+ elif input_ids is not None:
+ batch_size, seq_length = input_ids.shape[:2]
+ elif inputs_embeds is not None:
+ batch_size, seq_length = inputs_embeds.shape[:2]
+ else:
+ raise ValueError("You have to specify either input_ids or inputs_embeds")
+
+ past_key_values_length = 0
+ if past_key_values is not None:
+ past_key_values_length = past_key_values[0][0].shape[2]
+
+ if position_ids is None:
+ device = input_ids.device if input_ids is not None else inputs_embeds.device
+ position_ids = torch.arange(
+ past_key_values_length,
+ seq_length + past_key_values_length,
+ dtype=torch.long,
+ device=device,
+ )
+ position_ids = position_ids.unsqueeze(0)
+
+ if inputs_embeds is None:
+ inputs_embeds = self.embed_tokens(input_ids)
+
+ # 4d mask is passed through the layers
+ attention_mask = attention_mask if (attention_mask is not None and 0 in attention_mask) else None
+ if attention_mask is not None:
+ attention_mask = _gaudi_prepare_4d_causal_attention_mask(
+ attention_mask,
+ (batch_size, seq_length),
+ inputs_embeds,
+ past_key_values_length,
+ )
+
+ # embed positions
+ hidden_states = inputs_embeds
+
+ # decoder layers
+ all_hidden_states = () if output_hidden_states else None
+ all_self_attns = () if output_attentions else None
+ next_decoder_cache = () if use_cache else None
+
+ if lazy_mode:
+ htcore.mark_step()
+
+ for idx, decoder_layer in enumerate(self.layers):
+ if output_hidden_states:
+ all_hidden_states += (hidden_states,)
+
+ past_key_value = past_key_values[idx] if past_key_values is not None else None
+ if (
+ lazy_mode
+ and not self.training
+ and (torch.distributed.is_initialized() is False or torch.distributed.get_world_size() == 1)
+ ):
+ htcore.mark_step()
+ layer_outputs = decoder_layer(
+ hidden_states,
+ attention_mask=attention_mask,
+ position_ids=position_ids,
+ past_key_value=past_key_value,
+ output_attentions=output_attentions,
+ use_cache=use_cache,
+ token_idx=token_idx,
+ )
+ if (
+ lazy_mode
+ and not self.training
+ and (torch.distributed.is_initialized() is False or torch.distributed.get_world_size() == 1)
+ ):
+ htcore.mark_step()
+ hidden_states = layer_outputs[0]
+
+ if use_cache:
+ next_decoder_cache += (layer_outputs[2 if output_attentions else 1],)
+
+ if output_attentions:
+ all_self_attns += (layer_outputs[1],)
+
+ hidden_states = self.norm(hidden_states)
+
+ # add hidden states from the last decoder layer
+ if output_hidden_states:
+ all_hidden_states += (hidden_states,)
+
+ next_cache = next_decoder_cache if use_cache else None
+ if not return_dict:
+ return tuple(v for v in [hidden_states, next_cache, all_hidden_states, all_self_attns] if v is not None)
+ return BaseModelOutputWithPast(
+ last_hidden_state=hidden_states,
+ past_key_values=next_cache,
+ hidden_states=all_hidden_states,
+ attentions=all_self_attns,
+ )
+
+
+class DeepseekV3ForCausalLM(DeepseekV3PreTrainedModel, GenerationMixin):
+ _tied_weights_keys = ["lm_head.weight"]
+
+ def __init__(self, config):
+ super().__init__(config)
+ self.model = DeepseekV3Model(config)
+ self.vocab_size = config.vocab_size
+ self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
+
+ # Initialize weights and apply final processing
+ self.post_init()
+
+ def get_input_embeddings(self):
+ return self.model.embed_tokens
+
+ def set_input_embeddings(self, value):
+ self.model.embed_tokens = value
+
+ def get_output_embeddings(self):
+ return self.lm_head
+
+ def set_output_embeddings(self, new_embeddings):
+ self.lm_head = new_embeddings
+
+ def set_decoder(self, decoder):
+ self.model = decoder
+
+ def get_decoder(self):
+ return self.model
+
+ def allocate_kv_cache(self, batch_size, max_seq_len, inp_seq_len):
+ self.model.allocate_kv_cache(batch_size, max_seq_len, inp_seq_len)
+ self.kv_cache_len = max_seq_len
+
+ def reorder_kv_cache(self, beam_idx: torch.LongTensor):
+ return self.model.reorder_kv_cache(beam_idx)
+
+ def update_sincos_cache(self, seq_len):
+ self.model.update_sincos_cache(seq_len)
+
+ @add_start_docstrings_to_model_forward(DeepseekV3_INPUTS_DOCSTRING)
+ @replace_return_docstrings(output_type=CausalLMOutputWithPast, config_class=_CONFIG_FOR_DOC)
+ def forward(
+ self,
+ input_ids: torch.LongTensor = None,
+ attention_mask: Optional[torch.Tensor] = None,
+ position_ids: Optional[torch.LongTensor] = None,
+ past_key_values: Optional[List[torch.FloatTensor]] = None,
+ inputs_embeds: Optional[torch.FloatTensor] = None,
+ labels: Optional[torch.LongTensor] = None,
+ use_cache: Optional[bool] = None,
+ output_attentions: Optional[bool] = None,
+ output_hidden_states: Optional[bool] = None,
+ return_dict: Optional[bool] = None,
+ token_idx: Optional[torch.Tensor] = None,
+ ) -> Union[Tuple, CausalLMOutputWithPast]:
+ r"""
+ Args:
+ labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
+ Labels for computing the masked language modeling loss. Indices should either be in `[0, transformers.,
+ config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
+ (masked), the loss is only computed for the tokens with labels in `[0, transformers., config.vocab_size]`.
+
+ Returns:
+
+ Example:
+
+ ```python
+ >>> from transformers import AutoTokenizer, DeepseekV3ForCausalLM
+
+ >>> model = DeepseekV3ForCausalLM.from_pretrained(PATH_TO_CONVERTED_WEIGHTS)
+ >>> tokenizer = AutoTokenizer.from_pretrained(PATH_TO_CONVERTED_TOKENIZER)
+
+ >>> prompt = "Hey, are you conscious? Can you talk to me?"
+ >>> inputs = tokenizer(prompt, return_tensors="pt")
+
+ >>> # Generate
+ >>> generate_ids = model.generate(inputs.input_ids, max_length=30)
+ >>> tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
+ "Hey, are you conscious? Can you talk to me?\nI'm not conscious, but I can talk to you."
+ ```"""
+ output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
+ output_hidden_states = (
+ output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+ )
+ return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+ # decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)
+ outputs = self.model(
+ input_ids=input_ids,
+ attention_mask=attention_mask,
+ position_ids=position_ids,
+ past_key_values=past_key_values,
+ inputs_embeds=inputs_embeds,
+ use_cache=use_cache,
+ output_attentions=output_attentions,
+ output_hidden_states=output_hidden_states,
+ return_dict=return_dict,
+ token_idx=token_idx,
+ )
+
+ hidden_states = outputs[0]
+ logits = self.lm_head(hidden_states)
+ logits = logits.float()
+
+ loss = None
+ if labels is not None:
+ # Shift so that tokens < n predict n
+ shift_logits = logits[..., :-1, :].contiguous()
+ shift_labels = labels[..., 1:].contiguous()
+ # Flatten the tokens
+ loss_fct = CrossEntropyLoss()
+ shift_logits = shift_logits.view(-1, self.config.vocab_size)
+ shift_labels = shift_labels.view(-1)
+ # Enable model parallelism
+ shift_labels = shift_labels.to(shift_logits.device)
+ loss = loss_fct(shift_logits, shift_labels)
+
+ if not return_dict:
+ output = (logits,) + outputs[1:]
+ return (loss,) + output if loss is not None else output
+
+ return CausalLMOutputWithPast(
+ loss=loss,
+ logits=logits,
+ past_key_values=outputs.past_key_values,
+ hidden_states=outputs.hidden_states,
+ attentions=outputs.attentions,
+ )
+
+ def prepare_inputs_for_generation(
+ self,
+ input_ids,
+ past_key_values=None,
+ attention_mask=None,
+ inputs_embeds=None,
+ **kwargs,
+ ):
+ token_idx = kwargs.get("token_idx")
+ past_length = 0
+ max_cache_length = None
+ if past_key_values is not None:
+ if token_idx is not None:
+ input_ids = torch.index_select(input_ids, 1, token_idx - 1)
+ else:
+ if isinstance(past_key_values, Cache):
+ cache_length = past_key_values.get_seq_length()
+ past_length = past_key_values.seen_tokens
+ max_cache_length = past_key_values.get_max_length()
+ else:
+ cache_length = past_length = past_key_values[0][0].shape[2]
+ max_cache_length = None
+
+ # Keep only the unprocessed tokens:
+ # 1 - If the length of the attention_mask exceeds the length of input_ids, then we are in a setting where
+ # some of the inputs are exclusivelly passed as part of the cache (e.g. when passing input_embeds as
+ # input)
+ if attention_mask is not None and attention_mask.shape[1] > input_ids.shape[1]:
+ input_ids = input_ids[:, -(attention_mask.shape[1] - past_length) :]
+ # 2 - If the past_length is smaller than input_ids', then input_ids holds all input tokens. We can discard
+ # input_ids based on the past_length.
+ elif past_length < input_ids.shape[1]:
+ input_ids = input_ids[:, past_length:]
+ # 3 - Otherwise (past_length >= input_ids.shape[1]), let's assume input_ids only has unprocessed tokens.
+
+ # If we are about to go beyond the maximum cache length, we need to crop the input attention mask.
+ if (
+ max_cache_length is not None
+ and attention_mask is not None
+ and cache_length + input_ids.shape[1] > max_cache_length
+ ):
+ attention_mask = attention_mask[:, -max_cache_length:]
+
+ position_ids = kwargs.get("position_ids", None)
+ if attention_mask is not None and position_ids is None:
+ # create position_ids on the fly for batch generation
+ position_ids = attention_mask.long().cumsum(-1) - 1
+ position_ids.masked_fill_(attention_mask == 0, 1)
+ if past_key_values:
+ if token_idx is not None:
+ position_ids = torch.index_select(position_ids, 1, token_idx - 1)
+ else:
+ position_ids = position_ids[:, -input_ids.shape[1] :]
+
+ # if `inputs_embeds` are passed, we only want to use them in the 1st generation step
+ if inputs_embeds is not None and past_key_values is None:
+ model_inputs = {"inputs_embeds": inputs_embeds}
+ else:
+ model_inputs = {"input_ids": input_ids.contiguous()}
+
+ model_inputs.update(
+ {
+ "position_ids": position_ids,
+ "past_key_values": past_key_values,
+ "use_cache": kwargs.get("use_cache"),
+ "attention_mask": attention_mask,
+ "token_idx": token_idx,
+ }
+ )
+ return model_inputs
+
+
+@add_start_docstrings(
+ """
+ The DeepseekV3 Model transformer with a sequence classification head on top (linear layer).
+
+ [`DeepseekV3ForSequenceClassification`] uses the last token in order to do the classification, as other causal models
+ (e.g. GPT-2) do.
+
+ Since it does classification on the last token, it requires to know the position of the last token. If a
+ `pad_token_id` is defined in the configuration, it finds the last token that is not a padding token in each row. If
+ no `pad_token_id` is defined, it simply takes the last value in each row of the batch. Since it cannot guess the
+ padding tokens when `inputs_embeds` are passed instead of `input_ids`, it does the same (take the last value in
+ each row of the batch).
+ """,
+ DeepseekV3_START_DOCSTRING,
+)
+class DeepseekV3ForSequenceClassification(DeepseekV3PreTrainedModel):
+ def __init__(self, config):
+ super().__init__(config)
+ self.num_labels = config.num_labels
+ self.model = DeepseekV3Model(config)
+ self.score = nn.Linear(config.hidden_size, self.num_labels, bias=False)
+
+ # Initialize weights and apply final processing
+ self.post_init()
+
+ def get_input_embeddings(self):
+ return self.model.embed_tokens
+
+ def set_input_embeddings(self, value):
+ self.model.embed_tokens = value
+
+ @add_start_docstrings_to_model_forward(DeepseekV3_INPUTS_DOCSTRING)
+ def forward(
+ self,
+ input_ids: torch.LongTensor = None,
+ attention_mask: Optional[torch.Tensor] = None,
+ position_ids: Optional[torch.LongTensor] = None,
+ past_key_values: Optional[List[torch.FloatTensor]] = None,
+ inputs_embeds: Optional[torch.FloatTensor] = None,
+ labels: Optional[torch.LongTensor] = None,
+ use_cache: Optional[bool] = None,
+ output_attentions: Optional[bool] = None,
+ output_hidden_states: Optional[bool] = None,
+ return_dict: Optional[bool] = None,
+ ) -> Union[Tuple, SequenceClassifierOutputWithPast]:
+ r"""
+ labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
+ Labels for computing the sequence classification/regression loss. Indices should be in `[0, transformers.,
+ config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
+ `config.num_labels > 1` a classification loss is computed (Cross-Entropy).
+ """
+ return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+ transformer_outputs = self.model(
+ input_ids,
+ attention_mask=attention_mask,
+ position_ids=position_ids,
+ past_key_values=past_key_values,
+ inputs_embeds=inputs_embeds,
+ use_cache=use_cache,
+ output_attentions=output_attentions,
+ output_hidden_states=output_hidden_states,
+ return_dict=return_dict,
+ )
+ hidden_states = transformer_outputs[0]
+ logits = self.score(hidden_states)
+
+ if input_ids is not None:
+ batch_size = input_ids.shape[0]
+ else:
+ batch_size = inputs_embeds.shape[0]
+
+ if self.config.pad_token_id is None and batch_size != 1:
+ raise ValueError("Cannot handle batch sizes > 1 if no padding token is defined.")
+ if self.config.pad_token_id is None:
+ sequence_lengths = -1
+ else:
+ if input_ids is not None:
+ sequence_lengths = (torch.eq(input_ids, self.config.pad_token_id).int().argmax(-1) - 1).to(
+ logits.device
+ )
+ else:
+ sequence_lengths = -1
+
+ pooled_logits = logits[torch.arange(batch_size, device=logits.device), sequence_lengths]
+
+ loss = None
+ if labels is not None:
+ labels = labels.to(logits.device)
+ if self.config.problem_type is None:
+ if self.num_labels == 1:
+ self.config.problem_type = "regression"
+ elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
+ self.config.problem_type = "single_label_classification"
+ else:
+ self.config.problem_type = "multi_label_classification"
+
+ if self.config.problem_type == "regression":
+ loss_fct = MSELoss()
+ if self.num_labels == 1:
+ loss = loss_fct(pooled_logits.squeeze(), labels.squeeze())
+ else:
+ loss = loss_fct(pooled_logits, labels)
+ elif self.config.problem_type == "single_label_classification":
+ loss_fct = CrossEntropyLoss()
+ loss = loss_fct(pooled_logits.view(-1, self.num_labels), labels.view(-1))
+ elif self.config.problem_type == "multi_label_classification":
+ loss_fct = BCEWithLogitsLoss()
+ loss = loss_fct(pooled_logits, labels)
+ if not return_dict:
+ output = (pooled_logits,) + transformer_outputs[1:]
+ return ((loss,) + output) if loss is not None else output
+
+ return SequenceClassifierOutputWithPast(
+ loss=loss,
+ logits=pooled_logits,
+ past_key_values=transformer_outputs.past_key_values,
+ hidden_states=transformer_outputs.hidden_states,
+ attentions=transformer_outputs.attentions,
+ )