diff --git a/README.md b/README.md
index 45300b1930..466bf568aa 100644
--- a/README.md
+++ b/README.md
@@ -240,7 +240,7 @@ The following model architectures, tasks and device distributions have been vali
| Mllama | LoRA | :heavy_check_mark: | [image to text](https://github.com/huggingface/optimum-habana/tree/main/examples/image-to-text) |
| MiniCPM3 | | Single card | [text generation](https://github.com/huggingface/optimum-habana/tree/main/examples/text-generation) |
| Baichuan2 | | Single card | [text generation](https://github.com/huggingface/optimum-habana/tree/main/examples/text-generation) |
-
+| DeepSeek-V2 | | :heavy_check_mark: | [text generation](https://github.com/huggingface/optimum-habana/tree/main/examples/text-generation) |
- Diffusers:
diff --git a/docs/source/index.mdx b/docs/source/index.mdx
index 713fd17bd9..048456799b 100644
--- a/docs/source/index.mdx
+++ b/docs/source/index.mdx
@@ -107,6 +107,7 @@ In the tables below, ✅ means single-card, multi-card and DeepSpeed have all be
| Mllama | LoRA |✅ | [image to text](https://github.com/huggingface/optimum-habana/tree/main/examples/image-to-text) |
| MiniCPM3 | | Single card | [text generation](https://github.com/huggingface/optimum-habana/tree/main/examples/text-generation) |
| Baichuan2 | | Single card | [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) |
- Diffusers
diff --git a/examples/text-generation/run_generation.py b/examples/text-generation/run_generation.py
index f5a1951446..4b2ab96842 100755
--- a/examples/text-generation/run_generation.py
+++ b/examples/text-generation/run_generation.py
@@ -306,9 +306,9 @@ def setup_parser(parser):
parser.add_argument(
"--parallel_strategy",
type=str,
- choices=["tp", "none"], # Add other strategies as needed
+ choices=["tp", "ep", "none"], # Add other strategies as needed
default="none",
- help="Run multi card with the specified parallel strategy. Choices are 'tp' for Tensor Parallel Strategy or 'none'.",
+ help="Run multi card with the specified parallel strategy. Choices are 'tp' for Tensor Parallel Strategy or 'ep' for Expert Parallel Strategy or 'none'.",
)
parser.add_argument(
"--input_embeds",
diff --git a/examples/text-generation/utils.py b/examples/text-generation/utils.py
index 8adf2bde8a..be3abdf44d 100644
--- a/examples/text-generation/utils.py
+++ b/examples/text-generation/utils.py
@@ -387,6 +387,44 @@ def setup_distributed_model_tp(args, model_dtype, model_kwargs, logger, cache_di
return model, args.assistant_model
+def setup_distributed_model_ep(args, model_dtype, model_kwargs, logger):
+ logger.info("Multi-device ep run.")
+
+ assert args.quant_config == "", "Fp8 is not enabled, unset QUANT_CONFIG"
+ assert args.assistant_model is None, "Assistant model must be None"
+
+ from torch import distributed as dist
+
+ if args.device == "hpu":
+ dist.init_process_group(backend="hccl")
+ else:
+ assert False, "Supports EP only on HPU"
+
+ torch._C._distributed_c10d._register_process_group("default", dist.group.WORLD)
+ logger.info("Creating Model")
+ config = AutoConfig.from_pretrained(args.model_name_or_path, torch_dtype=model_dtype, **model_kwargs)
+ config.update({"ep_size": args.world_size})
+
+ model = AutoModelForCausalLM.from_pretrained(
+ args.model_name_or_path,
+ config=config,
+ torch_dtype=model_dtype,
+ **model_kwargs,
+ )
+
+ model = model.eval().to(args.device)
+
+ if args.use_hpu_graphs:
+ from habana_frameworks.torch.hpu import wrap_in_hpu_graph
+
+ model = wrap_in_hpu_graph(model)
+
+ if args.torch_compile:
+ model = get_torch_compiled_model(model)
+
+ return model, args.assistant_model
+
+
def setup_distributed_model(args, model_dtype, model_kwargs, logger):
import deepspeed
@@ -677,8 +715,10 @@ def initialize_model(args, logger):
setup_model(args, model_dtype, model_kwargs, logger)
if not use_deepspeed
else setup_distributed_model(args, model_dtype, model_kwargs, logger)
- if not args.parallel_strategy == "tp"
+ if args.parallel_strategy == "none"
else setup_distributed_model_tp(args, model_dtype, model_kwargs, logger, cache_dir)
+ if args.parallel_strategy == "tp"
+ else setup_distributed_model_ep(args, model_dtype, model_kwargs, logger)
)
tokenizer, model, assistant_model = setup_tokenizer(args, model, assistant_model, logger)
diff --git a/optimum/habana/transformers/generation/utils.py b/optimum/habana/transformers/generation/utils.py
index 453e1f22d1..fa298b3047 100644
--- a/optimum/habana/transformers/generation/utils.py
+++ b/optimum/habana/transformers/generation/utils.py
@@ -114,6 +114,7 @@
"mllama",
"minicpm3",
"baichuan",
+ "deepseek_v2",
]
diff --git a/optimum/habana/transformers/modeling_utils.py b/optimum/habana/transformers/modeling_utils.py
index b77e88e969..e36fb060e5 100644
--- a/optimum/habana/transformers/modeling_utils.py
+++ b/optimum/habana/transformers/modeling_utils.py
@@ -33,6 +33,9 @@
BaichuanTokenizer,
DeciLMConfig,
DeciLMForCausalLM,
+ DeepseekTokenizerFast,
+ DeepseekV2Config,
+ DeepseekV2ForCausalLM,
Gaudi2Idefics2ImageProcessor,
GaudiBloomForCausalLM,
GaudiBloomMLP,
@@ -683,6 +686,10 @@ def adapt_transformers_to_gaudi():
transformers.AutoConfig.register("deci", DeciLMConfig)
transformers.AutoModelForCausalLM.register(DeciLMConfig, DeciLMForCausalLM)
+ transformers.AutoConfig.register("deepseek_v2", DeepseekV2Config)
+ transformers.AutoModelForCausalLM.register(DeepseekV2Config, DeepseekV2ForCausalLM)
+ transformers.AutoTokenizer.register(DeepseekV2Config, fast_tokenizer_class=DeepseekTokenizerFast)
+
# Optimization for cohere on Gaudi
transformers.models.cohere.modeling_cohere.CohereDecoderLayer = GaudiCohereDecoderLayer
transformers.models.cohere.modeling_cohere.CohereForCausalLM = GaudiCohereForCausalLM
diff --git a/optimum/habana/transformers/models/__init__.py b/optimum/habana/transformers/models/__init__.py
index 9b7981a73f..22d067aa5a 100644
--- a/optimum/habana/transformers/models/__init__.py
+++ b/optimum/habana/transformers/models/__init__.py
@@ -60,6 +60,11 @@
DeciLMConfig,
DeciLMForCausalLM,
)
+from .deepseek_v2 import (
+ DeepseekTokenizerFast,
+ DeepseekV2Config,
+ DeepseekV2ForCausalLM,
+)
from .detr import gaudi_DetrConvModel_forward
from .esm import (
gaudi_esm_for_protein_folding_forward,
diff --git a/optimum/habana/transformers/models/deepseek_v2/__init__.py b/optimum/habana/transformers/models/deepseek_v2/__init__.py
new file mode 100644
index 0000000000..66d303b31f
--- /dev/null
+++ b/optimum/habana/transformers/models/deepseek_v2/__init__.py
@@ -0,0 +1,3 @@
+from .configuration_deepseek_v2 import DeepseekV2Config
+from .modeling_deepseek_v2 import DeepseekV2ForCausalLM
+from .tokenization_deepseek_v2 import DeepseekTokenizerFast
diff --git a/optimum/habana/transformers/models/deepseek_v2/configuration_deepseek_v2.py b/optimum/habana/transformers/models/deepseek_v2/configuration_deepseek_v2.py
new file mode 100644
index 0000000000..6f9d03ddcd
--- /dev/null
+++ b/optimum/habana/transformers/models/deepseek_v2/configuration_deepseek_v2.py
@@ -0,0 +1,206 @@
+"""
+DeepSeekV2 model configuration. Copied from https://huggingface.co/deepseek-ai/DeepSeek-V2-Lite/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 DeepseekV2Config(PretrainedConfig):
+ r"""
+ This is the configuration class to store the configuration of a [`DeepseekV2Model`]. 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-V2.
+ 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 102400):
+ Vocabulary size of the Deep model. Defines the number of different tokens that can be represented by the
+ `inputs_ids` passed when calling [`DeepseekV2Model`]
+ 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_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 DeepseekV2Model, DeepseekV2Config
+ >>> # Initializing a Deepseek-V2 style configuration
+ >>> configuration = DeepseekV2Config()
+ >>> # Accessing the model configuration
+ >>> configuration = model.config
+ ```"""
+
+ model_type = "deepseek_v2"
+ keys_to_ignore_at_inference = ["past_key_values"]
+
+ def __init__(
+ self,
+ vocab_size=102400,
+ hidden_size=4096,
+ intermediate_size=11008,
+ moe_intermediate_size=1407,
+ num_hidden_layers=30,
+ num_attention_heads=32,
+ num_key_value_heads=32,
+ n_shared_experts=None,
+ n_routed_experts=None,
+ ep_size=1,
+ routed_scaling_factor=1.0,
+ kv_lora_rank=512,
+ q_lora_rank=1536,
+ qk_rope_head_dim=64,
+ v_head_dim=128,
+ qk_nope_head_dim=128,
+ topk_method="gready",
+ n_group=None,
+ topk_group=None,
+ num_experts_per_tok=None,
+ moe_layer_freq=1,
+ first_k_dense_replace=0,
+ norm_topk_prob=False,
+ scoring_func="softmax",
+ aux_loss_alpha=0.001,
+ seq_aux=True,
+ hidden_act="silu",
+ max_position_embeddings=2048,
+ initializer_range=0.02,
+ rms_norm_eps=1e-6,
+ use_cache=True,
+ pad_token_id=None,
+ bos_token_id=100000,
+ eos_token_id=100001,
+ 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_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_v2/modeling_deepseek_v2.py b/optimum/habana/transformers/models/deepseek_v2/modeling_deepseek_v2.py
new file mode 100644
index 0000000000..ee271b7254
--- /dev/null
+++ b/optimum/habana/transformers/models/deepseek_v2/modeling_deepseek_v2.py
@@ -0,0 +1,1299 @@
+# 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 DeepSeekV2 model. Adapted from https://huggingface.co/deepseek-ai/DeepSeek-V2-Lite/resolve/main/modeling_deepseek.py"""
+
+import math
+import warnings
+from typing import List, Optional, Tuple, Union
+
+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 CrossEntropyLoss
+from transformers.activations import ACT2FN
+from transformers.cache_utils import Cache
+from transformers.modeling_outputs import (
+ BaseModelOutputWithPast,
+ CausalLMOutputWithPast,
+)
+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 .configuration_deepseek_v2 import DeepseekV2Config
+
+
+logger = logging.get_logger(__name__)
+
+_CONFIG_FOR_DOC = "DeepseekV2Config"
+
+
+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 DeepseekV2RMSNorm(nn.Module):
+ def __init__(self, hidden_size, eps=1e-6):
+ """
+ DeepseekV2RMSNorm is equivalent to T5LayerNorm
+ """
+ super().__init__()
+ self.weight = nn.Parameter(torch.ones(hidden_size))
+ self.variance_epsilon = eps
+
+ def forward(self, hidden_states):
+ 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(DeepseekV2RMSNorm)
+
+
+class DeepseekV2RotaryEmbedding(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.
+ 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 > 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->DeepseekV2
+class DeepseekV2LinearScalingRotaryEmbedding(DeepseekV2RotaryEmbedding):
+ """DeepseekV2RotaryEmbedding 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->DeepseekV2
+class DeepseekV2DynamicNTKScalingRotaryEmbedding(DeepseekV2RotaryEmbedding):
+ """DeepseekV2RotaryEmbedding 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 DeepseekV2YarnRotaryEmbedding(DeepseekV2RotaryEmbedding):
+ 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)
+ emb_cos = (emb.cos() * _mscale).to(dtype)
+ emb_sin = (emb.sin() * _mscale).to(dtype)
+ self.register_buffer("cos_cached", emb_cos, persistent=False)
+ self.register_buffer("sin_cached", emb_sin, persistent=False)
+
+
+# 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, k, cos, sin, position_ids, unsqueeze_dim=1):
+ """Applies Rotary Position Embedding to the query and key tensors.
+ Args:
+ q (`torch.Tensor`): The query tensor.
+ k (`torch.Tensor`): The key 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.
+ """
+ cos = cos[position_ids].unsqueeze(unsqueeze_dim)
+ sin = sin[position_ids].unsqueeze(unsqueeze_dim)
+
+ b, h, s, d = q.shape
+ q = q.view(b, h, s, d // 2, 2).transpose(4, 3).reshape(b, h, s, d)
+
+ b, h, s, d = k.shape
+ k = k.view(b, h, s, d // 2, 2).transpose(4, 3).reshape(b, h, s, d)
+
+ q_embed = (q * cos) + (rotate_half(q) * sin)
+ k_embed = (k * cos) + (rotate_half(k) * sin)
+ return q_embed, k_embed
+
+
+class DeepseekV2MLP(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.alpha = config.aux_loss_alpha
+ 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)))
+ 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 == "softmax":
+ scores = F.softmax(logits, dim=-1, dtype=torch.float32)
+ else:
+ raise NotImplementedError(f"insupportable scoring function for MoE gating: {self.scoring_func}")
+
+ ### select top-k experts
+ if self.topk_method == "greedy":
+ topk_weight, topk_idx = torch.topk(scores, self.top_k, dim=-1)
+ elif self.topk_method == "group_limited_greedy":
+ group_scores = scores.view(bsz * seq_len, self.n_group, -1).max(dim=-1).values # [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.masked_fill(~score_mask.bool(), 0.0) # [n, e]
+ topk_weight, topk_idx = torch.topk(tmp_scores, k=self.top_k, dim=-1, sorted=False)
+
+ ### 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
+ else:
+ topk_weight = topk_weight * self.routed_scaling_factor
+ ### expert-level computation auxiliary loss
+ if self.training and self.alpha > 0.0:
+ scores_for_aux = scores
+ aux_topk = self.top_k
+ # always compute aux loss based on the naive greedy topk method
+ topk_idx_for_aux_loss = topk_idx.view(bsz, -1)
+ if self.seq_aux:
+ scores_for_seq_aux = scores_for_aux.view(bsz, seq_len, -1)
+ ce = torch.zeros(bsz, self.n_routed_experts, device=hidden_states.device)
+ ce.scatter_add_(
+ 1,
+ topk_idx_for_aux_loss,
+ torch.ones(bsz, seq_len * aux_topk, device=hidden_states.device),
+ ).div_(seq_len * aux_topk / self.n_routed_experts)
+ aux_loss = (ce * scores_for_seq_aux.mean(dim=1)).sum(dim=1).mean() * self.alpha
+ else:
+ mask_ce = F.one_hot(topk_idx_for_aux_loss.view(-1), num_classes=self.n_routed_experts)
+ ce = mask_ce.float().mean(0)
+ Pi = scores_for_aux.mean(0)
+ fi = ce * self.n_routed_experts
+ aux_loss = (Pi * fi).sum() * self.alpha
+ else:
+ aux_loss = None
+ return topk_idx, topk_weight, aux_loss
+
+
+class AddAuxiliaryLoss(torch.autograd.Function):
+ """
+ The trick function of adding auxiliary (aux) loss,
+ which includes the gradient of the aux loss during backpropagation.
+ """
+
+ @staticmethod
+ def forward(ctx, x, loss):
+ assert loss.numel() == 1
+ ctx.dtype = loss.dtype
+ ctx.required_aux_loss = loss.requires_grad
+ return x
+
+ @staticmethod
+ def backward(ctx, grad_output):
+ grad_loss = None
+ if ctx.required_aux_loss:
+ grad_loss = torch.ones(1, dtype=ctx.dtype, device=grad_output.device)
+ return grad_output, grad_loss
+
+
+class DeepseekV2MoE(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(
+ [
+ (
+ DeepseekV2MLP(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(
+ [
+ DeepseekV2MLP(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 = DeepseekV2MLP(config=config, intermediate_size=intermediate_size)
+
+ def forward(self, hidden_states):
+ identity = hidden_states
+ orig_shape = hidden_states.shape
+ topk_idx, topk_weight, aux_loss = self.gate(hidden_states)
+ hidden_states = hidden_states.view(-1, hidden_states.shape[-1])
+ flat_topk_idx = topk_idx.view(-1)
+ if self.training:
+ hidden_states = hidden_states.repeat_interleave(self.num_experts_per_tok, dim=0)
+ y = torch.empty_like(hidden_states)
+ for i, expert in enumerate(self.experts):
+ y[flat_topk_idx == i] = expert(hidden_states[flat_topk_idx == i])
+ y = (y.view(*topk_weight.shape, -1) * topk_weight.unsqueeze(-1)).sum(dim=1)
+ y = y.to(hidden_states.dtype).view(*orig_shape)
+ y = AddAuxiliaryLoss.apply(y, aux_loss)
+ else:
+ y = self.moe_infer(hidden_states, topk_idx, topk_weight).view(*orig_shape)
+ if self.config.n_shared_experts is not None:
+ y = y + self.shared_experts(identity)
+ return y
+
+ @torch.no_grad()
+ def moe_infer(self, x, topk_ids, topk_weight):
+ """
+ Rewrite DeepseekV2MoE.moe_infer: https://huggingface.co/deepseek-ai/DeepSeek-V2-Lite/blob/main/modeling_deepseek.py for static expert support
+ """
+ out = torch.zeros_like(x)
+
+ seq_len, hidden_dim = x.shape
+ num_experts = len(self.experts)
+
+ padded_weights = torch.zeros((seq_len, num_experts), dtype=topk_weight.dtype, device=x.device)
+ padded_weights.scatter_(-1, topk_ids, topk_weight)
+ padded_weights = padded_weights.reshape(seq_len, num_experts)
+ padded_weights = padded_weights.permute(1, 0).unsqueeze(-1)
+
+ # Loop over all available experts in the model and perform the computation on each expert
+ for i in range(self.experts_per_rank):
+ expert_idx = i + self.ep_rank * self.experts_per_rank
+ expert = self.experts[expert_idx]
+ padded_weight = padded_weights[expert_idx]
+ x_static = expert(x) * padded_weight
+ out += x_static
+
+ if self.ep_size > 1:
+ out = _all_reduce(out)
+
+ return out
+
+
+# Copied from transformers.models.llama.modeling_llama.repeat_kv
+def repeat_kv(hidden_states: torch.Tensor, n_rep: int) -> torch.Tensor:
+ """
+ This is the equivalent of torch.repeat_interleave(x, dim=1, repeats=n_rep). The hidden states go from (batch,
+ num_key_value_heads, seqlen, head_dim) to (batch, num_attention_heads, seqlen, head_dim)
+ """
+ batch, num_key_value_heads, slen, head_dim = hidden_states.shape
+ if n_rep == 1:
+ return hidden_states
+ hidden_states = hidden_states[:, :, None, :, :].expand(batch, num_key_value_heads, n_rep, slen, head_dim)
+ return hidden_states.reshape(batch, num_key_value_heads * n_rep, slen, head_dim)
+
+
+# Copied from transformers.models.llama.modeling_llama.LlamaAttention with Llama->DeepseekV2
+class DeepseekV2Attention(nn.Module):
+ """Multi-headed attention from 'Attention Is All You Need' paper"""
+
+ def __init__(self, config: DeepseekV2Config, 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 = DeepseekV2RMSNorm(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 = DeepseekV2RMSNorm(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.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
+
+ def _init_rope(self):
+ if self.config.rope_scaling is None:
+ self.rotary_emb = DeepseekV2RotaryEmbedding(
+ 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 = DeepseekV2LinearScalingRotaryEmbedding(
+ 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 = DeepseekV2DynamicNTKScalingRotaryEmbedding(
+ 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 = DeepseekV2YarnRotaryEmbedding(
+ 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}")
+
+ 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 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: bool = False,
+ use_cache: bool = False,
+ token_idx: Optional[torch.Tensor] = None,
+ **kwargs,
+ ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
+ """
+ Copied from DeepseekV2Attention.forward: https://huggingface.co/deepseek-ai/DeepSeek-V2-Lite/blob/main/modeling_deepseek.py
+ deltas are:
+ - add token_idx
+ - optimize KV cache
+ """
+
+ 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:
+ kv_seq_len += past_key_value[0].shape[-2]
+ 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_rotary_pos_emb(q_pe, k_pe, cos, sin, position_ids)
+
+ 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:
+ if token_idx is None:
+ key_states = torch.cat([past_key_value[0], key_states], dim=2)
+ value_states = torch.cat([past_key_value[1], value_states], dim=2)
+ else:
+ past_key_value[0].index_add_(
+ 2, token_idx - 1, key_states - torch.index_select(past_key_value[0], 2, token_idx - 1)
+ )
+ past_key_value[1].index_add_(
+ 2, token_idx - 1, value_states - torch.index_select(past_key_value[1], 2, token_idx - 1)
+ )
+ key_states = past_key_value[0]
+ value_states = past_key_value[1]
+ past_key_value = (key_states, value_states) if use_cache else None
+
+ attn_weights = torch.matmul(query_states, key_states.transpose(2, 3)) * 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()}"
+ )
+ assert attention_mask is not None
+ if attention_mask is not None:
+ if attention_mask.size() != (bsz, 1, q_len, kv_seq_len):
+ raise ValueError(
+ f"Attention mask should be of size {(bsz, 1, q_len, kv_seq_len)}, but is {attention_mask.size()}"
+ )
+ attn_weights = attn_weights + attention_mask
+
+ # upcast attention to fp32
+ attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query_states.dtype)
+ attn_weights = nn.functional.dropout(attn_weights, p=self.attention_dropout, training=self.training)
+ attn_output = torch.matmul(attn_weights, value_states)
+
+ 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 DeepseekV2DecoderLayer(nn.Module):
+ def __init__(self, config: DeepseekV2Config, layer_idx: int):
+ super().__init__()
+ self.hidden_size = config.hidden_size
+
+ self.self_attn = DeepseekV2Attention(config=config, layer_idx=layer_idx)
+
+ self.mlp = (
+ DeepseekV2MoE(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 DeepseekV2MLP(config)
+ )
+ self.input_layernorm = DeepseekV2RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+ self.post_attention_layernorm = DeepseekV2RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+
+ 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,
+ **kwargs,
+ ) -> Tuple[torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]]]:
+ """
+ Copied from DeepseekV2DecoderLayer.forward: https://huggingface.co/deepseek-ai/DeepSeek-V2-Lite/blob/main/modeling_deepseek.py
+ The deltas are:
+ - add token_idx
+ """
+ """
+ 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,
+ **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
+
+
+DeepseekV2_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 ([`DeepseekV2Config`]):
+ 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 DeepseekV2 Model outputting raw hidden-states without any specific head on top.",
+ DeepseekV2_START_DOCSTRING,
+)
+class DeepseekV2PreTrainedModel(PreTrainedModel):
+ config_class = DeepseekV2Config
+ base_model_prefix = "model"
+ supports_gradient_checkpointing = True
+ _no_split_modules = ["DeepseekV2DecoderLayer"]
+ _skip_keys_device_placement = "past_key_values"
+ _supports_flash_attn_2 = True
+ _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_()
+
+
+DeepseekV2_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 DeepseekV2 Model outputting raw hidden-states without any specific head on top.",
+ DeepseekV2_START_DOCSTRING,
+)
+class DeepseekV2Model(DeepseekV2PreTrainedModel):
+ """
+ Transformer decoder consisting of *config.num_hidden_layers* layers. Each layer is a [`DeepseekV2DecoderLayer`]
+ Args:
+ config: DeepseekV2Config
+ """
+
+ def __init__(self, config: DeepseekV2Config):
+ 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(
+ [DeepseekV2DecoderLayer(config, layer_idx) for layer_idx in range(config.num_hidden_layers)]
+ )
+ self._use_flash_attention_2 = config._attn_implementation == "flash_attention_2"
+ self.norm = DeepseekV2RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+
+ self.gradient_checkpointing = False
+ # Initialize weights and apply final processing
+ self.post_init()
+
+ 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(DeepseekV2_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,
+ token_idx: Optional[torch.Tensor] = 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")
+
+ if self.gradient_checkpointing and self.training:
+ if use_cache:
+ logger.warning_once(
+ "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`transformers."
+ )
+ use_cache = False
+
+ 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
+
+ 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 self.gradient_checkpointing and self.training:
+ layer_outputs = self._gradient_checkpointing_func(
+ decoder_layer.__call__,
+ hidden_states,
+ attention_mask,
+ position_ids,
+ past_key_values,
+ output_attentions,
+ use_cache,
+ )
+ else:
+ 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,
+ )
+
+ 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 DeepseekV2ForCausalLM(DeepseekV2PreTrainedModel):
+ _tied_weights_keys = ["lm_head.weight"]
+
+ def __init__(self, config):
+ super().__init__(config)
+ self.model = DeepseekV2Model(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
+
+ @add_start_docstrings_to_model_forward(DeepseekV2_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, DeepseekV2ForCausalLM
+ >>> model = DeepseekV2ForCausalLM.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
diff --git a/optimum/habana/transformers/models/deepseek_v2/tokenization_deepseek_v2.py b/optimum/habana/transformers/models/deepseek_v2/tokenization_deepseek_v2.py
new file mode 100644
index 0000000000..12499a38b5
--- /dev/null
+++ b/optimum/habana/transformers/models/deepseek_v2/tokenization_deepseek_v2.py
@@ -0,0 +1,38 @@
+"""
+Copied from https://huggingface.co/deepseek-ai/DeepSeek-V2-Lite/resolve/main/tokenization_deepseek_fast.py
+"""
+
+from typing import List, Optional, Union
+
+from transformers.models.llama import LlamaTokenizerFast
+
+
+class DeepseekTokenizerFast(LlamaTokenizerFast):
+ def convert_ids_to_tokens(
+ self, ids: Union[int, List[int]], skip_special_tokens: bool = False
+ ) -> Union[str, List[str]]:
+ """
+ Converts a single index or a sequence of indices in a token or a sequence of tokens, using the vocabulary and
+ added tokens.
+ Args:
+ ids (`int` or `List[int]`):
+ The token id (or token ids) to convert to tokens.
+ skip_special_tokens (`bool`, *optional*, defaults to `False`):
+ Whether or not to remove special tokens in the decoding.
+ Returns:
+ `str` or `List[str]`: The decoded token(s).
+ """
+ if isinstance(ids, int):
+ return self._convert_id_to_token(ids)
+ tokens = []
+ for index in ids:
+ index = int(index)
+ if skip_special_tokens and index in self.all_special_ids:
+ continue
+ token = self._tokenizer.id_to_token(index)
+ tokens.append(token if token is not None else "")
+ return tokens
+
+ def _convert_id_to_token(self, index: int) -> Optional[str]:
+ token = self._tokenizer.id_to_token(int(index))
+ return token if token is not None else ""
diff --git a/tests/test_text_generation_example.py b/tests/test_text_generation_example.py
index ed1a094e47..4de08433bb 100644
--- a/tests/test_text_generation_example.py
+++ b/tests/test_text_generation_example.py
@@ -60,6 +60,7 @@
("openbmb/MiniCPM3-4B", 1, False, 65.116, False),
("baichuan-inc/Baichuan2-7B-Chat", 1, True, 108, False),
("baichuan-inc/Baichuan2-13B-Chat", 1, False, 66, False),
+ ("deepseek-ai/DeepSeek-V2-Lite", 1, False, 35, False),
],
"fp8": [
("tiiuae/falcon-180B", 4, 950, True, 128, 128, 2506.68),