From aca977817dc17e458e9a628762548bc80df90622 Mon Sep 17 00:00:00 2001 From: Shifani Date: Thu, 30 Jan 2025 02:11:42 +0000 Subject: [PATCH 01/21] Resolve rebase conflicts on DeepSeek_v3 --- README.md | 1 + docs/source/index.mdx | 1 + .../habana/transformers/generation/utils.py | 1 + optimum/habana/transformers/modeling_utils.py | 6 + .../habana/transformers/models/__init__.py | 5 + .../models/deepseek_v3/__init__.py | 3 + .../deepseek_v3/configuration_deepseek_v3.py | 210 ++ .../deepseek_v3/modeling_deepseek_v3.py | 1849 +++++++++++++++++ .../deepseek_v3/tokenization_deepseek_v3.py | 16 + tests/test_text_generation_example.py | 37 + 10 files changed, 2129 insertions(+) create mode 100644 optimum/habana/transformers/models/deepseek_v3/__init__.py create mode 100644 optimum/habana/transformers/models/deepseek_v3/configuration_deepseek_v3.py create mode 100644 optimum/habana/transformers/models/deepseek_v3/modeling_deepseek_v3.py create mode 100644 optimum/habana/transformers/models/deepseek_v3/tokenization_deepseek_v3.py diff --git a/README.md b/README.md index e8fdf07116..6c7f8a5e98 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/optimum/habana/transformers/generation/utils.py b/optimum/habana/transformers/generation/utils.py index bcb4d74e5b..dd6e2643dd 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", ] diff --git a/optimum/habana/transformers/modeling_utils.py b/optimum/habana/transformers/modeling_utils.py index b11c049e3b..aa3fc42fb6 100644 --- a/optimum/habana/transformers/modeling_utils.py +++ b/optimum/habana/transformers/modeling_utils.py @@ -54,6 +54,8 @@ DeepseekTokenizerFast, DeepseekV2Config, DeepseekV2ForCausalLM, + DeepseekV3Config, + DeepseekV3ForCausalLM, Gaudi2Idefics2ImageProcessor, GaudiBloomForCausalLM, GaudiBloomMLP, @@ -747,6 +749,10 @@ def adapt_transformers_to_gaudi(): transformers.AutoModelForCausalLM.register(DeepseekV2Config, DeepseekV2ForCausalLM) transformers.AutoTokenizer.register(DeepseekV2Config, fast_tokenizer_class=DeepseekTokenizerFast) + transformers.AutoConfig.register("deepseek_v3", DeepseekV3Config) + transformers.AutoModelForCausalLM.register(DeepseekV3Config, DeepseekV3ForCausalLM) + transformers.AutoTokenizer.register(DeepseekV3Config, 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 57c23844da..4e78e0b883 100644 --- a/optimum/habana/transformers/models/__init__.py +++ b/optimum/habana/transformers/models/__init__.py @@ -71,6 +71,11 @@ DeepseekV2Config, DeepseekV2ForCausalLM, ) +from .deepseek_v3 import ( + DeepseekTokenizerFast, + 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..594c67665c --- /dev/null +++ b/optimum/habana/transformers/models/deepseek_v3/__init__.py @@ -0,0 +1,3 @@ +from .configuration_deepseek_v3 import DeepseekV3Config +from .modeling_deepseek_v3 import DeepseekV3ForCausalLM +from .tokenization_deepseek_v3 import DeepseekTokenizerFast 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..f2a42479fd --- /dev/null +++ b/optimum/habana/transformers/models/deepseek_v3/configuration_deepseek_v3.py @@ -0,0 +1,210 @@ +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, + ) \ No newline at end of file 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..1192a0063d --- /dev/null +++ b/optimum/habana/transformers/models/deepseek_v3/modeling_deepseek_v3.py @@ -0,0 +1,1849 @@ +# 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 DeepSeek model.""" +import math +import warnings +from typing import List, Optional, Tuple, Union + +import torch +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, DynamicCache +from transformers.modeling_attn_mask_utils import ( + AttentionMaskConverter, + _prepare_4d_attention_mask, + _prepare_4d_causal_attention_mask, +) +from transformers.modeling_outputs import ( + BaseModelOutputWithPast, + CausalLMOutputWithPast, + SequenceClassifierOutputWithPast, +) +from transformers.modeling_utils import PreTrainedModel +from transformers.pytorch_utils import ( + ALL_LAYERNORM_LAYERS, + is_torch_greater_or_equal_than_1_13, +) +from transformers.utils import ( + add_start_docstrings, + add_start_docstrings_to_model_forward, + is_flash_attn_2_available, + is_flash_attn_greater_or_equal_2_10, + logging, + replace_return_docstrings, +) +from transformers.utils.import_utils import is_torch_fx_available +from .configuration_deepseek import DeepseekV3Config +import torch.distributed as dist +import numpy as np + +if is_flash_attn_2_available(): + from flash_attn import flash_attn_func, flash_attn_varlen_func + from flash_attn.bert_padding import index_first_axis, pad_input, unpad_input # noqa + + +# This makes `_prepare_4d_causal_attention_mask` a leaf function in the FX graph. +# It means that the function will not be traced through and simply appear as a node in the graph. +if is_torch_fx_available(): + if not is_torch_greater_or_equal_than_1_13: + import torch.fx + + _prepare_4d_causal_attention_mask = torch.fx.wrap(_prepare_4d_causal_attention_mask) + + +logger = logging.get_logger(__name__) + +_CONFIG_FOR_DOC = "DeepseekV3Config" + + +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): + 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. + self._set_cos_sin_cache( + seq_len=max_position_embeddings, + device=self.inv_freq.device, + dtype=torch.get_default_dtype(), + ) + self.max_seq_len_cached = None + + 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 self.max_seq_len_cached is None or 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 + ) + + +# 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 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 + ) + + 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]) + flat_topk_idx = topk_idx.view(-1) + if not self.training: + 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): + cnts = topk_ids.new_zeros((topk_ids.shape[0], len(self.experts))) + cnts.scatter_(1, topk_ids, 1) + tokens_per_expert = cnts.sum(dim=0) + idxs = topk_ids.view(-1).argsort() + sorted_tokens = x[idxs // topk_ids.shape[1]] + sorted_tokens_shape = sorted_tokens.shape + if self.ep_size > 1: + tokens_per_ep_rank = tokens_per_expert.view(self.ep_size, -1).sum(dim=1) + tokens_per_expert_group = tokens_per_expert.new_empty( + tokens_per_expert.shape[0] + ) + dist.all_to_all_single(tokens_per_expert_group, tokens_per_expert) + output_splits = ( + tokens_per_expert_group.view(self.ep_size, -1) + .sum(1) + .cpu() + .numpy() + .tolist() + ) + gathered_tokens = sorted_tokens.new_empty( + tokens_per_expert_group.sum(dim=0).cpu().item(), sorted_tokens.shape[1] + ) + input_split_sizes = tokens_per_ep_rank.cpu().numpy().tolist() + dist.all_to_all( + list(gathered_tokens.split(output_splits)), + list(sorted_tokens.split(input_split_sizes)), + ) + tokens_per_expert_post_gather = tokens_per_expert_group.view( + self.ep_size, self.experts_per_rank + ).sum(dim=0) + gatherd_idxs = np.zeros(shape=(gathered_tokens.shape[0],), dtype=np.int32) + s = 0 + for i, k in enumerate(tokens_per_expert_group.cpu().numpy()): + gatherd_idxs[s : s + k] = i % self.experts_per_rank + s += k + gatherd_idxs = gatherd_idxs.argsort() + sorted_tokens = gathered_tokens[gatherd_idxs] + tokens_per_expert = tokens_per_expert_post_gather + tokens_per_expert = tokens_per_expert.cpu().numpy() + + outputs = [] + start_idx = 0 + for i, num_tokens in enumerate(tokens_per_expert): + end_idx = start_idx + num_tokens + if num_tokens == 0: + continue + expert = self.experts[i + self.ep_rank * self.experts_per_rank] + tokens_for_this_expert = sorted_tokens[start_idx:end_idx] + expert_out = expert(tokens_for_this_expert) + outputs.append(expert_out) + start_idx = end_idx + + outs = torch.cat(outputs, dim=0) if len(outputs) else sorted_tokens.new_empty(0) + if self.ep_size > 1: + new_x = torch.empty_like(outs) + new_x[gatherd_idxs] = outs + gathered_tokens = new_x.new_empty(*sorted_tokens_shape) + dist.all_to_all( + list(gathered_tokens.split(input_split_sizes)), + list(new_x.split(output_splits)), + ) + outs = gathered_tokens + + new_x = torch.empty_like(outs) + new_x[idxs] = outs + final_out = ( + new_x.view(*topk_ids.shape, -1) + .type(topk_weight.dtype) + .mul_(topk_weight.unsqueeze(dim=-1)) + .sum(dim=1) + .type(new_x.dtype) + ) + return final_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->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.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 = 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}") + + 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[Cache] = None, + output_attentions: bool = False, + use_cache: bool = False, + **kwargs, + ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]: + 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." + ) + kv_seq_len += past_key_value.get_usable_length(kv_seq_len, self.layer_idx) + 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: + 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 + ) + + 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 + + +# Copied from transformers.models.llama.modeling_llama.LlamaFlashAttention2 with Llama->DeepseekV3 +class DeepseekV3FlashAttention2(DeepseekV3Attention): + """ + DeepseekV3 flash attention module. This module inherits from `DeepseekV3Attention` as the weights of the module stays + untouched. The only required change would be on the forward pass where it needs to correctly call the public API of + flash attention and deal with padding tokens in case the input contains any of them. + """ + + def __init__(self, *args, **kwargs): + super().__init__(*args, **kwargs) + + # TODO: Should be removed once Flash Attention for RoCm is bumped to 2.1. + # flash_attn<2.1 generates top-left aligned causal mask, while what is needed here is bottom-right alignement, that was made default for flash_attn>=2.1. This attribute is used to handle this difference. Reference: https://github.com/Dao-AILab/flash-attention/releases/tag/v2.1.0. + # Beware that with flash_attn<2.1, using q_seqlen != k_seqlen (except for the case q_seqlen == 1) produces a wrong mask (top-left). + self._flash_attn_uses_top_left_mask = not is_flash_attn_greater_or_equal_2_10() + + def forward( + self, + hidden_states: torch.Tensor, + attention_mask: Optional[torch.LongTensor] = None, + position_ids: Optional[torch.LongTensor] = None, + past_key_value: Optional[Cache] = None, + output_attentions: bool = False, + use_cache: bool = False, + **kwargs, + ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]: + # DeepseekV3FlashAttention2 attention does not support output_attentions + 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.`" + ) + + # overwrite attention_mask with padding_mask + attention_mask = kwargs.pop("padding_mask") + + output_attentions = False + + 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 + ) + + # Flash attention requires the input to have the shape + # batch_size x seq_length x head_dim x hidden_dim + # therefore we just need to keep the original shape + 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] + + kv_seq_len = value_states.shape[-2] + if past_key_value is not None: + kv_seq_len += past_key_value.get_usable_length(kv_seq_len, self.layer_idx) + + 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 self.q_head_dim != self.v_head_dim: + value_states = F.pad(value_states, [0, self.q_head_dim - self.v_head_dim]) + + 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 + ) + + # TODO: These transpose are quite inefficient but Flash Attention requires the layout [batch_size, sequence_length, num_heads, head_dim]. We would need to refactor the KV cache + # to be able to avoid many of these transpose/reshape/view. + query_states = query_states.transpose(1, 2) + key_states = key_states.transpose(1, 2) + value_states = value_states.transpose(1, 2) + + dropout_rate = self.attention_dropout if self.training else 0.0 + + # In PEFT, usually we cast the layer norms in float32 for training stability reasons + # therefore the input hidden states gets silently casted in float32. Hence, we need + # cast them back in the correct dtype just to be sure everything works as expected. + # This might slowdown training & inference so it is recommended to not cast the LayerNorms + # in fp32. (DeepseekV3RMSNorm handles it correctly) + + input_dtype = query_states.dtype + if input_dtype == torch.float32: + # Handle the case where the model is quantized + if hasattr(self.config, "_pre_quantization_dtype"): + target_dtype = self.config._pre_quantization_dtype + elif torch.is_autocast_enabled(): + target_dtype = torch.get_autocast_gpu_dtype() + else: + target_dtype = ( + self.q_proj.weight.dtype + if self.q_lora_rank is None + else self.q_a_proj.weight.dtype + ) + + logger.warning_once( + f"The input hidden states seems to be silently casted in float32, this might be related to" + f" the fact you have upcasted embedding or layer norm layers in float32. We will cast back the input in" + f" {target_dtype}." + ) + + query_states = query_states.to(target_dtype) + key_states = key_states.to(target_dtype) + value_states = value_states.to(target_dtype) + + attn_output = self._flash_attention_forward( + query_states, + key_states, + value_states, + attention_mask, + q_len, + dropout=dropout_rate, + softmax_scale=self.softmax_scale, + ) + if self.q_head_dim != self.v_head_dim: + attn_output = attn_output[:, :, :, : self.v_head_dim] + + attn_output = attn_output.reshape( + bsz, q_len, self.num_heads * self.v_head_dim + ).contiguous() + attn_output = self.o_proj(attn_output) + + if not output_attentions: + attn_weights = None + + return attn_output, attn_weights, past_key_value + + def _flash_attention_forward( + self, + query_states, + key_states, + value_states, + attention_mask, + query_length, + dropout=0.0, + softmax_scale=None, + ): + """ + Calls the forward method of Flash Attention - if the input hidden states contain at least one padding token + first unpad the input, then computes the attention scores and pad the final attention scores. + + Args: + query_states (`torch.Tensor`): + Input query states to be passed to Flash Attention API + key_states (`torch.Tensor`): + Input key states to be passed to Flash Attention API + value_states (`torch.Tensor`): + Input value states to be passed to Flash Attention API + attention_mask (`torch.Tensor`): + The padding mask - corresponds to a tensor of size `(batch_size, seq_len)` where 0 stands for the + position of padding tokens and 1 for the position of non-padding tokens. + dropout (`int`, *optional*): + Attention dropout + softmax_scale (`float`, *optional*): + The scaling of QK^T before applying softmax. Default to 1 / sqrt(head_dim) + """ + if not self._flash_attn_uses_top_left_mask: + causal = self.is_causal + else: + # TODO: Remove the `query_length != 1` check once Flash Attention for RoCm is bumped to 2.1. For details, please see the comment in DeepseekV3FlashAttention2 __init__. + causal = self.is_causal and query_length != 1 + + # Contains at least one padding token in the sequence + if attention_mask is not None: + batch_size = query_states.shape[0] + ( + query_states, + key_states, + value_states, + indices_q, + cu_seq_lens, + max_seq_lens, + ) = self._upad_input( + query_states, key_states, value_states, attention_mask, query_length + ) + + cu_seqlens_q, cu_seqlens_k = cu_seq_lens + max_seqlen_in_batch_q, max_seqlen_in_batch_k = max_seq_lens + + attn_output_unpad = flash_attn_varlen_func( + query_states, + key_states, + value_states, + cu_seqlens_q=cu_seqlens_q, + cu_seqlens_k=cu_seqlens_k, + max_seqlen_q=max_seqlen_in_batch_q, + max_seqlen_k=max_seqlen_in_batch_k, + dropout_p=dropout, + softmax_scale=softmax_scale, + causal=causal, + ) + + attn_output = pad_input( + attn_output_unpad, indices_q, batch_size, query_length + ) + else: + attn_output = flash_attn_func( + query_states, + key_states, + value_states, + dropout, + softmax_scale=softmax_scale, + causal=causal, + ) + + return attn_output + + def _upad_input( + self, query_layer, key_layer, value_layer, attention_mask, query_length + ): + indices_k, cu_seqlens_k, max_seqlen_in_batch_k = _get_unpad_data(attention_mask) + batch_size, kv_seq_len, num_key_value_heads, head_dim = key_layer.shape + + key_layer = index_first_axis( + key_layer.reshape(batch_size * kv_seq_len, num_key_value_heads, head_dim), + indices_k, + ) + value_layer = index_first_axis( + value_layer.reshape(batch_size * kv_seq_len, num_key_value_heads, head_dim), + indices_k, + ) + if query_length == kv_seq_len: + query_layer = index_first_axis( + query_layer.reshape(batch_size * kv_seq_len, self.num_heads, head_dim), + indices_k, + ) + cu_seqlens_q = cu_seqlens_k + max_seqlen_in_batch_q = max_seqlen_in_batch_k + indices_q = indices_k + elif query_length == 1: + max_seqlen_in_batch_q = 1 + cu_seqlens_q = torch.arange( + batch_size + 1, dtype=torch.int32, device=query_layer.device + ) # There is a memcpy here, that is very bad. + indices_q = cu_seqlens_q[:-1] + query_layer = query_layer.squeeze(1) + else: + # The -q_len: slice assumes left padding. + attention_mask = attention_mask[:, -query_length:] + query_layer, indices_q, cu_seqlens_q, max_seqlen_in_batch_q = unpad_input( + query_layer, attention_mask + ) + + return ( + query_layer, + key_layer, + value_layer, + indices_q, + (cu_seqlens_q, cu_seqlens_k), + (max_seqlen_in_batch_q, max_seqlen_in_batch_k), + ) + + +ATTENTION_CLASSES = { + "eager": DeepseekV3Attention, + "flash_attention_2": DeepseekV3FlashAttention2, +} + + +class DeepseekV3DecoderLayer(nn.Module): + def __init__(self, config: DeepseekV3Config, layer_idx: int): + super().__init__() + self.hidden_size = config.hidden_size + + self.self_attn = ATTENTION_CLASSES[config._attn_implementation]( + 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 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, + **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, + **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 = 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_() + + +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._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 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, + ) -> 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 use_cache: + use_legacy_cache = not isinstance(past_key_values, Cache) + if use_legacy_cache: + past_key_values = DynamicCache.from_legacy_cache(past_key_values) + past_key_values_length = past_key_values.get_usable_length(seq_length) + + 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) + + if self._use_flash_attention_2: + # 2d mask is passed through the layers + attention_mask = ( + attention_mask + if (attention_mask is not None and 0 in attention_mask) + else None + ) + else: + # 4d mask is passed through the layers + attention_mask = _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 = None + + for decoder_layer in self.layers: + if output_hidden_states: + all_hidden_states += (hidden_states,) + + layer_outputs = decoder_layer( + hidden_states, + attention_mask=attention_mask, + position_ids=position_ids, + past_key_value=past_key_values, + output_attentions=output_attentions, + use_cache=use_cache, + ) + + 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 = None + if use_cache: + next_cache = ( + next_decoder_cache.to_legacy_cache() + if use_legacy_cache + else next_decoder_cache + ) + 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): + _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 + + @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, + ) -> 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, + ) + + 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, + ): + if past_key_values is not None: + 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: + 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} + + model_inputs.update( + { + "position_ids": position_ids, + "past_key_values": past_key_values, + "use_cache": kwargs.get("use_cache"), + "attention_mask": attention_mask, + } + ) + return model_inputs + + @staticmethod + def _reorder_cache(past_key_values, beam_idx): + reordered_past = () + for layer_past in past_key_values: + reordered_past += ( + tuple( + past_state.index_select(0, beam_idx.to(past_state.device)) + for past_state in layer_past + ), + ) + return reordered_past + + +@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, + ) diff --git a/optimum/habana/transformers/models/deepseek_v3/tokenization_deepseek_v3.py b/optimum/habana/transformers/models/deepseek_v3/tokenization_deepseek_v3.py new file mode 100644 index 0000000000..5adefbb94a --- /dev/null +++ b/optimum/habana/transformers/models/deepseek_v3/tokenization_deepseek_v3.py @@ -0,0 +1,16 @@ +""" +Copied from https://cdn.deepseek.com/api-docs/deepseek_v3_tokenizer.zip +""" + +# pip3 install transformers +# python3 deepseek_tokenizer.py +import transformers + +chat_tokenizer_dir = "./" + +tokenizer = transformers.AutoTokenizer.from_pretrained( + chat_tokenizer_dir, trust_remote_code=True + ) + +result = tokenizer.encode("Hello!") +print(result) \ No newline at end of file diff --git a/tests/test_text_generation_example.py b/tests/test_text_generation_example.py index 0c513d8bb1..c778b065f4 100644 --- a/tests/test_text_generation_example.py +++ b/tests/test_text_generation_example.py @@ -60,6 +60,43 @@ ("THUDM/chatglm2-6b", 1, True, False), ("THUDM/chatglm3-6b", 1, True, False), ("Qwen/Qwen2.5-7B", 4, False, False), + ("bigscience/bloomz-7b1", 1, False, 130.0472971205316, False), + ("gpt2-xl", 1, False, 281.8734689674413, False), + ("EleutherAI/gpt-j-6b", 1, False, 160.5823842101192, False), + ("EleutherAI/gpt-neox-20b", 1, False, 50.67672679310354, False), + ("meta-llama/Llama-2-7b-hf", 1, True, 141.25776956002076, True), + ("tiiuae/falcon-40b", 1, True, 25.202450111088346, False), + ("bigcode/starcoder", 256, True, 6846.575763562658, True), + ("Salesforce/codegen2-1B", 1, False, 446.4029486883532, False), + ("mosaicml/mpt-30b", 1, False, 36.06464336116623, False), + ("mistralai/Mistral-7B-v0.1", 1, True, 130.2172236767782, True), + ("mistralai/Mixtral-8x7B-v0.1", 1, False, 23.7931001677926, True), + ("microsoft/phi-2", 1, False, 224.72307766211117, False), + ("meta-llama/Meta-Llama-3-8B", 1, True, 129, False), + ("meta-llama/Llama-2-7b-hf", 512, True, 12808, False), + ("meta-llama/Llama-2-7b-hf", 512, False, 8711, False), # in some cases like TGI, reuse_cache isn't used + ("stabilityai/stablelm-2-12b", 1, False, 74.8904496532218, False), + ("codellama/CodeLlama-34b-hf", 1, True, 32.644, False), + ("bigcode/starcoder2-3b", 1, False, 261.07213776344133, True), + ("adept/persimmon-8b-base", 4, False, 366.73968820698406, False), + # ("Qwen/Qwen1.5-7B", 4, False, 490.8621617893209, False), + ("google/gemma-7b", 1, False, 109.70751574382221, True), + ("google/gemma-2-9b", 1, False, 92.302359446567, True), + ("google/gemma-2-27b", 1, False, 36.578709544111, True), + ("state-spaces/mamba-130m-hf", 1536, False, 5385.511100161605, False), + # ("Deci/DeciLM-7B", 1, False, 115, False), + ("Qwen/Qwen2-7B", 256, False, 8870.945160540245, True), + ("Qwen/Qwen1.5-MoE-A2.7B", 1, True, 44.25834541569395, False), + # ("EleutherAI/gpt-neo-2.7B", 1, False, 257.2476416844122, False), + # ("facebook/xglm-1.7B", 1, False, 357.46365062825083, False), + # ("CohereForAI/c4ai-command-r-v01", 1, False, 29.50315234651154, False), + ("tiiuae/falcon-mamba-7b", 1, False, 47.1464839567739, False), + ("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), + ("deepseek-ai/DeepSeek-V3", 1, False, 35, False), + ("THUDM/chatglm3-6b", 1, True, 150, False), ], "fp8": [ ("tiiuae/falcon-180B", 4, 950, True, 128, 128), From a8fdecd371f2af75b706a97d0ff558b56e27cb15 Mon Sep 17 00:00:00 2001 From: Shifani Rajabose Date: Thu, 30 Jan 2025 09:22:40 -0500 Subject: [PATCH 02/21] Update __init__.py --- optimum/habana/transformers/models/deepseek_v3/__init__.py | 1 - 1 file changed, 1 deletion(-) diff --git a/optimum/habana/transformers/models/deepseek_v3/__init__.py b/optimum/habana/transformers/models/deepseek_v3/__init__.py index 594c67665c..f76872762e 100644 --- a/optimum/habana/transformers/models/deepseek_v3/__init__.py +++ b/optimum/habana/transformers/models/deepseek_v3/__init__.py @@ -1,3 +1,2 @@ from .configuration_deepseek_v3 import DeepseekV3Config from .modeling_deepseek_v3 import DeepseekV3ForCausalLM -from .tokenization_deepseek_v3 import DeepseekTokenizerFast From 3e7a00e66c276c21d29740450672379bddf71780 Mon Sep 17 00:00:00 2001 From: Shifani Rajabose Date: Thu, 30 Jan 2025 09:28:41 -0500 Subject: [PATCH 03/21] Update __init__.py --- optimum/habana/transformers/models/__init__.py | 1 - 1 file changed, 1 deletion(-) diff --git a/optimum/habana/transformers/models/__init__.py b/optimum/habana/transformers/models/__init__.py index 4e78e0b883..b784c1c895 100644 --- a/optimum/habana/transformers/models/__init__.py +++ b/optimum/habana/transformers/models/__init__.py @@ -72,7 +72,6 @@ DeepseekV2ForCausalLM, ) from .deepseek_v3 import ( - DeepseekTokenizerFast, DeepseekV3Config, DeepseekV3ForCausalLM, ) From 8629818324b48b73c6ae0fb38bc622ef31e7b8ae Mon Sep 17 00:00:00 2001 From: Shifani Rajabose Date: Thu, 30 Jan 2025 13:45:42 -0500 Subject: [PATCH 04/21] Update modeling_deepseek_v3.py --- .../transformers/models/deepseek_v3/modeling_deepseek_v3.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/optimum/habana/transformers/models/deepseek_v3/modeling_deepseek_v3.py b/optimum/habana/transformers/models/deepseek_v3/modeling_deepseek_v3.py index 1192a0063d..dc50dbb8bf 100644 --- a/optimum/habana/transformers/models/deepseek_v3/modeling_deepseek_v3.py +++ b/optimum/habana/transformers/models/deepseek_v3/modeling_deepseek_v3.py @@ -54,7 +54,7 @@ replace_return_docstrings, ) from transformers.utils.import_utils import is_torch_fx_available -from .configuration_deepseek import DeepseekV3Config +from .configuration_deepseek_v3 import DeepseekV3Config import torch.distributed as dist import numpy as np From a161a36834713c2121d20a24a67cbf6cdef5a9be Mon Sep 17 00:00:00 2001 From: "Kavulya, Soila P" Date: Thu, 30 Jan 2025 14:52:09 -0800 Subject: [PATCH 05/21] Support optimized KV cache, static MOE and expert parallelism --- optimum/habana/transformers/modeling_utils.py | 5 +- .../deepseek_v3/configuration_deepseek_v3.py | 49 +- .../deepseek_v3/modeling_deepseek_v3.py | 826 ++++-------------- .../deepseek_v3/tokenization_deepseek_v3.py | 16 - 4 files changed, 213 insertions(+), 683 deletions(-) delete mode 100644 optimum/habana/transformers/models/deepseek_v3/tokenization_deepseek_v3.py diff --git a/optimum/habana/transformers/modeling_utils.py b/optimum/habana/transformers/modeling_utils.py index aa3fc42fb6..e0db69d895 100644 --- a/optimum/habana/transformers/modeling_utils.py +++ b/optimum/habana/transformers/modeling_utils.py @@ -55,7 +55,7 @@ DeepseekV2Config, DeepseekV2ForCausalLM, DeepseekV3Config, - DeepseekV3ForCausalLM, + DeepseekV3ForCausalLM, Gaudi2Idefics2ImageProcessor, GaudiBloomForCausalLM, GaudiBloomMLP, @@ -745,13 +745,12 @@ 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) - transformers.AutoTokenizer.register(DeepseekV3Config, fast_tokenizer_class=DeepseekTokenizerFast) # Optimization for cohere on Gaudi transformers.models.cohere.modeling_cohere.CohereDecoderLayer = GaudiCohereDecoderLayer diff --git a/optimum/habana/transformers/models/deepseek_v3/configuration_deepseek_v3.py b/optimum/habana/transformers/models/deepseek_v3/configuration_deepseek_v3.py index f2a42479fd..af5eb623b1 100644 --- a/optimum/habana/transformers/models/deepseek_v3/configuration_deepseek_v3.py +++ b/optimum/habana/transformers/models/deepseek_v3/configuration_deepseek_v3.py @@ -1,9 +1,16 @@ +""" +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 @@ -119,30 +126,30 @@ def __init__( vocab_size=129280, hidden_size=7168, intermediate_size=18432, - moe_intermediate_size = 2048, + 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, + 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, @@ -207,4 +214,4 @@ def __init__( eos_token_id=eos_token_id, tie_word_embeddings=tie_word_embeddings, **kwargs, - ) \ No newline at end of file + ) diff --git a/optimum/habana/transformers/models/deepseek_v3/modeling_deepseek_v3.py b/optimum/habana/transformers/models/deepseek_v3/modeling_deepseek_v3.py index dc50dbb8bf..3c3990aa7a 100644 --- a/optimum/habana/transformers/models/deepseek_v3/modeling_deepseek_v3.py +++ b/optimum/habana/transformers/models/deepseek_v3/modeling_deepseek_v3.py @@ -17,24 +17,31 @@ # 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 DeepSeek model.""" +""" +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 static experts instead of dynamic +- Enable expert parallelism + +TODO: +- Add support for optimized Gaudi ops like fused rope and rms +""" + 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 BCEWithLogitsLoss, CrossEntropyLoss, MSELoss - from transformers.activations import ACT2FN -from transformers.cache_utils import Cache, DynamicCache -from transformers.modeling_attn_mask_utils import ( - AttentionMaskConverter, - _prepare_4d_attention_mask, - _prepare_4d_causal_attention_mask, -) +from transformers.cache_utils import Cache from transformers.modeling_outputs import ( BaseModelOutputWithPast, CausalLMOutputWithPast, @@ -43,33 +50,17 @@ from transformers.modeling_utils import PreTrainedModel from transformers.pytorch_utils import ( ALL_LAYERNORM_LAYERS, - is_torch_greater_or_equal_than_1_13, ) from transformers.utils import ( add_start_docstrings, add_start_docstrings_to_model_forward, - is_flash_attn_2_available, - is_flash_attn_greater_or_equal_2_10, logging, replace_return_docstrings, ) -from transformers.utils.import_utils import is_torch_fx_available -from .configuration_deepseek_v3 import DeepseekV3Config -import torch.distributed as dist -import numpy as np - -if is_flash_attn_2_available(): - from flash_attn import flash_attn_func, flash_attn_varlen_func - from flash_attn.bert_padding import index_first_axis, pad_input, unpad_input # noqa - -# This makes `_prepare_4d_causal_attention_mask` a leaf function in the FX graph. -# It means that the function will not be traced through and simply appear as a node in the graph. -if is_torch_fx_available(): - if not is_torch_greater_or_equal_than_1_13: - import torch.fx - - _prepare_4d_causal_attention_mask = torch.fx.wrap(_prepare_4d_causal_attention_mask) +from ....distributed.tensorparallel import _all_reduce +from ...modeling_attn_mask_utils import _gaudi_prepare_4d_causal_attention_mask +from .configuration_deepseek_v3 import DeepseekV3Config logger = logging.get_logger(__name__) @@ -81,9 +72,7 @@ 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) - ) + cu_seqlens = F.pad(torch.cumsum(seqlens_in_batch, dim=0, dtype=torch.torch.int32), (1, 0)) return ( indices, cu_seqlens, @@ -118,24 +107,20 @@ def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None): 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) - ) + 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=max_position_embeddings, + seq_len=self.max_seq_len_cached, device=self.inv_freq.device, dtype=torch.get_default_dtype(), ) - self.max_seq_len_cached = None 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 = 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 @@ -145,7 +130,7 @@ def _set_cos_sin_cache(self, seq_len, device, dtype): def forward(self, x, seq_len=None): # x: [bs, num_attention_heads, seq_len, head_size] - if self.max_seq_len_cached is None or seq_len > self.max_seq_len_cached: + if seq_len > self.max_seq_len_cached: self._set_cos_sin_cache(seq_len=seq_len, device=x.device, dtype=x.dtype) return ( @@ -171,9 +156,7 @@ def __init__( 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 = 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) @@ -203,17 +186,12 @@ def _set_cos_sin_cache(self, seq_len, device, dtype): if seq_len > self.max_position_embeddings: base = self.base * ( - (self.scaling_factor * seq_len / self.max_position_embeddings) - - (self.scaling_factor - 1) + (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) - ) + 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 - ) + 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 @@ -223,24 +201,14 @@ def _set_cos_sin_cache(self, seq_len, device, dtype): # 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) - ) +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) - ) +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 @@ -260,7 +228,6 @@ def yarn_linear_ramp_mask(min, max, dim): class DeepseekV3YarnRotaryEmbedding(DeepseekV3RotaryEmbedding): - def __init__( self, dim, @@ -286,14 +253,9 @@ 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_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) + self.scaling_factor * self.base ** (torch.arange(0, dim, 2, dtype=torch.float32, device=device) / dim) ) low, high = yarn_find_correction_range( @@ -303,9 +265,7 @@ def _set_cos_sin_cache(self, seq_len, device, dtype): 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_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) @@ -319,12 +279,8 @@ def _set_cos_sin_cache(self, seq_len, device, dtype): ) 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 - ) + self.register_buffer("cos_cached", (emb.cos() * _mscale).to(dtype), persistent=False) + self.register_buffer("sin_cached", (emb.sin() * _mscale).to(dtype), persistent=False) # Copied from transformers.models.llama.modeling_llama.rotate_half @@ -376,9 +332,7 @@ 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.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) @@ -406,13 +360,9 @@ def __init__(self, config): # 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.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.e_score_correction_bias = nn.Parameter(torch.empty((self.n_routed_experts))) self.reset_parameters() def reset_parameters(self) -> None: @@ -424,55 +374,42 @@ 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 - ) + 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}" - ) + 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) + 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_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 - ) + .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_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}" - ) + 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 + 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. @@ -491,11 +428,8 @@ def __init__(self, config): 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 + 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) @@ -507,25 +441,22 @@ def __init__(self, config): self.ep_rank = 0 self.experts = nn.ModuleList( [ - DeepseekV3MLP( - config, intermediate_size=config.moe_intermediate_size - ) + 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 - ) + self.shared_experts = DeepseekV3MLP(config=config, intermediate_size=intermediate_size) 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]) - flat_topk_idx = topk_idx.view(-1) + # Fix style error by commenting out unused flat_topk_idx variable in original code + # flat_topk_idx = topk_idx.view(-1) if not self.training: y = self.moe_infer(hidden_states, topk_idx, topk_weight).view(*orig_shape) if self.config.n_shared_experts is not None: @@ -534,79 +465,31 @@ def forward(self, hidden_states): @torch.no_grad() def moe_infer(self, x, topk_ids, topk_weight): - cnts = topk_ids.new_zeros((topk_ids.shape[0], len(self.experts))) - cnts.scatter_(1, topk_ids, 1) - tokens_per_expert = cnts.sum(dim=0) - idxs = topk_ids.view(-1).argsort() - sorted_tokens = x[idxs // topk_ids.shape[1]] - sorted_tokens_shape = sorted_tokens.shape - if self.ep_size > 1: - tokens_per_ep_rank = tokens_per_expert.view(self.ep_size, -1).sum(dim=1) - tokens_per_expert_group = tokens_per_expert.new_empty( - tokens_per_expert.shape[0] - ) - dist.all_to_all_single(tokens_per_expert_group, tokens_per_expert) - output_splits = ( - tokens_per_expert_group.view(self.ep_size, -1) - .sum(1) - .cpu() - .numpy() - .tolist() - ) - gathered_tokens = sorted_tokens.new_empty( - tokens_per_expert_group.sum(dim=0).cpu().item(), sorted_tokens.shape[1] - ) - input_split_sizes = tokens_per_ep_rank.cpu().numpy().tolist() - dist.all_to_all( - list(gathered_tokens.split(output_splits)), - list(sorted_tokens.split(input_split_sizes)), - ) - tokens_per_expert_post_gather = tokens_per_expert_group.view( - self.ep_size, self.experts_per_rank - ).sum(dim=0) - gatherd_idxs = np.zeros(shape=(gathered_tokens.shape[0],), dtype=np.int32) - s = 0 - for i, k in enumerate(tokens_per_expert_group.cpu().numpy()): - gatherd_idxs[s : s + k] = i % self.experts_per_rank - s += k - gatherd_idxs = gatherd_idxs.argsort() - sorted_tokens = gathered_tokens[gatherd_idxs] - tokens_per_expert = tokens_per_expert_post_gather - tokens_per_expert = tokens_per_expert.cpu().numpy() - - outputs = [] - start_idx = 0 - for i, num_tokens in enumerate(tokens_per_expert): - end_idx = start_idx + num_tokens - if num_tokens == 0: - continue - expert = self.experts[i + self.ep_rank * self.experts_per_rank] - tokens_for_this_expert = sorted_tokens[start_idx:end_idx] - expert_out = expert(tokens_for_this_expert) - outputs.append(expert_out) - start_idx = end_idx - - outs = torch.cat(outputs, dim=0) if len(outputs) else sorted_tokens.new_empty(0) + """ + Rewrite DeepseekV3MoE.moe_infer: https://huggingface.co/deepseek-ai/DeepSeek-R1/resolve/main/modeling_deepseek.py + """ + 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: - new_x = torch.empty_like(outs) - new_x[gatherd_idxs] = outs - gathered_tokens = new_x.new_empty(*sorted_tokens_shape) - dist.all_to_all( - list(gathered_tokens.split(input_split_sizes)), - list(new_x.split(output_splits)), - ) - outs = gathered_tokens - - new_x = torch.empty_like(outs) - new_x[idxs] = outs - final_out = ( - new_x.view(*topk_ids.shape, -1) - .type(topk_weight.dtype) - .mul_(topk_weight.unsqueeze(dim=-1)) - .sum(dim=1) - .type(new_x.dtype) - ) - return final_out + out = _all_reduce(out) + + return out # Copied from transformers.models.llama.modeling_llama.repeat_kv @@ -618,9 +501,7 @@ def repeat_kv(hidden_states: torch.Tensor, n_rep: int) -> torch.Tensor: 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 - ) + 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) @@ -655,17 +536,11 @@ def __init__(self, config: DeepseekV3Config, layer_idx: Optional[int] = None): 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 - ) + 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_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.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, @@ -675,8 +550,7 @@ def __init__(self, config: DeepseekV3Config, layer_idx: Optional[int] = None): 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), + self.num_heads * (self.q_head_dim - self.qk_rope_head_dim + self.v_head_dim), bias=False, ) @@ -742,22 +616,25 @@ def _init_rope(self): 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() - ) + 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[Cache] = 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 DeepseekV3Attention.forward: https://huggingface.co/deepseek-ai/DeepSeek-R1/resolve/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.`" @@ -769,14 +646,10 @@ def forward( 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 - ) + 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 - ) + 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)) @@ -784,9 +657,7 @@ def forward( .transpose(1, 2) ) - k_nope, value_states = torch.split( - kv, [self.qk_nope_head_dim, self.v_head_dim], dim=-1 - ) + 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: @@ -795,7 +666,10 @@ def forward( "for auto-regressive decoding with k/v caching, please make sure to initialize the attention class " "with a layer index." ) - kv_seq_len += past_key_value.get_usable_length(kv_seq_len, self.layer_idx) + 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) @@ -808,14 +682,21 @@ def forward( 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 - ) + 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 - ) + 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( @@ -831,12 +712,8 @@ def forward( 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_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): @@ -857,297 +734,12 @@ def forward( return attn_output, attn_weights, past_key_value -# Copied from transformers.models.llama.modeling_llama.LlamaFlashAttention2 with Llama->DeepseekV3 -class DeepseekV3FlashAttention2(DeepseekV3Attention): - """ - DeepseekV3 flash attention module. This module inherits from `DeepseekV3Attention` as the weights of the module stays - untouched. The only required change would be on the forward pass where it needs to correctly call the public API of - flash attention and deal with padding tokens in case the input contains any of them. - """ - - def __init__(self, *args, **kwargs): - super().__init__(*args, **kwargs) - - # TODO: Should be removed once Flash Attention for RoCm is bumped to 2.1. - # flash_attn<2.1 generates top-left aligned causal mask, while what is needed here is bottom-right alignement, that was made default for flash_attn>=2.1. This attribute is used to handle this difference. Reference: https://github.com/Dao-AILab/flash-attention/releases/tag/v2.1.0. - # Beware that with flash_attn<2.1, using q_seqlen != k_seqlen (except for the case q_seqlen == 1) produces a wrong mask (top-left). - self._flash_attn_uses_top_left_mask = not is_flash_attn_greater_or_equal_2_10() - - def forward( - self, - hidden_states: torch.Tensor, - attention_mask: Optional[torch.LongTensor] = None, - position_ids: Optional[torch.LongTensor] = None, - past_key_value: Optional[Cache] = None, - output_attentions: bool = False, - use_cache: bool = False, - **kwargs, - ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]: - # DeepseekV3FlashAttention2 attention does not support output_attentions - 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.`" - ) - - # overwrite attention_mask with padding_mask - attention_mask = kwargs.pop("padding_mask") - - output_attentions = False - - 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 - ) - - # Flash attention requires the input to have the shape - # batch_size x seq_length x head_dim x hidden_dim - # therefore we just need to keep the original shape - 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] - - kv_seq_len = value_states.shape[-2] - if past_key_value is not None: - kv_seq_len += past_key_value.get_usable_length(kv_seq_len, self.layer_idx) - - 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 self.q_head_dim != self.v_head_dim: - value_states = F.pad(value_states, [0, self.q_head_dim - self.v_head_dim]) - - 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 - ) - - # TODO: These transpose are quite inefficient but Flash Attention requires the layout [batch_size, sequence_length, num_heads, head_dim]. We would need to refactor the KV cache - # to be able to avoid many of these transpose/reshape/view. - query_states = query_states.transpose(1, 2) - key_states = key_states.transpose(1, 2) - value_states = value_states.transpose(1, 2) - - dropout_rate = self.attention_dropout if self.training else 0.0 - - # In PEFT, usually we cast the layer norms in float32 for training stability reasons - # therefore the input hidden states gets silently casted in float32. Hence, we need - # cast them back in the correct dtype just to be sure everything works as expected. - # This might slowdown training & inference so it is recommended to not cast the LayerNorms - # in fp32. (DeepseekV3RMSNorm handles it correctly) - - input_dtype = query_states.dtype - if input_dtype == torch.float32: - # Handle the case where the model is quantized - if hasattr(self.config, "_pre_quantization_dtype"): - target_dtype = self.config._pre_quantization_dtype - elif torch.is_autocast_enabled(): - target_dtype = torch.get_autocast_gpu_dtype() - else: - target_dtype = ( - self.q_proj.weight.dtype - if self.q_lora_rank is None - else self.q_a_proj.weight.dtype - ) - - logger.warning_once( - f"The input hidden states seems to be silently casted in float32, this might be related to" - f" the fact you have upcasted embedding or layer norm layers in float32. We will cast back the input in" - f" {target_dtype}." - ) - - query_states = query_states.to(target_dtype) - key_states = key_states.to(target_dtype) - value_states = value_states.to(target_dtype) - - attn_output = self._flash_attention_forward( - query_states, - key_states, - value_states, - attention_mask, - q_len, - dropout=dropout_rate, - softmax_scale=self.softmax_scale, - ) - if self.q_head_dim != self.v_head_dim: - attn_output = attn_output[:, :, :, : self.v_head_dim] - - attn_output = attn_output.reshape( - bsz, q_len, self.num_heads * self.v_head_dim - ).contiguous() - attn_output = self.o_proj(attn_output) - - if not output_attentions: - attn_weights = None - - return attn_output, attn_weights, past_key_value - - def _flash_attention_forward( - self, - query_states, - key_states, - value_states, - attention_mask, - query_length, - dropout=0.0, - softmax_scale=None, - ): - """ - Calls the forward method of Flash Attention - if the input hidden states contain at least one padding token - first unpad the input, then computes the attention scores and pad the final attention scores. - - Args: - query_states (`torch.Tensor`): - Input query states to be passed to Flash Attention API - key_states (`torch.Tensor`): - Input key states to be passed to Flash Attention API - value_states (`torch.Tensor`): - Input value states to be passed to Flash Attention API - attention_mask (`torch.Tensor`): - The padding mask - corresponds to a tensor of size `(batch_size, seq_len)` where 0 stands for the - position of padding tokens and 1 for the position of non-padding tokens. - dropout (`int`, *optional*): - Attention dropout - softmax_scale (`float`, *optional*): - The scaling of QK^T before applying softmax. Default to 1 / sqrt(head_dim) - """ - if not self._flash_attn_uses_top_left_mask: - causal = self.is_causal - else: - # TODO: Remove the `query_length != 1` check once Flash Attention for RoCm is bumped to 2.1. For details, please see the comment in DeepseekV3FlashAttention2 __init__. - causal = self.is_causal and query_length != 1 - - # Contains at least one padding token in the sequence - if attention_mask is not None: - batch_size = query_states.shape[0] - ( - query_states, - key_states, - value_states, - indices_q, - cu_seq_lens, - max_seq_lens, - ) = self._upad_input( - query_states, key_states, value_states, attention_mask, query_length - ) - - cu_seqlens_q, cu_seqlens_k = cu_seq_lens - max_seqlen_in_batch_q, max_seqlen_in_batch_k = max_seq_lens - - attn_output_unpad = flash_attn_varlen_func( - query_states, - key_states, - value_states, - cu_seqlens_q=cu_seqlens_q, - cu_seqlens_k=cu_seqlens_k, - max_seqlen_q=max_seqlen_in_batch_q, - max_seqlen_k=max_seqlen_in_batch_k, - dropout_p=dropout, - softmax_scale=softmax_scale, - causal=causal, - ) - - attn_output = pad_input( - attn_output_unpad, indices_q, batch_size, query_length - ) - else: - attn_output = flash_attn_func( - query_states, - key_states, - value_states, - dropout, - softmax_scale=softmax_scale, - causal=causal, - ) - - return attn_output - - def _upad_input( - self, query_layer, key_layer, value_layer, attention_mask, query_length - ): - indices_k, cu_seqlens_k, max_seqlen_in_batch_k = _get_unpad_data(attention_mask) - batch_size, kv_seq_len, num_key_value_heads, head_dim = key_layer.shape - - key_layer = index_first_axis( - key_layer.reshape(batch_size * kv_seq_len, num_key_value_heads, head_dim), - indices_k, - ) - value_layer = index_first_axis( - value_layer.reshape(batch_size * kv_seq_len, num_key_value_heads, head_dim), - indices_k, - ) - if query_length == kv_seq_len: - query_layer = index_first_axis( - query_layer.reshape(batch_size * kv_seq_len, self.num_heads, head_dim), - indices_k, - ) - cu_seqlens_q = cu_seqlens_k - max_seqlen_in_batch_q = max_seqlen_in_batch_k - indices_q = indices_k - elif query_length == 1: - max_seqlen_in_batch_q = 1 - cu_seqlens_q = torch.arange( - batch_size + 1, dtype=torch.int32, device=query_layer.device - ) # There is a memcpy here, that is very bad. - indices_q = cu_seqlens_q[:-1] - query_layer = query_layer.squeeze(1) - else: - # The -q_len: slice assumes left padding. - attention_mask = attention_mask[:, -query_length:] - query_layer, indices_q, cu_seqlens_q, max_seqlen_in_batch_q = unpad_input( - query_layer, attention_mask - ) - - return ( - query_layer, - key_layer, - value_layer, - indices_q, - (cu_seqlens_q, cu_seqlens_k), - (max_seqlen_in_batch_q, max_seqlen_in_batch_k), - ) - - -ATTENTION_CLASSES = { - "eager": DeepseekV3Attention, - "flash_attention_2": DeepseekV3FlashAttention2, -} - - class DeepseekV3DecoderLayer(nn.Module): def __init__(self, config: DeepseekV3Config, layer_idx: int): super().__init__() self.hidden_size = config.hidden_size - self.self_attn = ATTENTION_CLASSES[config._attn_implementation]( - config=config, layer_idx=layer_idx - ) + self.self_attn = DeepseekV3Attention(config=config, layer_idx=layer_idx) self.mlp = ( DeepseekV3MoE(config) @@ -1158,12 +750,8 @@ def __init__(self, config: DeepseekV3Config, layer_idx: int): ) 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 - ) + 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 forward( self, @@ -1173,10 +761,14 @@ def forward( 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]] - ]: + ) -> Tuple[torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]]]: + """ + Copied from DeepseekV3DecoderLayer.forward: https://huggingface.co/deepseek-ai/DeepSeek-R1/resolve/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)` @@ -1207,6 +799,7 @@ def forward( past_key_value=past_key_value, output_attentions=output_attentions, use_cache=use_cache, + token_idx=token_idx, **kwargs, ) hidden_states = residual + hidden_states @@ -1357,14 +950,9 @@ def __init__(self, config: DeepseekV3Config): 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.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) - ] + [DeepseekV3DecoderLayer(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 = DeepseekV3RMSNorm(config.hidden_size, eps=config.rms_norm_eps) @@ -1391,28 +979,19 @@ def forward( 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_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 + 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 - ) + 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" - ) + 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: @@ -1421,11 +1000,8 @@ def forward( raise ValueError("You have to specify either input_ids or inputs_embeds") past_key_values_length = 0 - if use_cache: - use_legacy_cache = not isinstance(past_key_values, Cache) - if use_legacy_cache: - past_key_values = DynamicCache.from_legacy_cache(past_key_values) - past_key_values_length = past_key_values.get_usable_length(seq_length) + 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 @@ -1440,16 +1016,10 @@ def forward( if inputs_embeds is None: inputs_embeds = self.embed_tokens(input_ids) - if self._use_flash_attention_2: - # 2d mask is passed through the layers - attention_mask = ( - attention_mask - if (attention_mask is not None and 0 in attention_mask) - else None - ) - else: - # 4d mask is passed through the layers - attention_mask = _prepare_4d_causal_attention_mask( + # 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, @@ -1462,25 +1032,28 @@ def forward( # decoder layers all_hidden_states = () if output_hidden_states else None all_self_attns = () if output_attentions else None - next_decoder_cache = None + next_decoder_cache = () if use_cache else None - for decoder_layer in self.layers: + 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 + layer_outputs = decoder_layer( hidden_states, attention_mask=attention_mask, position_ids=position_ids, - past_key_value=past_key_values, + 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] + next_decoder_cache += (layer_outputs[2 if output_attentions else 1],) if output_attentions: all_self_attns += (layer_outputs[1],) @@ -1491,19 +1064,9 @@ def forward( if output_hidden_states: all_hidden_states += (hidden_states,) - next_cache = None - if use_cache: - next_cache = ( - next_decoder_cache.to_legacy_cache() - if use_legacy_cache - else next_decoder_cache - ) + 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 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, @@ -1543,9 +1106,7 @@ def get_decoder(self): return self.model @add_start_docstrings_to_model_forward(DeepseekV3_INPUTS_DOCSTRING) - @replace_return_docstrings( - output_type=CausalLMOutputWithPast, config_class=_CONFIG_FOR_DOC - ) + @replace_return_docstrings(output_type=CausalLMOutputWithPast, config_class=_CONFIG_FOR_DOC) def forward( self, input_ids: torch.LongTensor = None, @@ -1558,6 +1119,7 @@ def forward( 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: @@ -1584,19 +1146,11 @@ def forward( >>> 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_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 + 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( @@ -1609,6 +1163,7 @@ def forward( output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, + token_idx=token_idx, ) hidden_states = outputs[0] @@ -1648,23 +1203,26 @@ def prepare_inputs_for_generation( 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 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() + if token_idx is not None: + input_ids = torch.index_select(input_ids, 1, token_idx - 1) else: - cache_length = past_length = past_key_values[0][0].shape[2] - max_cache_length = None + 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] - ): + 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. @@ -1686,13 +1244,16 @@ def prepare_inputs_for_generation( position_ids = attention_mask.long().cumsum(-1) - 1 position_ids.masked_fill_(attention_mask == 0, 1) if past_key_values: - position_ids = position_ids[:, -input_ids.shape[1] :] + 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} + model_inputs = {"input_ids": input_ids.contiguous()} model_inputs.update( { @@ -1700,22 +1261,11 @@ def prepare_inputs_for_generation( "past_key_values": past_key_values, "use_cache": kwargs.get("use_cache"), "attention_mask": attention_mask, + "token_idx": token_idx, } ) return model_inputs - @staticmethod - def _reorder_cache(past_key_values, beam_idx): - reordered_past = () - for layer_past in past_key_values: - reordered_past += ( - tuple( - past_state.index_select(0, beam_idx.to(past_state.device)) - for past_state in layer_past - ), - ) - return reordered_past - @add_start_docstrings( """ @@ -1768,9 +1318,7 @@ def forward( 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 - ) + return_dict = return_dict if return_dict is not None else self.config.use_return_dict transformer_outputs = self.model( input_ids, @@ -1792,22 +1340,18 @@ def forward( 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." - ) + 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) + 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 - ] + pooled_logits = logits[torch.arange(batch_size, device=logits.device), sequence_lengths] loss = None if labels is not None: @@ -1815,9 +1359,7 @@ def forward( 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 - ): + 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" @@ -1830,9 +1372,7 @@ def forward( 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) - ) + 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) diff --git a/optimum/habana/transformers/models/deepseek_v3/tokenization_deepseek_v3.py b/optimum/habana/transformers/models/deepseek_v3/tokenization_deepseek_v3.py deleted file mode 100644 index 5adefbb94a..0000000000 --- a/optimum/habana/transformers/models/deepseek_v3/tokenization_deepseek_v3.py +++ /dev/null @@ -1,16 +0,0 @@ -""" -Copied from https://cdn.deepseek.com/api-docs/deepseek_v3_tokenizer.zip -""" - -# pip3 install transformers -# python3 deepseek_tokenizer.py -import transformers - -chat_tokenizer_dir = "./" - -tokenizer = transformers.AutoTokenizer.from_pretrained( - chat_tokenizer_dir, trust_remote_code=True - ) - -result = tokenizer.encode("Hello!") -print(result) \ No newline at end of file From da80fba3daae12aac9d440454a4f4ac22eeeb877 Mon Sep 17 00:00:00 2001 From: pallavi jaini Date: Tue, 4 Feb 2025 22:35:36 +0000 Subject: [PATCH 06/21] Commented out attention_mask assertion for the mmlu tests --- .../transformers/models/deepseek_v3/modeling_deepseek_v3.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/optimum/habana/transformers/models/deepseek_v3/modeling_deepseek_v3.py b/optimum/habana/transformers/models/deepseek_v3/modeling_deepseek_v3.py index 3c3990aa7a..151cece150 100644 --- a/optimum/habana/transformers/models/deepseek_v3/modeling_deepseek_v3.py +++ b/optimum/habana/transformers/models/deepseek_v3/modeling_deepseek_v3.py @@ -703,7 +703,8 @@ def forward( 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 + # commenting the below line and enable it after attention_mask optimizations are in place for deepseek_v3 + # 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( From 34f8ff7bc52ef72a15c3aa2afad61f7611f08560 Mon Sep 17 00:00:00 2001 From: Shifani Date: Wed, 5 Feb 2025 20:59:32 +0000 Subject: [PATCH 07/21] Support optimized fusedDSPA, RoPE and RMS --- .../deepseek_v3/modeling_deepseek_v3.py | 674 +++++++++++++++--- 1 file changed, 568 insertions(+), 106 deletions(-) diff --git a/optimum/habana/transformers/models/deepseek_v3/modeling_deepseek_v3.py b/optimum/habana/transformers/models/deepseek_v3/modeling_deepseek_v3.py index 151cece150..412b921bcf 100644 --- a/optimum/habana/transformers/models/deepseek_v3/modeling_deepseek_v3.py +++ b/optimum/habana/transformers/models/deepseek_v3/modeling_deepseek_v3.py @@ -34,6 +34,7 @@ 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 @@ -67,7 +68,27 @@ _CONFIG_FOR_DOC = "DeepseekV3Config" +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() @@ -90,11 +111,23 @@ def __init__(self, hidden_size, eps=1e-6): 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) + if hidden_states.device.type == "hpu" and FusedRMSNorm: + # 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) @@ -130,7 +163,7 @@ def _set_cos_sin_cache(self, seq_len, device, dtype): def forward(self, x, seq_len=None): # x: [bs, num_attention_heads, seq_len, head_size] - if seq_len > self.max_seq_len_cached: + 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 ( @@ -282,6 +315,13 @@ def _set_cos_sin_cache(self, seq_len, device, dtype): 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): + if q.device.type == "hpu" and FusedRoPE: + return FusedRoPE.apply( + q, cos.unsqueeze(0).unsqueeze(0), sin.unsqueeze(0).unsqueeze(0), position_ids + ), FusedRoPE.apply(k, cos.unsqueeze(0).unsqueeze(0), sin.unsqueeze(0).unsqueeze(0), position_ids) + 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): @@ -292,12 +332,10 @@ def rotate_half(x): # 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): +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. - 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`): @@ -313,18 +351,19 @@ def apply_rotary_pos_emb(q, k, cos, sin, position_ids, unsqueeze_dim=1): 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 + 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): @@ -463,6 +502,7 @@ def forward(self, hidden_states): y = y + self.shared_experts(identity) return y + @torch.no_grad() def moe_infer(self, x, topk_ids, topk_weight): """ @@ -491,21 +531,129 @@ def moe_infer(self, x, topk_ids, topk_weight): return out +class Matmul(torch.nn.Module): + def __init__(self): + super().__init__() + + def forward(self, x, y): + return torch.matmul(x, y) -# Copied from transformers.models.llama.modeling_llama.repeat_kv -def repeat_kv(hidden_states: torch.Tensor, n_rep: int) -> torch.Tensor: + +def gaudi_deepseekv3_repeat_kv( + query_states: torch.Tensor, + key_states: torch.Tensor, + value_states: torch.Tensor, + attention_mask: torch.Tensor, + n_rep: int, +): """ - 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) + 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, 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) + 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) -# Copied from transformers.models.llama.modeling_llama.LlamaAttention with Llama->DeepseekV3 + return query_states, key_states, value_states, attention_mask + + +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) + + +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->DeepseekV2 class DeepseekV3Attention(nn.Module): """Multi-headed attention from 'Attention Is All You Need' paper""" @@ -561,6 +709,13 @@ def __init__(self, config: DeepseekV3Config, layer_idx: Optional[int] = None): ) self._init_rope() + self.num_key_value_groups = self.num_heads // config.num_key_value_heads + 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) @@ -569,6 +724,19 @@ def __init__(self, config: DeepseekV3Config, layer_idx: Optional[int] = None): mscale = yarn_get_mscale(scaling_factor, mscale_all_dim) self.softmax_scale = self.softmax_scale * mscale * mscale + self.norm_factor = self.softmax_scale + 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( @@ -615,107 +783,331 @@ def _init_rope(self): else: raise ValueError(f"Unknown RoPE scaling type {scaling_type}") + 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[Tuple[torch.Tensor]] = 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]]]: """ - Copied from DeepseekV3Attention.forward: https://huggingface.co/deepseek-ai/DeepSeek-R1/resolve/main/modeling_deepseek.py - deltas are: - - add token_idx - - optimize KV cache + 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.`" ) - 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) + 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) + ) - 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." + ) - 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) + + 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 ) - 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) + # optimization + 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: - past_key_value[0].index_add_( - 2, token_idx - 1, key_states - torch.index_select(past_key_value[0], 2, token_idx - 1) + query_states, key_states, value_states, attention_mask = gaudi_deepseekv2_repeat_kv( + query_states, key_states, value_states, attention_mask, self.num_key_value_groups ) - past_key_value[1].index_add_( - 2, token_idx - 1, value_states - torch.index_select(past_key_value[1], 2, token_idx - 1) + + 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 ) - 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_output = self.matmul_av(attn_weights, value_states) + else: + 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))) - attn_weights = torch.matmul(query_states, key_states.transpose(2, 3)) * self.softmax_scale + q = q.view(bsz, q_len, self.num_heads, self.q_head_dim).transpose(1, 2) - 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 the below line and enable it after attention_mask optimizations are in place for deepseek_v3 - # assert attention_mask is not None - if attention_mask is not None: - if attention_mask.size() != (bsz, 1, q_len, kv_seq_len): + 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 mask should be of size {(bsz, 1, q_len, kv_seq_len)}, but is {attention_mask.size()}" + f"Attention weights should be of size {(bsz, self.num_heads, q_len, kv_seq_len)}, but is" + f" {attn_weights.size()}" ) - attn_weights = attn_weights + attention_mask + 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) - # 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) + 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( @@ -754,6 +1146,14 @@ def __init__(self, config: DeepseekV3Config, layer_idx: int): 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, @@ -763,13 +1163,18 @@ def forward( 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]]]: - """ - Copied from DeepseekV3DecoderLayer.forward: https://huggingface.co/deepseek-ai/DeepSeek-R1/resolve/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)` @@ -801,6 +1206,16 @@ def forward( 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 @@ -849,7 +1264,7 @@ class DeepseekV3PreTrainedModel(PreTrainedModel): supports_gradient_checkpointing = True _no_split_modules = ["DeepseekV3DecoderLayer"] _skip_keys_device_placement = "past_key_values" - _supports_flash_attn_2 = True + _supports_flash_attn_2 = False _supports_cache_class = True def _init_weights(self, module): @@ -955,6 +1370,7 @@ def __init__(self, config: DeepseekV3Config): 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) @@ -962,6 +1378,17 @@ def __init__(self, config: DeepseekV3Config): # 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 @@ -980,7 +1407,18 @@ def forward( 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 = ( @@ -1000,6 +1438,7 @@ def forward( 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] @@ -1035,12 +1474,20 @@ def forward( 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, @@ -1050,7 +1497,12 @@ def forward( 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: @@ -1106,6 +1558,16 @@ def set_decoder(self, 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( From 937deeb5f863669f54568c915958da00d6e8fff3 Mon Sep 17 00:00:00 2001 From: pallavi jaini Date: Wed, 5 Feb 2025 23:41:11 +0000 Subject: [PATCH 08/21] Commented out attention_mask assertionn --- .../transformers/models/deepseek_v3/modeling_deepseek_v3.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/optimum/habana/transformers/models/deepseek_v3/modeling_deepseek_v3.py b/optimum/habana/transformers/models/deepseek_v3/modeling_deepseek_v3.py index 412b921bcf..eb497fbd90 100644 --- a/optimum/habana/transformers/models/deepseek_v3/modeling_deepseek_v3.py +++ b/optimum/habana/transformers/models/deepseek_v3/modeling_deepseek_v3.py @@ -1097,7 +1097,8 @@ def forward( 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 + # 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 From e32aaddf617cc428fc6af2d8e086cc909ba8d24f Mon Sep 17 00:00:00 2001 From: "Kavulya, Soila P" Date: Thu, 6 Feb 2025 13:09:40 -0800 Subject: [PATCH 09/21] Change references to deepseekv2 to deepseekv3 --- .../transformers/models/deepseek_v3/modeling_deepseek_v3.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/optimum/habana/transformers/models/deepseek_v3/modeling_deepseek_v3.py b/optimum/habana/transformers/models/deepseek_v3/modeling_deepseek_v3.py index eb497fbd90..120f2f6305 100644 --- a/optimum/habana/transformers/models/deepseek_v3/modeling_deepseek_v3.py +++ b/optimum/habana/transformers/models/deepseek_v3/modeling_deepseek_v3.py @@ -653,7 +653,7 @@ def forward( ) -# Copied from transformers.models.llama.modeling_llama.LlamaAttention with Llama->DeepseekV2 +# 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""" @@ -979,7 +979,7 @@ def forward( ) else: - query_states, key_states, value_states, attention_mask = gaudi_deepseekv2_repeat_kv( + 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 ) From 37a0431a52484d69d2dde8778f11931fa98e9334 Mon Sep 17 00:00:00 2001 From: "Kavulya, Soila P" Date: Thu, 6 Feb 2025 14:15:40 -0800 Subject: [PATCH 10/21] Override load_state_dict to support deepseek-R1 Copied from transformers v4.48.2 for DeepSeek-R1 support. Delete after upgrade transformers v4.45.2 to v4.48 --- optimum/habana/transformers/modeling_utils.py | 92 +++++++++++++++++++ .../deepseek_v3/modeling_deepseek_v3.py | 28 +++--- 2 files changed, 108 insertions(+), 12 deletions(-) diff --git a/optimum/habana/transformers/modeling_utils.py b/optimum/habana/transformers/modeling_utils.py index e0db69d895..56f2ee9dac 100644 --- a/optimum/habana/transformers/modeling_utils.py +++ b/optimum/habana/transformers/modeling_utils.py @@ -12,9 +12,25 @@ # 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 os +from typing import Optional, Union +from zipfile import is_zipfile import accelerate +import torch import transformers +import transformers.modeling_utils import transformers.utils.fx +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 from ..accelerate.utils import extract_model_from_parallel from ..accelerate.utils.modeling import gaudi_check_device_same @@ -291,6 +307,79 @@ ) +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." + ) + + def adapt_transformers_to_gaudi(): """ Replaces some Transformers' methods for equivalent methods optimized @@ -315,6 +404,9 @@ 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 diff --git a/optimum/habana/transformers/models/deepseek_v3/modeling_deepseek_v3.py b/optimum/habana/transformers/models/deepseek_v3/modeling_deepseek_v3.py index 120f2f6305..78b310c15f 100644 --- a/optimum/habana/transformers/models/deepseek_v3/modeling_deepseek_v3.py +++ b/optimum/habana/transformers/models/deepseek_v3/modeling_deepseek_v3.py @@ -89,6 +89,8 @@ 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() @@ -315,6 +317,7 @@ def _set_cos_sin_cache(self, seq_len, device, dtype): 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): if q.device.type == "hpu" and FusedRoPE: return FusedRoPE.apply( @@ -323,6 +326,7 @@ def apply_customized_rope(q, k, cos, sin, position_ids): 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.""" @@ -502,7 +506,6 @@ def forward(self, hidden_states): y = y + self.shared_experts(identity) return y - @torch.no_grad() def moe_infer(self, x, topk_ids, topk_weight): """ @@ -531,6 +534,7 @@ def moe_infer(self, x, topk_ids, topk_weight): return out + class Matmul(torch.nn.Module): def __init__(self): super().__init__() @@ -1155,6 +1159,7 @@ def reorder_kv_cache(self, beam_idx: torch.LongTensor): def update_sincos_cache(self, seq_len): self.self_attn.update_sincos_cache(seq_len) + def forward( self, hidden_states: torch.Tensor, @@ -1439,7 +1444,6 @@ def forward( 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] @@ -1484,11 +1488,11 @@ def forward( 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() + 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, @@ -1499,11 +1503,11 @@ def forward( 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() + 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: From 7b0b9cf6aebbe7546d6dc401355c2e2aa22c490b Mon Sep 17 00:00:00 2001 From: srajabos Date: Mon, 10 Feb 2025 17:34:29 +0000 Subject: [PATCH 11/21] Added dynamic MoE changes --- .../deepseek_v3/modeling_deepseek_v3.py | 100 +++++++++++++----- 1 file changed, 71 insertions(+), 29 deletions(-) diff --git a/optimum/habana/transformers/models/deepseek_v3/modeling_deepseek_v3.py b/optimum/habana/transformers/models/deepseek_v3/modeling_deepseek_v3.py index 78b310c15f..6eaa02c1d8 100644 --- a/optimum/habana/transformers/models/deepseek_v3/modeling_deepseek_v3.py +++ b/optimum/habana/transformers/models/deepseek_v3/modeling_deepseek_v3.py @@ -43,6 +43,7 @@ from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss from transformers.activations import ACT2FN from transformers.cache_utils import Cache +from transformers.integrations.deepspeed import is_deepspeed_available from transformers.modeling_outputs import ( BaseModelOutputWithPast, CausalLMOutputWithPast, @@ -68,6 +69,8 @@ _CONFIG_FOR_DOC = "DeepseekV3Config" +# default expert number per slice for dynamic MoE +SLICE_MAX_EXPERT = 80 try: from habana_frameworks.torch.hpex.kernels import RotaryPosEmbeddingHelperV2 as FusedRoPE @@ -493,46 +496,85 @@ def __init__(self, config): intermediate_size = config.moe_intermediate_size * config.n_shared_experts self.shared_experts = DeepseekV3MLP(config=config, intermediate_size=intermediate_size) + self.expert_slice = math.ceil(config.n_routed_experts / SLICE_MAX_EXPERT) + self.expert_chunk = self.config.n_routed_experts // 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]) - # Fix style error by commenting out unused flat_topk_idx variable in original code - # flat_topk_idx = topk_idx.view(-1) - if not self.training: - 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 + # 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] + 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) - @torch.no_grad() - def moe_infer(self, x, topk_ids, topk_weight): - """ - Rewrite DeepseekV3MoE.moe_infer: https://huggingface.co/deepseek-ai/DeepSeek-R1/resolve/main/modeling_deepseek.py - """ - out = torch.zeros_like(x) + 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 + ) + for idx in range(self.expert_slice): + experts_range = range(self.expert_chunk) + gate_proj_list = [ + self.experts[idx * self.expert_chunk + i].gate_proj.weight.squeeze() for i in experts_range + ] + down_proj_list = [ + self.experts[idx * self.expert_chunk + i].down_proj.weight.squeeze() for i in experts_range + ] + up_proj_list = [ + self.experts[idx * self.expert_chunk + i].up_proj.weight.squeeze() for i in experts_range + ] - seq_len, hidden_dim = x.shape - num_experts = len(self.experts) + 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=(self.expert_chunk * idx), + experts_max=(self.expert_chunk * (idx + 1) - 1), + ) + final_hidden_states = final_hidden_states + hidden_states_slice + htcore.mark_step() - 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) + if is_deepspeed_available(): + from deepspeed import comm as dist - # 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 dist.is_initialized(): + dist.all_reduce(final_hidden_states, op=dist.ReduceOp.SUM) - if self.ep_size > 1: - out = _all_reduce(out) + 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 out + return final_hidden_states class Matmul(torch.nn.Module): From 33e792c23e5b51de30e25e9b73bafcdf26e7ae3f Mon Sep 17 00:00:00 2001 From: "Kavulya, Soila P" Date: Tue, 11 Feb 2025 12:11:18 -0800 Subject: [PATCH 12/21] Fix multicard expert parallelism for deepseekv3 --- .../deepseek_v3/modeling_deepseek_v3.py | 33 +++++++++++-------- 1 file changed, 19 insertions(+), 14 deletions(-) diff --git a/optimum/habana/transformers/models/deepseek_v3/modeling_deepseek_v3.py b/optimum/habana/transformers/models/deepseek_v3/modeling_deepseek_v3.py index 6eaa02c1d8..48e3504d9b 100644 --- a/optimum/habana/transformers/models/deepseek_v3/modeling_deepseek_v3.py +++ b/optimum/habana/transformers/models/deepseek_v3/modeling_deepseek_v3.py @@ -23,11 +23,8 @@ The main differences are: - Use Gaudi Flash Attention - Optimized KV cache with support for static shapes -- Use static experts instead of dynamic +- Use fused Gaudi MoE, RoPE, and RMSNorm operators - Enable expert parallelism - -TODO: -- Add support for optimized Gaudi ops like fused rope and rms """ import math @@ -43,6 +40,7 @@ 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, @@ -496,8 +494,9 @@ def __init__(self, config): intermediate_size = config.moe_intermediate_size * config.n_shared_experts self.shared_experts = DeepseekV3MLP(config=config, intermediate_size=intermediate_size) - self.expert_slice = math.ceil(config.n_routed_experts / SLICE_MAX_EXPERT) - self.expert_chunk = self.config.n_routed_experts // self.expert_slice + self.expert_slice = math.ceil(self.experts_per_rank / SLICE_MAX_EXPERT) + self.expert_chunk = self.experts_per_rank // self.expert_slice + def forward(self, hidden_states): identity = hidden_states orig_shape = hidden_states.shape @@ -530,21 +529,25 @@ def forward(self, hidden_states): ) 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) + # 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 ) for idx in range(self.expert_slice): + expert_offset = self.ep_rank * self.experts_per_rank experts_range = range(self.expert_chunk) gate_proj_list = [ - self.experts[idx * self.expert_chunk + i].gate_proj.weight.squeeze() for i in experts_range + self.experts[idx * self.expert_chunk + i + expert_offset].gate_proj.weight.squeeze() + for i in experts_range ] down_proj_list = [ - self.experts[idx * self.expert_chunk + i].down_proj.weight.squeeze() for i in experts_range + self.experts[idx * self.expert_chunk + i + expert_offset].down_proj.weight.squeeze() + for i in experts_range ] up_proj_list = [ - self.experts[idx * self.expert_chunk + i].up_proj.weight.squeeze() for i in experts_range + self.experts[idx * self.expert_chunk + i + expert_offset].up_proj.weight.squeeze() + for i in experts_range ] hidden_states_slice = torch.ops.hpu.mixture_of_experts( @@ -556,13 +559,15 @@ def forward(self, hidden_states): w3=down_proj_list, permuted_weights=True, activation="silu", - experts_min=(self.expert_chunk * idx), - experts_max=(self.expert_chunk * (idx + 1) - 1), + experts_min=(self.expert_chunk * idx + expert_offset), + experts_max=(self.expert_chunk * (idx + 1) - 1 + expert_offset), ) final_hidden_states = final_hidden_states + hidden_states_slice htcore.mark_step() - if is_deepspeed_available(): + 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(): @@ -1575,7 +1580,7 @@ def forward( ) -class DeepseekV3ForCausalLM(DeepseekV3PreTrainedModel): +class DeepseekV3ForCausalLM(DeepseekV3PreTrainedModel, GenerationMixin): _tied_weights_keys = ["lm_head.weight"] def __init__(self, config): From 71de78c291b3add2764adee5cba32d2f03960ef3 Mon Sep 17 00:00:00 2001 From: "Kavulya, Soila P" Date: Thu, 13 Feb 2025 05:05:37 -0800 Subject: [PATCH 13/21] Delete duplicate tests accidentally copied --- tests/test_text_generation_example.py | 36 --------------------------- 1 file changed, 36 deletions(-) diff --git a/tests/test_text_generation_example.py b/tests/test_text_generation_example.py index c778b065f4..0d34ec6760 100644 --- a/tests/test_text_generation_example.py +++ b/tests/test_text_generation_example.py @@ -60,43 +60,7 @@ ("THUDM/chatglm2-6b", 1, True, False), ("THUDM/chatglm3-6b", 1, True, False), ("Qwen/Qwen2.5-7B", 4, False, False), - ("bigscience/bloomz-7b1", 1, False, 130.0472971205316, False), - ("gpt2-xl", 1, False, 281.8734689674413, False), - ("EleutherAI/gpt-j-6b", 1, False, 160.5823842101192, False), - ("EleutherAI/gpt-neox-20b", 1, False, 50.67672679310354, False), - ("meta-llama/Llama-2-7b-hf", 1, True, 141.25776956002076, True), - ("tiiuae/falcon-40b", 1, True, 25.202450111088346, False), - ("bigcode/starcoder", 256, True, 6846.575763562658, True), - ("Salesforce/codegen2-1B", 1, False, 446.4029486883532, False), - ("mosaicml/mpt-30b", 1, False, 36.06464336116623, False), - ("mistralai/Mistral-7B-v0.1", 1, True, 130.2172236767782, True), - ("mistralai/Mixtral-8x7B-v0.1", 1, False, 23.7931001677926, True), - ("microsoft/phi-2", 1, False, 224.72307766211117, False), - ("meta-llama/Meta-Llama-3-8B", 1, True, 129, False), - ("meta-llama/Llama-2-7b-hf", 512, True, 12808, False), - ("meta-llama/Llama-2-7b-hf", 512, False, 8711, False), # in some cases like TGI, reuse_cache isn't used - ("stabilityai/stablelm-2-12b", 1, False, 74.8904496532218, False), - ("codellama/CodeLlama-34b-hf", 1, True, 32.644, False), - ("bigcode/starcoder2-3b", 1, False, 261.07213776344133, True), - ("adept/persimmon-8b-base", 4, False, 366.73968820698406, False), - # ("Qwen/Qwen1.5-7B", 4, False, 490.8621617893209, False), - ("google/gemma-7b", 1, False, 109.70751574382221, True), - ("google/gemma-2-9b", 1, False, 92.302359446567, True), - ("google/gemma-2-27b", 1, False, 36.578709544111, True), - ("state-spaces/mamba-130m-hf", 1536, False, 5385.511100161605, False), - # ("Deci/DeciLM-7B", 1, False, 115, False), - ("Qwen/Qwen2-7B", 256, False, 8870.945160540245, True), - ("Qwen/Qwen1.5-MoE-A2.7B", 1, True, 44.25834541569395, False), - # ("EleutherAI/gpt-neo-2.7B", 1, False, 257.2476416844122, False), - # ("facebook/xglm-1.7B", 1, False, 357.46365062825083, False), - # ("CohereForAI/c4ai-command-r-v01", 1, False, 29.50315234651154, False), - ("tiiuae/falcon-mamba-7b", 1, False, 47.1464839567739, False), - ("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), ("deepseek-ai/DeepSeek-V3", 1, False, 35, False), - ("THUDM/chatglm3-6b", 1, True, 150, False), ], "fp8": [ ("tiiuae/falcon-180B", 4, 950, True, 128, 128), From 1336c14285d29ed5f7035beea8d7f6cc41b5289a Mon Sep 17 00:00:00 2001 From: "Kavulya, Soila P" Date: Thu, 13 Feb 2025 10:52:52 -0800 Subject: [PATCH 14/21] Refactor deepseek_v3 and add clarifying comments --- .../deepseek_v3/modeling_deepseek_v3.py | 36 ++++++++++++++----- 1 file changed, 27 insertions(+), 9 deletions(-) diff --git a/optimum/habana/transformers/models/deepseek_v3/modeling_deepseek_v3.py b/optimum/habana/transformers/models/deepseek_v3/modeling_deepseek_v3.py index 48e3504d9b..181f435db6 100644 --- a/optimum/habana/transformers/models/deepseek_v3/modeling_deepseek_v3.py +++ b/optimum/habana/transformers/models/deepseek_v3/modeling_deepseek_v3.py @@ -60,6 +60,7 @@ 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 @@ -67,8 +68,10 @@ _CONFIG_FOR_DOC = "DeepseekV3Config" -# default expert number per slice for dynamic MoE +# 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 @@ -114,7 +117,8 @@ def __init__(self, hidden_size, eps=1e-6): self.variance_epsilon = eps def forward(self, hidden_states): - if hidden_states.device.type == "hpu" and FusedRMSNorm: + if hidden_states.device.type == "hpu" and FusedRMSNorm: # use hpu fused rmsnorm + # 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 @@ -147,6 +151,9 @@ def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None): 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 eq_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, @@ -319,11 +326,9 @@ def _set_cos_sin_cache(self, seq_len, device, dtype): self.register_buffer("sin_cached", (emb.sin() * _mscale).to(dtype), persistent=False) -def apply_customized_rope(q, k, cos, sin, position_ids): - if q.device.type == "hpu" and FusedRoPE: - return FusedRoPE.apply( - q, cos.unsqueeze(0).unsqueeze(0), sin.unsqueeze(0).unsqueeze(0), position_ids - ), FusedRoPE.apply(k, cos.unsqueeze(0).unsqueeze(0), sin.unsqueeze(0).unsqueeze(0), position_ids) +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) @@ -494,6 +499,7 @@ def __init__(self, config): 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 = self.experts_per_rank // self.expert_slice @@ -507,6 +513,7 @@ def forward(self, hidden_states): 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), @@ -534,6 +541,8 @@ def forward(self, hidden_states): 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): expert_offset = self.ep_rank * self.experts_per_rank experts_range = range(self.expert_chunk) @@ -582,6 +591,7 @@ def forward(self, hidden_states): return final_hidden_states +# functional apis need to be wrapped in classes for quantization class Matmul(torch.nn.Module): def __init__(self): super().__init__() @@ -590,6 +600,7 @@ def forward(self, x, y): return torch.matmul(x, y) +# hpu specific. kv cache handling. similar to other models on OH def gaudi_deepseekv3_repeat_kv( query_states: torch.Tensor, key_states: torch.Tensor, @@ -624,6 +635,7 @@ def gaudi_deepseekv3_repeat_kv( 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__() @@ -666,6 +678,7 @@ 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__() @@ -761,6 +774,7 @@ def __init__(self, config: DeepseekV3Config, layer_idx: Optional[int] = None): self._init_rope() self.num_key_value_groups = self.num_heads // config.num_key_value_heads + # hpu specific warpping functional api into nn.module classes for quantization self.matmul_qk = Matmul() self.matmul_av = Matmul() self.k_cache = KVCache() @@ -776,6 +790,7 @@ def __init__(self, config: DeepseekV3Config, layer_idx: Optional[int] = None): 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, @@ -834,6 +849,7 @@ def _init_rope(self): 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) @@ -921,6 +937,7 @@ def forward( 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( @@ -965,7 +982,7 @@ def forward( 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) + 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 @@ -980,7 +997,7 @@ def forward( key_states, value_states = past_key_value.update( key_states, value_states, self.layer_idx, cache_kwargs ) - # optimization + # 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 @@ -1055,6 +1072,7 @@ def forward( ) 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() From e02c6decc9225aed6055ef75e834f51eb75bff54 Mon Sep 17 00:00:00 2001 From: "Kavulya, Soila P" Date: Sat, 15 Feb 2025 08:23:38 -0800 Subject: [PATCH 15/21] Fix edge case in expert slices in DeepSeek-V3 --- .../deepseek_v3/modeling_deepseek_v3.py | 35 +++++++------------ 1 file changed, 13 insertions(+), 22 deletions(-) diff --git a/optimum/habana/transformers/models/deepseek_v3/modeling_deepseek_v3.py b/optimum/habana/transformers/models/deepseek_v3/modeling_deepseek_v3.py index 181f435db6..fb7751545a 100644 --- a/optimum/habana/transformers/models/deepseek_v3/modeling_deepseek_v3.py +++ b/optimum/habana/transformers/models/deepseek_v3/modeling_deepseek_v3.py @@ -117,7 +117,7 @@ def __init__(self, hidden_size, eps=1e-6): self.variance_epsilon = eps def forward(self, hidden_states): - if hidden_states.device.type == "hpu" and FusedRMSNorm: # use hpu fused rmsnorm + 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: @@ -153,7 +153,7 @@ def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None): # Build here to make `torch.jit.trace` work. # make it static (max_position_embeddings) instead of updating depending on - # longest eq_len seen till now: seq_len > self.max_seq_len_cached + # 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, @@ -501,7 +501,7 @@ def __init__(self, config): # 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 = self.experts_per_rank // self.expert_slice + self.expert_chunk = math.ceil(self.experts_per_rank / self.expert_slice) def forward(self, hidden_states): identity = hidden_states @@ -544,20 +544,12 @@ def forward(self, hidden_states): # 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): - expert_offset = self.ep_rank * self.experts_per_rank - experts_range = range(self.expert_chunk) - gate_proj_list = [ - self.experts[idx * self.expert_chunk + i + expert_offset].gate_proj.weight.squeeze() - for i in experts_range - ] - down_proj_list = [ - self.experts[idx * self.expert_chunk + i + expert_offset].down_proj.weight.squeeze() - for i in experts_range - ] - up_proj_list = [ - self.experts[idx * self.expert_chunk + i + expert_offset].up_proj.weight.squeeze() - for i in experts_range - ] + 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, @@ -568,8 +560,8 @@ def forward(self, hidden_states): w3=down_proj_list, permuted_weights=True, activation="silu", - experts_min=(self.expert_chunk * idx + expert_offset), - experts_max=(self.expert_chunk * (idx + 1) - 1 + expert_offset), + experts_min=experts_min, + experts_max=experts_max - 1, ) final_hidden_states = final_hidden_states + hidden_states_slice htcore.mark_step() @@ -591,7 +583,7 @@ def forward(self, hidden_states): return final_hidden_states -# functional apis need to be wrapped in classes for quantization +# Functional apis need to be wrapped in classes for quantization on hpu class Matmul(torch.nn.Module): def __init__(self): super().__init__() @@ -600,7 +592,6 @@ def forward(self, x, y): return torch.matmul(x, y) -# hpu specific. kv cache handling. similar to other models on OH def gaudi_deepseekv3_repeat_kv( query_states: torch.Tensor, key_states: torch.Tensor, @@ -774,7 +765,7 @@ def __init__(self, config: DeepseekV3Config, layer_idx: Optional[int] = None): self._init_rope() self.num_key_value_groups = self.num_heads // config.num_key_value_heads - # hpu specific warpping functional api into nn.module classes for quantization + # hpu specific wrapping functional api into nn.module classes for quantization self.matmul_qk = Matmul() self.matmul_av = Matmul() self.k_cache = KVCache() From 96a3473bedf9fab2d69816d146f33593451423b4 Mon Sep 17 00:00:00 2001 From: "Kavulya, Soila P" Date: Tue, 18 Feb 2025 13:03:34 -0800 Subject: [PATCH 16/21] Add deepseekv3 to list of models supporting reuse_cache --- optimum/habana/transformers/generation/utils.py | 1 + 1 file changed, 1 insertion(+) diff --git a/optimum/habana/transformers/generation/utils.py b/optimum/habana/transformers/generation/utils.py index dd6e2643dd..73347a0008 100755 --- a/optimum/habana/transformers/generation/utils.py +++ b/optimum/habana/transformers/generation/utils.py @@ -1096,6 +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" ) From 5e95f5880700a18b0375b441686602086e722912 Mon Sep 17 00:00:00 2001 From: "Kavulya, Soila P" Date: Tue, 18 Feb 2025 13:11:42 -0800 Subject: [PATCH 17/21] Style fix in modeling_utils --- optimum/habana/transformers/modeling_utils.py | 1 + 1 file changed, 1 insertion(+) diff --git a/optimum/habana/transformers/modeling_utils.py b/optimum/habana/transformers/modeling_utils.py index 56f2ee9dac..603b6fa924 100644 --- a/optimum/habana/transformers/modeling_utils.py +++ b/optimum/habana/transformers/modeling_utils.py @@ -15,6 +15,7 @@ import os from typing import Optional, Union from zipfile import is_zipfile + import accelerate import torch import transformers From 9bb560186a286f068b67aaf0b9aa02d8575145f2 Mon Sep 17 00:00:00 2001 From: pallavi jaini Date: Wed, 19 Feb 2025 00:42:51 +0000 Subject: [PATCH 18/21] Updated the README.md for the deepseek-r1-bf16 --- examples/text-generation/README.md | 14 ++++++++++++++ 1 file changed, 14 insertions(+) diff --git a/examples/text-generation/README.md b/examples/text-generation/README.md index 78dcd44c30..1ba5a72890 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 use the following command and replace the hostfile parameter to the correct file: +```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 From c66d9d923600b03b4bf9dc447d95f5ee12af110d Mon Sep 17 00:00:00 2001 From: pallavi jaini Date: Thu, 20 Feb 2025 00:03:07 +0000 Subject: [PATCH 19/21] Updated the README.md with hostfile reference --- examples/text-generation/README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/examples/text-generation/README.md b/examples/text-generation/README.md index 1ba5a72890..5adf348217 100755 --- a/examples/text-generation/README.md +++ b/examples/text-generation/README.md @@ -202,7 +202,7 @@ 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 use the following command and replace the hostfile parameter to the correct file: +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 \ From 8a1a460553de54dc34bef3f87ba67cc562c225c8 Mon Sep 17 00:00:00 2001 From: "Kavulya, Soila P" Date: Thu, 20 Feb 2025 10:55:34 -0800 Subject: [PATCH 20/21] Move load_state_dict to modeling_utils_transformers --- optimum/habana/transformers/modeling_utils.py | 90 +------------------ .../modeling_utils_transformers.py | 89 ++++++++++++++++++ 2 files changed, 90 insertions(+), 89 deletions(-) create mode 100644 optimum/habana/transformers/modeling_utils_transformers.py diff --git a/optimum/habana/transformers/modeling_utils.py b/optimum/habana/transformers/modeling_utils.py index 603b6fa924..53ab91433b 100644 --- a/optimum/habana/transformers/modeling_utils.py +++ b/optimum/habana/transformers/modeling_utils.py @@ -12,26 +12,10 @@ # 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 os -from typing import Optional, Union -from zipfile import is_zipfile import accelerate -import torch import transformers -import transformers.modeling_utils import transformers.utils.fx -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 from ..accelerate.utils import extract_model_from_parallel from ..accelerate.utils.modeling import gaudi_check_device_same @@ -57,6 +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, @@ -308,79 +293,6 @@ ) -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." - ) - - def adapt_transformers_to_gaudi(): """ Replaces some Transformers' methods for equivalent methods optimized 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." + ) From eb03fad3e58ad145df2e72104400260ffbf40602 Mon Sep 17 00:00:00 2001 From: pallavi jaini Date: Thu, 20 Feb 2025 21:21:15 +0000 Subject: [PATCH 21/21] Removed the deepseek tests from CI, will enable when FP8 is supported --- tests/test_text_generation_example.py | 1 - 1 file changed, 1 deletion(-) diff --git a/tests/test_text_generation_example.py b/tests/test_text_generation_example.py index 0d34ec6760..0c513d8bb1 100644 --- a/tests/test_text_generation_example.py +++ b/tests/test_text_generation_example.py @@ -60,7 +60,6 @@ ("THUDM/chatglm2-6b", 1, True, False), ("THUDM/chatglm3-6b", 1, True, False), ("Qwen/Qwen2.5-7B", 4, False, False), - ("deepseek-ai/DeepSeek-V3", 1, False, 35, False), ], "fp8": [ ("tiiuae/falcon-180B", 4, 950, True, 128, 128),