updated mixtral backend (need this for something else)
This commit is contained in:
parent
1a26f789a5
commit
69c1d2991f
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@ -1006,7 +1006,7 @@ def example_usage():
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available_tasks = [] + (["tts-ar"] if "ar" in cfg.model.capabilities else []) + (["tts-nar"] if "len" in cfg.model.capabilities else [])
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model = AR_NAR(**kwargs).to(cfg.device)
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steps = 1000 // batch_size
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steps = 500 // batch_size
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optimizer = cfg.hyperparameters.optimizer.lower() if cfg.yaml_path is not None else "prodigy"
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scheduler = cfg.hyperparameters.scheduler.lower() if cfg.yaml_path is not None else ""
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@ -1154,12 +1154,12 @@ def example_usage():
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text_list, proms_list, resp_list, task_list = sample_data( task )
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if task == "tts-nar":
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len_list = engine(text_list, proms_list, task_list=["len"], max_steps=5, temperature=0.0 )
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len_list = engine( text_list=text_list, proms_list=proms_list, task_list=["len"], max_steps=5, temperature=0.0 )
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len_list = [ resp_list[0].shape[0] for l in len_list ]
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resps_list = engine( text_list, proms_list, len_list=len_list )
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resps_list = engine( text_list=text_list, proms_list=proms_list, len_list=len_list )
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else:
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resps_list = engine( text_list, proms_list, task_list=["tts"], max_duration=steps, temperature=1.0 )
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resps_list = engine( text_list, proms_list, resps_list=resps_list, temperature=0.0 )
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resps_list = engine( text_list=text_list, proms_list=proms_list, task_list=["tts"], max_duration=steps, temperature=1.0 )
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resps_list = engine( text_list=text_list, proms_list=proms_list, resps_list=resps_list, temperature=0.0 )
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for i, o in enumerate(resps_list):
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_ = decode_to_file(o.to(dtype=torch.int32), f"data/{cfg.model.arch_type}.{cfg.audio_backend}.{i}.{name}.{task}.wav", device=cfg.device)
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@ -46,11 +46,15 @@ except Exception as e:
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ERROR_ARCHES["bitnet"] = e
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pass
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from .mixtral import MixtralModel, MixtralConfig, MixtralAttention, MixtralAttention_Adapted, MixtralModel_Adapted, load_balancing_loss_func
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AVAILABLE_ARCHES.append("mixtral")
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"""
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try:
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from .mixtral import MixtralModel, MixtralConfig, MixtralAttention, MixtralAttention_Adapted, load_balancing_loss_func
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from .mixtral import MixtralModel, MixtralConfig, MixtralAttention, MixtralAttention_Adapted, MixtralModel_Adapted, load_balancing_loss_func
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AVAILABLE_ARCHES.append("mixtral")
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except Exception as e:
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ERROR_ARCHES["mixtral"] = e
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"""
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try:
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from .mamba import MambaModel, Mamba2Model, MambaConfig, Mamba2Config
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133
vall_e/models/arch/attention/__init__.py
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133
vall_e/models/arch/attention/__init__.py
Normal file
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@ -0,0 +1,133 @@
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import logging
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import torch
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_logger = logging.getLogger(__name__)
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AVAILABLE_ATTENTIONS = []
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try:
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from sageattention import sageattn
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AVAILABLE_ATTENTIONS.append("sageattn")
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except Exception as e:
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_logger.warning(f"Error while querying for `sageattn` support: {str(e)}")
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try:
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from torch.nn.attention.flex_attention import flex_attention, create_block_mask
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AVAILABLE_ATTENTIONS.append("flex")
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except Exception as e:
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_logger.warning(f"Error while querying for `flexattention` support: {str(e)}")
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try:
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from transformers.utils import is_flash_attn_2_available
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if is_flash_attn_2_available():
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AVAILABLE_ATTENTIONS.append("flash_attention_2")
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except Exception as e:
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_logger.warning(f"Error while querying for `flash_attention_2` support: {str(e)}")
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try:
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from .fused import attention as _fused_attention
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def fused_attn_func(q, k, v, softmax_scale=None, causal=False, *args, **kwargs):
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return _fused_attention( q, k, v, causal, softmax_scale )
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AVAILABLE_ATTENTIONS.append("fused_attn")
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except Exception as e:
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_logger.warning(f"Error while querying for `fused_attn` support: {str(e)}")
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is_rocm = any("AMD" in torch.cuda.get_device_properties(i).name for i in range(torch.cuda.device_count()))
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is_ampere_or_newer_gpu = any(torch.cuda.get_device_properties(i).major >= 8 for i in range(torch.cuda.device_count()))
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try:
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if is_rocm:
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# requires pain to set up on Navi3, and for no backwards (training) support
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from flash_attn import flash_attn_func
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AVAILABLE_ATTENTIONS.append("flash_attn")
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elif not is_ampere_or_newer_gpu:
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# Uses https://github.com/ZRayZzz/flash-attention-v100/
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# Currently doesn't work because it's hard-coded to use a head dim of 128, will throw NaNs otherwise...
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from flash_attn_v100 import flash_attn_func as flash_attn_v100_func
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AVAILABLE_ATTENTIONS.append("flash_attn")
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AVAILABLE_ATTENTIONS.append("flash_attn_v100") # needed to signal to use padding
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def flash_attn_func(q, k, v, softmax_scale=None, causal=False, *args, **kwargs):
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return flash_attn_v100_func(
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q,
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k,
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v,
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softmax_scale,
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causal
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)
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else:
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# Borrowed from https://github.com/turboderp/exllamav2/blob/master/exllamav2/attn.py#L32
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# Adapted to provide flash_attn_v1 support
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import flash_attn
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flash_attn_ver = [int(t) for t in flash_attn.__version__.split(".") if t.isdigit()]
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if flash_attn_ver <= [1, 0, 9]:
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AVAILABLE_ATTENTIONS.append("flash_attn")
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from flash_attn.flash_attn_interface import flash_attn_unpadded_func
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from einops import rearrange
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# converts the flash_attn_2 calling convention to flash_attn_1's
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def flash_attn_func(q, k, v, dropout_p=0.0, softmax_scale=None, causal=False, return_attn_probs=False, deterministic=False, *args, **kwargs):
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batch_size, seqlen_q = q.shape[0], q.shape[1]
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seqlen_k = k.shape[1]
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q, k, v = [rearrange(x, 'b s ... -> (b s) ...').contiguous() for x in [q, k, v]]
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cu_seqlens_q = torch.arange(0, (batch_size + 1) * seqlen_q, step=seqlen_q, dtype=torch.int32, device=q.device)
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cu_seqlens_k = cu_seqlens_q
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return flash_attn_unpadded_func(
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q, k, v,
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cu_seqlens_q, cu_seqlens_k, seqlen_q, seqlen_k,
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dropout_p, softmax_scale, causal, return_attn_probs, deterministic
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)
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has_flash_attn = True
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elif [2, 2, 1] <= flash_attn_ver < [2, 5, 7]:
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AVAILABLE_ATTENTIONS.append("flash_attn")
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from flash_attn import flash_attn_func
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has_flash_attn = True
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elif [2, 5, 7] <= flash_attn_ver:
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AVAILABLE_ATTENTIONS.append("flash_attn")
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from flash_attn import flash_attn_func, flash_attn_with_kvcache
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signature = list(inspect.signature(flash_attn_func).parameters)
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has_flash_attn_with_window = "window_size" in signature
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has_flash_attn_with_softcap = "softcap" in signature
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import flash_attn_2_cuda as flash_attn_cuda
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has_flash_attn = True
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has_flash_attn_with_paged = True
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except Exception as e:
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_logger.warning(f"Error while querying for `flash_attn` support: {str(e)}")
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try:
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from xformers.ops.fmha import memory_efficient_attention
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from xformers.ops.fmha.attn_bias import LowerTriangularFromBottomRightMask, LowerTriangularMask
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AVAILABLE_ATTENTIONS.append("xformers")
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except Exception as e:
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_logger.warning(f"Error while importing `xformers`: {str(e)}")
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# to-do: find a better way to query for if there's available kernels since these return true regardless
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if torch.backends.cuda.flash_sdp_enabled():
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AVAILABLE_ATTENTIONS.append("flash_(sdpa)")
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if torch.backends.cuda.mem_efficient_sdp_enabled():
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AVAILABLE_ATTENTIONS.append("mem_efficient")
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if torch.backends.cuda.math_sdp_enabled():
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AVAILABLE_ATTENTIONS.append("math")
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if torch.backends.cuda.cudnn_sdp_enabled():
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AVAILABLE_ATTENTIONS.append("cudnn")
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if AVAILABLE_ATTENTIONS:
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AVAILABLE_ATTENTIONS.append("sdpa")
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AVAILABLE_ATTENTIONS.append("default")
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@ -16,139 +16,10 @@ from transformers.cache_utils import Cache, DynamicCache, StaticCache
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from transformers.models.llama.modeling_llama import LlamaAttention, LlamaDecoderLayer, LlamaRMSNorm, LlamaRotaryEmbedding, apply_rotary_pos_emb, repeat_kv
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from transformers.modeling_attn_mask_utils import AttentionMaskConverter
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_logger = logging.getLogger(__name__)
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AVAILABLE_ATTENTIONS = []
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from .attention import *
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LN_2 = 0.69314718056
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try:
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from sageattention import sageattn
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AVAILABLE_ATTENTIONS.append("sageattn")
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except Exception as e:
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_logger.warning(f"Error while querying for `sageattn` support: {str(e)}")
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try:
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from torch.nn.attention.flex_attention import flex_attention, create_block_mask
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AVAILABLE_ATTENTIONS.append("flex")
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except Exception as e:
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_logger.warning(f"Error while querying for `flexattention` support: {str(e)}")
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try:
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from transformers.utils import is_flash_attn_2_available
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if is_flash_attn_2_available():
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AVAILABLE_ATTENTIONS.append("flash_attention_2")
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except Exception as e:
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_logger.warning(f"Error while querying for `flash_attention_2` support: {str(e)}")
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try:
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from .attention.fused import attention as _fused_attention
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def fused_attn_func(q, k, v, softmax_scale=None, causal=False, *args, **kwargs):
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return _fused_attention( q, k, v, causal, softmax_scale )
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AVAILABLE_ATTENTIONS.append("fused_attn")
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except Exception as e:
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_logger.warning(f"Error while querying for `fused_attn` support: {str(e)}")
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is_rocm = any("AMD" in torch.cuda.get_device_properties(i).name for i in range(torch.cuda.device_count()))
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is_ampere_or_newer_gpu = any(torch.cuda.get_device_properties(i).major >= 8 for i in range(torch.cuda.device_count()))
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try:
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if is_rocm:
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# requires pain to set up on Navi3, and for no backwards (training) support
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from flash_attn import flash_attn_func
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AVAILABLE_ATTENTIONS.append("flash_attn")
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elif not is_ampere_or_newer_gpu:
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# Uses https://github.com/ZRayZzz/flash-attention-v100/
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# Currently doesn't work because it's hard-coded to use a head dim of 128, will throw NaNs otherwise...
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from flash_attn_v100 import flash_attn_func as flash_attn_v100_func
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AVAILABLE_ATTENTIONS.append("flash_attn")
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AVAILABLE_ATTENTIONS.append("flash_attn_v100") # needed to signal to use padding
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def flash_attn_func(q, k, v, softmax_scale=None, causal=False, *args, **kwargs):
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return flash_attn_v100_func(
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q,
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k,
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v,
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softmax_scale,
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causal
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)
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else:
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# Borrowed from https://github.com/turboderp/exllamav2/blob/master/exllamav2/attn.py#L32
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# Adapted to provide flash_attn_v1 support
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import flash_attn
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flash_attn_ver = [int(t) for t in flash_attn.__version__.split(".") if t.isdigit()]
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if flash_attn_ver <= [1, 0, 9]:
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AVAILABLE_ATTENTIONS.append("flash_attn")
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from flash_attn.flash_attn_interface import flash_attn_unpadded_func
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from einops import rearrange
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# converts the flash_attn_2 calling convention to flash_attn_1's
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def flash_attn_func(q, k, v, dropout_p=0.0, softmax_scale=None, causal=False, return_attn_probs=False, deterministic=False, *args, **kwargs):
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batch_size, seqlen_q = q.shape[0], q.shape[1]
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seqlen_k = k.shape[1]
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q, k, v = [rearrange(x, 'b s ... -> (b s) ...').contiguous() for x in [q, k, v]]
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cu_seqlens_q = torch.arange(0, (batch_size + 1) * seqlen_q, step=seqlen_q, dtype=torch.int32, device=q.device)
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cu_seqlens_k = cu_seqlens_q
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return flash_attn_unpadded_func(
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q, k, v,
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cu_seqlens_q, cu_seqlens_k, seqlen_q, seqlen_k,
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dropout_p, softmax_scale, causal, return_attn_probs, deterministic
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)
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has_flash_attn = True
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elif [2, 2, 1] <= flash_attn_ver < [2, 5, 7]:
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AVAILABLE_ATTENTIONS.append("flash_attn")
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from flash_attn import flash_attn_func
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has_flash_attn = True
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elif [2, 5, 7] <= flash_attn_ver:
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AVAILABLE_ATTENTIONS.append("flash_attn")
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from flash_attn import flash_attn_func, flash_attn_with_kvcache
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signature = list(inspect.signature(flash_attn_func).parameters)
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has_flash_attn_with_window = "window_size" in signature
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has_flash_attn_with_softcap = "softcap" in signature
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import flash_attn_2_cuda as flash_attn_cuda
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has_flash_attn = True
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has_flash_attn_with_paged = True
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except Exception as e:
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_logger.warning(f"Error while querying for `flash_attn` support: {str(e)}")
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try:
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from xformers.ops.fmha import memory_efficient_attention
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from xformers.ops.fmha.attn_bias import LowerTriangularFromBottomRightMask, LowerTriangularMask
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AVAILABLE_ATTENTIONS.append("xformers")
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except Exception as e:
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_logger.warning(f"Error while importing `xformers`: {str(e)}")
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# to-do: find a better way to query for if there's available kernels since these return true regardless
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if torch.backends.cuda.flash_sdp_enabled():
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AVAILABLE_ATTENTIONS.append("flash_(sdpa)")
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if torch.backends.cuda.mem_efficient_sdp_enabled():
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AVAILABLE_ATTENTIONS.append("mem_efficient")
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if torch.backends.cuda.math_sdp_enabled():
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AVAILABLE_ATTENTIONS.append("math")
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if torch.backends.cuda.cudnn_sdp_enabled():
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AVAILABLE_ATTENTIONS.append("cudnn")
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if AVAILABLE_ATTENTIONS:
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AVAILABLE_ATTENTIONS.append("sdpa")
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AVAILABLE_ATTENTIONS.append("default")
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class LlamaAttention_Adapted(LlamaAttention):
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def __init__(self, *args, **kwargs):
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self.mode = kwargs.pop("mode", "sdpa")
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@ -3,32 +3,61 @@
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import math
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import torch
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import torch.nn.functional as F
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from typing import Literal, overload, Optional, Tuple
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from transformers.cache_utils import Cache
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from typing import Literal, overload, Optional, Tuple, List, Union
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from transformers import MixtralModel, MixtralConfig
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from transformers.models.mixtral.modeling_mixtral import load_balancing_loss_func, MixtralSparseMoeBlock, MixtralAttention, apply_rotary_pos_emb, repeat_kv
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from transformers.models.mixtral.modeling_mixtral import load_balancing_loss_func, MixtralSparseMoeBlock, MixtralAttention, MixtralDecoderLayer, MixtralRMSNorm, repeat_kv
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from transformers.modeling_outputs import BaseModelOutputWithPast, MoeModelOutputWithPast
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from transformers.cache_utils import Cache, DynamicCache, StaticCache
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from transformers.modeling_attn_mask_utils import AttentionMaskConverter
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from transformers.modeling_flash_attention_utils import FlashAttentionKwargs
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from transformers.modeling_rope_utils import ROPE_INIT_FUNCTIONS
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from transformers.processing_utils import Unpack
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try:
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from .llama import flash_attn_func
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except Exception as e:
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pass
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from .attention import *
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try:
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from .llama import fused_attn_func
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except Exception as e:
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pass
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def rotate_half(x):
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"""Rotates half the hidden dims of the input."""
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x1 = x[..., : x.shape[-1] // 2]
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x2 = x[..., x.shape[-1] // 2 :]
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return torch.cat((-x2, x1), dim=-1)
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try:
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from .llama import memory_efficient_attention
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except Exception as e:
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pass
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def apply_rotary_pos_emb(q, k, cos, sin, position_ids, unsqueeze_dim=1):
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"""Applies Rotary Position Embedding to the query and key tensors.
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Args:
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q (`torch.Tensor`): The query tensor.
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k (`torch.Tensor`): The key tensor.
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cos (`torch.Tensor`): The cosine part of the rotary embedding.
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sin (`torch.Tensor`): The sine part of the rotary embedding.
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position_ids (`torch.Tensor`):
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The position indices of the tokens corresponding to the query and key tensors. For example, this can be
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used to pass offsetted position ids when working with a KV-cache.
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unsqueeze_dim (`int`, *optional*, defaults to 1):
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The 'unsqueeze_dim' argument specifies the dimension along which to unsqueeze cos[position_ids] and
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sin[position_ids] so that they can be properly broadcasted to the dimensions of q and k. For example, note
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that cos[position_ids] and sin[position_ids] have the shape [batch_size, seq_len, head_dim]. Then, if q and
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||||
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)
|
||||
|
||||
q_embed = (q * cos) + (rotate_half(q) * sin)
|
||||
k_embed = (k * cos) + (rotate_half(k) * sin)
|
||||
return q_embed, k_embed
|
||||
|
||||
# This is required because batch sizes > 1 throws errors
|
||||
def MixtralSparseMoeBlock_forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
|
||||
""" """
|
||||
batch_size, sequence_length, hidden_dim = hidden_states.shape
|
||||
hidden_states = hidden_states.reshape(-1, hidden_dim) # was view()
|
||||
if self.training and self.jitter_noise > 0:
|
||||
hidden_states *= torch.empty_like(hidden_states).uniform_(1.0 - self.jitter_noise, 1.0 + self.jitter_noise)
|
||||
#hidden_states = hidden_states.view(-1, hidden_dim)
|
||||
hidden_states = hidden_states.reshape(-1, hidden_dim)
|
||||
# router_logits: (batch * sequence_length, n_experts)
|
||||
router_logits = self.gate(hidden_states)
|
||||
|
||||
|
|
@ -42,20 +71,23 @@ def MixtralSparseMoeBlock_forward(self, hidden_states: torch.Tensor) -> torch.Te
|
|||
(batch_size * sequence_length, hidden_dim), dtype=hidden_states.dtype, device=hidden_states.device
|
||||
)
|
||||
|
||||
# One hot encode the selected experts to create an expert mask
|
||||
# this will be used to easily index which expert is going to be sollicitated
|
||||
expert_mask = torch.nn.functional.one_hot(selected_experts, num_classes=self.num_experts).permute(2, 1, 0)
|
||||
|
||||
# Loop over all available experts in the model and perform the computation on each expert
|
||||
for expert_idx in range(self.num_experts):
|
||||
expert_layer = self.experts[expert_idx]
|
||||
idx, top_x = torch.where(expert_mask[expert_idx])
|
||||
|
||||
if top_x.shape[0] == 0:
|
||||
continue
|
||||
top_x_list = top_x.tolist()
|
||||
idx_list = idx.tolist()
|
||||
|
||||
current_state = hidden_states[None, top_x_list].reshape(-1, hidden_dim)
|
||||
current_hidden_states = expert_layer(current_state) * routing_weights[top_x_list, idx_list, None]
|
||||
# Index the correct hidden states and compute the expert hidden state for
|
||||
# the current expert. We need to make sure to multiply the output hidden
|
||||
# states by `routing_weights` on the corresponding tokens (top-1 and top-2)
|
||||
current_state = hidden_states[None, top_x].reshape(-1, hidden_dim)
|
||||
current_hidden_states = expert_layer(current_state) * routing_weights[top_x, idx, None]
|
||||
|
||||
# However `index_add_` only support torch tensors for indexing so we'll use
|
||||
# the `top_x` tensor here.
|
||||
final_hidden_states.index_add_(0, top_x, current_hidden_states.to(hidden_states.dtype))
|
||||
final_hidden_states = final_hidden_states.reshape(batch_size, sequence_length, hidden_dim)
|
||||
return final_hidden_states, router_logits
|
||||
|
|
@ -64,51 +96,158 @@ MixtralSparseMoeBlock.forward = MixtralSparseMoeBlock_forward
|
|||
|
||||
class MixtralAttention_Adapted(MixtralAttention):
|
||||
def __init__(self, *args, **kwargs):
|
||||
if 'mode' in kwargs:
|
||||
self.mode = kwargs['mode']
|
||||
kwargs.pop("mode")
|
||||
else:
|
||||
self.mode = "math"
|
||||
self.mode = kwargs.pop("mode", "sdpa")
|
||||
|
||||
if self.mode == "math":
|
||||
self.mode = torch.nn.attention.SDPBackend.MATH
|
||||
elif self.mode == "mem_efficient":
|
||||
self.mode = torch.nn.attention.SDPBackend.EFFICIENT_ATTENTION
|
||||
elif self.mode == "flash":
|
||||
elif self.mode == "flash_(sdpa)":
|
||||
self.mode = torch.nn.attention.SDPBackend.FLASH_ATTENTION
|
||||
elif self.mode == "cudnn":
|
||||
self.mode = torch.nn.attention.SDPBackend.CUDNN_ATTENTION
|
||||
|
||||
super().__init__(*args, **kwargs)
|
||||
|
||||
# Adapted from MixtralAttention.forward
|
||||
def forward(
|
||||
# extracts inputs from a batch based on requested causality
|
||||
def split_forward(
|
||||
self,
|
||||
hidden_states: torch.Tensor,
|
||||
attention_mask: Optional[torch.Tensor] = None,
|
||||
is_causal: Optional[list] = None,
|
||||
target_causal_state: Optional[bool] = True,
|
||||
position_ids: Optional[torch.LongTensor] = None,
|
||||
past_key_value: Optional[Cache] = None,
|
||||
output_attentions: bool = False,
|
||||
use_cache: bool = False,
|
||||
cache_position: Optional[torch.LongTensor] = None,
|
||||
position_embeddings: Optional[Tuple[torch.Tensor, torch.Tensor]] = None, # will become mandatory in v4.45
|
||||
) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
|
||||
if output_attentions:
|
||||
# TODO: Improve this warning with e.g. `model.config.attn_implementation = "manual"` once this is implemented.
|
||||
"""
|
||||
logger.warning_once(
|
||||
"MixtralModel is using MixtralSdpaAttention, but `torch.nn.functional.scaled_dot_product_attention` does not support `output_attentions=True`. Falling back to the manual attention implementation, "
|
||||
'but specifying the manual implementation will be required from Transformers version v5.0.0 onwards. This warning can be removed using the argument `attn_implementation="eager"` when loading the model.'
|
||||
)
|
||||
"""
|
||||
return super().forward(
|
||||
**kwargs,
|
||||
):
|
||||
indices = [ i for i, state in enumerate( is_causal ) if state == target_causal_state ]
|
||||
|
||||
# no matching inputs in batch
|
||||
if not indices:
|
||||
return indices, None, None, None
|
||||
|
||||
# entire batch is homogenous
|
||||
if len( indices ) == hidden_states.shape[0]:
|
||||
output_hidden_states, output_self_attn_weights, output_present_key_values = self.forward(
|
||||
hidden_states=hidden_states,
|
||||
attention_mask=attention_mask,
|
||||
is_causal=target_causal_state,
|
||||
position_ids=position_ids,
|
||||
past_key_value=past_key_value,
|
||||
output_attentions=output_attentions,
|
||||
use_cache=use_cache,
|
||||
use_cache=False,
|
||||
cache_position=cache_position,
|
||||
position_embeddings=position_embeddings,
|
||||
**kwargs,
|
||||
)
|
||||
return indices, output_hidden_states, output_self_attn_weights, output_present_key_values
|
||||
|
||||
input_hidden_states = torch.stack( [ hidden_states[i] for i in indices ] )
|
||||
input_attention_mask = torch.stack( [ attention_mask[i] for i in indices ] ) if attention_mask is not None else None
|
||||
input_position_ids = torch.stack( [ position_ids[i] for i in indices ] ) if position_ids is not None else None
|
||||
input_position_embeddings = (
|
||||
torch.stack( [ position_embeddings[0][i] for i in indices ] ),
|
||||
torch.stack( [ position_embeddings[1][i] for i in indices ] ),
|
||||
) if position_embeddings is not None else None
|
||||
|
||||
output_hidden_states, output_self_attn_weights, output_present_key_values = self.forward(
|
||||
hidden_states=input_hidden_states,
|
||||
attention_mask=input_attention_mask,
|
||||
is_causal=target_causal_state,
|
||||
position_ids=input_position_ids,
|
||||
past_key_value=past_key_value,
|
||||
output_attentions=output_attentions,
|
||||
use_cache=False,
|
||||
cache_position=cache_position,
|
||||
position_embeddings=input_position_embeddings,
|
||||
**kwargs,
|
||||
)
|
||||
return indices, output_hidden_states, output_self_attn_weights, output_present_key_values
|
||||
|
||||
# Adapted from LlamaAttention.forward
|
||||
def forward(
|
||||
self,
|
||||
hidden_states: torch.Tensor,
|
||||
attention_mask: Optional[torch.Tensor] = None,
|
||||
is_causal: bool = True,
|
||||
position_ids: Optional[torch.LongTensor] = None,
|
||||
past_key_value: Optional[Cache] = None,
|
||||
output_attentions: bool = False,
|
||||
use_cache: bool = False,
|
||||
cache_position: Optional[torch.LongTensor] = None,
|
||||
position_embeddings: Optional[Tuple[torch.Tensor, torch.Tensor]] = None, # will become mandatory in v4.45
|
||||
**kwargs,
|
||||
) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
|
||||
mode = "default" if output_attentions else self.mode
|
||||
non_split_attention = [
|
||||
"default",
|
||||
torch.nn.attention.SDPBackend.MATH,
|
||||
torch.nn.attention.SDPBackend.EFFICIENT_ATTENTION,
|
||||
torch.nn.attention.SDPBackend.FLASH_ATTENTION,
|
||||
torch.nn.attention.SDPBackend.CUDNN_ATTENTION
|
||||
]
|
||||
|
||||
# split per batch because other attention mechanisms do not have a conditional is_causal per-batch, only for the entire input
|
||||
if isinstance( is_causal, list ) and mode not in non_split_attention:
|
||||
# initialize lists
|
||||
attn_hidden_states = [ None for _ in is_causal ]
|
||||
self_attn_weights = [ None for _ in is_causal ]
|
||||
present_key_values = [ None for _ in is_causal ]
|
||||
|
||||
# process causal inputs in a batch
|
||||
causal_indices, causal_hidden_states, causal_self_attn_weights, causal_present_key_values = self.split_forward(
|
||||
hidden_states=hidden_states,
|
||||
attention_mask=attention_mask,
|
||||
is_causal=is_causal,
|
||||
target_causal_state=True,
|
||||
position_ids=position_ids,
|
||||
past_key_value=past_key_value,
|
||||
output_attentions=output_attentions,
|
||||
use_cache=False,
|
||||
cache_position=cache_position,
|
||||
position_embeddings=position_embeddings,
|
||||
**kwargs,
|
||||
)
|
||||
|
||||
# process non-causal inputs in a batch
|
||||
non_causal_indices, non_causal_hidden_states, non_causal_self_attn_weights, non_causal_present_key_values = self.split_forward(
|
||||
hidden_states=hidden_states,
|
||||
attention_mask=attention_mask,
|
||||
is_causal=is_causal,
|
||||
target_causal_state=False,
|
||||
position_ids=position_ids,
|
||||
past_key_value=past_key_value,
|
||||
output_attentions=output_attentions,
|
||||
use_cache=False,
|
||||
cache_position=cache_position,
|
||||
position_embeddings=position_embeddings,
|
||||
**kwargs,
|
||||
)
|
||||
|
||||
# insert causal outputs to batch
|
||||
for i, idx in enumerate( causal_indices ):
|
||||
attn_hidden_states[idx] = causal_hidden_states[i]
|
||||
|
||||
if output_attentions:
|
||||
self_attn_weights[idx] = causal_self_attn_weights[i]
|
||||
|
||||
# insert non-causal outputs to batch
|
||||
for i, idx in enumerate( non_causal_indices ):
|
||||
attn_hidden_states[idx] = non_causal_hidden_states[i]
|
||||
|
||||
if output_attentions:
|
||||
self_attn_weights[idx] = non_causal_self_attn_weights[i]
|
||||
|
||||
# combine list
|
||||
attn_hidden_states = torch.stack( attn_hidden_states, dim=0 )
|
||||
if output_attentions:
|
||||
self_attn_weights = torch.stack( self_attn_weights, dim=0 )
|
||||
|
||||
return attn_hidden_states, output_attentions, []
|
||||
|
||||
dropout_rate = self.attention_dropout if self.training else 0.0
|
||||
bsz, q_len, _ = hidden_states.size()
|
||||
|
|
@ -123,23 +262,32 @@ class MixtralAttention_Adapted(MixtralAttention):
|
|||
|
||||
kv_seq_len = key_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)
|
||||
|
||||
if position_embeddings is None:
|
||||
cos, sin = self.rotary_emb(value_states, seq_len=kv_seq_len)
|
||||
else:
|
||||
cos, sin = position_embeddings
|
||||
|
||||
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)
|
||||
query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin, position_ids)
|
||||
|
||||
if past_key_value is not None:
|
||||
cache_kwargs = {"sin": sin, "cos": cos, "cache_position": cache_position} # Specific to RoPE models
|
||||
key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs)
|
||||
cache_kwargs = {"sin": sin, "cos": cos, "cache_position": cache_position} # Specific to RoPE models
|
||||
key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs)
|
||||
|
||||
# repeat k/v heads if n_kv_heads < n_heads
|
||||
key_states = repeat_kv(key_states, self.num_key_value_groups)
|
||||
value_states = repeat_kv(value_states, self.num_key_value_groups)
|
||||
|
||||
if self.mode in ["xformers", "flash_attn"]:
|
||||
if past_key_value is not None:
|
||||
# sin and cos are specific to RoPE models; cache_position needed for the static cache
|
||||
cache_kwargs = {"sin": sin, "cos": cos, "cache_position": cache_position}
|
||||
key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs)
|
||||
|
||||
attn_scores = None
|
||||
|
||||
if mode in ["xformers", "flash_attn"]:
|
||||
# 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)
|
||||
|
|
@ -168,69 +316,484 @@ class MixtralAttention_Adapted(MixtralAttention):
|
|||
value_states = value_states.to(target_dtype)
|
||||
"""
|
||||
|
||||
if self.mode == "flash_attn":
|
||||
if mode == "flash_attn":
|
||||
attn_output = flash_attn_func(
|
||||
query_states,
|
||||
key_states,
|
||||
value_states,
|
||||
causal=True,
|
||||
causal=is_causal,
|
||||
softmax_scale=1.0 / math.sqrt(self.head_dim),
|
||||
dropout_p=dropout_rate,
|
||||
)
|
||||
|
||||
attn_output = attn_output.reshape(bsz, q_len, -1).contiguous()
|
||||
elif self.mode == "xformers":
|
||||
elif mode == "xformers":
|
||||
attn_output = memory_efficient_attention(
|
||||
query_states,
|
||||
key_states,
|
||||
value_states,
|
||||
attn_bias = LowerTriangularMask() if attention_mask is None or attention_mask[0, 0, 0, 1] == 0 else None,
|
||||
attn_bias = LowerTriangularMask(),
|
||||
scale = 1.0 / math.sqrt(self.head_dim),
|
||||
p=dropout_rate
|
||||
)
|
||||
attn_output = attn_output.reshape(bsz, q_len, self.hidden_size)
|
||||
|
||||
attn_output = self.o_proj(attn_output)
|
||||
return attn_output, None, past_key_value
|
||||
return attn_output, attn_scores, past_key_value
|
||||
|
||||
causal_mask = attention_mask
|
||||
key_states = repeat_kv(key_states, self.num_key_value_groups)
|
||||
value_states = repeat_kv(value_states, self.num_key_value_groups)
|
||||
|
||||
x_mask = attention_mask
|
||||
|
||||
if attention_mask is not None:
|
||||
causal_mask = causal_mask[:, :, :, : key_states.shape[-2]]
|
||||
x_mask = x_mask[:, :, :, : key_states.shape[-2]]
|
||||
|
||||
# SDPA with memory-efficient backend is currently (torch==2.1.2) bugged with non-contiguous inputs with custom attn_mask,
|
||||
# Reference: https://github.com/pytorch/pytorch/issues/112577.
|
||||
if query_states.device.type == "cuda" and causal_mask is not None:
|
||||
if query_states.device.type == "cuda" and x_mask is not None:
|
||||
query_states = query_states.contiguous()
|
||||
key_states = key_states.contiguous()
|
||||
value_states = value_states.contiguous()
|
||||
|
||||
# We dispatch to SDPA's Flash Attention or Efficient kernels via this `is_causal` if statement instead of an inline conditional assignment
|
||||
# in SDPA to support both torch.compile's dynamic shapes and full graph options. An inline conditional prevents dynamic shapes from compiling.
|
||||
is_causal = True if causal_mask is None and q_len > 1 else False
|
||||
|
||||
if self.mode in ["fused_attn"]:
|
||||
if mode in ["sageattn"]:
|
||||
attn_output = sageattn(
|
||||
query_states,
|
||||
key_states,
|
||||
value_states,
|
||||
tensor_layout="HND",
|
||||
is_causal=is_causal
|
||||
)
|
||||
elif mode in ["fused_attn"]:
|
||||
attn_output = fused_attn_func(
|
||||
query_states,
|
||||
key_states,
|
||||
value_states,
|
||||
causal=True,
|
||||
causal=is_causal,
|
||||
softmax_scale=1.0 / math.sqrt(self.head_dim),
|
||||
dropout_p=dropout_rate,
|
||||
)
|
||||
elif mode in ["default"]:
|
||||
attn_scores = torch.matmul(query_states, key_states.transpose(2, 3)) / math.sqrt(self.head_dim)
|
||||
# cringe logic
|
||||
attn_weights = (attn_scores + x_mask) if attention_mask is not None else (attn_scores)
|
||||
# upcast attention to fp32
|
||||
attn_weights = torch.nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query_states.dtype)
|
||||
attn_weights = torch.nn.functional.dropout(attn_weights, p=self.attention_dropout, training=self.training)
|
||||
attn_output = torch.matmul(attn_weights, value_states)
|
||||
|
||||
if attn_output.size() != (bsz, self.num_heads, q_len, self.head_dim):
|
||||
raise ValueError(
|
||||
f"`attn_output` should be of size {(bsz, self.num_heads, q_len, self.head_dim)}, but is"
|
||||
f" {attn_output.size()}"
|
||||
)
|
||||
else:
|
||||
# We dispatch to SDPA's Flash Attention or Efficient kernels via this `is_causal` if statement instead of an inline conditional assignment
|
||||
# in SDPA to support both torch.compile's dynamic shapes and full graph options. An inline conditional prevents dynamic shapes from compiling.
|
||||
# is_causal = True if x_mask is None and q_len > 1 else False
|
||||
is_causal = True if x_mask is None and q_len > 1 else False
|
||||
with torch.nn.attention.sdpa_kernel(self.mode):
|
||||
attn_output = torch.nn.functional.scaled_dot_product_attention(
|
||||
query_states,
|
||||
key_states,
|
||||
value_states,
|
||||
attn_mask=causal_mask,
|
||||
attn_mask=x_mask,
|
||||
dropout_p=dropout_rate,
|
||||
is_causal=is_causal,
|
||||
)
|
||||
|
||||
# cringe
|
||||
if attn_scores is None and output_attentions:
|
||||
attn_scores = attn_output
|
||||
|
||||
attn_output = attn_output.transpose(1, 2).contiguous()
|
||||
attn_output = attn_output.view(bsz, q_len, -1)
|
||||
|
||||
attn_output = self.o_proj(attn_output)
|
||||
|
||||
return attn_output, None, past_key_value
|
||||
return attn_output, attn_scores, past_key_value
|
||||
|
||||
class MixtralDecoderLayer_Adapted(MixtralDecoderLayer):
|
||||
def forward(
|
||||
self,
|
||||
hidden_states: torch.Tensor,
|
||||
attention_mask: Optional[torch.Tensor] = None,
|
||||
is_causal: Optional[torch.Tensor] = None,
|
||||
position_ids: Optional[torch.LongTensor] = None,
|
||||
past_key_value: Optional[Tuple[torch.Tensor]] = None,
|
||||
output_attentions: Optional[bool] = False,
|
||||
output_router_logits: Optional[bool] = False,
|
||||
use_cache: Optional[bool] = False,
|
||||
cache_position: Optional[torch.LongTensor] = None,
|
||||
position_embeddings: Optional[Tuple[torch.Tensor, torch.Tensor]] = None, # necessary, but kept here for BC
|
||||
**kwargs: Unpack[FlashAttentionKwargs],
|
||||
) -> 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, sequence_length)` where padding elements are indicated by 0.
|
||||
past_key_value (`Tuple(torch.FloatTensor)`, *optional*): cached past key and value projection states
|
||||
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_router_logits (`bool`, *optional*):
|
||||
Whether or not to return the logits of all the routers. They are useful for computing the router loss, and
|
||||
should not be returned during inference.
|
||||
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`).
|
||||
cache_position (`torch.LongTensor` of shape `(sequence_length)`, *optional*):
|
||||
Indices depicting the position of the input sequence tokens in the sequence.
|
||||
kwargs (`dict`, *optional*):
|
||||
Arbitrary kwargs to be ignored, used for FSDP and other methods that injects code
|
||||
into the model
|
||||
"""
|
||||
|
||||
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,
|
||||
position_embeddings=position_embeddings,
|
||||
attention_mask=attention_mask,
|
||||
is_causal=is_causal,
|
||||
position_ids=position_ids,
|
||||
past_key_value=past_key_value,
|
||||
output_attentions=output_attentions,
|
||||
use_cache=use_cache,
|
||||
cache_position=cache_position,
|
||||
**kwargs,
|
||||
)
|
||||
hidden_states = residual + hidden_states
|
||||
|
||||
# Fully Connected
|
||||
residual = hidden_states
|
||||
hidden_states = self.post_attention_layernorm(hidden_states)
|
||||
hidden_states, router_logits = self.block_sparse_moe(hidden_states)
|
||||
hidden_states = residual + hidden_states
|
||||
|
||||
outputs = (hidden_states,)
|
||||
|
||||
if output_attentions:
|
||||
outputs += (self_attn_weights,)
|
||||
|
||||
if output_router_logits:
|
||||
outputs += (router_logits,)
|
||||
|
||||
return outputs
|
||||
|
||||
class MixtralRotaryEmbedding(torch.nn.Module):
|
||||
def __init__(self, config: MixtralConfig, device=None):
|
||||
super().__init__()
|
||||
# BC: "rope_type" was originally "type"
|
||||
if hasattr(config, "rope_scaling") and config.rope_scaling is not None:
|
||||
self.rope_type = config.rope_scaling.get("rope_type", config.rope_scaling.get("type"))
|
||||
else:
|
||||
self.rope_type = "default"
|
||||
self.max_seq_len_cached = config.max_position_embeddings
|
||||
self.original_max_seq_len = config.max_position_embeddings
|
||||
|
||||
self.config = config
|
||||
self.rope_init_fn = ROPE_INIT_FUNCTIONS[self.rope_type]
|
||||
|
||||
inv_freq, self.attention_scaling = self.rope_init_fn(self.config, device)
|
||||
self.register_buffer("inv_freq", inv_freq, persistent=False)
|
||||
self.original_inv_freq = self.inv_freq
|
||||
|
||||
def _dynamic_frequency_update(self, position_ids, device):
|
||||
"""
|
||||
dynamic RoPE layers should recompute `inv_freq` in the following situations:
|
||||
1 - growing beyond the cached sequence length (allow scaling)
|
||||
2 - the current sequence length is in the original scale (avoid losing precision with small sequences)
|
||||
"""
|
||||
seq_len = torch.max(position_ids) + 1
|
||||
if seq_len > self.max_seq_len_cached: # growth
|
||||
inv_freq, self.attention_scaling = self.rope_init_fn(self.config, device, seq_len=seq_len)
|
||||
self.register_buffer("inv_freq", inv_freq, persistent=False) # TODO joao: may break with compilation
|
||||
self.max_seq_len_cached = seq_len
|
||||
|
||||
if seq_len < self.original_max_seq_len and self.max_seq_len_cached > self.original_max_seq_len: # reset
|
||||
# This .to() is needed if the model has been moved to a device after being initialized (because
|
||||
# the buffer is automatically moved, but not the original copy)
|
||||
self.original_inv_freq = self.original_inv_freq.to(device)
|
||||
self.register_buffer("inv_freq", self.original_inv_freq, persistent=False)
|
||||
self.max_seq_len_cached = self.original_max_seq_len
|
||||
|
||||
@torch.no_grad()
|
||||
def forward(self, x, position_ids):
|
||||
if "dynamic" in self.rope_type:
|
||||
self._dynamic_frequency_update(position_ids, device=x.device)
|
||||
|
||||
# Core RoPE block
|
||||
inv_freq_expanded = self.inv_freq[None, :, None].float().expand(position_ids.shape[0], -1, 1)
|
||||
position_ids_expanded = position_ids[:, None, :].float()
|
||||
# Force float32 (see https://github.com/huggingface/transformers/pull/29285)
|
||||
device_type = x.device.type
|
||||
device_type = device_type if isinstance(device_type, str) and device_type != "mps" else "cpu"
|
||||
with torch.autocast(device_type=device_type, enabled=False):
|
||||
freqs = (inv_freq_expanded.float() @ position_ids_expanded.float()).transpose(1, 2)
|
||||
emb = torch.cat((freqs, freqs), dim=-1)
|
||||
cos = emb.cos()
|
||||
sin = emb.sin()
|
||||
|
||||
# Advanced RoPE types (e.g. yarn) apply a post-processing scaling factor, equivalent to scaling attention
|
||||
cos = cos * self.attention_scaling
|
||||
sin = sin * self.attention_scaling
|
||||
|
||||
return cos.to(dtype=x.dtype), sin.to(dtype=x.dtype)
|
||||
|
||||
class MixtralModel_Adapted(MixtralModel):
|
||||
def __init__(self, config: MixtralConfig):
|
||||
#super().__init__(config)
|
||||
super(MixtralModel, self).__init__(config)
|
||||
|
||||
self.padding_idx = config.pad_token_id
|
||||
self.vocab_size = config.vocab_size
|
||||
|
||||
self.embed_tokens = torch.nn.Embedding(config.vocab_size, config.hidden_size, self.padding_idx)
|
||||
self.layers = torch.nn.ModuleList(
|
||||
[MixtralDecoderLayer_Adapted(config, layer_idx) for layer_idx in range(config.num_hidden_layers)]
|
||||
)
|
||||
self.norm = MixtralRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
|
||||
self.rotary_emb = MixtralRotaryEmbedding(config)
|
||||
self.gradient_checkpointing = False
|
||||
|
||||
# Initialize weights and apply final processing
|
||||
self.post_init()
|
||||
|
||||
def _update_noncausal_mask(
|
||||
self,
|
||||
attention_mask,
|
||||
inputs_embeds,
|
||||
past_key_values_length,
|
||||
):
|
||||
# create noncausal mask
|
||||
# [bsz, seq_len] -> [bsz, 1, seq_len, seq_len]
|
||||
|
||||
bsz, seq_len, _ = inputs_embeds.size()
|
||||
|
||||
# generate default mask based on input
|
||||
if attention_mask is None:
|
||||
attention_mask = torch.ones( (bsz, seq_len), dtype=torch.bool, device=inputs_embeds.device )
|
||||
|
||||
# make square
|
||||
expanded_mask = attention_mask[:, None, None, :].expand( bsz, 1, seq_len, seq_len ).to( dtype=inputs_embeds.dtype )
|
||||
|
||||
# invert from 1.0 = attend, 0.0 = masked to 0.0 = valid, -inf = masked
|
||||
inverted_mask = 1.0 - expanded_mask
|
||||
return inverted_mask.masked_fill( inverted_mask.to(dtype=torch.bool), torch.finfo(inputs_embeds.dtype).min )
|
||||
|
||||
# gut out the things that just shoves responsibility on SDPA's is_causal generating a mask because this causes problems
|
||||
def _update_causal_mask(
|
||||
self,
|
||||
attention_mask: torch.Tensor,
|
||||
input_tensor: torch.Tensor,
|
||||
cache_position: torch.Tensor,
|
||||
past_key_values: Cache,
|
||||
output_attentions: bool,
|
||||
):
|
||||
"""
|
||||
if self.config._attn_implementation == "flash_attention_2":
|
||||
if attention_mask is not None and 0.0 in attention_mask:
|
||||
return attention_mask
|
||||
return None
|
||||
"""
|
||||
|
||||
past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0
|
||||
using_static_cache = isinstance(past_key_values, StaticCache)
|
||||
|
||||
"""
|
||||
# For SDPA, when possible, we will rely on its `is_causal` argument instead of its `attn_mask` argument, in
|
||||
# order to dispatch on Flash Attention 2. This feature is not compatible with static cache, as SDPA will fail
|
||||
# to infer the attention mask.
|
||||
# When output attentions is True, sdpa implementation's forward method calls the eager implementation's forward
|
||||
if self.config._attn_implementation == "sdpa" and not using_static_cache and not output_attentions:
|
||||
if AttentionMaskConverter._ignore_causal_mask_sdpa(
|
||||
attention_mask,
|
||||
inputs_embeds=input_tensor,
|
||||
past_key_values_length=past_seen_tokens,
|
||||
is_training=self.training,
|
||||
):
|
||||
return None
|
||||
"""
|
||||
|
||||
dtype, device = input_tensor.dtype, input_tensor.device
|
||||
sequence_length = input_tensor.shape[1]
|
||||
if using_static_cache:
|
||||
target_length = past_key_values.get_max_cache_shape()
|
||||
else:
|
||||
target_length = (
|
||||
attention_mask.shape[-1]
|
||||
if isinstance(attention_mask, torch.Tensor)
|
||||
else past_seen_tokens + sequence_length + 1
|
||||
)
|
||||
|
||||
# In case the provided `attention` mask is 2D, we generate a causal mask here (4D).
|
||||
causal_mask = self._prepare_4d_causal_attention_mask_with_cache_position(
|
||||
attention_mask,
|
||||
sequence_length=sequence_length,
|
||||
target_length=target_length,
|
||||
dtype=dtype,
|
||||
device=device,
|
||||
cache_position=cache_position,
|
||||
batch_size=input_tensor.shape[0],
|
||||
config=self.config,
|
||||
past_key_values=past_key_values,
|
||||
)
|
||||
|
||||
if (
|
||||
self.config._attn_implementation == "sdpa"
|
||||
and attention_mask is not None
|
||||
and attention_mask.device.type == "cuda"
|
||||
and not output_attentions
|
||||
):
|
||||
# Attend to all tokens in fully masked rows in the causal_mask, for example the relevant first rows when
|
||||
# using left padding. This is required by F.scaled_dot_product_attention memory-efficient attention path.
|
||||
# Details: https://github.com/pytorch/pytorch/issues/110213
|
||||
min_dtype = torch.finfo(dtype).min
|
||||
causal_mask = AttentionMaskConverter._unmask_unattended(causal_mask, min_dtype)
|
||||
|
||||
return causal_mask
|
||||
|
||||
def forward(
|
||||
self,
|
||||
input_ids: torch.LongTensor = None,
|
||||
attention_mask: Optional[torch.Tensor] = None,
|
||||
is_causal: 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,
|
||||
output_router_logits: Optional[bool] = None,
|
||||
return_dict: Optional[bool] = None,
|
||||
cache_position: Optional[torch.LongTensor] = None,
|
||||
**flash_attn_kwargs: Unpack[FlashAttentionKwargs],
|
||||
) -> Union[Tuple, BaseModelOutputWithPast]:
|
||||
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
|
||||
output_router_logits = (
|
||||
output_router_logits if output_router_logits is not None else self.config.output_router_logits
|
||||
)
|
||||
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
|
||||
|
||||
if (input_ids is None) ^ (inputs_embeds is not None):
|
||||
raise ValueError("You must specify exactly one of input_ids or inputs_embeds")
|
||||
|
||||
if self.gradient_checkpointing and self.training:
|
||||
if use_cache:
|
||||
logger.warning_once(
|
||||
"`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..."
|
||||
)
|
||||
use_cache = False
|
||||
|
||||
if use_cache and past_key_values is None:
|
||||
past_key_values = DynamicCache()
|
||||
|
||||
if inputs_embeds is None:
|
||||
inputs_embeds = self.embed_tokens(input_ids)
|
||||
|
||||
if cache_position is None:
|
||||
past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0
|
||||
cache_position = torch.arange(
|
||||
past_seen_tokens, past_seen_tokens + inputs_embeds.shape[1], device=inputs_embeds.device
|
||||
)
|
||||
if position_ids is None:
|
||||
position_ids = cache_position.unsqueeze(0)
|
||||
|
||||
#causal_mask = self._update_causal_mask(
|
||||
# attention_mask, inputs_embeds, cache_position, past_key_values, output_attentions
|
||||
#)
|
||||
# because we can attend to both a causal and a non-causal sequence, generate both masks then pick among which to use per batch
|
||||
if is_causal is not None:
|
||||
"""
|
||||
causal_mask = self._prepare_4d_causal_attention_mask_with_cache_position(
|
||||
attention_mask,
|
||||
sequence_length=inputs_embeds.shape[1],
|
||||
target_length=attention_mask.shape[-1] if attention_mask is not None else inputs_embeds.shape[1],
|
||||
dtype=inputs_embeds.dtype,
|
||||
device=inputs_embeds.device,
|
||||
cache_position=cache_position,
|
||||
batch_size=inputs_embeds.shape[0],
|
||||
)
|
||||
"""
|
||||
causal_mask = self._update_causal_mask(attention_mask, inputs_embeds, cache_position, past_key_values, output_attentions)
|
||||
noncausal_mask = self._update_noncausal_mask(attention_mask, inputs_embeds, past_key_values)
|
||||
|
||||
x_mask = torch.stack( [ causal_mask[i, :, :, :] if state else noncausal_mask[i, :, :, :] for i, state in enumerate( is_causal ) ], dim=0 )
|
||||
else:
|
||||
x_mask = self._update_causal_mask(attention_mask, inputs_embeds, cache_position, past_key_values, output_attentions)
|
||||
|
||||
hidden_states = inputs_embeds
|
||||
|
||||
# create position embeddings to be shared across the decoder layers
|
||||
position_embeddings = self.rotary_emb(hidden_states, position_ids)
|
||||
|
||||
# decoder layers
|
||||
all_hidden_states = () if output_hidden_states else None
|
||||
all_self_attns = () if output_attentions else None
|
||||
all_router_logits = () if output_router_logits else None
|
||||
|
||||
for decoder_layer in self.layers:
|
||||
if output_hidden_states:
|
||||
all_hidden_states += (hidden_states,)
|
||||
|
||||
if self.gradient_checkpointing and self.training:
|
||||
layer_outputs = self._gradient_checkpointing_func(
|
||||
decoder_layer.__call__,
|
||||
hidden_states,
|
||||
causal_mask,
|
||||
is_causal,
|
||||
position_ids,
|
||||
past_key_values,
|
||||
output_attentions,
|
||||
output_router_logits,
|
||||
use_cache,
|
||||
cache_position,
|
||||
position_embeddings,
|
||||
)
|
||||
else:
|
||||
layer_outputs = decoder_layer(
|
||||
hidden_states,
|
||||
attention_mask=causal_mask,
|
||||
is_causal=is_causal,
|
||||
position_ids=position_ids,
|
||||
past_key_value=past_key_values,
|
||||
output_attentions=output_attentions,
|
||||
output_router_logits=output_router_logits,
|
||||
use_cache=use_cache,
|
||||
cache_position=cache_position,
|
||||
position_embeddings=position_embeddings,
|
||||
**flash_attn_kwargs,
|
||||
)
|
||||
|
||||
hidden_states = layer_outputs[0]
|
||||
|
||||
if output_attentions:
|
||||
all_self_attns += (layer_outputs[1],)
|
||||
|
||||
if output_router_logits:
|
||||
all_router_logits += (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,)
|
||||
|
||||
output = MoeModelOutputWithPast(
|
||||
last_hidden_state=hidden_states,
|
||||
past_key_values=past_key_values,
|
||||
hidden_states=all_hidden_states,
|
||||
attentions=all_self_attns,
|
||||
router_logits=all_router_logits,
|
||||
)
|
||||
return output if return_dict else output.to_tuple()
|
||||
|
|
@ -650,55 +650,11 @@ class Base(nn.Module):
|
|||
norm_type="ln", # adaln
|
||||
n_levels=self.n_resp_levels,
|
||||
) for _ in range(n_layers) ])
|
||||
elif self.arch_type in ["mistral", "mixtral"]:
|
||||
if n_experts <= 1:
|
||||
self.model = MistralModel(MistralConfig(
|
||||
vocab_size=n_vocab,
|
||||
hidden_size=d_model,
|
||||
max_position_embeddings=max_position_embeddings,
|
||||
intermediate_size=d_model*4,
|
||||
num_hidden_layers=n_layers,
|
||||
num_attention_heads=n_heads,
|
||||
attention_dropout=p_dropout if training else 0.0,
|
||||
num_key_value_heads=self.config.experimental.kv_heads if self.config is not None and self.config.experimental.kv_heads > 0 else n_heads,
|
||||
hidden_act="gelu",
|
||||
is_encoder_decoder=False,
|
||||
is_decoder=True,
|
||||
attn_implementation=hf_attention,
|
||||
#gradient_checkpointing=self.gradient_checkpointing,
|
||||
))
|
||||
else:
|
||||
self.model = MixtralModel(MixtralConfig(
|
||||
vocab_size =n_resp_tokens,
|
||||
hidden_size=d_model,
|
||||
max_position_embeddings=max_position_embeddings,
|
||||
intermediate_size=d_model*4,
|
||||
num_hidden_layers=n_layers,
|
||||
num_attention_heads=n_heads,
|
||||
attention_dropout=p_dropout if training else 0.0,
|
||||
num_key_value_heads=self.config.experimental.kv_heads if self.config is not None and self.config.experimental.kv_heads > 0 else n_heads,
|
||||
sliding_window=75 * 12, # 12 second context window
|
||||
output_router_logits=training,
|
||||
hidden_act="gelu",
|
||||
is_encoder_decoder=False,
|
||||
is_decoder=True,
|
||||
num_local_experts=n_experts,
|
||||
num_experts_per_tok=min(2, n_experts),
|
||||
attn_implementation=hf_attention,
|
||||
#gradient_checkpointing=self.gradient_checkpointing,
|
||||
))
|
||||
if attention_backend not in HF_ATTENTIONS:
|
||||
self.model = ml.replace_attention( self.model, klass=MixtralAttention_Adapted, target=MixtralAttention, mode=attention_backend )
|
||||
|
||||
if self.gradient_checkpointing and not self.model.gradient_checkpointing:
|
||||
self.model.gradient_checkpointing_enable(gradient_checkpointing_kwargs=dict(
|
||||
use_reentrant=False
|
||||
))
|
||||
elif self.arch_type == "llama":
|
||||
elif self.arch_type in ["llama", "mistral", "mixtral"]:
|
||||
LlamaClass = LlamaModel_Adapted # if (self.layerskip or "len" in self.capabilities) else LlamaModel
|
||||
|
||||
if n_experts <= 1:
|
||||
config = LlamaConfig(
|
||||
self.model = LlamaClass(LlamaConfig(
|
||||
vocab_size=n_vocab,
|
||||
hidden_size=d_model,
|
||||
max_position_embeddings=max_position_embeddings,
|
||||
|
|
@ -707,20 +663,19 @@ class Base(nn.Module):
|
|||
num_attention_heads=n_heads,
|
||||
attention_dropout=p_dropout if training else 0.0,
|
||||
num_key_value_heads=n_heads,
|
||||
sliding_window=75 * 12, # 12 second context window
|
||||
#sliding_window=75 * 12, # 12 second context window
|
||||
hidden_act="gelu",
|
||||
is_encoder_decoder=False,
|
||||
is_decoder=True,
|
||||
attn_implementation=hf_attention,
|
||||
#gradient_checkpointing=self.gradient_checkpointing,
|
||||
)
|
||||
self.model = LlamaClass(config)
|
||||
))
|
||||
|
||||
# replace with desired attention
|
||||
if attention_backend not in HF_ATTENTIONS:
|
||||
self.model = ml.replace_attention( self.model, klass=LlamaAttention_Adapted, target=LlamaAttention, mode=attention_backend )
|
||||
else:
|
||||
self.model = MixtralModel(MixtralConfig(
|
||||
self.model = MixtralModel_Adapted(MixtralConfig(
|
||||
vocab_size =n_resp_tokens,
|
||||
hidden_size=d_model,
|
||||
max_position_embeddings=max_position_embeddings,
|
||||
|
|
@ -729,7 +684,7 @@ class Base(nn.Module):
|
|||
num_attention_heads=n_heads,
|
||||
attention_dropout=p_dropout if training else 0.0,
|
||||
num_key_value_heads=n_heads,
|
||||
sliding_window=75 * 12, # 12 second context window
|
||||
#sliding_window=75 * 12, # 12 second context window
|
||||
output_router_logits=training,
|
||||
hidden_act="gelu",
|
||||
is_encoder_decoder=False,
|
||||
|
|
@ -886,8 +841,9 @@ class Base(nn.Module):
|
|||
hidden_states = output["hidden_states"]
|
||||
|
||||
if self.n_experts > 1 and self.training:
|
||||
router_logits = output["aux_loss"]
|
||||
aux_loss = self.model.config.router_aux_loss_coef * load_balancing_loss_func( router_logits, self.model.config.num_local_experts, self.model.config.num_experts_per_tok )
|
||||
router_logits = output["router_logits"]
|
||||
aux_loss = self.model.config.router_aux_loss_coef * load_balancing_loss_func( router_logits, self.model.config.num_local_experts, self.model.config.num_experts_per_tok, m )
|
||||
|
||||
elif self.arch_type == "transformer":
|
||||
# ensures we specify a quant_level for the transformer implementation's AdaLN
|
||||
l = torch.zeros((batch_size,), dtype=torch.int32) if quant_levels is None else quant_levels
|
||||
|
|
|
|||
Loading…
Reference in New Issue
Block a user