# coding=utf-8 # Copyright 2024 AI21 Labs Ltd. 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 FastSLM model.""" import inspect import math import copy import warnings from dataclasses import dataclass, field from typing import Any, Dict, List, Optional, Tuple, Union import time from collections import OrderedDict from functools import partial import numpy as np import os import torch import torch.nn.functional as F import torch.utils.checkpoint from torch import nn from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss torch._inductor.config.max_autotune_gemm_backends = ["aten"] from transformers.activations import ACT2FN from transformers.cache_utils import Cache, DynamicCache from transformers.modeling_outputs import ( MoeCausalLMOutputWithPast, MoeModelOutputWithPast, SequenceClassifierOutputWithPast, ) from transformers.modeling_utils import PreTrainedModel try: from transformers.modeling_utils import ALL_ATTENTION_FUNCTIONS except ImportError: pass from transformers.pytorch_utils import is_torch_greater_or_equal_than_1_13 from transformers.utils import ( add_start_docstrings, add_start_docstrings_to_model_forward, is_flash_attn_greater_or_equal_2_10, logging, replace_return_docstrings, ) from transformers.utils.import_utils import is_torch_fx_available from .configuration_fast_slm import FastSLMConfig from torch.utils.checkpoint import checkpoint import torch.distributed as dist import math import random 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 _flash_supports_window_size = "window_size" in list(inspect.signature(flash_attn_func).parameters) from einops import rearrange, repeat, reduce, pack, unpack from einops.layers.torch import Rearrange from mamba_ssm.ops.selective_scan_interface import mamba_inner_fn, selective_scan_fn from mamba_ssm.ops.triton.selective_state_update import selective_state_update from causal_conv1d import causal_conv1d_fn, causal_conv1d_update from .fused_mha_with_cache import fused_mha_interface from .mamba2 import Mamba2 from mamba_ssm.utils.generation import InferenceParams from .delta_net import Cache as fla_cache from .delta_net import DeltaNet import torch._dynamo torch._dynamo.config.suppress_errors = True from torch.cuda import CUDAGraph logger = logging.get_logger(__name__) _CONFIG_FOR_DOC = "FastSLMConfig" # Copied from transformers.models.llama.modeling_llama._get_unpad_data 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.int32), (1, 0)) return ( indices, cu_seqlens, max_seqlen_in_batch, ) ### Copied from transformers.models.llama.modeling_llama.LlamaRMSNorm with Llama->FastSLM class FastSLMRMSNorm(nn.Module): def __init__(self, hidden_size, learnable_weight=True, eps=1e-6): """ FastSLMRMSNorm is equivalent to T5LayerNorm """ super().__init__() if learnable_weight: self.weight = nn.Parameter(torch.ones(hidden_size)) else: self.weight = None 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) if self.weight is not None: return self.weight * hidden_states.to(input_dtype) else: return hidden_states.to(input_dtype) class LlamaRotaryEmbedding(nn.Module): def __init__(self, config, dim, base=10000, device=None, scaling_factor=1.0): super().__init__() self.scaling_factor = scaling_factor self.dim = dim self.base = base self.config = config self.rope_type = config.rope_type self.factor = 2 max_position_embeddings = self.config.max_position_embeddings if config.rope_type is None or config.rope_type == "default": inv_freq = 1.0 / (self.base ** (torch.arange(0, self.dim, 2, dtype=torch.int64).float().to(device) / self.dim)) self.max_seq_len_cached = max_position_embeddings elif config.rope_type == 'ntk': assert self.config.orig_max_position_embeddings is not None orig_max_position_embeddings = self.config.orig_max_position_embeddings base = base * ((self.factor * max_position_embeddings / orig_max_position_embeddings) - (self.factor - 1)) ** (self.dim / (self.dim - 2)) inv_freq = 1.0 / (base ** (torch.arange(0, self.dim, 2, dtype=torch.int64).float().to(device) / self.dim)) self.max_seq_len_cached = orig_max_position_embeddings elif config.rope_type == 'dynamic_ntk': inv_freq = 1.0 / (self.base ** (torch.arange(0, self.dim, 2, dtype=torch.int64).float().to(device) / self.dim)) self.original_inv_freq = inv_freq self.max_seq_len_cached = self.config.orig_max_position_embeddings else: raise ValueError(f"Not support rope_type: {config.rope_type}") self.register_buffer("inv_freq", inv_freq, persistent=False) 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 base = self.base * ((self.factor * seq_len / self.config.orig_max_position_embeddings) - (self.factor - 1)) ** (self.dim / (self.dim - 2)) inv_freq = 1.0 / (base ** (torch.arange(0, self.dim, 2, dtype=torch.int64).float().to(device) / self.dim)) self.register_buffer("inv_freq", inv_freq, persistent=False) self.max_seq_len_cached = seq_len if seq_len < self.config.orig_max_position_embeddings and self.max_seq_len_cached > self.config.orig_max_position_embeddings: # reset self.register_buffer("inv_freq", self.original_inv_freq, persistent=False) self.max_seq_len_cached = self.config.orig_max_position_embeddings @torch.no_grad() def forward(self, x, position_ids): if self.rope_type == 'dynamic_ntk': self._dynamic_frequency_update(position_ids, device=x.device) # x: [bs, num_attention_heads, seq_len, head_size] 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 since bfloat16 loses precision on long contexts # 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() return cos.to(dtype=x.dtype), sin.to(dtype=x.dtype) 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) def apply_rotary_pos_emb(q, k, cos, sin, position_ids=None, 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`, *optional*): Deprecated and unused. 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.unsqueeze(unsqueeze_dim) sin = sin.unsqueeze(unsqueeze_dim) if q is not None: q_embed = (q * cos) + (rotate_half(q) * sin) else: q_embed = None if k is not None: k_embed = (k * cos) + (rotate_half(k) * sin) else: k_embed = None return q_embed, k_embed # 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) class HybridMambaAttentionDynamicCache(DynamicCache): """ A dynamic cache that can handle both the attention cache (which has a seq_len dimension) and the mamba cache (which has a constant shape regardless of seq_len). This cache has two sets of lists of tensors: `key_cache` and `value_cache` for attention cache and `conv_states` and `ssm_states` for mamba cache. Each of these lists has `num_layers` tensors. The expected shape for each tensor For attention layers, `key_cache` and `value_cache` have a shape of `(batch_size, num_heads, seq_len, head_dim)`, while `conv_states` and `ssm_states` have a shape of `(batch_size, 0)` (empty tensors). For mamba layers, `key_cache` and `value_cache` have a shape of `(batch_size, 0)` (empty tensors), while `conv_states` represents the convolution state and has a shape of `(batch_size, d_inner, d_conv)`, and `ssm_states` represents the ssm state and has a shape of `(batch_size, d_inner, d_state)`. """ def __init__(self, config, batch_size, dtype=torch.float16, device=None, layer_type=None): self.dtype = dtype # self.layers_block_type = config.layers_block_type intermediate_size = config.mamba_expand * config.hidden_size ssm_state_size = config.mamba_d_state conv_kernel_size = config.mamba_d_conv self.conv_states = [] self.ssm_states = [] self.layer_type = layer_type for i in range(config.num_hidden_layers): has_mamba_state = self.layer_type[i] == 'h' or self.layer_type[i] == 'm' if has_mamba_state: if hasattr(config, 'conv_dim'): conv_dim = config.conv_dim[str(i)] else: conv_dim = intermediate_size self.conv_states += [ torch.zeros(batch_size, conv_dim, conv_kernel_size, device=device, dtype=dtype) ] self.ssm_states += [ torch.zeros(batch_size, intermediate_size, ssm_state_size, device=device, dtype=dtype) ] else: self.conv_states += [torch.tensor([[]] * batch_size, device=device)] self.ssm_states += [torch.tensor([[]] * batch_size, device=device)] self.key_cache = [torch.tensor([[]] * batch_size, device=device) for _ in range(config.num_hidden_layers)] self.value_cache = [torch.tensor([[]] * batch_size, device=device) for _ in range(config.num_hidden_layers)] self.mamba_past_length = [0 for _ in range(config.num_hidden_layers)] def update( self, key_states: torch.Tensor, value_states: torch.Tensor, layer_idx: int, cache_kwargs: Optional[Dict[str, Any]] = None, ) -> Tuple[torch.Tensor, torch.Tensor]: # Update the cache if self.key_cache[layer_idx].shape[-1] == 0: self.key_cache[layer_idx] = key_states self.value_cache[layer_idx] = value_states else: self.key_cache[layer_idx] = torch.cat([self.key_cache[layer_idx], key_states], dim=2) self.value_cache[layer_idx] = torch.cat([self.value_cache[layer_idx], value_states], dim=2) return self.key_cache[layer_idx], self.value_cache[layer_idx] def reorder_cache(self, beam_idx: torch.LongTensor): """Reorders the cache for beam search, given the selected beam indices.""" for layer_idx in range(len(self.key_cache)): device = self.key_cache[layer_idx].device self.key_cache[layer_idx] = self.key_cache[layer_idx].index_select(0, beam_idx.to(device)) device = self.value_cache[layer_idx].device self.value_cache[layer_idx] = self.value_cache[layer_idx].index_select(0, beam_idx.to(device)) device = self.conv_states[layer_idx].device self.conv_states[layer_idx] = self.conv_states[layer_idx].index_select(0, beam_idx.to(device)) device = self.ssm_states[layer_idx].device self.ssm_states[layer_idx] = self.ssm_states[layer_idx].index_select(0, beam_idx.to(device)) def get_seq_length(self, layer_idx=None) -> int: """Returns the sequence length of the cached states. A layer index can be optionally passed.""" # take any layer that contains cache and not empty tensor if layer_idx is None: max_mamba_len = max(self.mamba_past_length) if max_mamba_len > 0: return max_mamba_len else: max_key_len = max(cache.shape[-2] for cache in self.key_cache) return max_key_len if self.layer_type[layer_idx] == 'm': return self.mamba_past_length[layer_idx] if self.key_cache[layer_idx].shape[-1] == 0: return 0 return self.key_cache[layer_idx].shape[-2] # def get_max_length(self) -> Optional[int]: # """Returns the maximum sequence length of the cached states. Cache does not have a maximum length.""" # return None def to_legacy_cache(self) -> Tuple[Tuple[torch.Tensor], Tuple[torch.Tensor]]: raise NotImplementedError("HybridMambaAttentionDynamicCache does not have a legacy cache equivalent.") @classmethod def from_legacy_cache(cls, past_key_values: Optional[Tuple[Tuple[torch.FloatTensor]]] = None) -> "DynamicCache": raise NotImplementedError("HybridMambaAttentionDynamicCache does not have a legacy cache equivalent.") @dataclass class MambaCacheParams: seqlen_offset: int = 0 conv_states: Dict[int, torch.Tensor] = field(default_factory=dict) ssm_states: Dict[int, torch.Tensor] = field(default_factory=dict) # Adapted from transformers.models.mistral.modeling_mistral.MistralAttention with Mistral->FastSLM class FastSLMAttention(nn.Module): """ Multi-headed attention from 'Attention Is All You Need' paper. Modified to use sliding window attention: Longformer and "Generating Long Sequences with Sparse Transformers". """ def __init__(self, config: FastSLMConfig, layer_idx: Optional[int] = None, input_hidden_size=None, output_hidden_size=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 a `layer_idx` is not recommended and will " "lead to errors during the forward call if caching is used. Please make sure to provide a `layer_idx` " "when creating this class." ) # self.hidden_size = config.hidden_size self.hidden_size = config.attn_hidden_size if config.attn_hidden_size > 0 else config.hidden_size self.num_heads = config.num_attention_heads self.head_dim = self.hidden_size // self.num_heads self.max_position_embeddings = config.max_position_embeddings self.rope_theta = config.rope_theta self.kq_head_dim = config.kq_head_dim if config.kq_head_dim > 0 else self.head_dim self.v_head_dim = config.v_head_dim if config.v_head_dim > 0 else self.head_dim self.num_key_value_heads = config.num_key_value_heads self.num_key_value_groups = self.num_heads // self.num_key_value_heads self.is_causal = True self.attention_dropout = config.attention_dropout if (self.head_dim * self.num_heads) != self.hidden_size and self.kq_head_dim == self.head_dim: raise ValueError( f"hidden_size must be divisible by num_heads (got `hidden_size`: {self.hidden_size}" f" and `num_heads`: {self.num_heads})." ) self.q_proj = nn.Linear(self.hidden_size if input_hidden_size is None else input_hidden_size, self.num_heads * self.kq_head_dim, bias=False) self.k_proj = nn.Linear(self.hidden_size if input_hidden_size is None else input_hidden_size, self.num_key_value_heads * self.kq_head_dim, bias=False) self.v_proj = nn.Linear(self.hidden_size if input_hidden_size is None else input_hidden_size, self.num_key_value_heads * self.v_head_dim, bias=False) if output_hidden_size is None: output_hidden_size = self.hidden_size self.o_proj = nn.Linear(self.num_heads * self.v_head_dim, output_hidden_size, bias=False) if self.config.kq_norm == "rms": self.k_norm = FastSLMRMSNorm(self.kq_head_dim) self.q_norm = FastSLMRMSNorm(self.kq_head_dim) elif self.config.kq_norm == "none": self.k_norm = None self.q_norm = None else: raise NotImplementedError(f"Unknown kq_norm: {self.config.kq_norm}") if self.config.rope: # print("===> Using Rotary Position Embedding") self._init_rope() def _init_rope(self): # assert 1==0, f"max_position_embeddings: {self.max_position_embeddings}" self.rotary_emb = LlamaRotaryEmbedding( config=self.config, dim=self.kq_head_dim, base=self.rope_theta, device=torch.device("cuda"), ) def _shape(self, tensor: torch.Tensor, seq_len: int, bsz: int): return tensor.view(bsz, seq_len, self.num_heads, self.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, # kv_proj_last_layer = None, use_swa=False, query_states = None, key_states=None, value_states=None, **kwargs, ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]: raise NotImplementedError("FastSLMAttention is an abstract class. Use one of the subclasses.") # Adapted from transformers.models.mistral.modeling_mistral.MistralFlashAttention2 with Mistral->FastSLM class FastSLMFlashAttention2(FastSLMAttention): """ FastSLM flash attention module. This module inherits from `FastSLMAttention` 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. """ # Copied from transformers.models.llama.modeling_llama.LlamaFlashAttention2.__init__ 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 = None, 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, # kv_proj_last_layer = None, use_swa=False, query_states = None, key_states=None, value_states=None, **kwargs, ): 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") bsz, q_len, _ = hidden_states.size() query_states = self.q_proj(hidden_states) query_states = query_states.view(bsz, q_len, self.num_heads, self.kq_head_dim).transpose(1, 2).contiguous() if self.q_norm is not None: query_states = self.q_norm(query_states) # we do kq_norm first before rope according to # https://github.com/huggingface/transformers/blob/6c1d0b069de22d7ed8aa83f733c25045eea0585d/src/transformers/models/cohere/modeling_cohere.py#L568 if self.config.rope: cos, sin = self.rotary_emb(hidden_states, position_ids) query_states, _ = apply_rotary_pos_emb(query_states, None, cos, sin) key_states = self.k_proj(hidden_states) value_states = self.v_proj(hidden_states) key_states = key_states.view(bsz, q_len, self.num_key_value_heads, self.kq_head_dim).transpose(1, 2) value_states = value_states.view(bsz, q_len, self.num_key_value_heads, self.v_head_dim).transpose(1, 2) if self.k_norm is not None: key_states = self.k_norm(key_states) if self.config.rope: _, key_states = apply_rotary_pos_emb(None, key_states, cos, sin) kv_seq_len = key_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) use_sliding_windows = ( _flash_supports_window_size and getattr(self.config, "sliding_window", None) is not None # and kv_seq_len > (self.config.sliding_window + self.config.num_memory_tokens if self.config.num_memory_tokens > 0 else self.config.sliding_window) and kv_seq_len > self.config.sliding_window and use_swa ) if not _flash_supports_window_size: logger.warning_once( "The current flash attention version does not support sliding window attention, for a more memory efficient implementation" " make sure to upgrade flash-attn library." ) swa_processed_flag = False if past_key_value is not None and use_cache: kv_layer_idx = self.layer_idx cache_has_contents = past_key_value.get_seq_length(kv_layer_idx) > 0 if ( getattr(self.config, "sliding_window", None) is not None # and kv_seq_len > (self.config.sliding_window + self.config.num_memory_tokens if self.config.num_memory_tokens > 0 else self.config.sliding_window) and kv_seq_len > self.config.sliding_window and cache_has_contents and use_swa ): slicing_tokens = 1 - self.config.sliding_window past_key = past_key_value[kv_layer_idx][0] past_value = past_key_value[kv_layer_idx][1] past_key = past_key[:, :, slicing_tokens:, :].contiguous() past_value = past_value[:, :, slicing_tokens:, :].contiguous() past_key_value.key_cache[kv_layer_idx] = past_key past_key_value.value_cache[kv_layer_idx] = past_value if attention_mask is not None: attention_mask = attention_mask[:, slicing_tokens:] attention_mask = torch.cat([attention_mask, torch.ones_like(attention_mask[:, -1:])], dim=-1) swa_processed_flag = True key_states, value_states = past_key_value.update(key_states, value_states, kv_layer_idx) # repeat k/v heads if n_kv_heads < n_heads key_states_no_repeat = key_states value_states_no_repeat = value_states key_states = repeat_kv(key_states, self.num_key_value_groups) value_states = repeat_kv(value_states, self.num_key_value_groups) dropout_rate = 0.0 if not self.training else self.attention_dropout # 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 float16 just to be sure everything works as expected. input_dtype = query_states.dtype if input_dtype == torch.float32: if torch.is_autocast_enabled(): target_dtype = torch.get_autocast_gpu_dtype() # Handle the case where the model is quantized elif hasattr(self.config, "_pre_quantization_dtype"): target_dtype = self.config._pre_quantization_dtype else: target_dtype = self.q_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) # Reashape to the expected shape for Flash Attention query_states = query_states.transpose(1, 2) # (batch, slen, num_heads, head_dim) key_states = key_states.transpose(1, 2) # (batch, slen, num_heads, head_dim) value_states = value_states.transpose(1, 2) # (batch, slen, num_heads, head_dim) attn_output = self._flash_attention_forward( query_states, key_states, value_states, attention_mask, q_len, dropout=dropout_rate, use_sliding_windows=use_sliding_windows and not swa_processed_flag, ) v_dim = value_states.shape[-2] * value_states.shape[-1] attn_output = attn_output.reshape(-1, q_len, v_dim).contiguous() attn_output = self.o_proj(attn_output) if not output_attentions: attn_weights = None return attn_output, attn_weights, past_key_value, (key_states_no_repeat, value_states_no_repeat) def _flash_attention_forward( self, query_states, key_states, value_states, attention_mask, query_length, dropout=0.0, softmax_scale=None, use_sliding_windows=False, ): """ 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) use_sliding_windows (`bool`, *optional*): Whether to activate sliding window attention. """ 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 LlamaFlashAttention2 __init__. causal = self.is_causal and query_length != 1 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 if not use_sliding_windows: 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, ) else: 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, window_size=(self.config.sliding_window, self.config.sliding_window), ) attn_output = pad_input(attn_output_unpad, indices_q, batch_size, query_length) else: if not use_sliding_windows: attn_output = flash_attn_func( query_states, key_states, value_states, dropout, softmax_scale=softmax_scale, causal=causal, ) else: attn_output = flash_attn_func( query_states, key_states, value_states, dropout, softmax_scale=softmax_scale, causal=causal, window_size=(self.config.sliding_window, self.config.sliding_window), ) return attn_output def _upad_input(self, query_layer, key_layer, value_layer, attention_mask, query_length): batch_size, kv_seq_len, num_heads, head_dim = key_layer.shape # On the first iteration we need to properly re-create the padding mask # by slicing it on the proper place if kv_seq_len != attention_mask.shape[-1]: attention_mask_num_tokens = attention_mask.shape[-1] attention_mask = attention_mask[:, attention_mask_num_tokens - kv_seq_len :] indices_k, cu_seqlens_k, max_seqlen_in_batch_k = _get_unpad_data(attention_mask) if not self.training and not type(key_layer) == torch.Tensor: ## this is for handling Mamba2 with output type key_layer = torch.tensor(key_layer.clone()) value_layer = torch.tensor(value_layer.clone()) query_layer = torch.tensor(query_layer.clone()) key_layer = index_first_axis(key_layer.reshape(batch_size * kv_seq_len, num_heads, head_dim), indices_k) value_layer = index_first_axis(value_layer.reshape(batch_size * kv_seq_len, num_heads, head_dim), indices_k) if query_length == kv_seq_len: query_layer = index_first_axis( query_layer.reshape(batch_size * kv_seq_len, 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), ) class FastSLMSDPAAttention(nn.Module): def __init__(self, config, layer_idx: int, reuse_kv=False): super().__init__() self.config = config self.layer_idx = layer_idx self.head_dim = getattr(config, "head_dim", config.hidden_size // config.num_attention_heads) self.num_key_value_groups = config.num_attention_heads // config.num_key_value_heads self.scaling = self.head_dim**-0.5 self.attention_dropout = config.attention_dropout self.is_causal = True self.q_proj = nn.Linear( config.hidden_size, config.num_attention_heads * self.head_dim, bias=False ) self.k_proj = nn.Linear( config.hidden_size, config.num_key_value_heads * self.head_dim, bias=False ) self.v_proj = nn.Linear( config.hidden_size, config.num_key_value_heads * self.head_dim, bias=False ) self.o_proj = nn.Linear( config.num_attention_heads * self.head_dim, config.hidden_size, bias=False ) self.sliding_window = self.config.sliding_window if self.layer_idx not in self.config.global_attn_idx else None def forward( self, hidden_states: torch.Tensor, # position_embeddings: tuple[torch.Tensor, torch.Tensor], attention_mask: Optional[torch.Tensor], past_key_value: Optional[Cache] = None, **kwargs, ) -> tuple[torch.Tensor, Optional[torch.Tensor]]: input_shape = hidden_states.shape[:-1] hidden_shape = (*input_shape, -1, self.head_dim) query_states = self.q_proj(hidden_states).view(hidden_shape).transpose(1, 2) key_states = self.k_proj(hidden_states).view(hidden_shape).transpose(1, 2) value_states = self.v_proj(hidden_states).view(hidden_shape).transpose(1, 2) # cos, sin = position_embeddings # query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin) if past_key_value is not None: key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx) # , cache_kwargs) attention_interface = ALL_ATTENTION_FUNCTIONS['flash_attention_2'] attn_output, attn_weights = attention_interface( self, query_states, key_states, value_states, attention_mask, dropout=0.0 if not self.training else self.attention_dropout, scaling=self.scaling, sliding_window=self.sliding_window, # diff with Llama **kwargs, ) attn_output = attn_output.reshape(*input_shape, -1).contiguous() attn_output = self.o_proj(attn_output) return attn_output, attn_weights, past_key_value, (key_states, value_states) class FastSLMFused_MHA(FastSLMAttention): """ FastSLM flash attention module. This module inherits from `FastSLMAttention` 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. """ # Copied from transformers.models.llama.modeling_llama.LlamaFlashAttention2.__init__ def __init__(self, *args, **kwargs): super().__init__(*args, **kwargs) self.fused_mha_interface = fused_mha_interface # self.init_kv_cache(max_batch_size=1, max_seq_len=8000) def init_kv_cache(self, max_batch_size, max_seq_len, page_size=-1): if hasattr(self, 'k_cache'): del self.k_cache del self.v_cache if hasattr(self, 'page_table') and self.page_table is not None: del self.page_table import gc gc.collect() torch.cuda.empty_cache() if page_size is not None and page_size > 0: batch_max_pages = (max_seq_len + page_size - 1) // page_size cache_max_pages = (max_batch_size * max_seq_len + page_size - 1) // page_size self.k_cache = torch.zeros(cache_max_pages, page_size, self.num_key_value_heads, self.kq_head_dim).to(self.q_proj.weight) self.v_cache = torch.zeros(cache_max_pages, page_size, self.num_key_value_heads, self.v_head_dim).to(self.q_proj.weight) self.page_table = torch.zeros(max_batch_size, batch_max_pages, device=self.q_proj.weight.device, dtype=torch.int32) else: self.k_cache = torch.zeros(max_batch_size, max_seq_len, self.num_key_value_heads, self.kq_head_dim).to(self.q_proj.weight) self.v_cache = torch.zeros(max_batch_size, max_seq_len, self.num_key_value_heads, self.v_head_dim).to(self.q_proj.weight) self.page_table = None self.max_seq_len = max_seq_len def reset_kv_cache(self): self.k_cache = self.k_cache.zero_() self.v_cache = self.v_cache.zero_() if self.page_table is not None: self.page_table = self.page_table.zero_() def forward( self, hidden_states: torch.Tensor = None, 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, use_swa=False, query_states = None, key_states=None, value_states=None, **kwargs, ): # print(f"Flash Attn - layer_idx: {self.layer_idx}, attn_mask is none: {attention_mask is None}") # print(f"layer_idx: {self.layer_idx}, use_swq: {use_swa}") if not hasattr(self, 'k_cache'): self.init_kv_cache(max_batch_size=1, max_seq_len=8000) 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.`" ) attention_mask = kwargs.pop("padding_mask") bsz, q_len, _ = hidden_states.size() query_states = self.q_proj(hidden_states) query_states = query_states.view(bsz, q_len, self.num_heads, self.kq_head_dim).transpose(1, 2).contiguous() if self.q_norm is not None: query_states = self.q_norm(query_states) # we do kq_norm first before rope according to # https://github.com/huggingface/transformers/blob/6c1d0b069de22d7ed8aa83f733c25045eea0585d/src/transformers/models/cohere/modeling_cohere.py#L568 if self.config.rope: cos, sin = self.rotary_emb(hidden_states, position_ids) query_states, _ = apply_rotary_pos_emb(query_states, None, cos, sin) key_states = self.k_proj(hidden_states) value_states = self.v_proj(hidden_states) key_states = key_states.view(bsz, q_len, self.num_key_value_heads, self.kq_head_dim).transpose(1, 2) value_states = value_states.view(bsz, q_len, self.num_key_value_heads, self.v_head_dim).transpose(1, 2) if self.k_norm is not None: key_states = self.k_norm(key_states) if self.config.rope: # cos, sin = self.rotary_emb(hidden_states, position_ids) _, key_states = apply_rotary_pos_emb(None, key_states, cos, sin) key_states_no_repeat = key_states value_states_no_repeat = value_states # Reashape to the expected shape for Flash Attention query_states = query_states.transpose(1, 2) # (batch, slen, num_heads, head_dim) key_states = key_states.transpose(1, 2) # (batch, slen, num_kv_heads, head_dim) value_states = value_states.transpose(1, 2) # (batch, slen, num_kv_heads, head_dim) if self.k_cache.device != query_states.device: self.k_cache = self.k_cache.to(query_states) self.v_cache = self.v_cache.to(query_states) attn_output = self.fused_mha_interface( query_states, key_states, value_states, k_cache=self.k_cache, v_cache=self.v_cache, page_table=self.page_table, max_seq_len=self.max_seq_len, position_ids=position_ids, ) v_dim = query_states.shape[-2] * value_states.shape[-1] attn_output = attn_output.reshape(bsz, q_len, v_dim).contiguous() attn_output = self.o_proj(attn_output) if not output_attentions: attn_weights = None return attn_output, attn_weights, past_key_value, (key_states_no_repeat, value_states_no_repeat) JAMBA_ATTENTION_CLASSES = { "flash_attention_2": FastSLMFlashAttention2, "fused_mha": FastSLMFused_MHA, "sdpa": FastSLMSDPAAttention, } class FastSLMMLP(nn.Module): def __init__(self, config: FastSLMConfig, layer_idx: int): super().__init__() self.config = config self.act_fn_name = config.mlp_hidden_act self.act_fn = ACT2FN[self.act_fn_name] if config.ffn_expand_ratio is not None: self.ffn_dim = int(config.ffn_expand_ratio * config.hidden_size) // 128 * 128 else: self.ffn_dim = config.intermediate_size self.hidden_dim = config.hidden_size self.layer_idx = layer_idx if self.act_fn_name == "silu": self.gate_proj = nn.Linear(self.hidden_dim, self.ffn_dim, bias=False) self.down_proj = nn.Linear(self.ffn_dim, self.hidden_dim, bias=False) self.up_proj = nn.Linear(self.hidden_dim, self.ffn_dim, bias=False) def forward(self, x): if self.act_fn_name == "silu": output = self.down_proj(self.act_fn(self.gate_proj(x)) * self.up_proj(x)) elif self.act_fn_name == "relu2": output = self.down_proj(self.act_fn(self.up_proj(x))) else: raise NotImplementedError(f"No such hidden_act: {self.act_fn_name}") return output # Adapted from transformers.models.mixtral.modeling_mixtral.MixtralSparseMoeBlock with Mistral->FastSLM class FastSLMSparseMoeBlock(nn.Module): """ This implementation is strictly equivalent to standard MoE with full capacity (no dropped tokens). It's faster since it formulates MoE operations in terms of block-sparse operations to accomodate imbalanced assignments of tokens to experts, whereas standard MoE either (1) drop tokens at the cost of reduced performance or (2) set capacity factor to number of experts and thus waste computation and memory on padding. """ def __init__(self, config: FastSLMConfig, num_experts: int, num_experts_per_tok: int, layer_idx: int): super().__init__() self.hidden_dim = config.hidden_size self.ffn_dim = config.intermediate_size self.layer_idx = layer_idx # these values are decided on runtime depending on the layer index self.num_experts = num_experts self.top_k = num_experts_per_tok if num_experts > 1: # expert routing self.router = nn.Linear(self.hidden_dim, self.num_experts, bias=False) else: self.router = None self.experts = nn.ModuleList([FastSLMMLP(config, layer_idx=layer_idx) for _ in range(self.num_experts)]) def forward(self, hidden_states: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]: """ """ if len(hidden_states.shape) == 3: batch_size, sequence_length, hidden_dim = hidden_states.shape bs_times_seq_len = batch_size * sequence_length elif len(hidden_states.shape) == 2: assert self.num_experts == 1 bs_times_seq_len, hidden_dim = hidden_states.shape else: batch_size, sequence_length, _, hidden_dim = hidden_states.shape bs_times_seq_len = batch_size * sequence_length if self.num_experts == 1: # in this case we have a single MLP block and don't need to do any routing final_hidden_states = self.experts[0](hidden_states) router_logits = torch.ones( (bs_times_seq_len, 1), device=hidden_states.device, dtype=hidden_states.dtype, requires_grad=hidden_states.requires_grad, ) return final_hidden_states, router_logits # in this case we have multiple experts and need to do routing hidden_states = hidden_states.view(-1, hidden_dim) # router_logits: (batch * sequence_length, n_experts) router_logits = self.router(hidden_states) routing_weights = F.softmax(router_logits, dim=1, dtype=torch.float) routing_weights, selected_experts = torch.topk(routing_weights, self.top_k, dim=-1) # we cast back to the input dtype routing_weights = routing_weights.to(hidden_states.dtype) final_hidden_states = torch.zeros( (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 # in torch it is faster to index using lists than torch tensors top_x_list = top_x.tolist() idx_list = idx.tolist() # 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_list].reshape(-1, hidden_dim) current_hidden_states = expert_layer(current_state) * routing_weights[top_x_list, idx_list, 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 class FastSLMAttentionDecoderLayer(nn.Module): def __init__(self, config: FastSLMConfig, num_experts: int, layer_idx: int,): super().__init__() self.config = config self.layer_idx = layer_idx self.self_attn = JAMBA_ATTENTION_CLASSES[config.attn_implementation](config, layer_idx) if self.config.intermediate_size > 0: num_experts_per_tok = config.num_experts_per_tok if num_experts > 1 else 1 self.moe = FastSLMSparseMoeBlock(config, num_experts=num_experts, num_experts_per_tok=num_experts_per_tok, layer_idx=layer_idx) else: self.moe = None self.input_layernorm = FastSLMRMSNorm(config.hidden_size, eps=config.rms_norm_eps) self.pre_moe_layernorm = FastSLMRMSNorm(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, output_router_logits: Optional[bool] = False, use_cache: Optional[bool] = False, use_swa=False, **kwargs, ) -> Tuple[torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]]]: 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.`" ) """ 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`). """ if position_ids is not None and position_ids.shape[1] != hidden_states.shape[1]: position_ids = torch.arange(hidden_states.shape[1], device=hidden_states.device).unsqueeze(0) residual = hidden_states if self.input_layernorm is not None: hidden_states = self.input_layernorm(hidden_states) hidden_states, self_attn_weights, present_key_value, current_kv = 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, use_swa=use_swa, ) hidden_states = residual + hidden_states if self.moe is not None: residual = hidden_states if self.pre_moe_layernorm is not None: hidden_states = self.pre_moe_layernorm(hidden_states) hidden_states, router_logits = self.moe(hidden_states) hidden_states = residual + hidden_states else: router_logits = None outputs = (hidden_states,) if output_attentions: outputs += (self_attn_weights,) if use_cache: outputs += (present_key_value,) if output_router_logits: outputs += (router_logits,) outputs += (current_kv,) return outputs class FFNDecoderLayer(nn.Module): def __init__(self, config: FastSLMConfig, num_experts: int, layer_idx: int): super().__init__() self.config = config self.layer_idx = layer_idx num_experts_per_tok = config.num_experts_per_tok if num_experts > 1 else 1 self.moe = FastSLMSparseMoeBlock(config, num_experts=num_experts, num_experts_per_tok=num_experts_per_tok, layer_idx=layer_idx) self.pre_moe_layernorm = FastSLMRMSNorm(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, output_router_logits: Optional[bool] = False, use_cache: Optional[bool] = False, use_swa=False, **kwargs, ) -> Tuple[torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]]]: 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.`" ) """ 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`). """ residual = hidden_states if self.pre_moe_layernorm is not None: hidden_states = self.pre_moe_layernorm(hidden_states) hidden_states, router_logits = self.moe(hidden_states) hidden_states = residual + hidden_states outputs = (hidden_states,) if output_attentions: outputs += (None,) if use_cache: outputs += (None,) if output_router_logits: outputs += (router_logits,) return outputs class FastSLMMambaDecoderLayer(nn.Module): def __init__(self, config: FastSLMConfig, num_experts: int, layer_idx: int): super().__init__() self.config = config self.layer_idx = layer_idx self.mamba = Mamba2(config=config, layer_idx=layer_idx) self.intermediate_size = config.intermediate_size if self.intermediate_size > 0: num_experts_per_tok = config.num_experts_per_tok if num_experts > 1 else 1 self.moe = FastSLMSparseMoeBlock(config, num_experts=num_experts, num_experts_per_tok=num_experts_per_tok, layer_idx=layer_idx) self.input_layernorm = FastSLMRMSNorm(config.hidden_size, eps=config.rms_norm_eps) if self.intermediate_size > 0: self.pre_moe_layernorm = FastSLMRMSNorm(config.hidden_size, eps=config.rms_norm_eps) else: self.pre_moe_layernorm = None self.meta_added_flag = False def forward( self, hidden_states: torch.Tensor, attention_mask: Optional[torch.Tensor] = None, position_ids: Optional[torch.LongTensor] = None, past_key_value: Optional[HybridMambaAttentionDynamicCache] = None, output_attentions: Optional[bool] = False, output_router_logits: Optional[bool] = False, use_cache: Optional[bool] = False, use_swa=False, mamba_inference_params=None, **kwargs, ) -> Tuple[torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]]]: 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.`" ) """ 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`). """ if position_ids is not None and position_ids.shape[1] != hidden_states.shape[1]: position_ids = torch.arange(hidden_states.shape[1], device=hidden_states.device).unsqueeze(0) residual = hidden_states if self.input_layernorm is not None: hidden_states = self.input_layernorm(hidden_states) hidden_states, present_key_value = self.mamba( hidden_states=hidden_states, past_key_value=past_key_value, attention_mask=attention_mask, inference_params=mamba_inference_params, ) attn_key_value = None hidden_states = residual + hidden_states if self.intermediate_size > 0: residual = hidden_states if self.pre_moe_layernorm is not None: hidden_states = self.pre_moe_layernorm(hidden_states) hidden_states, router_logits = self.moe(hidden_states) hidden_states = residual + hidden_states outputs = (hidden_states,) if use_cache: outputs += (present_key_value,) if output_router_logits: outputs += (router_logits,) outputs += (attn_key_value,) return outputs def _get_past_seqlen(self, past_key_value, seqlen): if past_key_value is None: return seqlen past_seqlen = past_key_value.get_seq_length(self.layer_idx) if past_seqlen == 0: return seqlen return past_seqlen class FastSLMHybridDecoderLayer(nn.Module): def __init__(self, config: FastSLMConfig, num_experts: int, layer_idx: int): super().__init__() self.config = config self.layer_idx = layer_idx if config.hybrid_decoder_layer == 'mamba': self.mamba = Mamba2(config=config, layer_idx=layer_idx) if config.hybrid_decoder_layer == 'deltanet': ## this is to properly handle cache index if config.layer_types is not None: deltanet_idx = sum(1 for i in range(layer_idx) if config.layer_types[i] == 'deltanet') else: deltanet_idx = layer_idx self.gla = DeltaNet(hidden_size=config.hidden_size, num_heads=config.num_attention_heads, layer_idx=deltanet_idx, config=self.config) else: raise ValueError(f"Not supported: {config.hybrid_decoder_layer}") self.config = config if self.config.intermediate_size > 0: num_experts_per_tok = config.num_experts_per_tok if num_experts > 1 else 1 self.moe = FastSLMSparseMoeBlock(config, num_experts=num_experts, num_experts_per_tok=num_experts_per_tok, layer_idx=layer_idx) self.pre_moe_layernorm = FastSLMRMSNorm(config.hidden_size, eps=config.rms_norm_eps) else: self.moe = None self.pre_moe_layernorm = None self.input_layernorm = FastSLMRMSNorm(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[HybridMambaAttentionDynamicCache] = None, output_attentions: Optional[bool] = False, output_router_logits: Optional[bool] = False, use_cache: Optional[bool] = False, fla_past_key_values = None, mamba_inference_params = None, use_swa=False, **kwargs, ) -> Tuple[torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]]]: 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.`" ) """ 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`). """ residual = hidden_states hidden_states = self.input_layernorm(hidden_states) if self.config.hybrid_decoder_layer == 'mamba': hybrid_op_hidden_states, mamba_present_key_value = self.mamba( hidden_states=hidden_states, past_key_value=past_key_value, attention_mask=attention_mask, inference_params=mamba_inference_params, ) else: hybrid_op_hidden_states, _, fla_past_key_values = self.gla( hidden_states=hidden_states, attention_mask=attention_mask, past_key_values=fla_past_key_values, use_cache=use_cache, ) self_attn_weights = self_attn_present_key_value = current_kv = None hidden_states = residual + hybrid_op_hidden_states if self.moe is not None: residual = hidden_states hidden_states = self.pre_moe_layernorm(hidden_states) hidden_states, router_logits = self.moe(hidden_states) hidden_states = residual + hidden_states outputs = (hidden_states,) if output_attentions: outputs += (self_attn_weights,) if use_cache: outputs += (self_attn_present_key_value,) if output_router_logits: outputs += (router_logits,) outputs += (current_kv,) return outputs # Adapted from transformers.models.mistral.modeling_mistral.MistralPreTrainedModel with Mistral->FastSLM class FastSLMPreTrainedModel(PreTrainedModel): config_class = FastSLMConfig base_model_prefix = "model" supports_gradient_checkpointing = True _no_split_modules = ["FastSLMAttentionDecoderLayer", "FastSLMMambaDecoderLayer"] _skip_keys_device_placement = "past_key_values" _supports_flash_attn_2 = True _supports_sdpa = True _supports_cache_class = True def _init_weights(self, module): std = self.config.initializer_range if isinstance(module, (nn.Linear, nn.Conv1d)): 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_() @staticmethod def _convert_to_standard_cache( past_key_value: Tuple[Tuple[torch.Tensor, torch.Tensor]], batch_size: int ) -> Tuple[Tuple[torch.Tensor, torch.Tensor]]: """ Standardizes the format of the cache so as to match most implementations, i.e. have the seqlen as the third dim also for mamba layers """ attn_layer_index = [k.shape == v.shape for k, v in past_key_value].index(True) seqlen = past_key_value[attn_layer_index][0].shape[2] standard_past_key_value = () for k, v in past_key_value: if k.shape != v.shape: # mamba layer # expand doesn't use more memory, so it's fine to do it here standard_past_key_value += ((k.expand(-1, -1, seqlen, -1), v.expand(-1, -1, seqlen, -1)),) else: standard_past_key_value += ((k, v),) return standard_past_key_value @staticmethod def _convert_to_jamba_cache( past_key_value: Tuple[Tuple[torch.Tensor, torch.Tensor]], ) -> Tuple[Tuple[torch.Tensor, torch.Tensor]]: """ Converts the cache to the format expected by FastSLM, i.e. dummy seqlen dimesion with size 1 for mamba layers """ jamba_past_key_value = () for k, v in past_key_value: if k.shape != v.shape: # mamba layer jamba_past_key_value += ((k[:, :, :1, :], v[:, :, :1, :]),) else: jamba_past_key_value += ((k, v),) return jamba_past_key_value # Adapted from transformers.models.mistral.modeling_mistral.MistralModel with MISTRAL->JAMBA, Mistral->FastSLM class FastSLMModel(FastSLMPreTrainedModel): """ Transformer decoder consisting of *config.num_hidden_layers* layers. Each layer is a [`FastSLMDecoderLayer`] Args: config: FastSLMConfig """ def __init__(self, config: FastSLMConfig): super().__init__(config) config.attn_implementation = config.attn_implementation_new config._attn_implementation = config.attn_implementation_new self.config = 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) decoder_layers = [] layer_type = [] for i in range(config.num_hidden_layers): num_experts = 1 if config.layer_types[i] in ['deltanet']: layer_type.append('m') config_new = copy.deepcopy(config) config_new.hybrid_decoder_layer = 'deltanet' decoder_layer = FastSLMHybridDecoderLayer(config_new, num_experts=num_experts, layer_idx=i) elif config.layer_types[i] in ['m', 'm2']: layer_type.append('m') decoder_layer = FastSLMMambaDecoderLayer(config, num_experts=num_experts, layer_idx=i) elif config.layer_types[i] == 'a': layer_type.append('a') decoder_layer = FastSLMAttentionDecoderLayer(config, num_experts=num_experts, layer_idx=i) elif config.layer_types[i] == 'f': layer_type.append('a') decoder_layer = FFNDecoderLayer(config, num_experts=num_experts, layer_idx=i) else: raise ValueError(f"Unsupported layer type {config.layer_types[i]}") decoder_layers.append(decoder_layer) config.layer_type = layer_type if config.sliding_window is not None: self.sliding_window = config.sliding_window self.global_attn_idx = config.global_attn_idx else: self.sliding_window = None self.global_attn_idx = None if not any(isinstance(layer, FastSLMAttentionDecoderLayer) for layer in decoder_layers): # raise ValueError("At least one layer in the decoder must be an attention layer") self._attn_layer_index = [] else: self._attn_layer_index = [isinstance(layer, FastSLMAttentionDecoderLayer) for layer in decoder_layers].index( True ) if not any(isinstance(layer, FastSLMMambaDecoderLayer) for layer in decoder_layers): # raise ValueError("At least one layer in the decoder must be a Mamba layer") self._mamba_layer_index = [] else: self._mamba_layer_index = [isinstance(layer, FastSLMMambaDecoderLayer) for layer in decoder_layers].index(True) # if ( # decoder_layers[self._mamba_layer_index].mamba.ssm_state_size # == decoder_layers[self._mamba_layer_index].mamba.conv_kernel_size # ): # raise ValueError("Mamba state size and convolution size must be different") self.layers = nn.ModuleList(decoder_layers) self._attn_implementation = config.attn_implementation self.final_layernorm = FastSLMRMSNorm(config.hidden_size, eps=config.rms_norm_eps) if self.config.num_memory_tokens > 0: self.memory_tokens = nn.Parameter(torch.randn(self.config.num_memory_tokens, self.config.hidden_size)) self.gradient_checkpointing = False # Initialize weights and apply final processing self.post_init() self.has_previous_state = False def get_input_embeddings(self): return self.embed_tokens def set_input_embeddings(self, value): self.embed_tokens = value def forward( self, input_ids: torch.LongTensor = None, attention_mask: Optional[torch.Tensor] = None, position_ids: Optional[torch.LongTensor] = None, past_key_values: Optional[Union[List[torch.FloatTensor], HybridMambaAttentionDynamicCache]] = 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, fla_past_key_values = None, mamba_inference_params = None, ) -> Union[Tuple, MoeModelOutputWithPast]: 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 # 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 elif inputs_embeds is not None: batch_size, seq_length, _ = inputs_embeds.shape else: raise ValueError("You have to specify either input_ids or inputs_embeds") if self.gradient_checkpointing and self.training: if use_cache: logger.warning_once( "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..." ) use_cache = False if position_ids is None: device = input_ids.device if input_ids is not None else inputs_embeds.device position_ids = torch.arange(0, seq_length, dtype=torch.long, device=device ) position_ids = position_ids.unsqueeze(0).view(-1, seq_length) else: if self.config.num_memory_tokens > 0 and past_key_values is not None and not self.has_previous_state: position_ids = position_ids.view(-1, seq_length + self.config.num_memory_tokens).long() else: position_ids = position_ids.view(-1, seq_length).long() if inputs_embeds is None: inputs_embeds = self.embed_tokens(input_ids) ori_b, ori_n = inputs_embeds.shape[0], inputs_embeds.shape[1] if self.config.num_memory_tokens > 0 and (past_key_values is None or not self.has_previous_state): mem = repeat(self.memory_tokens, 'n d -> b n d', b = inputs_embeds.shape[0]) # prepend the memory to every segment of m by repeating the memory tokens inputs_embeds, mem_packed_shape = pack((mem, inputs_embeds), 'b * d') if position_ids is not None and position_ids.shape[1] != inputs_embeds.shape[1]: position_ids = torch.arange(inputs_embeds.shape[1], device=inputs_embeds.device).unsqueeze(0) if attention_mask is not None and attention_mask.shape[1] < inputs_embeds.shape[1]: assert attention_mask.shape[1] + self.config.num_memory_tokens == inputs_embeds.shape[1] attention_mask = torch.cat([torch.ones(inputs_embeds.shape[0], self.config.num_memory_tokens, device=attention_mask.device), attention_mask], dim=1) if attention_mask is not None and self._attn_implementation == "flash_attention_2" and use_cache: is_padding_right = attention_mask[:, -1].sum().item() != batch_size if is_padding_right: raise ValueError( "You are attempting to perform batched generation with padding_side='right'" " this may lead to unexpected behaviour for Flash Attention version of FastSLM. Make sure to " " call `tokenizer.padding_side = 'left'` before tokenizing the input. " ) attention_mask = attention_mask if (attention_mask is not None and 0 in attention_mask) else None hidden_states = inputs_embeds # 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 next_decoder_cache = None for i, decoder_layer in enumerate(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, attention_mask, position_ids, past_key_values, output_attentions, output_router_logits, use_cache, ) else: layer_outputs = decoder_layer( hidden_states, attention_mask=attention_mask, position_ids=position_ids, past_key_value=past_key_values, output_attentions=output_attentions, output_router_logits=output_router_logits, use_cache=use_cache, use_swa=self.sliding_window is not None and i not in self.global_attn_idx, fla_past_key_values=fla_past_key_values, mamba_inference_params=mamba_inference_params, ) 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],) if output_router_logits: all_router_logits += (layer_outputs[3],) if self.final_layernorm is not None: hidden_states = self.final_layernorm(hidden_states) if output_hidden_states: all_hidden_states += (hidden_states,) if self.config.num_memory_tokens > 0 and (past_key_values is None or not self.has_previous_state): mem, hidden_states = unpack(hidden_states, mem_packed_shape, 'b * d') hidden_states = hidden_states[:, :ori_n, :] if past_key_values is not None and not self.has_previous_state: self.has_previous_state = True next_cache = None if use_cache: next_cache = next_decoder_cache if not return_dict: return tuple( v for v in [hidden_states, next_cache, all_hidden_states, all_self_attns, all_router_logits] if v is not None ) return MoeModelOutputWithPast( last_hidden_state=hidden_states, past_key_values=past_key_values if (fla_past_key_values is None and mamba_inference_params is None) else (past_key_values, fla_past_key_values, mamba_inference_params), hidden_states=all_hidden_states, attentions=all_self_attns, router_logits=all_router_logits, ) # Adapted from transformers.models.mixtral.modeling_mixtral.MixtralForCausalLM with MIXTRAL->JAMBA, Mixtral->FastSLM class FastSLMForCausalLM(FastSLMPreTrainedModel): _tied_weights_keys = ["lm_head.weight"] def __init__(self, config: FastSLMConfig): super().__init__(config) self.config = config self.model = FastSLMModel(config) self.vocab_size = config.vocab_size self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False) self.router_aux_loss_coef = config.router_aux_loss_coef self.num_experts = config.num_experts self.num_experts_per_tok = config.num_experts_per_tok # 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 @replace_return_docstrings(output_type=MoeCausalLMOutputWithPast, config_class=_CONFIG_FOR_DOC) # Ignore copy 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, output_router_logits: Optional[bool] = None, return_dict: Optional[bool] = None, calc_logits_for_entire_prompt: Optional[bool] = True, fla_past_key_values = None, mamba_inference_params = None, ) -> Union[Tuple, MoeCausalLMOutputWithPast]: 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, ..., 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, ..., config.vocab_size]`. calc_logits_for_entire_prompt (`bool`, *optional*): Whether or not to calculate the logits for the entire prompt, or just the last token. Only last token logits are needed for generation, and calculating them only for that token can save memory, which becomes pretty significant for long sequences. Returns: ```""" # print(input_ids.max()) 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 ) 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, output_router_logits=output_router_logits, fla_past_key_values=fla_past_key_values, mamba_inference_params=mamba_inference_params, return_dict=return_dict, ) hidden_states = outputs[0] if calc_logits_for_entire_prompt: logits = self.lm_head(hidden_states) else: logits = self.lm_head(hidden_states[..., -1:, :]) logits = logits / self.lm_head.weight.norm(p=2, dim=1) 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() 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 # print("hidden_states.shape:", hidden_states.shape, "input_ids.shape:", input_ids.shape, "logits.shape:", logits.shape) return MoeCausalLMOutputWithPast( loss=loss, logits=logits, past_key_values=outputs.past_key_values, hidden_states=outputs.hidden_states, attentions=outputs.attentions, router_logits=outputs.router_logits, ) def get_init_cache(self, max_seqlen, batch_size=1): past_key_values = HybridMambaAttentionDynamicCache( self.config, batch_size, self.dtype, device=self.device, layer_type=self.config.layer_type ) mamba_inference_params = InferenceParams(max_seqlen=max_seqlen, max_batch_size=batch_size) fla_past_key_values = fla_cache.from_legacy_cache(None) return past_key_values, fla_past_key_values, mamba_inference_params def init_cuda_graph_generation( self, max_new_tokens=128, batch_size=1, device=None, ): """ Initialize CUDA graph for generation with proper cache handling and warmup. This function should be called once before generation to set up the graph. Args: max_new_tokens: Maximum number of new tokens to generate batch_size: Batch size for generation device: Device to use (defaults to model device) Returns: generation_state: Dictionary containing all necessary state for generation """ if device is None: device = next(self.parameters()).device self.eval() # Initialize caches max_seqlen = max_new_tokens + 2048 + self.config.num_memory_tokens # Add buffer for input past_key_values, fla_past_key_values, mamba_inference_params = self.get_init_cache( max_seqlen=max_seqlen, batch_size=batch_size ) # Initialize KV caches for all modules for module in self.modules(): if hasattr(module, 'init_kv_cache'): module.init_kv_cache(max_batch_size=batch_size, max_seq_len=max_seqlen) with torch.no_grad(): # Warmup runs dummy_input = torch.ones((batch_size, 10), dtype=torch.long, device=device) for _ in range(10): self(dummy_input) # Prepare static tensors for CUDA graph static_current_input = torch.zeros((batch_size, 1), dtype=torch.long, device=device) static_position_ids = torch.zeros((batch_size, 1), dtype=torch.long, device=device) static_logits = torch.zeros((batch_size, self.config.vocab_size), device=device) # Set up for graph capture self.model.has_previous_state = True if mamba_inference_params is not None: mamba_inference_params.seqlen_offset = 1 # Warmup runs for graph capture for _ in range(10): model_kwargs_warmup = { 'input_ids': static_current_input, 'fla_past_key_values': fla_past_key_values, 'mamba_inference_params': mamba_inference_params, 'past_key_values': past_key_values, 'use_cache': True, 'position_ids': static_position_ids, } warmup_outputs = self(**model_kwargs_warmup) # Capture CUDA graph generation_graph = CUDAGraph() with torch.cuda.graph(generation_graph): model_kwargs_graph = { 'input_ids': static_current_input, 'fla_past_key_values': fla_past_key_values, 'mamba_inference_params': mamba_inference_params, 'past_key_values': past_key_values, 'use_cache': True, 'position_ids': static_position_ids, } graph_outputs = self(**model_kwargs_graph) static_logits.copy_(graph_outputs.logits[:, -1, :]) if fla_past_key_values is not None: fla_past_key_values.reset() if mamba_inference_params is not None: mamba_inference_params.reset(mamba_inference_params.max_seqlen, mamba_inference_params.max_batch_size) for key in mamba_inference_params.key_value_memory_dict: conv_state, ssm_state = mamba_inference_params.key_value_memory_dict[key] conv_state.zero_() ssm_state.zero_() for module in self.modules(): if hasattr(module, 'reset_kv_cache'): module.reset_kv_cache() self.model.has_previous_state = False # Return generation state generation_state = { 'generation_graph': generation_graph, 'static_current_input': static_current_input, 'static_position_ids': static_position_ids, 'static_logits': static_logits, 'past_key_values': past_key_values, 'fla_past_key_values': fla_past_key_values, 'mamba_inference_params': mamba_inference_params, 'max_seqlen': max_seqlen, 'batch_size': batch_size, 'device': device, } return generation_state def generate_with_cuda_graph( self, input_ids, generation_state, max_new_tokens=128, temperature=1.0, top_k=0, top_p=0.9, eos_token_id=None, verbose=False, profiling=False, multi_round=False, ): """ Generate text using pre-initialized CUDA graph state. Args: input_ids: Input token IDs tensor of shape (batch_size, seq_len) generation_state: State dictionary returned by init_cuda_graph_generation max_new_tokens: Maximum number of new tokens to generate temperature: Sampling temperature (0 for greedy) top_k: Top-k filtering (0 to disable) top_p: Top-p filtering (1.0 to disable) eos_token_id: End-of-sequence token ID pad_token_id: Padding token ID verbose: Whether to print generated tokens profiling: Whether to return timing information Returns: generated_ids: Tensor of shape (batch_size, input_len + generated_len) or decode_latency if profiling=True """ self.eval() batch_size = input_ids.shape[0] device = input_ids.device # Extract state generation_graph = generation_state['generation_graph'] static_current_input = generation_state['static_current_input'] static_position_ids = generation_state['static_position_ids'] static_logits = generation_state['static_logits'] past_key_values = generation_state['past_key_values'] fla_past_key_values = generation_state['fla_past_key_values'] mamba_inference_params = generation_state['mamba_inference_params'] with torch.no_grad(): if not multi_round or mamba_inference_params.seqlen_offset == 0: if fla_past_key_values is not None: fla_past_key_values.reset() if mamba_inference_params is not None: mamba_inference_params.reset(mamba_inference_params.max_seqlen, mamba_inference_params.max_batch_size) for key in mamba_inference_params.key_value_memory_dict: conv_state, ssm_state = mamba_inference_params.key_value_memory_dict[key] conv_state.zero_() ssm_state.zero_() for module in self.modules(): if hasattr(module, 'reset_kv_cache'): module.reset_kv_cache() self.model.has_previous_state = False # Prefill phase - process input sequence position_ids = torch.arange( self.config.num_memory_tokens + input_ids.shape[1], dtype=torch.long, device=device ).unsqueeze(0).expand(batch_size, -1) else: # Prefill phase - process input sequence position_ids = torch.arange( mamba_inference_params.seqlen_offset, mamba_inference_params.seqlen_offset + input_ids.shape[1], dtype=torch.long, device=device ).unsqueeze(0).expand(batch_size, -1) current_input = input_ids model_kwargs = { 'input_ids': current_input, 'past_key_values': past_key_values, 'fla_past_key_values': fla_past_key_values, 'mamba_inference_params': mamba_inference_params, 'use_cache': True, 'position_ids': position_ids, } if profiling: torch.cuda.synchronize() t1 = time.time() # Forward pass for prefill outputs = self(**model_kwargs) if mamba_inference_params is not None: if mamba_inference_params.seqlen_offset == 0: mamba_inference_params.seqlen_offset = current_input.shape[1] + self.config.num_memory_tokens else: mamba_inference_params.seqlen_offset += current_input.shape[1] static_position_ids.fill_(position_ids[0, -1]) logits = outputs.logits[:, -1, :] # (batch_size, vocab_size) generated_tokens = [] # Generation loop using CUDA graph replay for step in range(max_new_tokens): # Sample next token using current logits if temperature == 0: next_token = torch.argmax(logits, dim=-1, keepdim=True) else: next_token = sample_token(logits, temperature=temperature, top_k=top_k, top_p=top_p) generated_tokens.append(next_token) # Check for EOS if not profiling and eos_token_id is not None and (next_token == eos_token_id).all(): if verbose: print("\nEOS reached") break # Update static tensors for graph replay static_current_input.copy_(next_token) static_position_ids.add_(1) # Replay the captured graph generation_graph.replay() if mamba_inference_params is not None: mamba_inference_params.seqlen_offset += 1 logits = static_logits.clone() generated_ids = torch.cat([input_ids] + generated_tokens, dim=1) if profiling: torch.cuda.synchronize() t2 = time.time() decode_latency = t2 - t1 return generated_ids, decode_latency return generated_ids def prepare_inputs_for_generation( self, input_ids, past_key_values=None, attention_mask=None, inputs_embeds=None, output_router_logits=False, **kwargs, ): if self.config.num_memory_tokens > 0: attention_mask = torch.cat([torch.ones(input_ids.shape[0], self.config.num_memory_tokens, device=attention_mask.device), attention_mask], dim=1) past_key_values = None # Disable cache for now 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) 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: if input_ids.shape[1] == 0: model_inputs = {"inputs_embeds": inputs_embeds} else: inputs_embeds_new = self.model.embed_tokens(input_ids) model_inputs = {"inputs_embeds": torch.cat([inputs_embeds, inputs_embeds_new], dim=1)} 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 def sample_token(logits, temperature=1.0, top_k=0, top_p=0.9): """ Sample a token from logits with temperature, top-k, and top-p filtering. This matches the implementation in fast_slm_gen.py for consistency. Args: logits: Tensor of shape (batch_size, vocab_size) temperature: Sampling temperature top_k: Top-k filtering (0 to disable) top_p: Top-p filtering (1.0 to disable) Returns: next_token: Tensor of shape (batch_size, 1) """ if temperature == 0: return torch.argmax(logits, dim=-1, keepdim=True) logits = logits / temperature # Top-k filtering - match fast_slm_gen.py implementation if top_k > 0: indices_to_remove = logits < torch.topk(logits, top_k)[0][..., -1, None] logits.masked_fill_(indices_to_remove, float('-inf')) # Top-p filtering - match fast_slm_gen.py implementation if top_p < 1.0: sorted_logits, sorted_indices = torch.sort(logits, descending=True, dim=-1) cumulative_probs = torch.cumsum(F.softmax(sorted_logits, dim=-1), dim=-1) # Remove tokens with cumulative probability above the threshold sorted_indices_to_remove = cumulative_probs > top_p # Shift the indices to the right to keep also the first token above the threshold sorted_indices_to_remove[..., 1:] = sorted_indices_to_remove[..., :-1].clone() sorted_indices_to_remove[..., 0] = 0 indices_to_remove = sorted_indices_to_remove.scatter(-1, sorted_indices, sorted_indices_to_remove) logits.masked_fill_(indices_to_remove, float('-inf')) probs = F.softmax(logits, dim=-1) return torch.multinomial(probs, num_samples=1)