diff --git "a/modeling_fast_slm.py" "b/modeling_fast_slm.py" new file mode 100644--- /dev/null +++ "b/modeling_fast_slm.py" @@ -0,0 +1,2258 @@ +# 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 +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 + self.has_previous_state = False + 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 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() + + if past_key_value is not None: + past_key_value.has_previous_state = True + + 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, +} + +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() + + + 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 + + past_key_values_length = 0 + if use_cache: + if past_key_values is not None: + past_key_values_length = past_key_values.get_usable_length(seq_length, 0) + else: + 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( + past_key_values_length, seq_length + past_key_values_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 past_key_values.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 past_key_values.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 past_key_values.has_previous_state): + mem, hidden_states = unpack(hidden_states, mem_packed_shape, 'b * d') + hidden_states = hidden_states[:, :ori_n, :] + + if past_key_values and not past_key_values.has_previous_state: + for layer_idx_ in range(len(self.layers)): + if past_key_values.get_seq_length(layer_idx_) > 0: + past_key_values.has_previous_state = True + break + + if mamba_inference_params is not None and mamba_inference_params.seqlen_offset > 0: + past_key_values.has_previous_state = True + + if fla_past_key_values is not None and len(fla_past_key_values.states) > 0: + past_key_values.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 + past_key_values.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() + + past_key_values.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() + + past_key_values.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 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) +