# coding=utf-8 # Copyright 2024 HuggingFace Inc. team. # Copyright (c) 2025, NVIDIA CORPORATION. All rights reserved. # # 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 NemotronH model.""" import math from dataclasses import dataclass from typing import Any, Dict, Optional, Tuple, Union import torch import torch.utils.checkpoint from torch import nn from torch.nn import CrossEntropyLoss from transformers.activations import ACT2FN from transformers.cache_utils import DynamicCache # we need __iter__ and __len__ of pkv from transformers.generation import GenerationMixin from transformers.modeling_attn_mask_utils import ( AttentionMaskConverter, ) from transformers.modeling_utils import PreTrainedModel from transformers.utils import ( ModelOutput, add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward, logging, ) from transformers.utils.import_utils import ( is_causal_conv1d_available, is_flash_attn_2_available, is_flash_attn_greater_or_equal_2_10, is_mamba_2_ssm_available, ) from .configuration_nemotron_h import NemotronHConfig logger = logging.get_logger(__name__) # Copied from transformers.models.mamba.modeling_mamba2.modeling_mamba2.py with MAMBA2->NEMOTRONH,Mamba2->NemotronH # For Mamba2 components Mamba2->NemotronHMamba2 if is_mamba_2_ssm_available(): from mamba_ssm.ops.triton.selective_state_update import selective_state_update from mamba_ssm.ops.triton.ssd_combined import mamba_chunk_scan_combined, mamba_split_conv1d_scan_combined else: mamba_chunk_scan_combined, mamba_split_conv1d_scan_combined, selective_state_update = None, None, None try: #from mamba_ssm.ops.triton.layernorm_gated import RMSNorm as RMSNormGated from mamba_ssm.ops.triton.layernorm_gated import rmsnorm_fn except ImportError: raise ImportError("mamba-ssm is required by the Mamba model but cannot be imported") if is_causal_conv1d_available(): from causal_conv1d import causal_conv1d_fn, causal_conv1d_update else: causal_conv1d_update, causal_conv1d_fn = None, None if is_flash_attn_2_available(): from transformers.modeling_flash_attention_utils import _flash_attention_forward is_fast_path_available = all( ( selective_state_update, mamba_chunk_scan_combined, mamba_split_conv1d_scan_combined, causal_conv1d_fn, causal_conv1d_update, ) ) _CHECKPOINT_FOR_DOC = "nvidia/Nemotron-H-56B-Base-8K" _CONFIG_FOR_DOC = "NemotronHConfig" # Helper methods for segment sum computation def pad_tensor_by_size(input_tensor: torch.Tensor, pad_size: int): """ Padding x tensor with `pad_size` on the seq_len dim (dim=1) Assumes that we only have tensors of either size 4 or 3 """ pad_shape = (0, 0, 0, 0, 0, pad_size, 0, 0) if len(input_tensor.shape) == 4 else (0, 0, 0, pad_size, 0, 0) return torch.nn.functional.pad(input_tensor, pad_shape, mode="constant", value=0) def reshape_into_chunks(input_tensor, pad_size, chunk_size): """ Padding input_tensor with `pad_size` on the seq_len dim (dim=1) and simultaneously splitting it into chunk sequences. Assumes that we only have tensors of either size 4 or 3 """ # [bsz, seq_len, ...] -> [bsz, seq_len multiple of chunk_size, ...] input_tensor = pad_tensor_by_size(input_tensor, pad_size) if len(input_tensor.shape) == 3: # [bsz, seq_len multiple of chunk_size, num_heads] -> [bsz, -1, chunk_size, num_heads] return input_tensor.reshape(input_tensor.shape[0], -1, chunk_size, input_tensor.shape[2]) else: # [bsz, seq_len multiple of chunk_size, num_heads, head_dim or state_size] -> [bsz, -1, chunk_size, num_heads, head_dim or state_size] return input_tensor.reshape( input_tensor.shape[0], -1, chunk_size, input_tensor.shape[2], input_tensor.shape[3] ) def segment_sum(input_tensor): """ More stable segment sum calculation. Uses cumulative sums and masking instead of direct subtractions. """ chunk_size = input_tensor.size(-1) # 1. expand input tensor to have an additional dimension and repeat along that dimension # [..., chunk_size] -> [..., chunk_size, chunk_size] input_tensor = input_tensor[..., None].expand(*input_tensor.size(), chunk_size) # 2. create a lower triangular mask with the diagonal set to 0 to 0 out elements above diag mask = torch.tril(torch.ones(chunk_size, chunk_size, device=input_tensor.device, dtype=torch.bool), diagonal=-1) input_tensor = input_tensor.masked_fill(~mask, 0) # 3. compute actual cumsum tensor_segsum = torch.cumsum(input_tensor, dim=-2) # 4. apply mask to keep only the lower triangular part of the cumulative sum result (incl diagonal this time) mask = torch.tril(torch.ones(chunk_size, chunk_size, device=input_tensor.device, dtype=torch.bool), diagonal=0) tensor_segsum = tensor_segsum.masked_fill(~mask, -torch.inf) return tensor_segsum def apply_mask_to_padding_states(hidden_states, attention_mask): """ Tunes out the hidden states for padding tokens, see https://github.com/state-spaces/mamba/issues/66 """ if attention_mask is not None and attention_mask.shape[1] > 1 and attention_mask.shape[0] > 1: dtype = hidden_states.dtype hidden_states = (hidden_states * attention_mask[:, :, None]).to(dtype) return hidden_states # Copied from https://github.com/huggingface/transformers/blob/main/src/transformers/models/jamba/modeling_jamba.py 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): super().__init__() self.dtype = dtype self.hybrid_override_pattern = config.hybrid_override_pattern self.has_previous_state = False # only used by mamba intermediate_size = config.expand * config.hidden_size ssm_state_size = config.ssm_state_size conv_kernel_size = config.conv_kernel self.conv_states = [] self.ssm_states = [] self.transformer_layers = [] for i in range(config.num_hidden_layers): if self.hybrid_override_pattern[i] == "M": # Mamba layer self.conv_states += [ torch.zeros(batch_size, intermediate_size, conv_kernel_size, device=device, dtype=dtype) ] self.ssm_states += [ torch.zeros(batch_size, intermediate_size, ssm_state_size, device=device, dtype=dtype) ] else: # Attention or MLP layer self.conv_states += [torch.tensor([[]] * batch_size, device=device)] self.ssm_states += [torch.tensor([[]] * batch_size, device=device)] self.transformer_layers.append(i) 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)] 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: Optional[int] = 0) -> 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 layer_idx = self.transformer_layers[0] if layer_idx not in self.transformer_layers else layer_idx if len(self.key_cache) <= layer_idx: return 0 return self.key_cache[layer_idx].shape[-2] 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.") # Copied from modeling_mamba2.py def update_conv_state( self, layer_idx: int, new_conv_state: torch.Tensor, cache_init: bool = False ) -> torch.Tensor: if cache_init: self.conv_states[layer_idx] = new_conv_state.to(self.conv_states.device) else: self.conv_states[layer_idx] = self.conv_states[layer_idx].roll(shifts=-1, dims=-1) self.conv_states[layer_idx][:, :, -1] = new_conv_state[:, 0, :].to(self.conv_states.device) return self.conv_states[layer_idx] def update_ssm_state(self, layer_idx: int, new_ssm_state: torch.Tensor): self.ssm_states[layer_idx] = new_ssm_state.to(self.ssm_states.device) return self.ssm_states[layer_idx] def reset(self): self.conv_states.zero_() self.ssm_states.zero_() class MambaRMSNormGated(torch.nn.Module): def __init__(self, hidden_size, group_size, eps=1e-5): super().__init__() self.weight = nn.Parameter(torch.ones(hidden_size)) self.variance_epsilon = eps self.group_size = group_size # jan28b version def forward(self, hidden_states, gate=None): return rmsnorm_fn(x=hidden_states, weight=self.weight, bias=None, # No bias z=gate, eps=self.variance_epsilon, group_size=self.group_size, norm_before_gate=False ) class NemotronHMamba2Mixer(nn.Module): """ Compute ∆, A, B, C, and D the state space parameters and compute the `contextualized_states`. A, D are input independent (see Mamba paper [1] Section 3.5.2 "Interpretation of A" for why A isn't selective) ∆, B, C are input-dependent (this is a key difference between Mamba and the linear time invariant S4, and is why Mamba is called **selective** state spaces) """ def __init__(self, config: NemotronHConfig, layer_idx: int): super().__init__() self.num_heads = config.mamba_num_heads self.hidden_size = config.hidden_size self.ssm_state_size = config.ssm_state_size self.conv_kernel_size = config.conv_kernel self.intermediate_size = config.mamba_num_heads * config.mamba_head_dim self.layer_idx = layer_idx self.use_conv_bias = config.use_conv_bias self.activation = config.mamba_hidden_act self.act = ACT2FN[config.mamba_hidden_act] self.layer_norm_epsilon = config.layer_norm_epsilon self.n_groups = config.n_groups self.head_dim = config.mamba_head_dim self.chunk_size = config.chunk_size self.time_step_limit = config.time_step_limit self.time_step_min = config.time_step_min self.time_step_max = config.time_step_max self.conv_dim = self.intermediate_size + 2 * self.n_groups * self.ssm_state_size self.conv1d = nn.Conv1d( in_channels=self.conv_dim, out_channels=self.conv_dim, bias=config.use_conv_bias, kernel_size=config.conv_kernel, groups=self.conv_dim, padding=config.conv_kernel - 1, ) # projection of the input hidden states projection_size = self.intermediate_size + self.conv_dim + self.num_heads self.in_proj = nn.Linear( self.hidden_size, projection_size, bias=config.use_bias, ) # selective projection used to make dt, B and C input dependant # time step projection (discretization) # instantiate once and copy inv_dt in init_weights of PretrainedModel self.dt_bias = nn.Parameter(torch.ones(self.num_heads)) # S4D real initialization. These are not discretized! # The core is to load them, compute the discrete states, then write the updated state. Keeps the memory bounded A = torch.arange(1, self.num_heads + 1) self.A_log = nn.Parameter(torch.log(A)) self.A_log._no_weight_decay = True self.norm = MambaRMSNormGated(self.intermediate_size, eps=self.layer_norm_epsilon, group_size=self.intermediate_size // self.n_groups) self.D = nn.Parameter(torch.ones(self.num_heads)) self.D._no_weight_decay = True self.out_proj = nn.Linear(self.intermediate_size, self.hidden_size, bias=config.use_bias) self.use_bias = config.use_bias if not is_fast_path_available: logger.warning_once( "The fast path is not available because on of `(selective_state_update, causal_conv1d_fn, causal_conv1d_update)`" " is None. Falling back to the naive implementation. To install follow https://github.com/state-spaces/mamba/#installation and" " https://github.com/Dao-AILab/causal-conv1d" ) def cuda_kernels_forward( self, hidden_states: torch.Tensor, cache_params: Optional[HybridMambaAttentionDynamicCache] = None, cache_position: Optional[torch.LongTensor] = None, attention_mask: Optional[torch.Tensor] = None, ): # 1. Gated MLP's linear projection hidden_states = apply_mask_to_padding_states(hidden_states, attention_mask) projected_states = self.in_proj(hidden_states) # Set up dimensions for reshapes later batch_size, seq_len, _ = hidden_states.shape groups_time_state_size = self.n_groups * self.ssm_state_size d_mlp = ( projected_states.shape[-1] - 2 * self.intermediate_size - 2 * self.n_groups * self.ssm_state_size - self.num_heads ) // 2 # Single step calculations via cache if cache_params is not None and cache_position is not None and cache_position[0] > 0: _, _, gate, hidden_states_B_C, dt = projected_states.squeeze(1).split( [d_mlp, d_mlp, self.intermediate_size, self.conv_dim, self.num_heads], dim=-1 ) # 2. Convolution sequence transformation hidden_states_B_C = causal_conv1d_update( hidden_states_B_C, cache_params.conv_states[self.layer_idx], self.conv1d.weight.squeeze(1), self.conv1d.bias, self.activation, ) hidden_states, B, C = torch.split( hidden_states_B_C, [self.intermediate_size, groups_time_state_size, groups_time_state_size], dim=-1, ) # 3. SSM transformation A = -torch.exp(self.A_log.float()) # (nheads,) A = A[:, None, ...][:, :, None].expand(-1, self.head_dim, self.ssm_state_size).to(dtype=torch.float32) dt = dt[:, :, None].expand(-1, -1, self.head_dim) dt_bias = self.dt_bias[:, None, ...].expand(-1, self.head_dim) D = self.D[:, None, ...].expand(-1, self.head_dim) B = B.view(batch_size, self.n_groups, B.shape[1] // self.n_groups) C = C.view(batch_size, self.n_groups, C.shape[1] // self.n_groups) hidden_states_reshaped = hidden_states.view(batch_size, self.num_heads, self.head_dim) hidden_states = selective_state_update( cache_params.ssm_states[self.layer_idx], hidden_states_reshaped, dt, A, B, C, D, z=None, dt_bias=dt_bias, dt_softplus=True, ) hidden_states = hidden_states.view(batch_size, self.num_heads * self.head_dim) breakpoint() hidden_states = self.norm(hidden_states, gate) # 4. Final linear projection out = self.out_proj(hidden_states)[:, None, ...] # Fused calculations or step by step if no initialized cache is found else: A = -torch.exp(self.A_log.float()) # (num_heads) or (intermediate_size, state_size) dt_limit_kwargs = {} if self.time_step_limit == (0.0, float("inf")) else {"dt_limit": self.time_step_limit} # 2-4. Fused kernel for conv1d, SSM, and the final projection if self.training and cache_params is None: out = mamba_split_conv1d_scan_combined( projected_states, self.conv1d.weight.squeeze(1), self.conv1d.bias, self.dt_bias, A, D=self.D, chunk_size=self.chunk_size, seq_idx=None, # was seq_idx activation=self.activation, rmsnorm_weight=self.norm.weight, rmsnorm_eps=self.norm.variance_epsilon, outproj_weight=self.out_proj.weight, outproj_bias=self.out_proj.bias, headdim=self.head_dim, ngroups=self.n_groups, norm_before_gate=False, return_final_states=False, **dt_limit_kwargs, ) else: _, _, gate, hidden_states_B_C, dt = projected_states.split( [d_mlp, d_mlp, self.intermediate_size, self.conv_dim, self.num_heads], dim=-1 ) # 2. Convolution sequence transformation # Init cache if cache_params is not None: hidden_states_B_C_transposed = hidden_states_B_C.transpose(1, 2) conv_states = nn.functional.pad( hidden_states_B_C_transposed, (cache_params.conv_kernel_size - hidden_states_B_C_transposed.shape[-1], 0), ) cache_params.update_conv_state( layer_idx=self.layer_idx, new_conv_state=conv_states, cache_init=True ) if self.activation not in ["silu", "swish"]: hidden_states_B_C = self.act( self.conv1d(hidden_states_B_C.transpose(1, 2))[..., :seq_len].transpose(1, 2) ) else: hidden_states_B_C = causal_conv1d_fn( x=hidden_states_B_C.transpose(1, 2), weight=self.conv1d.weight.squeeze(1), bias=self.conv1d.bias, activation=self.activation, ).transpose(1, 2) hidden_states_B_C = apply_mask_to_padding_states(hidden_states_B_C, attention_mask) hidden_states, B, C = torch.split( hidden_states_B_C, [self.intermediate_size, groups_time_state_size, groups_time_state_size], dim=-1, ) # 3. SSM transformation scan_output, ssm_state = mamba_chunk_scan_combined( hidden_states.view(batch_size, seq_len, -1, self.head_dim), dt, A, B.view(batch_size, seq_len, self.n_groups, -1), C.view(batch_size, seq_len, self.n_groups, -1), chunk_size=self.chunk_size, D=self.D, z=None, seq_idx=None, return_final_states=True, dt_bias=self.dt_bias, dt_softplus=True, **dt_limit_kwargs, ) # Init cache if ssm_state is not None and cache_params is not None: cache_params.update_ssm_state(layer_idx=self.layer_idx, new_ssm_state=ssm_state) scan_output = scan_output.view(batch_size, seq_len, -1) # Multiply "gate" branch and apply extra normalization layer scan_output = self.norm(scan_output, gate) # 4. Final linear projection out = self.out_proj(scan_output) return out # fmt: off def torch_forward(self, input_states, cache_params: Optional[HybridMambaAttentionDynamicCache]=None, cache_position:Optional[torch.LongTensor]=None, attention_mask: Optional[torch.Tensor]=None): batch_size, seq_len, _ = input_states.shape dtype = input_states.dtype # 1. Gated MLP's linear projection input_states = apply_mask_to_padding_states(input_states, attention_mask) projected_states = self.in_proj(input_states) d_mlp = (projected_states.shape[-1] - 2 * self.intermediate_size - 2 * self.n_groups * self.ssm_state_size-self.num_heads) // 2 _, _, gate, hidden_states_B_C, dt = projected_states.split( [d_mlp, d_mlp, self.intermediate_size, self.conv_dim, self.num_heads], dim=-1 ) # 2. Convolution sequence transformation if cache_params is not None and cache_position is not None and cache_position[0] > 0: cache_params.update_conv_state(layer_idx=self.layer_idx, new_conv_state=hidden_states_B_C, cache_init=False) # We need to guarantee that anything regarding the cache is on the same device conv_states = cache_params.conv_states[self.layer_idx].to(device=self.conv1d.weight.device) hidden_states_B_C = torch.sum( conv_states * self.conv1d.weight.squeeze(1), dim=-1 ) if self.use_conv_bias: hidden_states_B_C = hidden_states_B_C + self.conv1d.bias hidden_states_B_C = self.act(hidden_states_B_C) else: # Init cache if cache_params is not None: hidden_states_B_C_transposed = hidden_states_B_C.transpose(1, 2) conv_states = nn.functional.pad( hidden_states_B_C_transposed, (cache_params.conv_kernel_size - hidden_states_B_C_transposed.shape[-1], 0) ) cache_params.update_conv_state(layer_idx=self.layer_idx, new_conv_state=conv_states, cache_init=True) hidden_states_B_C = self.act(self.conv1d(hidden_states_B_C.transpose(1, 2))[..., :seq_len].transpose(1, 2)) hidden_states_B_C = apply_mask_to_padding_states(hidden_states_B_C, attention_mask) hidden_states, B, C = torch.split( hidden_states_B_C, [self.intermediate_size, self.n_groups * self.ssm_state_size, self.n_groups * self.ssm_state_size], dim=-1 ) # 3. SSM transformation A = -torch.exp(self.A_log.float()) # [num_heads] if cache_params is not None and cache_position is not None and cache_position[0] > 0: # We need to guarantee that anything regarding the cache is on the same device cache_device = cache_params.ssm_states.device # Note: there is no need to pad parameter matrices here, as there is just one new token # for batched generation dt = dt[:, 0, :][:, None, ...] dt = dt.transpose(1, 2).expand(batch_size, dt.shape[-1], self.head_dim) # [num_heads] -> [num_heads, head_dim] dt_bias = self.dt_bias[..., None].expand(self.dt_bias.shape[0], self.head_dim) dt = torch.nn.functional.softplus(dt + dt_bias.to(dt.dtype)) dt = torch.clamp(dt, self.time_step_limit[0], self.time_step_limit[1]) A = A[..., None, None].expand(self.num_heads, self.head_dim, self.ssm_state_size).to(dtype=torch.float32) # [bsz, num_heads, head_dim, state_size] dA = (torch.exp(dt[..., None] * A)).to(device=cache_device) # Discretize B # [bsz, n_groups * state_size] -> [bsz, n_groups, 1, state_size] -> # -> [bsz, n_groups, group to head repetition factor, state_size] -> [bsz, num_heads, state_size] B = B.reshape(batch_size, self.n_groups, -1)[..., None, :] B = B.expand(batch_size, self.n_groups, self.num_heads // self.n_groups, B.shape[-1]).contiguous() B = B.reshape(batch_size, -1, B.shape[-1]) # [bsz, num_heads, head_dim, state_size] dB = dt[..., None] * B[..., None, :] # Discretize x into dB # [bsz, intermediate_size] -> [bsz, num_heads, head_dim] hidden_states = hidden_states.reshape(batch_size, -1, self.head_dim) dBx = (dB * hidden_states[..., None]).to(device=cache_device) # State calculation cache_params.update_ssm_state( layer_idx=self.layer_idx, new_ssm_state=cache_params.ssm_states[self.layer_idx] * dA + dBx ) # Subsequent output # [bsz, n_groups * state_size] -> [bsz, num_heads, state_size] C = C.reshape(batch_size, self.n_groups, -1)[..., None, :] C = C.expand(batch_size, self.n_groups, self.num_heads // self.n_groups, C.shape[-1]).contiguous() C = C.reshape(batch_size, -1, C.shape[-1]) # [bsz, num_heads, head_dim] ssm_states = cache_params.ssm_states[self.layer_idx].to(device=C.device, dtype=C.dtype) # Shape: [b, h, d, n] # Reshape ssm_states to merge the first two dimensions ssm_states_reshaped = ssm_states.view(batch_size * self.num_heads, self.head_dim, self.ssm_state_size) # Shape: [b*h, d, n] C_reshaped = C.view(batch_size * self.num_heads, self.ssm_state_size, 1) # Shape: [b*h, n, 1] y = torch.bmm(ssm_states_reshaped, C_reshaped) y = y.view(batch_size, self.num_heads, self.head_dim) # D skip connection # [num_heads] -> [num_heads, head_dim] D = self.D[..., None].expand(self.D.shape[0], self.head_dim) y = (y + hidden_states * D).to(y.dtype) # [bsz, num_heads, head_dim] -> [bsz, 1, intermediate_size] y = y.reshape(batch_size, -1)[:, None, ...] else: # begin ssd naive implementation without einsums dt = nn.functional.softplus(dt + self.dt_bias) dt = torch.clamp(dt, self.time_step_limit[0], self.time_step_limit[1]) hidden_states = hidden_states.reshape(batch_size, seq_len, -1, self.head_dim).float() B = B.reshape(batch_size, seq_len, -1, self.ssm_state_size).float() C = C.reshape(batch_size, seq_len, -1, self.ssm_state_size).float() B = B.repeat(1, 1, self.num_heads // self.n_groups, 1) C = C.repeat(1, 1, self.num_heads // self.n_groups, 1) pad_size = (self.chunk_size - seq_len % self.chunk_size) % self.chunk_size D_residual = self.D[..., None] * pad_tensor_by_size(hidden_states, pad_size) # Discretize x and A hidden_states = hidden_states * dt[..., None] A = A.to(hidden_states.dtype) * dt # Rearrange into blocks/chunks hidden_states, A, B, C = [reshape_into_chunks(t, pad_size, self.chunk_size) for t in (hidden_states, A, B, C)] # [bsz, -1, chunk_size, num_heads] -> [bsz, num_heads, -1, chunk_size] A = A.permute(0, 3, 1, 2) A_cumsum = torch.cumsum(A, dim=-1) # 1. Compute the output for each intra-chunk (diagonal blocks) # This is the analog of a causal mask L = torch.exp(segment_sum(A)) # Contraction of C and B to get G (attention-weights like) G_intermediate = C[:, :, :, None, :, :] * B[:, :, None, :, :, :] # shape: (b, c, l, s, h, n) G = G_intermediate.sum(dim=-1) # shape: (b, c, l, s, h) # Compute M, equivalent to applying attention mask to weights M_intermediate = G[..., None] * L.permute(0, 2, 3, 4, 1)[..., None] M = M_intermediate.sum(dim=-1) # Compute Y_diag (apply to values) Y_diag = (M[..., None] * hidden_states[:, :, None]).sum(dim=3) # 2. Compute the state for each intra-chunk # (right term of low-rank factorization of off-diagonal blocks; B terms) decay_states = torch.exp((A_cumsum[:, :, :, -1:] - A_cumsum)) B_decay = B * decay_states.permute(0, -2, -1, 1)[..., None] states = (B_decay[..., None, :] * hidden_states[..., None]).sum(dim=2) # 3. Compute the inter-chunk SSM recurrence; produces correct SSM states at chunk boundaries # (middle term of factorization of off-diag blocks; A terms) if cache_params is not None and cache_position is not None and cache_position[0] > 0: previous_states = cache_params.ssm_states[self.layer_idx][:, None, ...].to(device=states.device) else: previous_states = torch.zeros_like(states[:, :1]) states = torch.cat([previous_states, states], dim=1) decay_chunk = torch.exp(segment_sum(nn.functional.pad(A_cumsum[:, :, :, -1], (1, 0)))) decay_chunk = decay_chunk.transpose(1, 3) new_states = (decay_chunk[..., None, None] * states[:, :, None, ...]).sum(dim=1) states, ssm_state = new_states[:, :-1], new_states[:, -1] # 4. Compute state -> output conversion per chunk # (left term of low-rank factorization of off-diagonal blocks; C terms) state_decay_out = torch.exp(A_cumsum) C_times_states = (C[..., None, :] * states[:, :, None, ...]) state_decay_out_permuted = state_decay_out.permute(0, 2, 3, 1) Y_off = (C_times_states.sum(-1) * state_decay_out_permuted[..., None]) # Add output of intra-chunk and inter-chunk terms (diagonal and off-diagonal blocks) y = Y_diag + Y_off # [bsz, -1, self.chunk_size, num_heads, head_dim] -> [bsz, (padded) seq_len, num_heads, head_dim] y = y.reshape(batch_size, -1, self.num_heads, self.head_dim) y = y + D_residual # Cutting off padded chunks if pad_size > 0: y = y[:, :seq_len, :, :] y = y.reshape(batch_size, seq_len, -1) # Init cache if ssm_state is not None and cache_params is not None: cache_params.update_ssm_state(layer_idx=self.layer_idx, new_ssm_state=ssm_state) scan_output = self.norm(y, gate) # end ssd naive # 4. Final linear projection contextualized_states = self.out_proj(scan_output.to(dtype)) # [batch, seq_len, hidden_size] return contextualized_states # fmt: on def forward( self, hidden_states, cache_params: Optional[HybridMambaAttentionDynamicCache] = None, cache_position: Optional[torch.LongTensor] = None, attention_mask: Optional[torch.Tensor] = None, ): if is_fast_path_available and "cuda" in self.in_proj.weight.device.type: return self.cuda_kernels_forward(hidden_states, cache_params, cache_position, attention_mask) dtype = hidden_states.dtype if attention_mask is not None and attention_mask.shape[1] > 1 and attention_mask.shape[0] > 1: # tune out hidden states for pad tokens, see https://github.com/state-spaces/mamba/issues/66 hidden_states = (hidden_states * attention_mask[:, :, None]).to(dtype) return self.torch_forward(hidden_states, cache_params, cache_position, attention_mask) class NemotronHRMSNorm(nn.Module): def __init__(self, hidden_size, eps=1e-6): """ NemotronHRMSNorm is equivalent to T5LayerNorm and LlamaRMSNorm """ super().__init__() self.weight = nn.Parameter(torch.ones(hidden_size)) self.variance_epsilon = eps def forward(self, hidden_states): input_dtype = hidden_states.dtype hidden_states = hidden_states.to(torch.float32) variance = hidden_states.pow(2).mean(-1, keepdim=True) hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon) # Weights are in float32 return (self.weight.to(torch.float32) * hidden_states).to(input_dtype) class NemotronHBlock(nn.Module): def __init__(self, config, layer_idx): super().__init__() self.config = config self.layer_idx = layer_idx self.residual_in_fp32 = config.residual_in_fp32 self.norm = NemotronHRMSNorm(config.hidden_size, eps=config.layer_norm_epsilon) # M: Mamba2, *: Attention, -: MLP self.block_type = config.layers_block_type[layer_idx] if self.block_type == "mamba": self.mixer = NemotronHMamba2Mixer(config, layer_idx=layer_idx) elif self.block_type == "attention": self.mixer = NEMOTRONH_ATTENTION_CLASSES[config._attn_implementation](config, layer_idx=layer_idx) elif self.block_type == "mlp": self.mixer = NemotronHMLP(config, layer_idx=layer_idx) else: raise ValueError(f"Invalid layer pattern {config.hybrid_override_pattern[layer_idx]}") def forward( self, hidden_states, cache_params: Optional[HybridMambaAttentionDynamicCache] = None, cache_position: Optional[torch.LongTensor] = None, attention_mask: Optional[torch.Tensor] = None, ): with torch.cuda.stream(torch.cuda.default_stream(hidden_states.device)): # * Use torch.cuda.stream() to avoid NaN issues when using multiple GPUs residual = hidden_states hidden_states = self.norm(hidden_states.to(dtype=self.norm.weight.dtype)) if self.residual_in_fp32: residual = residual.to(torch.float32) if self.block_type == "mamba": hidden_states = self.mixer( hidden_states, cache_params=cache_params, cache_position=cache_position ) elif self.block_type == "attention": hidden_states = self.mixer( hidden_states, cache_position=cache_position ) hidden_states = hidden_states[0] elif self.block_type == "mlp": hidden_states = self.mixer( hidden_states ) else: raise ValueError(f"Invalid block_type: {self.block_type}") hidden_states = residual + hidden_states return hidden_states # Copied from transformers.models.nemotron.modeling_nemotron Nemotron->NemotronH class NemotronHMLP(nn.Module): def __init__(self, config, layer_idx: Optional[int] = None): super().__init__() self.config = config self.layer_idx = layer_idx if layer_idx is None: logger.warning_once( f"Instantiating {self.__class__.__name__} without passing 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.intermediate_size = config.intermediate_size self.up_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=config.mlp_bias) self.down_proj = nn.Linear(self.intermediate_size, self.hidden_size, bias=config.mlp_bias) self.act_fn = ACT2FN[config.mlp_hidden_act] def forward(self, x): return self.down_proj(self.act_fn(self.up_proj(x))) # 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 NemotronHAttention(nn.Module): """Multi-headed attention from 'Attention Is All You Need' paper""" def __init__(self, config: NemotronHConfig, layer_idx: Optional[int] = None): super().__init__() self.config = config self.layer_idx = layer_idx if layer_idx is None: logger.warning_once( f"Instantiating {self.__class__.__name__} without passing 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.attention_dropout = config.attention_dropout self.hidden_size = config.hidden_size self.num_heads = config.num_attention_heads if config.attention_head_dim is not None: self.head_dim = config.attention_head_dim else: self.head_dim = config.hidden_size // config.num_attention_heads self.num_key_value_heads = config.num_key_value_heads self.num_key_value_groups = self.num_heads // self.num_key_value_heads self.max_position_embeddings = config.max_position_embeddings self.is_causal = True self.q_proj = nn.Linear(self.hidden_size, self.num_heads * self.head_dim, bias=config.attention_bias) self.k_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=config.attention_bias) self.v_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=config.attention_bias) self.o_proj = nn.Linear(self.head_dim * self.num_heads, self.hidden_size, bias=config.attention_bias) def forward( self, hidden_states: torch.Tensor, # position_embeddings: Tuple[torch.Tensor, torch.Tensor], #TODO attention_mask: Optional[torch.Tensor] = None, position_ids: Optional[torch.LongTensor] = None, past_key_value: Optional[HybridMambaAttentionDynamicCache] = None, output_attentions: bool = False, use_cache: bool = False, cache_position: Optional[torch.LongTensor] = None, ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]: bsz, q_len, _ = hidden_states.size() query_states = self.q_proj(hidden_states) key_states = self.k_proj(hidden_states) value_states = self.v_proj(hidden_states) query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2) key_states = key_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2) value_states = value_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2) if past_key_value is not None: key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx) key_states = repeat_kv(key_states, self.num_key_value_groups) value_states = repeat_kv(value_states, self.num_key_value_groups) causal_mask = attention_mask if attention_mask is not None: # no matter the length, we just slice it causal_mask = attention_mask[:, :, :, : key_states.shape[-2]] if query_states.device.type == "cuda" and attention_mask is not None: query_states = query_states.contiguous() key_states = key_states.contiguous() value_states = value_states.contiguous() is_causal = True if causal_mask is None and q_len > 1 else False attn_output = torch.nn.functional.scaled_dot_product_attention( query_states, key_states, value_states, attn_mask=causal_mask, dropout_p=self.attention_dropout if self.training else 0.0, is_causal=is_causal, ) attn_output = attn_output.transpose(1, 2).contiguous() #attn_output = attn_output.view(bsz, q_len, self.hidden_size) attn_output = attn_output.view(bsz, q_len, self.num_heads * self.head_dim) attn_output = self.o_proj(attn_output) return attn_output, None, past_key_value # Adapted from transformers.models.mistral.modeling_mistral.MistralFlashAttention2 with Mistral->Jamba #class JambaFlashAttention2(JambaAttention): class NemotronHFlashAttention2(NemotronHAttention): """ Jamba flash attention module. This module inherits from `JambaAttention` as the weights of the module stays untouched. The only required change would be on the forward pass where it needs to correctly call the public API of flash attention and deal with padding tokens in case the input contains any of them. """ def __init__(self, *args, **kwargs): super().__init__(*args, **kwargs) # TODO: Should be removed once Flash Attention for RoCm is bumped to 2.1. # flash_attn<2.1 generates top-left aligned causal mask, while what is needed here is bottom-right alignement, that was made default for flash_attn>=2.1. This attribute is used to handle this difference. Reference: https://github.com/Dao-AILab/flash-attention/releases/tag/v2.1.0. # Beware that with flash_attn<2.1, using q_seqlen != k_seqlen (except for the case q_seqlen == 1) produces a wrong mask (top-left). self._flash_attn_uses_top_left_mask = not is_flash_attn_greater_or_equal_2_10() def forward( self, hidden_states: torch.Tensor, attention_mask: Optional[torch.Tensor] = None, position_ids: Optional[torch.LongTensor] = None, past_key_value: Optional[HybridMambaAttentionDynamicCache] = None, output_attentions: bool = False, use_cache: bool = False, cache_position: Optional[torch.LongTensor] = None, **kwargs, ): bsz, q_len, _ = hidden_states.size() query_states = self.q_proj(hidden_states) key_states = self.k_proj(hidden_states) value_states = self.v_proj(hidden_states) # Flash attention requires the input to have the shape # batch_size x seq_length x head_dim x hidden_dim # therefore we just need to keep the original shape query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim) key_states = key_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2) value_states = value_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2) if past_key_value is not None: key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx) # repeat k/v heads if n_kv_heads < n_heads key_states = repeat_kv(key_states, self.num_key_value_groups) value_states = repeat_kv(value_states, self.num_key_value_groups) 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 key_states = key_states.transpose(1, 2) value_states = value_states.transpose(1, 2) attn_output = _flash_attention_forward( query_states, key_states, value_states, attention_mask, q_len, dropout=dropout_rate, sliding_window=getattr(self.config, "sliding_window", None), is_causal=self.is_causal, use_top_left_mask=self._flash_attn_uses_top_left_mask, ) #attn_output = attn_output.reshape(bsz, q_len, self.hidden_size).contiguous() attn_output = attn_output.reshape(bsz, q_len, self.num_heads * self.head_dim).contiguous() attn_output = self.o_proj(attn_output) if not output_attentions: attn_weights = None return attn_output, attn_weights, past_key_value # Adapted from transformers.models.mistral.modeling_mistral.MistralSdpaAttention with Mistral->Jamba #class JambaSdpaAttention(JambaAttention): class NemotronHSdpaAttention(NemotronHAttention): """ Jamba attention module using torch.nn.functional.scaled_dot_product_attention. This module inherits from `JambaAttention` as the weights of the module stays untouched. The only changes are on the forward pass to adapt to SDPA API. """ # Adapted from NemotronHAttention.forward 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: bool = False, use_cache: bool = False, cache_position: Optional[torch.LongTensor] = None, ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]: if output_attentions: # TODO: Improve this warning with e.g. `model.config.attn_implementation = "manual"` once this is implemented. logger.warning_once( "NemotronHModel is using NemotronHSdpaAttention, but `torch.nn.functional.scaled_dot_product_attention` does not support `output_attentions=True`. Falling back to the manual attention implementation, " 'but specifying the manual implementation will be required from Transformers version v5.0.0 onwards. This warning can be removed using the argument `attn_implementation="eager"` when loading the model.' ) return super().forward( 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, ) bsz, q_len, _ = hidden_states.size() query_states = self.q_proj(hidden_states) key_states = self.k_proj(hidden_states) value_states = self.v_proj(hidden_states) query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2) key_states = key_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2) value_states = value_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2) if past_key_value is not None: key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx) key_states = repeat_kv(key_states, self.num_key_value_groups) value_states = repeat_kv(value_states, self.num_key_value_groups) causal_mask = attention_mask if attention_mask is not None: causal_mask = causal_mask[:, :, :, : key_states.shape[-2]] # SDPA with memory-efficient backend is currently (torch==2.1.2) bugged with non-contiguous inputs with custom attn_mask, # Reference: https://github.com/pytorch/pytorch/issues/112577. if query_states.device.type == "cuda" and attention_mask is not None: query_states = query_states.contiguous() key_states = key_states.contiguous() value_states = value_states.contiguous() # We dispatch to SDPA's Flash Attention or Efficient kernels via this `is_causal` if statement instead of an inline conditional assignment # in SDPA to support both torch.compile's dynamic shapes and full graph options. An inline conditional prevents dynamic shapes from compiling. # The q_len > 1 is necessary to match with AttentionMaskConverter.to_causal_4d that does not create a causal mask in case q_len == 1. is_causal = True if self.is_causal and causal_mask is None and q_len > 1 else False attn_output = torch.nn.functional.scaled_dot_product_attention( query_states, key_states, value_states, attn_mask=causal_mask, dropout_p=self.attention_dropout if self.training else 0.0, is_causal=is_causal, ) attn_output = attn_output.transpose(1, 2).contiguous() attn_output = attn_output.view(bsz, q_len, self.hidden_size) attn_output = self.o_proj(attn_output) return attn_output, None, past_key_value NEMOTRONH_ATTENTION_CLASSES = { "eager": NemotronHAttention, "flash_attention_2": NemotronHFlashAttention2, "sdpa": NemotronHSdpaAttention, } # Copied from transformers.models.mamba.modeling_mamba2.Mamba2PreTrainedModel class NemotronHPreTrainedModel(PreTrainedModel): """ An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained models. """ config_class = NemotronHConfig base_model_prefix = "backbone" _no_split_modules = ["NemotronHBlock"] supports_gradient_checkpointing = True _is_stateful = True def _init_weights(self, module): """Initialize the weights.""" if isinstance(module, NemotronHMamba2Mixer): module.A_log._no_weight_decay = True module.D._no_weight_decay = True dt = torch.exp( torch.rand(self.config.mamba_num_heads) * (math.log(self.config.time_step_max) - math.log(self.config.time_step_min)) + math.log(self.config.time_step_min) ).clamp(min=self.config.time_step_floor) # # Inverse of softplus: https://github.com/pytorch/pytorch/issues/72759 inv_dt = dt + torch.log(-torch.expm1(-dt)) with torch.no_grad(): module.dt_bias.copy_(inv_dt) module.dt_bias._no_reinit = True if isinstance(module, nn.Linear): if module.bias is not None: if not getattr(module.bias, "_no_reinit", False): nn.init.zeros_(module.bias) elif isinstance(module, nn.Embedding): nn.init.normal_(module.weight, std=self.config.initializer_range) # TODO: Check if self.config.rescale_prenorm_residual: # Reinitialize selected weights subject to the OpenAI GPT-2 Paper Scheme: # > A modified initialization which accounts for the accumulation on the residual path with model depth. Scale # > the weights of residual layers at initialization by a factor of 1/√N where N is the # of residual layers. # > -- GPT-2 :: https://openai.com/blog/better-language-models/ # # Reference (Megatron-LM): https://github.com/NVIDIA/Megatron-LM/blob/main/megatron/model/gpt_model.py for name, p in module.named_parameters(): if name in ["out_proj.weight"]: # Special Scaled Initialization --> There are 2 Layer Norms per Transformer Block # Following Pytorch init, except scale by 1/sqrt(2 * n_layer) # We need to reinit p since this code could be called multiple times # Having just p *= scale would repeatedly scale it down nn.init.kaiming_uniform_(p, a=math.sqrt(5)) with torch.no_grad(): p /= math.sqrt(self.config.num_hidden_layers) @dataclass # Copied from transformers.models.mamba.modeling_mamba2.Mamba2Output with MAMBA2->NemotronH,Mamba2->NemotronH class NemotronHOutput(ModelOutput): """ Class for the NemotronH model outputs. Args: last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`): Sequence of hidden-states at the output of the last layer of the model. cache_params (`HybridMambaAttentionDynamicCache`): The state of the model at the last time step. Can be used in a forward method with the next `input_ids` to avoid providing the old `input_ids`. Includes both the State space model state matrices after the selective scan, and the Convolutional states hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, + one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`. Hidden-states of the model at the output of each layer plus the optional initial embedding outputs. """ last_hidden_state: Optional[torch.FloatTensor] = None cache_params: Optional[HybridMambaAttentionDynamicCache] = None hidden_states: Optional[Tuple[torch.FloatTensor]] = None attentions: Optional[Tuple[torch.FloatTensor]] = None @dataclass # Copied from transformers.models.mamba2.modeling_mamba2.MambaCausalLMOutput with Mamba2->NemotronH class NemotronHCausalLMOutput(ModelOutput): """ Base class for causal language model (or autoregressive) outputs. Args: loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided): Language modeling loss (for next-token prediction). logits (`torch.FloatTensor` of shape `(batch_size, sequence_length, config.vocab_size)`): Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax). cache_params (`HybridMambaAttentionDynamicCache`): The state of the model at the last time step. Can be used in a forward method with the next `input_ids` to avoid providing the old `input_ids`. Includes both the State space model state matrices after the selective scan, and the Convolutional states hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, + one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`. Hidden-states of the model at the output of each layer plus the optional initial embedding outputs. """ loss: Optional[torch.FloatTensor] = None logits: Optional[torch.FloatTensor] = None cache_params: Optional[HybridMambaAttentionDynamicCache] = None hidden_states: Optional[Tuple[torch.FloatTensor]] = None attentions: Optional[Tuple[torch.FloatTensor]] = None NEMOTRONH_START_DOCSTRING = r""" This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads etc.) This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and behavior. Parameters: config ([`NemotronHConfig`]): Model configuration class with all the parameters of the model. Initializing with a config file does not load the weights associated with the model, only the configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights. """ NEMOTRONH_INPUTS_DOCSTRING = r""" Args: input_ids (`torch.LongTensor` of shape `(batch_size, input_ids_length)`, *optional*): Indices of input sequence tokens in the vocabulary. If `cache_params.seqlen_offset>0`, only `input_ids` that do not have their past calculated should be passed as `input_ids`. Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and [`PreTrainedTokenizer.__call__`] for details. [What are input IDs?](../glossary#input-ids) inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*): Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This is useful if you want more control over how to convert `input_ids` indices into associated vectors than the model's internal embedding lookup matrix. position_ids (`torch.LongTensor` of shape `(batch_size,)`, *optional*): Indices of positions of each input sequence tokens in the position embeddings. cache_params (`HybridMambaAttentionDynamicCache`, *optional*): If passed along, the model uses the previous state in all the blocks (which will give the output for the `input_ids` provided as if the model add `state_input_ids + input_ids` as context). use_cache (`bool`, *optional*): If set to `True`, the `cache_params` is returned and can be used to quickly generate the next logits. output_attentions (`bool`, *optional*): Whether or not to return the attentions tensors of all attention layers. output_hidden_states (`bool`, *optional*): Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for more detail. return_dict (`bool`, *optional*): Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. cache_position (`torch.LongTensor` of shape `(batch_size,)`, *optional*): The position of the current input in the cache. This is used to ensure that the cache is correctly updated. If `cache_params` is passed, `cache_position` should also be passed. attention_mask (`torch.FloatTensor` of shape `(batch_size, sequence_length)`, *optional*): Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`: - 1 for tokens that are **not masked**, - 0 for tokens that are **masked**. [What are attention masks?](../glossary#attention-mask) """ @add_start_docstrings( "The bare NemotronH Model transformer outputting raw hidden-states without any specific head on top.", NEMOTRONH_START_DOCSTRING, ) class NemotronHModel(NemotronHPreTrainedModel): def __init__(self, config): super().__init__(config) self.embeddings = nn.Embedding(config.vocab_size, config.hidden_size) self.layers = nn.ModuleList([NemotronHBlock(config, layer_idx=idx) for idx in range(config.num_hidden_layers)]) self.gradient_checkpointing = False self.norm_f = NemotronHRMSNorm(config.hidden_size, eps=config.layer_norm_epsilon) # Initialize weights and apply final processing self._register_load_state_dict_pre_hook(self.load_hook) self.post_init() def load_hook(self, state_dict, prefix, *args): for k in state_dict: if "embedding." in k: state_dict[k.replace("embedding.", "embeddings.")] = state_dict.pop(k) break def get_input_embeddings(self): return self.embeddings def set_input_embeddings(self, new_embeddings): self.embeddings = new_embeddings @add_start_docstrings_to_model_forward(NEMOTRONH_INPUTS_DOCSTRING) @add_code_sample_docstrings( checkpoint=_CHECKPOINT_FOR_DOC, output_type=NemotronHOutput, config_class=_CONFIG_FOR_DOC, ) def forward( self, input_ids: Optional[torch.LongTensor] = None, inputs_embeds: Optional[torch.LongTensor] = None, position_ids: Optional[torch.LongTensor] = None, cache_params: Optional[HybridMambaAttentionDynamicCache] = None, use_cache: Optional[bool] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, cache_position: Optional[torch.LongTensor] = None, attention_mask: Optional[torch.Tensor] = None, **kwargs, ) -> Union[Tuple, NemotronHOutput]: output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions output_hidden_states = ( output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states ) # use_cache = use_cache if use_cache is not None else self.config.use_cache use_cache = use_cache if use_cache is not None else (self.config.use_cache if not self.training else False) return_dict = return_dict if return_dict is not None else self.config.use_return_dict if (input_ids is None) ^ (inputs_embeds is not None): # ^ is python for xor raise ValueError("You must specify exactly one of input_ids or inputs_embeds") if inputs_embeds is None: inputs_embeds = self.embeddings(input_ids) if self.gradient_checkpointing and self.training and use_cache: logger.warning_once( "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`." ) use_cache = False # From zamba_modeling.py if use_cache and cache_params is None: logger.warning_once( "NemotronH requires an initialized `NemotronHHybridDynamicCache` to return a cache. None was " "provided, so no cache will be returned." ) hidden_states = inputs_embeds if cache_position is None: cache_position = torch.arange(hidden_states.shape[1], device=hidden_states.device) if position_ids is None: position_ids = cache_position.unsqueeze(0) causal_mask = self._update_causal_mask(attention_mask, inputs_embeds, cache_position) mamba_mask = self._update_mamba_mask(attention_mask, cache_position) all_hidden_states = () if output_hidden_states else None all_self_attns = () if output_attentions else None # Until HERE for layer_idx, mixer_block in enumerate(self.layers): # Depending on the layer type we opt for 2D base attention mask (Mamba) or 4D causal mask (Attention) if mixer_block.block_type == "mamba": layer_mask = mamba_mask elif mixer_block.block_type == "attention": layer_mask = causal_mask elif mixer_block.block_type == "mlp": layer_mask = None else: raise ValueError(f"Invalid block_type: {self.block_type}") if output_hidden_states: all_hidden_states += (hidden_states,) if self.gradient_checkpointing and self.training: hidden_states = self._gradient_checkpointing_func( mixer_block.__call__, hidden_states, cache_params, cache_position, layer_mask ) else: hidden_states = mixer_block( hidden_states, cache_params=cache_params, cache_position=cache_position, attention_mask=layer_mask, ) # TODO: Store attentions # if output_attentions: # if layer_outputs[1] is not None: # # append attentions only of attention layers. Mamba layers return `None` as the attention weights # all_self_attns += (layer_outputs[1],) # TODO (Check): should it happen before the forward pass? # if output_hidden_states: # all_hidden_states = all_hidden_states + (hidden_states,) hidden_states = self.norm_f(hidden_states) if output_hidden_states: all_hidden_states = all_hidden_states + (hidden_states,) if not return_dict: return tuple(v for v in [hidden_states, cache_params, all_hidden_states] if v is not None) return NemotronHOutput( last_hidden_state=hidden_states, cache_params=cache_params if use_cache else None, hidden_states=all_hidden_states, attentions=all_self_attns, ) # Copied from transformers.models.jamba.modeling_jamba.JambaModel._update_causal_mask def _update_causal_mask(self, attention_mask, input_tensor, cache_position): if self.config._attn_implementation == "flash_attention_2": if attention_mask is not None and 0.0 in attention_mask: return attention_mask return None dtype, device = input_tensor.dtype, input_tensor.device min_dtype = torch.finfo(dtype).min sequence_length = input_tensor.shape[1] target_length = cache_position[-1] + 1 causal_mask = torch.full((sequence_length, target_length), fill_value=min_dtype, dtype=dtype, device=device) if sequence_length != 1: causal_mask = torch.triu(causal_mask, diagonal=1) causal_mask *= torch.arange(target_length, device=device) > cache_position.reshape(-1, 1) causal_mask = causal_mask[None, None, :, :].expand(input_tensor.shape[0], 1, -1, -1) if attention_mask is not None: causal_mask = causal_mask.clone() # copy to contiguous memory for in-place edit if attention_mask.dim() == 2: mask_length = attention_mask.shape[-1] padding_mask = causal_mask[..., :mask_length].eq(0.0) * attention_mask[:, None, None, :].eq(0.0) causal_mask[..., :mask_length] = causal_mask[..., :mask_length].masked_fill(padding_mask, min_dtype) if ( self.config._attn_implementation == "sdpa" and attention_mask is not None and attention_mask.device.type == "cuda" ): # Attend to all tokens in fully masked rows in the causal_mask, for example the relevant first rows when # using left padding. This is required by F.scaled_dot_product_attention memory-efficient attention path. # Details: https://github.com/pytorch/pytorch/issues/110213 causal_mask = AttentionMaskConverter._unmask_unattended(causal_mask, min_dtype) return causal_mask def _update_mamba_mask(self, attention_mask, cache_position): """ No need for zeroing states when 1. Cached forward 2. Attending to all inputs """ mamba_mask = attention_mask if cache_position[0] > 0 or (attention_mask is not None and torch.all(attention_mask == 1)): mamba_mask = None return mamba_mask @add_start_docstrings( """ The NEMOTRONH Model transformer with a language modeling head on top (linear layer with weights not tied to the input embeddings). """, NEMOTRONH_START_DOCSTRING, ) class NemotronHForCausalLM(NemotronHPreTrainedModel, GenerationMixin): _tied_weights_keys = ["lm_head.weight"] def __init__(self, config): super().__init__(config) self.backbone = NemotronHModel(config) self.vocab_size = config.vocab_size self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False) # Initialize weights and apply final processing self.post_init() def get_input_embeddings(self): return self.backbone.get_input_embeddings() def set_input_embeddings(self, new_embeddings): return self.backbone.set_input_embeddings(new_embeddings) def get_output_embeddings(self): return self.lm_head def set_output_embeddings(self, new_embeddings): self.lm_head = new_embeddings def get_decoder(self): return self.model def set_decoder(self, decoder): self.model = decoder def prepare_inputs_for_generation( self, input_ids, past_key_values=None, attention_mask=None, inputs_embeds=None, cache_position=None, position_ids=None, use_cache=True, **kwargs, ): # Copy from https://github.com/huggingface/transformers/blob/main/src/transformers/models/jamba/modeling_jamba.py # Overwitten -- uses `cache_params` as opposed to `past_key_values` empty_past_kv = past_key_values is None # If we have cache: let's slice `input_ids` through `cache_position`, to keep only the unprocessed tokens # Exception 1: when passing input_embeds, input_ids may be missing entries # Exception 2: some generation methods do special slicing of input_ids, so we don't need to do it here # Exception 3: with synced GPUs cache_position may go out of bounds, but we only want dummy token in that case. # (we can't check exception 3 while compiling) if not empty_past_kv: if ( inputs_embeds is not None # Exception 1 or cache_position[-1] >= input_ids.shape[1] # Exception 3 ): input_ids = input_ids[:, -cache_position.shape[0] :] elif input_ids.shape[1] != cache_position.shape[0]: # Default case (the "else", a no op, is Exception 2) input_ids = input_ids[:, cache_position] else: past_key_values = HybridMambaAttentionDynamicCache( self.config, input_ids.shape[0], self.dtype, device=self.device ) if attention_mask is not None and position_ids is None: # create position_ids on the fly for batch generation position_ids = attention_mask.long().cumsum(-1) - 1 position_ids.masked_fill_(attention_mask == 0, 1) if not empty_past_kv: position_ids = position_ids[:, -input_ids.shape[1] :] # if `inputs_embeds` are passed, we only want to use them in the 1st generation step if inputs_embeds is not None and empty_past_kv: model_inputs = {"inputs_embeds": inputs_embeds} else: model_inputs = {"input_ids": input_ids.contiguous()} # `contiguous()` needed for compilation use cases model_inputs.update( { "position_ids": position_ids, "past_key_values": past_key_values, "use_cache": use_cache, "attention_mask": attention_mask, "logits_to_keep": self.config.num_logits_to_keep, "cache_position": cache_position, } ) return model_inputs @add_start_docstrings_to_model_forward(NEMOTRONH_INPUTS_DOCSTRING) @add_code_sample_docstrings( checkpoint=_CHECKPOINT_FOR_DOC, output_type=NemotronHCausalLMOutput, config_class=_CONFIG_FOR_DOC, ) def forward( self, input_ids: Optional[torch.LongTensor] = None, inputs_embeds: Optional[torch.FloatTensor] = None, position_ids: Optional[torch.LongTensor] = None, cache_params: Optional[HybridMambaAttentionDynamicCache] = None, labels: Optional[torch.LongTensor] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, use_cache: Optional[bool] = None, cache_position: Optional[torch.Tensor] = None, attention_mask: Optional[torch.Tensor] = None, **kwargs, # for now we need this for generation ) -> Union[Tuple, NemotronHCausalLMOutput]: r""" labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): Labels for language modeling. Note that the labels **are shifted** inside the model, i.e. you can set `labels = input_ids` Indices are selected in `[-100, 0, ..., config.vocab_size]` All labels set to `-100` are ignored (masked), the loss is only computed for labels in `[0, ..., config.vocab_size]` """ output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions output_hidden_states = ( output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states ) return_dict = return_dict if return_dict is not None else self.config.use_return_dict nemotron_h_outputs = self.backbone( input_ids, cache_params=cache_params, inputs_embeds=inputs_embeds, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, use_cache=use_cache, cache_position=cache_position, attention_mask=attention_mask, ) hidden_states = nemotron_h_outputs[0] # TODO: Check zamba_modeling.py: https://github.com/huggingface/transformers/blob/d7188ba600e36d3fd191b12e19f1b3bb81a8404f/src/transformers/models/zamba/modeling_zamba.py#L1284C1-L1286C2 #logits = self.lm_head(hidden_states.to(self.lm_head.weight.dtype)).float() logits = self.lm_head(hidden_states.to(self.lm_head.weight.dtype)).float() loss = None if labels is not None: # move labels to correct device to enable model parallelism labels = labels.to(logits.device) # Shift so that tokens < n predict n shift_logits = logits[..., :-1, :].contiguous() shift_labels = labels[..., 1:].contiguous() # Flatten the tokens loss_fct = CrossEntropyLoss() loss = loss_fct(shift_logits.view(-1, shift_logits.size(-1)), shift_labels.view(-1)) if not return_dict: output = (logits,) + nemotron_h_outputs[1:] return ((loss,) + output) if loss is not None else output return NemotronHCausalLMOutput( loss=loss, logits=logits, cache_params=nemotron_h_outputs.cache_params, hidden_states=nemotron_h_outputs.hidden_states, attentions=nemotron_h_outputs.attentions, )