import math import importlib.util import os from typing import Optional, Tuple import torch import torch.nn as nn import torch.nn.functional as F from transformers import ( AutoConfig, AutoModel, AutoModelForCausalLM, PreTrainedModel, PretrainedConfig, ) from transformers.modeling_outputs import CausalLMOutput, CausalLMOutputWithPast flash_attn_func = None _flash_attn_available = False if importlib.util.find_spec("flash_attn") is not None: try: from flash_attn.flash_attn_interface import flash_attn_func _flash_attn_available = True except ImportError: _flash_attn_available = False # One-time startup log of which attention kernel is in effect, because the # sliding-window local attention is only implemented on the flash-attn path # and silently degrades to full attention on the SDPA/math fallbacks. _attention_path_logged = False class ArgonneConfig(PretrainedConfig): """Configuration for the Argonne v3.0 family of models.""" model_type = "argonne2" def __init__( self, vocab_size: int = 151936, hidden_size: int = 3072, num_hidden_layers: int = 24, num_attention_heads: int = 12, num_key_value_heads: Optional[int] = 4, intermediate_size: Optional[int] = 8192, max_position_embeddings: int = 1024, attention_dropout: float = 0.0, hidden_dropout: float = 0.0, rms_norm_eps: float = 1e-6, rope_theta: float = 1000000.0, sliding_window: Optional[int] = None, use_flash_attention: bool = True, use_gradient_checkpointing: bool = False, qk_norm: bool = True, v_norm: bool = True, sandwich_norm: bool = True, z_loss_weight: float = 0.0, mtp_horizon: int = 1, mtp_loss_weight: float = 0.0, interleaved_local_attention: bool = True, local_attention_window: Optional[int] = 256, logit_softcap: float = 15.0, loss_chunk_size: int = 0, tie_word_embeddings: bool = True, attention_bias: bool = False, mlp_bias: bool = False, pad_token_id: Optional[int] = None, bos_token_id: Optional[int] = None, eos_token_id: Optional[int] = None, **kwargs, ) -> None: pad_token_id = pad_token_id if pad_token_id is not None else kwargs.pop("pad_token_id", None) bos_token_id = bos_token_id if bos_token_id is not None else kwargs.pop("bos_token_id", None) eos_token_id = eos_token_id if eos_token_id is not None else kwargs.pop("eos_token_id", None) super().__init__( pad_token_id=pad_token_id, bos_token_id=bos_token_id, eos_token_id=eos_token_id, **kwargs, ) # Backwards compatibility with Argonne 1.x naming. if "n_layer" in kwargs: num_hidden_layers = kwargs["n_layer"] if "n_head" in kwargs: num_attention_heads = kwargs["n_head"] if "n_embd" in kwargs: hidden_size = kwargs["n_embd"] if "block_size" in kwargs: max_position_embeddings = kwargs["block_size"] self.vocab_size = vocab_size self.hidden_size = hidden_size self.num_hidden_layers = num_hidden_layers self.num_attention_heads = num_attention_heads self.num_key_value_heads = ( num_key_value_heads if num_key_value_heads is not None else num_attention_heads // 2 ) if self.num_key_value_heads < 1: self.num_key_value_heads = 1 if num_attention_heads % self.num_key_value_heads != 0: raise ValueError("num_attention_heads must be divisible by num_key_value_heads") if intermediate_size is None: width = int(8 * hidden_size / 3) self.intermediate_size = ((width + 255) // 256) * 256 else: self.intermediate_size = intermediate_size self.max_position_embeddings = max_position_embeddings self.attention_dropout = attention_dropout self.hidden_dropout = hidden_dropout self.rms_norm_eps = rms_norm_eps self.rope_theta = rope_theta self.sliding_window = sliding_window self.use_flash_attention = use_flash_attention self.use_gradient_checkpointing = use_gradient_checkpointing self.qk_norm = qk_norm self.v_norm = v_norm self.sandwich_norm = sandwich_norm self.z_loss_weight = z_loss_weight self.mtp_horizon = max(1, int(mtp_horizon)) self.mtp_loss_weight = float(mtp_loss_weight) self.interleaved_local_attention = bool(interleaved_local_attention) self.local_attention_window = ( int(local_attention_window) if local_attention_window is not None and int(local_attention_window) > 0 else None ) self.logit_softcap = float(logit_softcap) # >0 enables chunked cross-entropy (frees the full (batch*seq, vocab) fp32 logit transient # that caps batch at long context). 0 = off (default; unchanged full-logit path). self.loss_chunk_size = int(loss_chunk_size) self.tie_word_embeddings = tie_word_embeddings self.attention_bias = attention_bias self.mlp_bias = mlp_bias if self.pad_token_id is None and self.eos_token_id is not None: self.pad_token_id = self.eos_token_id # Backwards compatibility aliases self.n_embd = self.hidden_size self.n_layer = self.num_hidden_layers self.n_head = self.num_attention_heads self.block_size = self.max_position_embeddings class RMSNorm(nn.Module): def __init__(self, hidden_size: int, eps: float = 1e-6) -> None: super().__init__() self.eps = eps self.weight = nn.Parameter(torch.ones(hidden_size)) def forward(self, x: torch.Tensor) -> torch.Tensor: orig_dtype = x.dtype x = x.to(torch.float32) # Clamp values to prevent overflow in pow(2) x = torch.clamp(x, min=-65504.0, max=65504.0) variance = x.pow(2).mean(-1, keepdim=True) x = x * torch.rsqrt(variance + self.eps) return (self.weight * x.to(orig_dtype)) class RotaryEmbedding(nn.Module): def __init__( self, dim: int, max_position_embeddings: int = 2048, base: float = 10000.0, device: Optional[torch.device] = None, ) -> None: super().__init__() self.dim = dim self.max_position_embeddings = max_position_embeddings self.base = base # Always compute inv_freq on CPU to avoid meta-device issues during from_pretrained # (low_cpu_mem_usage=True initializes model on meta device, which would give uninitialized values) inv_freq = 1.0 / ( self.base ** (torch.arange(0, dim, 2, dtype=torch.float32, device="cpu") / dim) ) self.register_buffer("inv_freq", inv_freq, persistent=False) self._set_cos_sin_cache(max_position_embeddings, torch.device("cpu"), torch.float32) def _set_cos_sin_cache(self, seq_len: int, device: torch.device, dtype: torch.dtype) -> None: self.max_seq_len_cached = seq_len t = torch.arange(seq_len, device=device, dtype=self.inv_freq.dtype) freqs = torch.outer(t, self.inv_freq) emb = torch.cat((freqs, freqs), dim=-1) # Always store cache in float32 to avoid precision loss when model dtype is bfloat16 # (large freqs lose precision in bfloat16, corrupting cos/sin values) self.register_buffer("cos_cached", emb.cos().to(torch.float32), persistent=False) self.register_buffer("sin_cached", emb.sin().to(torch.float32), persistent=False) def forward(self, x: torch.Tensor, seq_len: int) -> Tuple[torch.Tensor, torch.Tensor]: if seq_len > self.max_seq_len_cached: self._set_cos_sin_cache(seq_len, x.device, x.dtype) return ( self.cos_cached[:seq_len].to(dtype=x.dtype, device=x.device), self.sin_cached[:seq_len].to(dtype=x.dtype, device=x.device), ) def init_buffers(self) -> None: """Re-initialize inv_freq, cos_cached, sin_cached. Call after from_pretrained to fix meta-device corruption (low_cpu_mem_usage=True destroys buffer values).""" device = self.inv_freq.device if device.type == "meta": device = torch.device("cpu") inv_freq = 1.0 / ( self.base ** (torch.arange(0, self.dim, 2, dtype=torch.float32, device="cpu") / self.dim) ) self.inv_freq = inv_freq.to(device) self._set_cos_sin_cache( self.max_position_embeddings, device, torch.float32, ) def rotate_half(x: torch.Tensor) -> torch.Tensor: 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: torch.Tensor, k: torch.Tensor, cos: torch.Tensor, sin: torch.Tensor, position_ids: Optional[torch.Tensor] = None, ) -> Tuple[torch.Tensor, torch.Tensor]: if position_ids is None: cos = cos.unsqueeze(0).unsqueeze(0) sin = sin.unsqueeze(0).unsqueeze(0) else: cos = cos[position_ids].unsqueeze(1) sin = sin[position_ids].unsqueeze(1) return ( (q * cos) + (rotate_half(q) * sin), (k * cos) + (rotate_half(k) * sin), ) class GroupedQueryAttention(nn.Module): def __init__(self, config: ArgonneConfig) -> None: super().__init__() self.hidden_size = config.hidden_size self.num_heads = config.num_attention_heads self.num_kv_heads = config.num_key_value_heads self.head_dim = self.hidden_size // self.num_heads self.num_key_value_groups = self.num_heads // self.num_kv_heads self.sliding_window = config.sliding_window self.qk_norm = config.qk_norm self.v_norm = config.v_norm 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_kv_heads * self.head_dim, bias=config.attention_bias, ) self.v_proj = nn.Linear( self.hidden_size, self.num_kv_heads * self.head_dim, bias=config.attention_bias, ) self.o_proj = nn.Linear( self.num_heads * self.head_dim, self.hidden_size, bias=config.attention_bias, ) self.o_proj._is_residual = True self.attention_dropout = config.attention_dropout self.use_flash_attention = config.use_flash_attention if self.qk_norm: self.q_norm = RMSNorm(self.head_dim, eps=config.rms_norm_eps) self.k_norm = RMSNorm(self.head_dim, eps=config.rms_norm_eps) if self.v_norm: self.v_norm_layer = RMSNorm(self.head_dim, eps=config.rms_norm_eps) def _repeat_kv(self, x: torch.Tensor) -> torch.Tensor: if self.num_key_value_groups == 1: return x bsz, num_kv, seqlen, head_dim = x.shape x = x[:, :, None, :, :].expand(bsz, num_kv, self.num_key_value_groups, seqlen, head_dim) return x.reshape(bsz, num_kv * self.num_key_value_groups, seqlen, head_dim) def forward( self, hidden_states: torch.Tensor, position_embeddings: Tuple[torch.Tensor, torch.Tensor], attention_mask: Optional[torch.Tensor] = None, past_kv: Optional[Tuple[torch.Tensor, torch.Tensor]] = None, use_cache: bool = False, ): bsz, seqlen, _ = hidden_states.shape query = self.q_proj(hidden_states) key = self.k_proj(hidden_states) value = self.v_proj(hidden_states) query = query.view(bsz, seqlen, self.num_heads, self.head_dim).transpose(1, 2) key = key.view(bsz, seqlen, self.num_kv_heads, self.head_dim).transpose(1, 2) value = value.view(bsz, seqlen, self.num_kv_heads, self.head_dim).transpose(1, 2) if self.qk_norm: query = self.q_norm(query) key = self.k_norm(key) if self.v_norm: value = self.v_norm_layer(value) cos, sin = position_embeddings query, key = apply_rotary_pos_emb(query, key, cos, sin) # ---- KV cache (inference only; training keeps past_kv=None/use_cache=False) ---- # Cache pre-repeat (num_kv_heads), post-rotary K/V. The SDPA/math path # ignores the sliding window (full causal), so storing all keys and having # the new query attend to everything reproduces the no-cache result. is_decode = past_kv is not None if is_decode: past_k, past_v = past_kv key = torch.cat([past_k, key], dim=2) value = torch.cat([past_v, value], dim=2) new_kv = (key, value) if use_cache else None kv_len = key.shape[2] # Additive mask used only when decoding with a cache: query at absolute # position (kv_len - seqlen + i) attends to keys j <= that position. decode_mask = None if is_decode: q_pos = torch.arange(kv_len - seqlen, kv_len, device=hidden_states.device) k_pos = torch.arange(kv_len, device=hidden_states.device) allowed = k_pos[None, :] <= q_pos[:, None] decode_mask = torch.zeros(seqlen, kv_len, dtype=query.dtype, device=hidden_states.device) decode_mask = decode_mask.masked_fill(~allowed, -65504.0)[None, None] key = self._repeat_kv(key) value = self._repeat_kv(value) # Argonne-3.5 speedup (hardware accel, training-neutral): qk/v-norm return fp32 # (fp32 norm-weight * bf16 activation type-promotes), which trips the dtype gate below # and forces the hand-rolled O(seq^2) math attention. Under bf16 autocast the matmuls # already run in bf16, so casting q/k/v to the autocast dtype here is numerically # equivalent (measured rel diff 3.7e-3 < bf16 roundoff 7.8e-3) and lets SDPA pick a fused # FLASH/cuDNN kernel: ~2x lower attention memory + faster. Training-under-autocast only; # inference/decode (self.training False) is untouched. if self.training and torch.is_autocast_enabled() and query.dtype not in (torch.float16, torch.bfloat16): _ac_dtype = torch.get_autocast_dtype("cuda") if hasattr(torch, "get_autocast_dtype") else torch.get_autocast_gpu_dtype() query = query.to(_ac_dtype) key = key.to(_ac_dtype) value = value.to(_ac_dtype) use_flash_attn_2 = ( not is_decode and _flash_attn_available and self.use_flash_attention and attention_mask is None and query.dtype in (torch.float16, torch.bfloat16) and self.head_dim % 4 == 0 ) use_scaled_dot = ( hasattr(F, "scaled_dot_product_attention") and self.use_flash_attention and query.dtype in (torch.float16, torch.bfloat16) and self.head_dim % 4 == 0 ) attn_output = None if use_flash_attn_2: try: flash_dropout = self.attention_dropout if self.training else 0.0 window = ( (self.sliding_window, self.sliding_window) if self.sliding_window is not None else (-1, -1) ) q = query.transpose(1, 2).contiguous() k = key.transpose(1, 2).contiguous() v = value.transpose(1, 2).contiguous() attn_output = flash_attn_func( q, k, v, dropout_p=flash_dropout, softmax_scale=None, causal=True, window_size=window, ).transpose(1, 2) except RuntimeError: attn_output = None if attn_output is None and use_scaled_dot: try: if decode_mask is not None: attn_output = F.scaled_dot_product_attention( query, key, value, attn_mask=decode_mask, dropout_p=0.0, is_causal=False, ) elif attention_mask is None: # Prefill / training: fast causal path. attn_output = F.scaled_dot_product_attention( query, key, value, attn_mask=None, dropout_p=self.attention_dropout if self.training else 0.0, is_causal=True, ) else: # With attention_mask: need to pass it explicitly (slower but correct) # attention_mask should be 4D: (bsz, 1, seq, seq) or broadcastable attn_output = F.scaled_dot_product_attention( query, key, value, attn_mask=attention_mask, dropout_p=self.attention_dropout if self.training else 0.0, is_causal=False, # Mask already includes causal component ) except RuntimeError: # Fallback to math attention when kernels are unavailable attn_output = None if attn_output is None: scores = torch.matmul(query, key.transpose(2, 3)) / math.sqrt(self.head_dim) mask_value = -65504.0 # Large negative instead of -inf if decode_mask is not None: scores = scores + decode_mask else: # Apply causal mask - use large negative instead of -inf for stability causal_mask = torch.triu( torch.ones(seqlen, kv_len, dtype=torch.bool, device=hidden_states.device), diagonal=1, ) scores = scores.masked_fill(causal_mask, mask_value) if attention_mask is not None: scores = scores + attention_mask attn_weights = torch.softmax(scores, dim=-1, dtype=torch.float32).to(query.dtype) attn_weights = F.dropout(attn_weights, p=self.attention_dropout, training=self.training) attn_output = torch.matmul(attn_weights, value) attn_output = ( attn_output.transpose(1, 2) .contiguous() .view(bsz, seqlen, self.num_heads * self.head_dim) ) out = self.o_proj(attn_output) if use_cache: return out, new_kv return out class SwiGLUMLP(nn.Module): def __init__(self, config: ArgonneConfig) -> None: super().__init__() self.gate_proj = nn.Linear( config.hidden_size, config.intermediate_size, bias=config.mlp_bias, ) self.up_proj = nn.Linear( config.hidden_size, config.intermediate_size, bias=config.mlp_bias, ) self.down_proj = nn.Linear( config.intermediate_size, config.hidden_size, bias=config.mlp_bias, ) self.down_proj._is_residual = True self.dropout = nn.Dropout(config.hidden_dropout) def forward(self, x: torch.Tensor) -> torch.Tensor: # Clamp intermediate values to prevent overflow gate = self.gate_proj(x) gate = torch.clamp(gate, min=-65504.0, max=65504.0) up = self.up_proj(x) up = torch.clamp(up, min=-65504.0, max=65504.0) return self.dropout(self.down_proj(F.silu(gate) * up)) class Block(nn.Module): """Transformer block with GQA attention and SwiGLU feed-forward.""" def __init__(self, config: ArgonneConfig, layer_idx: int = 0) -> None: super().__init__() self.layer_idx = layer_idx self.attn = GroupedQueryAttention(config) self.input_norm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps) self.post_norm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps) self.sandwich_norm = config.sandwich_norm if self.sandwich_norm: self.attn_out_norm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps) self.mlp_out_norm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps) self.mlp = SwiGLUMLP(config) def forward( self, hidden_states: torch.Tensor, position_embeddings: Tuple[torch.Tensor, torch.Tensor], attention_mask: Optional[torch.Tensor] = None, past_kv: Optional[Tuple[torch.Tensor, torch.Tensor]] = None, use_cache: bool = False, ): residual = hidden_states hidden_states = self.input_norm(hidden_states) attn_out = self.attn( hidden_states, position_embeddings, attention_mask, past_kv=past_kv, use_cache=use_cache, ) new_kv = None if use_cache: hidden_states, new_kv = attn_out else: hidden_states = attn_out if self.sandwich_norm: hidden_states = self.attn_out_norm(hidden_states) hidden_states = residual + hidden_states residual = hidden_states hidden_states = self.post_norm(hidden_states) hidden_states = self.mlp(hidden_states) if self.sandwich_norm: hidden_states = self.mlp_out_norm(hidden_states) hidden_states = residual + hidden_states if use_cache: return hidden_states, new_kv return hidden_states class ArgonneModel(PreTrainedModel): config_class = ArgonneConfig _no_split_modules = ["Block"] _tied_weights_keys = {"lm_head.weight": "embed_tokens.weight"} def __init__(self, config: ArgonneConfig) -> None: super().__init__(config) self.config = config self.embed_tokens = nn.Embedding(config.vocab_size, config.hidden_size) self.blocks = nn.ModuleList([Block(config, idx) for idx in range(config.num_hidden_layers)]) if config.interleaved_local_attention and config.local_attention_window is not None: for idx, block in enumerate(self.blocks): block.attn.sliding_window = config.local_attention_window if (idx % 2 == 1) else None self.norm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps) self.rotary_emb = RotaryEmbedding( config.hidden_size // config.num_attention_heads, max_position_embeddings=config.max_position_embeddings, base=config.rope_theta, ) self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False) if config.tie_word_embeddings: self.lm_head.weight = self.embed_tokens.weight self.gradient_checkpointing = config.use_gradient_checkpointing # Selective activation checkpointing (ported from argonne4.0). Runtime-only -- # NOT part of config or state_dict, so checkpoint/resume compatibility with every # existing argonne3.5 checkpoint is unaffected. checkpoint_stride S: # S == 1 : checkpoint ALL layers (default; identical to prior behavior). # S >= 2 : checkpoint every layer EXCEPT store (un-checkpoint) every Sth layer # -> store ceil(n_layers/S), recompute the rest. Smaller S stores MORE # (more HBM, less recompute, faster); too-small S OOMs. # Numerically identical either way: it only chooses store-vs-recompute. self.checkpoint_stride = 1 self._nan_loss_count = 0 self.post_init() global _attention_path_logged rank = os.environ.get("RANK") or os.environ.get("SLURM_PROCID") or "0" if not _attention_path_logged and rank == "0": _attention_path_logged = True window = ( config.local_attention_window if config.interleaved_local_attention else None ) if _flash_attn_available and config.use_flash_attention: note = ( f"flash-attn-2; sliding window {window} active on odd layers" if window is not None else "flash-attn-2; full attention" ) else: reason = ( "flash-attn unavailable" if config.use_flash_attention else "use_flash_attention=False" ) note = f"SDPA/math ({reason}); full attention" if window is not None: note += ( f" — local_attention_window={window} is configured but" " IGNORED on this path" ) print(f"[ArgonneModel] attention path: {note}", flush=True) def get_input_embeddings(self) -> nn.Embedding: return self.embed_tokens def set_input_embeddings(self, new_embeddings: nn.Embedding) -> None: self.embed_tokens = new_embeddings self.config.vocab_size = new_embeddings.num_embeddings if self.config.tie_word_embeddings: self.lm_head.weight = self.embed_tokens.weight def get_output_embeddings(self) -> nn.Module: return self.lm_head def set_output_embeddings(self, new_embeddings: nn.Module) -> None: self.lm_head = new_embeddings if isinstance(new_embeddings, nn.Linear): self.config.vocab_size = new_embeddings.out_features def tie_weights(self, **kwargs) -> None: if self.config.tie_word_embeddings: self.lm_head.weight = self.embed_tokens.weight @classmethod def from_pretrained(cls, *args, **kwargs): """Wrap PreTrainedModel.from_pretrained to self-heal after loading. from_pretrained may materialize the model via the meta device (low_cpu_mem_usage), which fills the rotary embedding's non-persistent buffers with uninitialized memory and can drop the embed_tokens/lm_head tie. Callers previously had to remember to call init_buffers() and tie_weights() by hand; do it here so every loader gets a working model. """ model = super().from_pretrained(*args, **kwargs) for module in model.modules(): if isinstance(module, RotaryEmbedding): module.init_buffers() model.tie_weights() return model def _init_weights(self, module: nn.Module) -> None: if isinstance(module, nn.Linear): std = self.config.hidden_size ** -0.5 if hasattr(module, "_is_residual"): std = (2 * self.config.num_hidden_layers) ** -0.5 nn.init.normal_(module.weight, mean=0.0, std=std) if module.bias is not None: nn.init.zeros_(module.bias) elif isinstance(module, nn.Embedding): nn.init.normal_(module.weight, mean=0.0, std=self.config.hidden_size ** -0.5) def set_gradient_checkpointing(self, enabled: bool = True) -> None: self.gradient_checkpointing = enabled def gradient_checkpointing_enable(self, gradient_checkpointing_kwargs=None) -> None: self.set_gradient_checkpointing(True) def gradient_checkpointing_disable(self) -> None: self.set_gradient_checkpointing(False) def forward( self, input_ids: torch.LongTensor, attention_mask: Optional[torch.Tensor] = None, labels: Optional[torch.LongTensor] = None, position_ids: Optional[torch.LongTensor] = None, past_key_values: Optional[list] = None, use_cache: bool = False, **kwargs, # Accept extra args from newer transformers (e.g., num_items_in_batch) ) -> CausalLMOutput: _, seq_length = input_ids.shape device = self.embed_tokens.weight.device if input_ids.device != device: input_ids = input_ids.to(device) hidden_states = self.embed_tokens(input_ids) # The training path does not use attention masks. attention_mask = None # RoPE positions are offset by the cached length so incremental decode # uses the correct absolute positions. past_len = past_key_values[0][0].shape[2] if past_key_values else 0 cos_full, sin_full = self.rotary_emb(hidden_states, past_len + seq_length) rotary = (cos_full[past_len:past_len + seq_length], sin_full[past_len:past_len + seq_length]) new_cache = [] if use_cache else None for i, layer in enumerate(self.blocks): _skip_ckpt = (self.checkpoint_stride >= 2) and (i % self.checkpoint_stride == 0) if self.gradient_checkpointing and self.training and not _skip_ckpt: hidden_states = torch.utils.checkpoint.checkpoint( layer, hidden_states, rotary, attention_mask, use_reentrant=False, ) elif use_cache: past_kv = past_key_values[i] if past_key_values else None hidden_states, layer_kv = layer( hidden_states, rotary, attention_mask, past_kv=past_kv, use_cache=True, ) new_cache.append(layer_kv) else: hidden_states = layer(hidden_states, rotary, attention_mask) hidden_states = self.norm(hidden_states) # Chunked cross-entropy (training only, flag-gated via config.loss_chunk_size): avoids # materializing the full (batch*seq, vocab) fp32 logit tensor -- the HBM wall at long context. # Early-return so the standard full-logit path below stays byte-for-byte unchanged (zero risk to # non-chunked / default runs). Only taken when MTP + z-loss are off (both default off). _chunk = getattr(self.config, "loss_chunk_size", 0) if (self.training and labels is not None and _chunk > 0 and self.config.mtp_horizon == 1 and self.config.z_loss_weight == 0): loss = self._chunked_lm_loss(hidden_states, labels, _chunk) if torch.isnan(loss): self._nan_loss_count += 1 if self._nan_loss_count <= 5 or self._nan_loss_count % 100 == 0: print(f"WARNING: NaN loss detected (occurrence {self._nan_loss_count}); " "zeroing this step's loss.", flush=True) zero = torch.zeros((), device=loss.device, dtype=torch.float32) for p in self.parameters(): if p.requires_grad: zero = zero + torch.nan_to_num(p).sum().float() * 0.0 loss = zero.to(loss.dtype) return CausalLMOutput(logits=None, loss=loss) logits = self.lm_head(hidden_states) if self.config.logit_softcap > 0: cap = self.config.logit_softcap logits = torch.tanh(logits / cap) * cap # Sanitize logits only at inference. During training the full-tensor # scan forces a GPU sync every step over [batch, seq, vocab]; a NaN # would surface in the loss check below anyway. if not self.training and torch.isnan(logits).any(): logits = torch.nan_to_num(logits, nan=0.0, posinf=65504.0, neginf=-65504.0) loss = None if labels is not None: if labels.device != logits.device: labels = labels.to(logits.device) loss = F.cross_entropy( logits.view(-1, logits.size(-1)), labels.view(-1), ignore_index=-100, ) if self.training and self.config.mtp_horizon > 1: mtp_terms = [] max_horizon = min(self.config.mtp_horizon, labels.shape[1]) for horizon in range(2, max_horizon + 1): shift = horizon - 1 shifted_logits = logits[:, :-shift, :] shifted_labels = labels[:, shift:] if shifted_logits.numel() == 0: continue mtp_loss = F.cross_entropy( shifted_logits.reshape(-1, shifted_logits.size(-1)), shifted_labels.reshape(-1), ignore_index=-100, ) mtp_terms.append(mtp_loss / horizon) if mtp_terms: loss = loss + (self.config.mtp_loss_weight * torch.stack(mtp_terms).mean()).to(loss.dtype) if self.config.z_loss_weight > 0: z = torch.logsumexp(logits.float(), dim=-1) loss = loss + (self.config.z_loss_weight * z.pow(2).mean()).to(loss.dtype) # A NaN loss becomes a zero loss that is still connected to every # parameter — but through the parameters directly, NOT through the # network's (possibly NaN) activations. Backward then delivers an # exact zero gradient to each parameter: DDP/FSDP gradient hooks # all fire (no collective desync/hang) and the optimizer step is a # true no-op. Warn (bounded) instead of hiding it. if torch.isnan(loss): self._nan_loss_count += 1 if self._nan_loss_count <= 5 or self._nan_loss_count % 100 == 0: print( f"WARNING: NaN loss detected (occurrence {self._nan_loss_count}); " "zeroing this step's loss.", flush=True, ) zero = torch.zeros((), device=loss.device, dtype=torch.float32) for p in self.parameters(): if p.requires_grad: zero = zero + torch.nan_to_num(p).sum().float() * 0.0 loss = zero.to(loss.dtype) if use_cache: return CausalLMOutputWithPast(logits=logits, loss=loss, past_key_values=new_cache) return CausalLMOutput(logits=logits, loss=loss) @torch.compiler.disable def _chunked_lm_loss(self, hidden_states, labels, chunk_size): """Cross-entropy without ever materializing the full (batch*seq, vocab) fp32 logit tensor. MUST run eager (@torch.compiler.disable): torch.compile/inductor otherwise fuses the chunks back into the full (N, vocab) logit tensor, re-creating the OOM this is meant to avoid. The transformer body still compiles (+FP8); only this loss runs eager (a small fraction of compute). Splits the flattened rows into chunks; torch.utils.checkpoint recomputes each chunk's logits in backward, so peak logit memory = chunk_size*vocab (not N*vocab). Numerically identical to F.cross_entropy(full_logits, labels.view(-1), ignore_index=-100) with per-chunk softcap (mean over non-ignored). Caller guarantees mtp_horizon==1 and z_loss_weight==0. Uses self.lm_head(...) per chunk so the fp8 (Float8Linear) GEMM path is preserved.""" from torch.utils.checkpoint import checkpoint as _ckpt cap = float(self.config.logit_softcap) H = hidden_states.reshape(-1, hidden_states.size(-1)) L = labels.reshape(-1).to(H.device) N = H.size(0) def _seg(hc, lc): lg = self.lm_head(hc) # (chunk, vocab); Float8Linear under fp8 if cap > 0: lg = torch.tanh(lg / cap) * cap return F.cross_entropy(lg, lc, ignore_index=-100, reduction="sum") total = H.new_zeros((), dtype=torch.float32) ntok = torch.zeros((), device=H.device, dtype=torch.float32) cs = max(1, int(chunk_size)) for i in range(0, N, cs): hc = H[i:i + cs] lc = L[i:i + cs] seg = _ckpt(_seg, hc, lc, use_reentrant=False) if self.training else _seg(hc, lc) total = total + seg.float() ntok = ntok + (lc != -100).sum().float() return total / ntok.clamp(min=1.0) @torch.no_grad() def generate( self, input_ids: torch.Tensor, max_length: int = 1024, temperature: float = 1.0, top_k: Optional[int] = None, top_p: Optional[float] = None, do_sample: bool = True, repetition_penalty: float = 1.0, no_repeat_ngram_size: int = 0, ) -> torch.Tensor: self.eval() device = self.embed_tokens.weight.device input_ids = input_ids.to(device) ctx = self.config.max_position_embeddings # KV-cache decode: prefill the prompt once, then feed a single token per # step reusing past_key_values. This yields identical logits to the # recompute-the-whole-prefix path (gated by verify_cache.py) but turns the # per-step cost from O(seq_len) down to O(1). When the running sequence # would exceed the context window we rebuild the cache from the truncated # window, matching the original chunk = input_ids[:, -ctx:] behavior. past = None outputs = None while input_ids.shape[1] < max_length: if past is None or past[0][0].shape[2] >= ctx: chunk = input_ids[:, -ctx:] outputs = self.forward(chunk, use_cache=True) past = outputs.past_key_values logits = outputs.logits[:, -1, :] / temperature if repetition_penalty != 1.0: for b in range(input_ids.shape[0]): seen_tokens = torch.unique(input_ids[b]) seen_logits = logits[b, seen_tokens] adjusted = torch.where( seen_logits < 0, seen_logits * repetition_penalty, seen_logits / repetition_penalty, ) logits[b, seen_tokens] = adjusted if no_repeat_ngram_size > 0 and input_ids.shape[1] + 1 >= no_repeat_ngram_size: n = int(no_repeat_ngram_size) for b in range(input_ids.shape[0]): seq = input_ids[b].tolist() ngrams = {} for i in range(len(seq) - n + 1): prefix = tuple(seq[i : i + n - 1]) if n > 1 else tuple() next_token = seq[i + n - 1] if prefix not in ngrams: ngrams[prefix] = set() ngrams[prefix].add(next_token) current_prefix = tuple(seq[-(n - 1) :]) if n > 1 else tuple() banned = ngrams.get(current_prefix, set()) if banned: logits[b, list(banned)] = float("-inf") if do_sample: if top_k is not None: top_values, _ = torch.topk(logits, min(top_k, logits.size(-1))) logits = logits.masked_fill(logits < top_values[:, [-1]], float("-inf")) if top_p is not None: sorted_logits, sorted_indices = torch.sort(logits, descending=True) cumulative_probs = torch.cumsum(F.softmax(sorted_logits, dim=-1), dim=-1) sorted_indices_to_remove = cumulative_probs > top_p 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 = logits.masked_fill(indices_to_remove, float("-inf")) probs = F.softmax(logits, dim=-1) next_token = torch.multinomial(probs, num_samples=1) else: next_token = torch.argmax(logits, dim=-1, keepdim=True) next_token = next_token.to(input_ids.device) input_ids = torch.cat([input_ids, next_token], dim=-1) if input_ids.shape[1] >= max_length: break # Advance the cache by one token unless we must rebuild it next loop # (cache full); the top-of-loop guard re-prefills in that case. if past[0][0].shape[2] < ctx: outputs = self.forward(next_token, past_key_values=past, use_cache=True) past = outputs.past_key_values return input_ids.to(device) AutoConfig.register("argonne2", ArgonneConfig) AutoModel.register(ArgonneConfig, ArgonneModel) AutoModelForCausalLM.register(ArgonneConfig, ArgonneModel) # Backwards compatibility exports CausalSelfAttention = GroupedQueryAttention MLP = SwiGLUMLP