Add distill-math239k-coldstart-forward-kl-260430-172233/epoch_3_step_179601 (weights + config + modeling)

distill-math239k-coldstart-forward-kl-260430-172233/config.json

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+{
+  "architectures": [
+    "DFlashDraftModel"
+  ],
+  "attention_bias": false,
+  "attention_dropout": 0.0,
+  "auto_map": {
+    "AutoModel": "dflash.DFlashDraftModel"
+  },
+  "block_size": 16,
+  "bos_token_id": 151643,
+  "dflash_config": {
+    "causal_head": true,
+    "mask_token_id": 151669,
+    "target_layer_ids": [
+      1,
+      9,
+      17,
+      25,
+      33
+    ]
+  },
+  "dtype": "bfloat16",
+  "eos_token_id": 151645,
+  "head_dim": 128,
+  "hidden_act": "silu",
+  "hidden_size": 4096,
+  "initializer_range": 0.02,
+  "intermediate_size": 12288,
+  "layer_types": [
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention"
+  ],
+  "max_position_embeddings": 40960,
+  "max_window_layers": 5,
+  "model_type": "qwen3",
+  "num_attention_heads": 32,
+  "num_hidden_layers": 5,
+  "num_key_value_heads": 8,
+  "num_target_layers": 36,
+  "rms_norm_eps": 1e-06,
+  "rope_scaling": null,
+  "rope_theta": 1000000,
+  "sliding_window": null,
+  "tie_word_embeddings": false,
+  "transformers_version": "4.57.1",
+  "use_cache": true,
+  "use_sliding_window": false,
+  "vocab_size": 151936
+}

distill-math239k-coldstart-forward-kl-260430-172233/dflash.py

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+from typing import Callable, Optional
+
+import torch
+from torch import nn
+from transformers import DynamicCache
+from transformers.cache_utils import Cache
+from transformers.modeling_outputs import CausalLMOutputWithPast
+from transformers.models.qwen3.modeling_qwen3 import (
+    ALL_ATTENTION_FUNCTIONS,
+    FlashAttentionKwargs,
+    GradientCheckpointingLayer,
+    Qwen3Config,
+    Qwen3MLP,
+    Qwen3PreTrainedModel,
+    Qwen3RMSNorm,
+    Qwen3RotaryEmbedding,
+    eager_attention_forward,
+    rotate_half,
+)
+from typing_extensions import Tuple, Unpack
+
+
+def sample(logits: torch.Tensor, temperature: float = 0.0) -> torch.Tensor:
+    if temperature < 1e-5:
+        return torch.argmax(logits, dim=-1)
+    bsz, seq_len, vocab_size = logits.shape
+    logits = logits.view(-1, vocab_size)
+    logits = logits / temperature
+    probs = torch.softmax(logits, dim=-1)
+    return torch.multinomial(probs, num_samples=1).view(bsz, seq_len)
+
+
+def apply_rotary_pos_emb(q, k, cos, sin, position_ids=None, unsqueeze_dim=1):
+    cos = cos.unsqueeze(unsqueeze_dim)
+    sin = sin.unsqueeze(unsqueeze_dim)
+    q_len = q.size(-2)
+    q_embed = (q * cos[..., -q_len:, :]) + (rotate_half(q) * sin[..., -q_len:, :])
+    k_embed = (k * cos) + (rotate_half(k) * sin)
+    return q_embed, k_embed
+
+
+def _to_additive_attention_mask(
+    attention_mask: torch.Tensor,
+    *,
+    query_dtype: torch.dtype,
+    device: torch.device,
+    key_len: int,
+) -> torch.Tensor:
+    if attention_mask.ndim == 4:
+        attention_mask = attention_mask[:, :, :, :key_len]
+    if attention_mask.dtype == torch.bool:
+        additive_mask = torch.zeros_like(attention_mask, dtype=query_dtype, device=device)
+        return additive_mask.masked_fill(
+            attention_mask.logical_not().to(device=device),
+            torch.finfo(query_dtype).min,
+        )
+    return attention_mask.to(device=device, dtype=query_dtype)
+
+
+def _build_dflash_causal_attention_mask(
+    *,
+    query: torch.Tensor,
+    key: torch.Tensor,
+    cached_kv_len: int,
+    ctx_len: int,
+) -> torch.Tensor:
+    q_len = query.shape[-2]
+    kv_len = key.shape[-2]
+    key_positions = torch.arange(kv_len, device=query.device)
+    query_positions = cached_kv_len + ctx_len + torch.arange(q_len, device=query.device)
+    can_attend = key_positions.unsqueeze(0) <= query_positions.unsqueeze(1)
+    mask = torch.zeros((1, 1, q_len, kv_len), dtype=query.dtype, device=query.device)
+    return mask.masked_fill(can_attend.logical_not().unsqueeze(0).unsqueeze(0), torch.finfo(query.dtype).min)
+
+
+class Qwen3DFlashAttention(nn.Module):
+    """Multi-headed attention from 'Attention Is All You Need' paper"""
+
+    def __init__(self, config: Qwen3Config, layer_idx: int):
+        super().__init__()
+        self.config = config
+        self.layer_idx = layer_idx
+        self.head_dim = getattr(
+            config, "head_dim", config.hidden_size // config.num_attention_heads
+        )
+        self.num_key_value_groups = (
+            config.num_attention_heads // config.num_key_value_heads
+        )
+        self.scaling = self.head_dim**-0.5
+        self.attention_dropout = config.attention_dropout
+        dflash_config = getattr(config, "dflash_config", {}) or {}
+        self.is_causal = bool(dflash_config.get("causal_head", False))
+        self.q_proj = nn.Linear(
+            config.hidden_size,
+            config.num_attention_heads * self.head_dim,
+            bias=config.attention_bias,
+        )
+        self.k_proj = nn.Linear(
+            config.hidden_size,
+            config.num_key_value_heads * self.head_dim,
+            bias=config.attention_bias,
+        )
+        self.v_proj = nn.Linear(
+            config.hidden_size,
+            config.num_key_value_heads * self.head_dim,
+            bias=config.attention_bias,
+        )
+        self.o_proj = nn.Linear(
+            config.num_attention_heads * self.head_dim,
+            config.hidden_size,
+            bias=config.attention_bias,
+        )
+        self.q_norm = Qwen3RMSNorm(self.head_dim, eps=config.rms_norm_eps)
+        self.k_norm = Qwen3RMSNorm(self.head_dim, eps=config.rms_norm_eps)
+        self.sliding_window = (
+            config.sliding_window
+            if config.layer_types[layer_idx] == "sliding_attention"
+            else None
+        )
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        target_hidden: torch.Tensor,
+        position_embeddings: tuple[torch.Tensor, torch.Tensor],
+        attention_mask: Optional[torch.Tensor],
+        past_key_values: Optional[Cache] = None,
+        cache_position: Optional[torch.LongTensor] = None,
+        **kwargs: Unpack[FlashAttentionKwargs],
+    ) -> tuple[torch.Tensor, Optional[torch.Tensor]]:
+        bsz, q_len = hidden_states.shape[:-1]
+        ctx_len = target_hidden.shape[1]
+        is_causal = kwargs.pop("is_causal", None)
+        if is_causal is None:
+            is_causal = self.is_causal
+        q = self.q_proj(hidden_states)
+        q = q.view(bsz, q_len, -1, self.head_dim)
+        q = self.q_norm(q).transpose(1, 2)
+        k_ctx = self.k_proj(target_hidden)
+        k_noise = self.k_proj(hidden_states)
+        v_ctx = self.v_proj(target_hidden)
+        v_noise = self.v_proj(hidden_states)
+        k = torch.cat([k_ctx, k_noise], dim=1).view(
+            bsz, ctx_len + q_len, -1, self.head_dim
+        )
+        v = torch.cat([v_ctx, v_noise], dim=1).view(
+            bsz, ctx_len + q_len, -1, self.head_dim
+        )
+        k = self.k_norm(k).transpose(1, 2)
+        v = v.transpose(1, 2)
+        cos, sin = position_embeddings
+        q, k = apply_rotary_pos_emb(q, k, cos, sin)
+        cached_kv_len = past_key_values.get_seq_length() if past_key_values is not None else 0
+        if past_key_values is not None:
+            cache_kwargs = {"sin": sin, "cos": cos, "cache_position": cache_position}
+            k, v = past_key_values.update(k, v, self.layer_idx, cache_kwargs)
+
+        attn_backend = self.config._attn_implementation
+        use_explicit_dflash_causal_mask = bool(is_causal) and attn_backend in {"eager", "sdpa"}
+        if use_explicit_dflash_causal_mask:
+            dflash_causal_mask = _build_dflash_causal_attention_mask(
+                query=q, key=k, cached_kv_len=cached_kv_len, ctx_len=ctx_len,
+            )
+            if attention_mask is not None:
+                dflash_causal_mask = dflash_causal_mask + _to_additive_attention_mask(
+                    attention_mask, query_dtype=q.dtype, device=q.device, key_len=k.shape[-2],
+                )
+            attention_mask = dflash_causal_mask
+            is_causal = False
+
+        kwargs["is_causal"] = is_causal
+
+        attn_fn: Callable = eager_attention_forward
+        if self.config._attn_implementation != "eager":
+            attn_fn = ALL_ATTENTION_FUNCTIONS[self.config._attn_implementation]
+        attn_output, attn_weights = attn_fn(
+            self,
+            q,
+            k,
+            v,
+            attention_mask,
+            dropout=0.0 if not self.training else self.attention_dropout,
+            scaling=self.scaling,
+            sliding_window=self.sliding_window,
+            **kwargs,
+        )
+        attn_output = attn_output.reshape(bsz, q_len, -1)
+        attn_output = self.o_proj(attn_output)
+        return attn_output, attn_weights
+
+
+class Qwen3DFlashDecoderLayer(GradientCheckpointingLayer):
+    def __init__(self, config: Qwen3Config, layer_idx: int):
+        super().__init__()
+        self.hidden_size = config.hidden_size
+        self.self_attn = Qwen3DFlashAttention(config=config, layer_idx=layer_idx)
+        self.mlp = Qwen3MLP(config)
+        self.input_layernorm = Qwen3RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+        self.post_attention_layernorm = Qwen3RMSNorm(
+            config.hidden_size, eps=config.rms_norm_eps
+        )
+
+    def forward(
+        self,
+        target_hidden: Optional[torch.Tensor] = None,
+        hidden_states: Optional[torch.Tensor] = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        past_key_value: Optional[Cache] = None,
+        output_attentions: Optional[bool] = False,
+        use_cache: Optional[bool] = False,
+        cache_position: Optional[torch.LongTensor] = None,
+        position_embeddings: Optional[
+            Tuple[torch.Tensor, torch.Tensor]
+        ] = None,  # necessary, but kept here for BC
+        **kwargs: Unpack[FlashAttentionKwargs],
+    ) -> Tuple[
+        torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]]
+    ]:
+        residual = hidden_states
+        hidden_states = self.input_layernorm(hidden_states)
+        hidden_states = self.self_attn(
+            hidden_states=hidden_states,
+            target_hidden=target_hidden,
+            attention_mask=attention_mask,
+            position_ids=position_ids,
+            past_key_values=past_key_value,
+            output_attentions=output_attentions,
+            use_cache=use_cache,
+            cache_position=cache_position,
+            position_embeddings=position_embeddings,
+            **kwargs,
+        )[0]
+        hidden_states = residual + hidden_states
+        residual = hidden_states
+        hidden_states = self.post_attention_layernorm(hidden_states)
+        hidden_states = self.mlp(hidden_states)
+        hidden_states = residual + hidden_states
+        return hidden_states
+
+
+def build_target_layer_ids(num_target_layers: int, num_draft_layers: int):
+    if num_draft_layers == 1:
+        return [(num_target_layers // 2)]
+    start = 1
+    end = num_target_layers - 3
+    span = end - start
+    target_layer_ids = [
+        int(round(start + (i * span) / (num_draft_layers - 1)))
+        for i in range(num_draft_layers)
+    ]
+    return target_layer_ids
+
+
+def extract_context_feature(
+    hidden_states: list[torch.Tensor],
+    layer_ids: Optional[list[int]],
+) -> torch.Tensor:
+    offset = 1
+    selected_states = []
+    for layer_id in layer_ids:
+        selected_states.append(hidden_states[layer_id + offset])
+    target_hidden = torch.cat(selected_states, dim=-1)
+    return target_hidden
+
+
+class DFlashDraftModel(Qwen3PreTrainedModel):
+    config_class = Qwen3Config
+    _no_split_modules = ["Qwen3DFlashDecoderLayer"]
+
+    def __init__(self, config) -> None:
+        super().__init__(config)
+        self.config = config
+        if not hasattr(self.config, "dflash_config") or self.config.dflash_config is None:
+            self.config.dflash_config = {}
+        self.causal_head = bool(self.config.dflash_config.get("causal_head", False))
+        self.layers = nn.ModuleList(
+            [
+                Qwen3DFlashDecoderLayer(config, layer_idx)
+                for layer_idx in range(config.num_hidden_layers)
+            ]
+        )
+        dflash_config = getattr(config, "dflash_config", {}) or {}
+        self.target_layer_ids = dflash_config.get(
+            "target_layer_ids",
+            build_target_layer_ids(config.num_target_layers, config.num_hidden_layers),
+        )
+        self.norm = Qwen3RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+        self.rotary_emb = Qwen3RotaryEmbedding(config)
+        self.fc = nn.Linear(
+            len(self.target_layer_ids) * config.hidden_size,
+            config.hidden_size,
+            bias=False,
+        )
+        self.hidden_norm = Qwen3RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+        self.block_size = config.block_size
+        self.mask_token_id = dflash_config.get("mask_token_id", None)
+        self.post_init()
+
+    def resolve_causal_head(self, head_type: str = "auto") -> bool:
+        if head_type == "auto":
+            return bool(self.causal_head)
+        if head_type == "bidirectional":
+            return False
+        if head_type == "causal":
+            return True
+        raise ValueError(
+            f"Unsupported head_type={head_type!r}. Expected one of: auto, bidirectional, causal."
+        )
+
+    def forward(
+        self,
+        position_ids: torch.LongTensor,
+        attention_mask: Optional[torch.Tensor] = None,
+        noise_embedding: Optional[torch.Tensor] = None,
+        target_hidden: Optional[torch.Tensor] = None,
+        past_key_values: Optional[Cache] = None,
+        use_cache: bool = False,
+        **kwargs,
+    ) -> CausalLMOutputWithPast:
+        hidden_states = noise_embedding
+        target_hidden = self.hidden_norm(self.fc(target_hidden))
+        position_embeddings = self.rotary_emb(hidden_states, position_ids)
+        for layer in self.layers:
+            hidden_states = layer(
+                hidden_states=hidden_states,
+                target_hidden=target_hidden,
+                attention_mask=attention_mask,
+                position_ids=position_ids,
+                past_key_value=past_key_values,
+                use_cache=use_cache,
+                position_embeddings=position_embeddings,
+                **kwargs,
+            )
+        return self.norm(hidden_states)
+
+    @torch.inference_mode()
+    def spec_generate(
+        self,
+        target: nn.Module,
+        input_ids: torch.LongTensor,
+        max_new_tokens: int,
+        stop_token_ids: list[int],
+        temperature: float,
+    ):
+        self.eval()
+        num_input_tokens = input_ids.shape[1]
+        max_length = num_input_tokens + max_new_tokens
+
+        block_size = self.block_size
+        output_ids = torch.full(
+            (1, max_length + block_size),
+            self.mask_token_id,
+            dtype=torch.long,
+            device=target.device,
+        )
+        position_ids = torch.arange(
+            output_ids.shape[1], device=target.device
+        ).unsqueeze(0)
+
+        past_key_values_target = DynamicCache()
+        past_key_values_draft = DynamicCache()
+
+        # Prefill stage
+        output = target(
+            input_ids,
+            position_ids=position_ids[:, :num_input_tokens],
+            past_key_values=past_key_values_target,
+            use_cache=True,
+            logits_to_keep=1,
+            output_hidden_states=True,
+        )
+
+        output_ids[:, :num_input_tokens] = input_ids
+        output_ids[:, num_input_tokens : num_input_tokens + 1] = sample(
+            output.logits, temperature
+        )
+        target_hidden = extract_context_feature(
+            output.hidden_states, self.target_layer_ids
+        )
+
+        # Decode stage
+        acceptance_lengths = []
+        start = input_ids.shape[1]
+        while start < max_length:
+            block_output_ids = output_ids[:, start : start + block_size].clone()
+            block_position_ids = position_ids[:, start : start + block_size]
+            noise_embedding = target.model.embed_tokens(block_output_ids)
+            draft_logits = target.lm_head(
+                self(
+                    target_hidden=target_hidden,
+                    noise_embedding=noise_embedding,
+                    position_ids=position_ids[
+                        :, past_key_values_draft.get_seq_length() : start + block_size
+                    ],
+                    past_key_values=past_key_values_draft,
+                    use_cache=True,
+                    is_causal=False,
+                )[:, -block_size + 1 :, :]
+            )
+            past_key_values_draft.crop(start)
+            block_output_ids[:, 1:] = sample(draft_logits)
+            output_ids[:, start + 1 : start + block_size] = block_output_ids[:, 1:]
+
+            output = target(
+                block_output_ids,
+                position_ids=block_position_ids,
+                past_key_values=past_key_values_target,
+                use_cache=True,
+                output_hidden_states=True,
+            )
+
+            posterior = sample(output.logits, temperature)
+            acceptance_length = (
+                (block_output_ids[:, 1:] == posterior[:, :-1])
+                .cumprod(dim=1)
+                .sum(dim=1)[0]
+                .item()
+            )
+            output_ids[:, start : start + acceptance_length + 1] = block_output_ids[
+                :, : acceptance_length + 1
+            ]
+            output_ids[:, start + acceptance_length + 1] = posterior[
+                :, acceptance_length
+            ]
+            start += acceptance_length + 1
+            past_key_values_target.crop(start)
+            target_hidden = extract_context_feature(
+                output.hidden_states, self.target_layer_ids
+            )[:, : acceptance_length + 1, :]
+            acceptance_lengths.append(acceptance_length + 1)
+            if stop_token_ids is not None and any(
+                stop_token_id in output_ids[:, num_input_tokens:]
+                for stop_token_id in stop_token_ids
+            ):
+                break
+        output_ids = output_ids[:, :max_length]
+        output_ids = output_ids[:, output_ids[0] != self.mask_token_id]
+        if stop_token_ids is not None:
+            stop_token_ids = torch.tensor(stop_token_ids, device=output_ids.device)
+            stop_token_indices = torch.isin(
+                output_ids[0][num_input_tokens:], stop_token_ids
+            ).nonzero(as_tuple=True)[0]
+            if stop_token_indices.numel() > 0:
+                output_ids = output_ids[
+                    :, : num_input_tokens + stop_token_indices[0] + 1
+                ]
+
+        return output_ids

distill-math239k-coldstart-forward-kl-260430-172233/model.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:47d993f0ff478e54bc0053025f0e82d2aaca023bf120d39d0edbb95404bc0296
+size 2097259104