1271 lines
56 KiB
Python
1271 lines
56 KiB
Python
# Inference-only Qwen3.6-27B (Qwen3_5 architecture) for Iluvatar BI-V100.
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# Pure-PyTorch DeltaNet (no fla / causal_conv1d dependency).
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# Text-only (no VL, no MTP).
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from typing import Iterable, List, Optional, Tuple
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import torch
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import torch.nn.functional as F
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from torch import nn
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from vllm.attention import Attention, AttentionMetadata
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from vllm.config import CacheConfig, LoRAConfig, SchedulerConfig
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from vllm.distributed import (get_tensor_model_parallel_rank,
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get_tensor_model_parallel_world_size,
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tensor_model_parallel_all_reduce)
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from vllm.model_executor.layers.activation import SiluAndMul
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from vllm.model_executor.layers.layernorm import GemmaRMSNorm
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from vllm.model_executor.layers.linear import (ColumnParallelLinear,
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MergedColumnParallelLinear,
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ReplicatedLinear,
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RowParallelLinear)
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from vllm.model_executor.layers.fused_moe import FusedMoE
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from vllm.model_executor.layers.logits_processor import LogitsProcessor
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from vllm.model_executor.layers.quantization import QuantizationConfig
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from vllm.model_executor.layers.rotary_embedding import get_rope
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from vllm.model_executor.layers.sampler import Sampler, SamplerOutput
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from vllm.model_executor.layers.vocab_parallel_embedding import (
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ParallelLMHead, VocabParallelEmbedding)
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from vllm.model_executor.model_loader.weight_utils import (
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default_weight_loader, sharded_weight_loader)
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from vllm.model_executor.models.mamba_cache import MambaCacheManager
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from vllm.model_executor.sampling_metadata import SamplingMetadata
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from vllm.model_executor.utils import set_weight_attrs
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from vllm.sequence import IntermediateTensors
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from vllm.worker.model_runner import (_BATCH_SIZES_TO_CAPTURE,
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_get_graph_batch_size)
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from vllm.logger import init_logger
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from vllm.model_executor.models.interfaces import HasInnerState, SupportsLoRA
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logger = init_logger(__name__)
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# ---------------------------------------------------------------------------
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# Pure-PyTorch DeltaNet kernels (fallbacks from transformers 5.2.0)
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# ---------------------------------------------------------------------------
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def _l2norm(x: torch.Tensor, dim: int = -1, eps: float = 1e-6) -> torch.Tensor:
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return x * torch.rsqrt((x * x).sum(dim=dim, keepdim=True) + eps)
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def _torch_causal_conv1d_update(
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hidden_states: torch.Tensor, # (batch, channels, seq=1)
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conv_state: torch.Tensor, # (batch, channels, state_len) modified in-place
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weight: torch.Tensor, # (channels, kernel_size)
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bias: Optional[torch.Tensor] = None,
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activation: Optional[str] = None,
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) -> torch.Tensor:
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_, channels, seq_len = hidden_states.shape
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state_len = conv_state.shape[-1]
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cat = torch.cat([conv_state, hidden_states], dim=-1).to(weight.dtype)
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conv_state.copy_(cat[:, :, -state_len:])
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out = F.conv1d(cat, weight.unsqueeze(1), bias, padding=0, groups=channels)
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out = out[:, :, -seq_len:]
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if activation is not None:
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out = F.silu(out)
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return out.to(hidden_states.dtype)
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def _torch_chunk_gated_delta_rule(
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query: torch.Tensor, # (batch, seq, num_heads, head_k_dim)
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key: torch.Tensor,
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value: torch.Tensor, # (batch, seq, num_heads, head_v_dim)
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g: torch.Tensor, # (batch, seq, num_heads)
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beta: torch.Tensor, # (batch, seq, num_heads)
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chunk_size: int = 64,
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initial_state: Optional[torch.Tensor] = None,
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output_final_state: bool = False,
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use_qk_l2norm_in_kernel: bool = False,
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) -> Tuple[torch.Tensor, Optional[torch.Tensor]]:
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initial_dtype = query.dtype
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if use_qk_l2norm_in_kernel:
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query = _l2norm(query)
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key = _l2norm(key)
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# Transpose to (batch, num_heads, seq, dim)
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query, key, value, beta, g = [
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x.transpose(1, 2).contiguous().to(torch.float32)
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for x in (query, key, value, beta, g)
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]
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batch, num_heads, seq_len, k_dim = key.shape
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v_dim = value.shape[-1]
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pad = (chunk_size - seq_len % chunk_size) % chunk_size
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query = F.pad(query, (0, 0, 0, pad))
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key = F.pad(key, (0, 0, 0, pad))
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value = F.pad(value, (0, 0, 0, pad))
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beta = F.pad(beta, (0, pad))
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g = F.pad(g, (0, pad))
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total_len = seq_len + pad
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scale = 1.0 / (query.shape[-1] ** 0.5)
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query = query * scale
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v_beta = value * beta.unsqueeze(-1)
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k_beta = key * beta.unsqueeze(-1)
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query, key, value, k_beta, v_beta = [
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x.reshape(x.shape[0], x.shape[1], -1, chunk_size, x.shape[-1])
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for x in (query, key, value, k_beta, v_beta)
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]
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g = g.reshape(g.shape[0], g.shape[1], -1, chunk_size)
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mask_upper = torch.triu(
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torch.ones(chunk_size, chunk_size, dtype=torch.bool, device=query.device),
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diagonal=0)
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g = g.cumsum(dim=-1)
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decay_mask = ((g.unsqueeze(-1) - g.unsqueeze(-2)).tril().exp().float()).tril()
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attn = -((k_beta @ key.transpose(-1, -2)) * decay_mask).masked_fill(mask_upper, 0)
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for i in range(1, chunk_size):
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row = attn[..., i, :i].clone()
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sub = attn[..., :i, :i].clone()
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attn[..., i, :i] = row + (row.unsqueeze(-1) * sub).sum(-2)
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attn = attn + torch.eye(chunk_size, dtype=attn.dtype, device=attn.device)
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value = attn @ v_beta
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k_cumdecay = attn @ (k_beta * g.exp().unsqueeze(-1))
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last_state = (
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torch.zeros(batch, num_heads, k_dim, v_dim, dtype=value.dtype, device=value.device)
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if initial_state is None
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else initial_state.to(value)
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)
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core_out = torch.zeros_like(value)
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mask_upper2 = torch.triu(
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torch.ones(chunk_size, chunk_size, dtype=torch.bool, device=query.device),
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diagonal=1)
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for i in range(total_len // chunk_size):
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q_i, k_i, v_i = query[:, :, i], key[:, :, i], value[:, :, i]
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attn_i = (q_i @ k_i.transpose(-1, -2) * decay_mask[:, :, i]).masked_fill_(mask_upper2, 0)
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v_prime = k_cumdecay[:, :, i] @ last_state
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v_new = v_i - v_prime
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attn_inter = (q_i * g[:, :, i, :, None].exp()) @ last_state
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core_out[:, :, i] = attn_inter + attn_i @ v_new
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last_state = (
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last_state * g[:, :, i, -1, None, None].exp()
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+ (k_i * (g[:, :, i, -1, None] - g[:, :, i]).exp()[..., None])
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.transpose(-1, -2) @ v_new
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)
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if not output_final_state:
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last_state = None
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core_out = core_out.reshape(batch, num_heads, -1, v_dim)[:, :, :seq_len]
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core_out = core_out.transpose(1, 2).contiguous().to(initial_dtype)
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return core_out, last_state
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def _torch_recurrent_gated_delta_rule(
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query: torch.Tensor, # (batch, 1, num_heads, head_k_dim)
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key: torch.Tensor,
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value: torch.Tensor,
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g: torch.Tensor, # (batch, 1, num_heads)
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beta: torch.Tensor,
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initial_state: Optional[torch.Tensor] = None,
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output_final_state: bool = False,
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use_qk_l2norm_in_kernel: bool = False,
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) -> Tuple[torch.Tensor, Optional[torch.Tensor]]:
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initial_dtype = query.dtype
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if use_qk_l2norm_in_kernel:
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query = _l2norm(query)
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key = _l2norm(key)
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query, key, value, beta, g = [
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x.transpose(1, 2).contiguous().to(torch.float32)
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for x in (query, key, value, beta, g)
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]
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batch, num_heads, seq_len, k_dim = key.shape
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v_dim = value.shape[-1]
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scale = 1.0 / (query.shape[-1] ** 0.5)
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query = query * scale
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core_out = torch.zeros(batch, num_heads, seq_len, v_dim,
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dtype=value.dtype, device=value.device)
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last_state = (
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torch.zeros(batch, num_heads, k_dim, v_dim,
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dtype=value.dtype, device=value.device)
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if initial_state is None
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else initial_state.to(value)
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)
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for t in range(seq_len):
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q_t = query[:, :, t]
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k_t = key[:, :, t]
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v_t = value[:, :, t]
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g_t = g[:, :, t].exp().unsqueeze(-1).unsqueeze(-1)
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beta_t = beta[:, :, t].unsqueeze(-1)
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last_state = last_state * g_t
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kv_mem = (last_state * k_t.unsqueeze(-1)).sum(dim=-2)
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delta = (v_t - kv_mem) * beta_t
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last_state = last_state + k_t.unsqueeze(-1) * delta.unsqueeze(-2)
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core_out[:, :, t] = (last_state * q_t.unsqueeze(-1)).sum(dim=-2)
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if not output_final_state:
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last_state = None
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core_out = core_out.transpose(1, 2).contiguous().to(initial_dtype)
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return core_out, last_state
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# ---------------------------------------------------------------------------
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# Gated RMSNorm (for DeltaNet output normalisation)
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# ---------------------------------------------------------------------------
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class Qwen3_5RMSNormGated(nn.Module):
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def __init__(self, hidden_size: int, eps: float = 1e-6):
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super().__init__()
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self.weight = nn.Parameter(torch.ones(hidden_size))
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self.variance_epsilon = eps
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def forward(self, hidden_states: torch.Tensor,
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gate: torch.Tensor) -> torch.Tensor:
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input_dtype = hidden_states.dtype
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hs = hidden_states.to(torch.float32)
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variance = hs.pow(2).mean(-1, keepdim=True)
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hs = hs * torch.rsqrt(variance + self.variance_epsilon)
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hs = self.weight * hs.to(input_dtype)
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return (hs * F.silu(gate.to(torch.float32))).to(input_dtype)
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# ---------------------------------------------------------------------------
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# Gated DeltaNet (linear_attention layers)
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# ---------------------------------------------------------------------------
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class GatedDeltaNet(nn.Module):
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def __init__(
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self,
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text_cfg,
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layer_idx: int,
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quant_config: Optional[QuantizationConfig] = None,
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) -> None:
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super().__init__()
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self.layer_idx = layer_idx
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self.hidden_size = text_cfg.hidden_size
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self.num_v_heads = text_cfg.linear_num_value_heads # 48
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self.num_k_heads = text_cfg.linear_num_key_heads # 16
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self.head_k_dim = text_cfg.linear_key_head_dim # 128
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self.head_v_dim = text_cfg.linear_value_head_dim # 128
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self.key_dim = self.num_k_heads * self.head_k_dim # 2048
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self.value_dim = self.num_v_heads * self.head_v_dim # 6144
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self.conv_dim = self.key_dim * 2 + self.value_dim # 10240
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self.conv_kernel_size = text_cfg.linear_conv_kernel_dim # 4
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self.head_expand_ratio = self.num_v_heads // self.num_k_heads # 3
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tp_size = get_tensor_model_parallel_world_size()
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# Sharded projections — MergedColumnParallelLinear shards each of q/k/v
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# independently so each TP rank gets [q_shard, k_shard, v_shard].
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# Plain ColumnParallelLinear would shard contiguously, giving rank 0
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# [q_all, k_partial] — completely wrong Q/K/V after the split below.
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self.in_proj_qkv = MergedColumnParallelLinear(
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self.hidden_size, [self.key_dim, self.key_dim, self.value_dim],
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bias=False, quant_config=quant_config)
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self.in_proj_z = ColumnParallelLinear(
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self.hidden_size, self.value_dim,
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bias=False, quant_config=quant_config)
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self.in_proj_b = ColumnParallelLinear(
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self.hidden_size, self.num_v_heads,
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bias=False, quant_config=quant_config)
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self.in_proj_a = ColumnParallelLinear(
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self.hidden_size, self.num_v_heads,
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bias=False, quant_config=quant_config)
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self.out_proj = RowParallelLinear(
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self.value_dim, self.hidden_size,
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bias=False, quant_config=quant_config)
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# Depthwise conv weight — sharded along channel dim (dim 0)
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local_conv_dim = self.conv_dim // tp_size
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self.conv1d_weight = nn.Parameter(
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torch.empty(local_conv_dim, 1, self.conv_kernel_size))
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set_weight_attrs(self.conv1d_weight, {
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"weight_loader": self._conv1d_weight_loader})
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# Per-head scalar parameters — sharded along dim 0
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local_num_v = self.num_v_heads // tp_size
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self.A_log = nn.Parameter(torch.zeros(local_num_v))
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self.dt_bias = nn.Parameter(torch.zeros(local_num_v))
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set_weight_attrs(self.A_log, {"weight_loader": sharded_weight_loader(0)})
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set_weight_attrs(self.dt_bias, {"weight_loader": sharded_weight_loader(0)})
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# Gated RMSNorm on head_v_dim — replicated (head_v_dim=128 is small)
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self.norm = Qwen3_5RMSNormGated(self.head_v_dim,
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eps=text_cfg.rms_norm_eps)
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def _conv1d_weight_loader(self, param: torch.Tensor,
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loaded_weight: torch.Tensor) -> None:
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# loaded_weight: (conv_dim=10240, 1, kernel) ordered as [q, k, v] channels
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# Must gather channels in the same non-contiguous pattern that
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# MergedColumnParallelLinear uses for in_proj_qkv, so that each rank's
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# conv1d_weight[i] applies to the correct in_proj_qkv output channel.
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tp_rank = get_tensor_model_parallel_rank()
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tp_size = get_tensor_model_parallel_world_size()
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key_local = self.key_dim // tp_size # 512 with TP=4
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val_local = self.value_dim // tp_size # 1536 with TP=4
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q_s = loaded_weight[tp_rank * key_local : (tp_rank + 1) * key_local]
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k_s = loaded_weight[self.key_dim + tp_rank * key_local :
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self.key_dim + (tp_rank + 1) * key_local]
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v_s = loaded_weight[2 * self.key_dim + tp_rank * val_local :
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2 * self.key_dim + (tp_rank + 1) * val_local]
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param.data.copy_(torch.cat([q_s, k_s, v_s], dim=0))
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def forward(
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self,
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hidden_states: torch.Tensor, # (total_tokens, hidden_size)
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attn_metadata: AttentionMetadata,
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conv_state: torch.Tensor, # (batch, local_conv_dim, kernel-1) in-place
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temporal_state: torch.Tensor, # (batch, local_v_heads, k_dim, v_dim) in-place
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) -> torch.Tensor:
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tp_size = get_tensor_model_parallel_world_size()
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local_key_dim = self.key_dim // tp_size
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local_val_dim = self.value_dim // tp_size
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local_num_v = self.num_v_heads // tp_size
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local_num_k = self.num_k_heads // tp_size
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local_conv_dim = self.conv_dim // tp_size
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is_prefill = attn_metadata.num_prefill_tokens > 0
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# Compute all projections for every token at once (batched, efficient)
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mixed_qkv_all, _ = self.in_proj_qkv(hidden_states) # (total, local_conv_dim)
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z_all, _ = self.in_proj_z(hidden_states) # (total, local_val_dim)
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b_all, _ = self.in_proj_b(hidden_states) # (total, local_num_v)
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a_all, _ = self.in_proj_a(hidden_states) # (total, local_num_v)
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if is_prefill:
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seq_starts = attn_metadata.query_start_loc.tolist()
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outputs = []
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state_len = self.conv_kernel_size - 1
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weight_2d = self.conv1d_weight.squeeze(1) # (local_conv_dim, kernel)
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for si in range(len(seq_starts) - 1):
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s, e = int(seq_starts[si]), int(seq_starts[si + 1])
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seq_len = e - s
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# Shape: (1, local_conv_dim, seq_len)
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mixed_qkv = (mixed_qkv_all[s:e]
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.transpose(0, 1).unsqueeze(0)
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.to(weight_2d.dtype))
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# Load prev conv state BEFORE overwriting (needed for causal conv padding).
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# For first prefill of a request: mamba_cache is zeros → correct.
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# For chunked prefill chunk 2+: carries last state_len tokens from prev chunk.
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prev_conv = conv_state[si:si + 1].clone().to(weight_2d.dtype) # [1, local_conv_dim, state_len]
|
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# Save conv state (last state_len positions)
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if seq_len >= state_len:
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conv_state[si].copy_(mixed_qkv[0, :, -state_len:])
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else:
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conv_state[si, :, state_len - seq_len:].copy_(
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mixed_qkv[0])
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conv_state[si, :, :state_len - seq_len] = 0
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# Causal conv: left-pad with previous conv state (not zeros).
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padded = torch.cat([prev_conv, mixed_qkv], dim=2)
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mixed_qkv_conv = F.conv1d(
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padded, self.conv1d_weight,
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bias=None, padding=0, groups=local_conv_dim)
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mixed_qkv_conv = F.silu(mixed_qkv_conv)
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# (1, seq_len, local_conv_dim)
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mixed_qkv_conv = mixed_qkv_conv.squeeze(0).transpose(0, 1).unsqueeze(0)
|
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q, k, v = torch.split(
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mixed_qkv_conv,
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[local_key_dim, local_key_dim, local_val_dim], dim=-1)
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q = q.reshape(1, seq_len, local_num_k, self.head_k_dim)
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k = k.reshape(1, seq_len, local_num_k, self.head_k_dim)
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v = v.reshape(1, seq_len, local_num_v, self.head_v_dim)
|
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beta = b_all[s:e].sigmoid().unsqueeze(0) # (1, seq_len, local_num_v)
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g = (-self.A_log.float().exp()
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* F.softplus(a_all[s:e].float() + self.dt_bias)
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).unsqueeze(0) # (1, seq_len, local_num_v)
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# Expand k/q to match num_v_heads
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q = q.repeat_interleave(self.head_expand_ratio, dim=2)
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k = k.repeat_interleave(self.head_expand_ratio, dim=2)
|
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|
||
# Sub-sequence chunking: call _torch_chunk_gated_delta_rule
|
||
# on _DNN_CHUNK tokens at a time to cap peak memory.
|
||
# Full 18K: tensors [1,6,282,64,64]=220 MB each → ~990 MB/call.
|
||
# With _DNN_CHUNK=4096: [1,6,64,64,64]=6 MB each → ~137 MB/call.
|
||
# State is chained via initial_state / output_final_state.
|
||
_DNN_CHUNK = 4096
|
||
cur_state = temporal_state[si:si + 1].clone()
|
||
core_out_parts = []
|
||
for sc_start in range(0, seq_len, _DNN_CHUNK):
|
||
sc_end = min(sc_start + _DNN_CHUNK, seq_len)
|
||
c_out, cur_state = _torch_chunk_gated_delta_rule(
|
||
q[:, sc_start:sc_end],
|
||
k[:, sc_start:sc_end],
|
||
v[:, sc_start:sc_end],
|
||
g[:, sc_start:sc_end],
|
||
beta[:, sc_start:sc_end],
|
||
initial_state=cur_state,
|
||
output_final_state=True,
|
||
use_qk_l2norm_in_kernel=True,
|
||
)
|
||
core_out_parts.append(c_out)
|
||
if cur_state is not None:
|
||
temporal_state[si].copy_(cur_state[0])
|
||
# [1, seq_len, num_v_heads, head_v_dim]
|
||
core_out = torch.cat(core_out_parts, dim=1)
|
||
|
||
# Gate + norm + output proj
|
||
z = z_all[s:e].reshape(seq_len, local_num_v, self.head_v_dim)
|
||
core_out = core_out.reshape(seq_len, local_num_v, self.head_v_dim)
|
||
normed = self.norm(
|
||
core_out.reshape(-1, self.head_v_dim),
|
||
z.reshape(-1, self.head_v_dim))
|
||
normed = normed.reshape(seq_len, -1)
|
||
out, _ = self.out_proj(normed)
|
||
outputs.append(out)
|
||
|
||
result = torch.cat(outputs, dim=0)
|
||
if torch.isnan(result).any():
|
||
logger.warning("NaN in prefill GatedDeltaNet layer %d (frac=%.4f), replacing with zeros",
|
||
self.layer_idx, torch.isnan(result).float().mean().item())
|
||
result = torch.nan_to_num(result, nan=0.0)
|
||
return result
|
||
|
||
else:
|
||
# Decode: one token per sequence
|
||
num_seqs = hidden_states.shape[0]
|
||
weight_2d = self.conv1d_weight.squeeze(1)
|
||
|
||
# (num_seqs, local_conv_dim, 1)
|
||
mixed_qkv = (mixed_qkv_all
|
||
.to(weight_2d.dtype)
|
||
.unsqueeze(-1))
|
||
|
||
mixed_qkv_conv = _torch_causal_conv1d_update(
|
||
mixed_qkv, conv_state, weight_2d,
|
||
bias=None, activation='silu')
|
||
# (num_seqs, local_conv_dim, 1) → (num_seqs, 1, local_conv_dim)
|
||
mixed_qkv_conv = mixed_qkv_conv.squeeze(-1).unsqueeze(1)
|
||
|
||
q, k, v = torch.split(
|
||
mixed_qkv_conv,
|
||
[local_key_dim, local_key_dim, local_val_dim], dim=-1)
|
||
q = q.reshape(num_seqs, 1, local_num_k, self.head_k_dim)
|
||
k = k.reshape(num_seqs, 1, local_num_k, self.head_k_dim)
|
||
v = v.reshape(num_seqs, 1, local_num_v, self.head_v_dim)
|
||
|
||
beta = b_all.sigmoid().unsqueeze(1) # (num_seqs, 1, local_num_v)
|
||
g = (-self.A_log.float().exp()
|
||
* F.softplus(a_all.float() + self.dt_bias)
|
||
).unsqueeze(1) # (num_seqs, 1, local_num_v)
|
||
|
||
q = q.repeat_interleave(self.head_expand_ratio, dim=2)
|
||
k = k.repeat_interleave(self.head_expand_ratio, dim=2)
|
||
|
||
# Inlined decode recurrent step (seq_len=1).
|
||
# Replaces _torch_recurrent_gated_delta_rule to avoid 5 transpose+
|
||
# contiguous+float32 copies, core_out allocation, and Python loop.
|
||
# Uses bmm/baddbmm_ to eliminate 3 large (B,H,k,v) intermediate tensors.
|
||
# temporal_state: (B, H_v, k_dim, v_dim) float32 — updated in-place.
|
||
orig_dtype = q.dtype
|
||
_scale = self.head_k_dim ** -0.5
|
||
|
||
q_t = _l2norm(q.squeeze(1)).float() * _scale # (B, H_v, k_dim)
|
||
k_t = _l2norm(k.squeeze(1)).float() # (B, H_v, k_dim)
|
||
v_t = v.squeeze(1).float() # (B, H_v, v_dim)
|
||
g_t = g.squeeze(1).float().exp_() # (B, H_v)
|
||
bt = beta.squeeze(1).float() # (B, H_v)
|
||
|
||
# Decay state in-place: (B, H_v, k_dim, v_dim) *= scalar per head
|
||
temporal_state.mul_(g_t[:, :, None, None])
|
||
|
||
# Reshape to batched-matmul layout: (B*H_v, k_dim, v_dim)
|
||
ts_flat = temporal_state.view(-1, self.head_k_dim, self.head_v_dim)
|
||
BH = ts_flat.shape[0]
|
||
|
||
# kv_mem = k_t @ temporal_state shape: (B*H_v, 1, k_dim) @ (B*H_v, k_dim, v_dim)
|
||
kv_mem = torch.bmm(
|
||
k_t.view(BH, 1, self.head_k_dim), ts_flat
|
||
).view(num_seqs, local_num_v, self.head_v_dim) # (B, H_v, v_dim)
|
||
|
||
delta = (v_t - kv_mem) * bt[:, :, None] # (B, H_v, v_dim)
|
||
|
||
# State update: temporal_state += outer(k_t, delta) fused, no intermediate
|
||
ts_flat.baddbmm_(
|
||
k_t.view(BH, self.head_k_dim, 1),
|
||
delta.view(BH, 1, self.head_v_dim),
|
||
)
|
||
|
||
# Output: core_out = q_t @ updated temporal_state
|
||
core_out = torch.bmm(
|
||
q_t.view(BH, 1, self.head_k_dim), ts_flat
|
||
).view(num_seqs, local_num_v, self.head_v_dim).to(orig_dtype)
|
||
# core_out: (B, H_v, v_dim) = (num_seqs, local_num_v, head_v_dim) already
|
||
|
||
z = z_all.reshape(num_seqs, local_num_v, self.head_v_dim)
|
||
normed = self.norm(
|
||
core_out.reshape(-1, self.head_v_dim),
|
||
z.reshape(-1, self.head_v_dim))
|
||
normed = normed.reshape(num_seqs, -1)
|
||
out, _ = self.out_proj(normed)
|
||
if torch.isnan(out).any():
|
||
logger.warning("NaN in decode GatedDeltaNet layer %d (frac=%.4f), replacing with zeros",
|
||
self.layer_idx, torch.isnan(out).float().mean().item())
|
||
out = torch.nan_to_num(out, nan=0.0)
|
||
return out
|
||
|
||
|
||
# ---------------------------------------------------------------------------
|
||
# Full Attention (with gated q — unique to Qwen3.5)
|
||
# ---------------------------------------------------------------------------
|
||
|
||
class Qwen3_5FullAttention(nn.Module):
|
||
def __init__(
|
||
self,
|
||
text_cfg,
|
||
layer_idx: int,
|
||
cache_config: Optional[CacheConfig] = None,
|
||
quant_config: Optional[QuantizationConfig] = None,
|
||
prefix: str = "",
|
||
) -> None:
|
||
super().__init__()
|
||
self.layer_idx = layer_idx
|
||
self.hidden_size = text_cfg.hidden_size # 5120
|
||
self.num_heads = text_cfg.num_attention_heads # 24
|
||
self.num_kv_heads = text_cfg.num_key_value_heads # 4
|
||
self.head_dim = text_cfg.head_dim # 256
|
||
self.rms_norm_eps = text_cfg.rms_norm_eps
|
||
|
||
tp_size = get_tensor_model_parallel_world_size()
|
||
self.local_num_heads = self.num_heads // tp_size
|
||
self.scaling = self.head_dim ** -0.5
|
||
|
||
# When num_kv_heads < tp_size we cannot shard KV further (would give
|
||
# fractional heads per rank). Use ReplicatedLinear so every rank holds
|
||
# all KV heads; local_num_kv_heads equals the full count.
|
||
# When num_kv_heads >= tp_size standard ColumnParallel sharding applies.
|
||
if tp_size > self.num_kv_heads:
|
||
# GQA-aware TP sharding: ixformer kernel only supports num_kv_heads=1
|
||
# per rank. With num_kv_heads=2 < tp_size=4 we cannot shard KV
|
||
# evenly, but we CAN assign each rank the ONE KV head that serves
|
||
# its Q heads:
|
||
# q_per_kv = num_heads // num_kv_heads (e.g. 16//2 = 8)
|
||
# Rank r uses KV head r * local_num_heads // q_per_kv
|
||
# e.g. ranks 0,1 → KV head 0; ranks 2,3 → KV head 1.
|
||
# We replicate all KV heads to every rank and select in forward().
|
||
self.proj_kv_heads = self.num_kv_heads # heads available from projection
|
||
self.local_num_kv_heads = 1 # heads after rank-local selection
|
||
self.q_per_kv_global = self.num_heads // self.num_kv_heads
|
||
self.k_proj = ReplicatedLinear(
|
||
self.hidden_size, self.num_kv_heads * self.head_dim,
|
||
bias=False, quant_config=quant_config)
|
||
self.v_proj = ReplicatedLinear(
|
||
self.hidden_size, self.num_kv_heads * self.head_dim,
|
||
bias=False, quant_config=quant_config)
|
||
else:
|
||
# Standard sharding: each rank gets num_kv_heads // tp_size heads.
|
||
self.local_num_kv_heads = self.num_kv_heads // tp_size
|
||
self.proj_kv_heads = self.local_num_kv_heads # already sharded
|
||
self.q_per_kv_global = None
|
||
self.k_proj = ColumnParallelLinear(
|
||
self.hidden_size, self.num_kv_heads * self.head_dim,
|
||
bias=False, quant_config=quant_config,
|
||
prefix=f"{prefix}.k_proj")
|
||
self.v_proj = ColumnParallelLinear(
|
||
self.hidden_size, self.num_kv_heads * self.head_dim,
|
||
bias=False, quant_config=quant_config,
|
||
prefix=f"{prefix}.v_proj")
|
||
|
||
self.local_q_dim = self.local_num_heads * self.head_dim
|
||
self.local_kv_dim = self.local_num_kv_heads * self.head_dim
|
||
|
||
# q_proj includes gate: output = num_heads * head_dim * 2
|
||
self.q_proj = ColumnParallelLinear(
|
||
self.hidden_size, self.num_heads * self.head_dim * 2,
|
||
bias=False, quant_config=quant_config,
|
||
prefix=f"{prefix}.q_proj")
|
||
self.o_proj = RowParallelLinear(
|
||
self.num_heads * self.head_dim, self.hidden_size,
|
||
bias=False, quant_config=quant_config,
|
||
prefix=f"{prefix}.o_proj")
|
||
|
||
self.q_norm = GemmaRMSNorm(self.head_dim, eps=self.rms_norm_eps)
|
||
self.k_norm = GemmaRMSNorm(self.head_dim, eps=self.rms_norm_eps)
|
||
|
||
# Partial RoPE: rotary_dim = head_dim * partial_rotary_factor = 256 * 0.25 = 64
|
||
rope_params = getattr(text_cfg, "rope_parameters", {}) or {}
|
||
rope_theta = rope_params.get("rope_theta", 10_000_000)
|
||
partial_factor = rope_params.get("partial_rotary_factor", 0.25)
|
||
rotary_dim = int(self.head_dim * partial_factor)
|
||
|
||
self.rotary_emb = get_rope(
|
||
self.head_dim,
|
||
rotary_dim=rotary_dim,
|
||
max_position=text_cfg.max_position_embeddings,
|
||
base=rope_theta,
|
||
)
|
||
|
||
self.attn = Attention(
|
||
self.local_num_heads,
|
||
self.head_dim,
|
||
self.scaling,
|
||
num_kv_heads=self.local_num_kv_heads,
|
||
cache_config=cache_config,
|
||
quant_config=quant_config,
|
||
prefix=f"{prefix}.attn",
|
||
)
|
||
|
||
def forward(
|
||
self,
|
||
positions: torch.Tensor,
|
||
hidden_states: torch.Tensor,
|
||
kv_cache: torch.Tensor,
|
||
attn_metadata: AttentionMetadata,
|
||
) -> torch.Tensor:
|
||
total_tokens = hidden_states.shape[0]
|
||
|
||
# q_proj output includes gate (dim doubled)
|
||
qg, _ = self.q_proj(hidden_states) # (total, local_num_heads * head_dim * 2)
|
||
qg = qg.view(total_tokens, self.local_num_heads, self.head_dim * 2)
|
||
q = qg[:, :, :self.head_dim].reshape(total_tokens, -1)
|
||
gate = qg[:, :, self.head_dim:].reshape(total_tokens, -1)
|
||
|
||
k, _ = self.k_proj(hidden_states) # (total, proj_kv_heads * head_dim)
|
||
v, _ = self.v_proj(hidden_states)
|
||
|
||
# q_norm on local Q heads
|
||
q = self.q_norm.forward_cuda(
|
||
q.view(total_tokens, self.local_num_heads, self.head_dim)
|
||
.contiguous()).view(total_tokens, -1)
|
||
|
||
# GQA-aware TP: select rank-local KV head BEFORE k_norm and rope so
|
||
# that ixformer kernels always see num_kv_heads=1 (same as 27B path).
|
||
# Doing k_norm/rope on 2 KV heads (proj_kv_heads=2) triggers ixformer
|
||
# paths that can produce NaN; restricting to 1 head avoids the issue.
|
||
if self.q_per_kv_global is not None:
|
||
tp_rank = get_tensor_model_parallel_rank()
|
||
kv_idx = (tp_rank * self.local_num_heads) // self.q_per_kv_global
|
||
k = (k.view(total_tokens, self.proj_kv_heads, self.head_dim)
|
||
[:, kv_idx, :].contiguous()) # (T, head_dim) — 1 head
|
||
v = (v.view(total_tokens, self.proj_kv_heads, self.head_dim)
|
||
[:, kv_idx, :].contiguous()) # (T, head_dim) — 1 head
|
||
|
||
# k_norm on the (now always 1) rank-local KV head
|
||
k = self.k_norm.forward_cuda(
|
||
k.view(total_tokens, self.local_num_kv_heads, self.head_dim)
|
||
.contiguous()).view(total_tokens, -1)
|
||
|
||
# rope: q=(T, local_num_heads*head_dim), k=(T, 1*head_dim) — mirrors 27B
|
||
q, k = self.rotary_emb(positions, q, k)
|
||
|
||
attn_out = self.attn(q, k, v, kv_cache, attn_metadata)
|
||
|
||
# Multiply by sigmoid gate before output projection
|
||
attn_out = attn_out * torch.sigmoid(gate.float()).to(attn_out.dtype)
|
||
output, _ = self.o_proj(attn_out)
|
||
return output
|
||
|
||
|
||
# ---------------------------------------------------------------------------
|
||
# MLP (SwiGLU, same as Qwen2/Qwen3)
|
||
# ---------------------------------------------------------------------------
|
||
|
||
class Qwen3_5MLP(nn.Module):
|
||
def __init__(
|
||
self,
|
||
hidden_size: int,
|
||
intermediate_size: int,
|
||
hidden_act: str,
|
||
quant_config: Optional[QuantizationConfig] = None,
|
||
) -> None:
|
||
super().__init__()
|
||
self.gate_up_proj = MergedColumnParallelLinear(
|
||
hidden_size, [intermediate_size] * 2,
|
||
bias=False, quant_config=quant_config)
|
||
self.down_proj = RowParallelLinear(
|
||
intermediate_size, hidden_size,
|
||
bias=False, quant_config=quant_config)
|
||
if hidden_act != "silu":
|
||
raise ValueError(f"Unsupported activation: {hidden_act}")
|
||
self.act_fn = SiluAndMul()
|
||
|
||
def forward(self, x: torch.Tensor) -> torch.Tensor:
|
||
gate_up, _ = self.gate_up_proj(x)
|
||
x = self.act_fn(gate_up)
|
||
x, _ = self.down_proj(x)
|
||
return x
|
||
|
||
|
||
# ---------------------------------------------------------------------------
|
||
# MoE sparse block (Qwen3.5-MoE / Qwen3.6-35B-A3B)
|
||
# ---------------------------------------------------------------------------
|
||
|
||
class Qwen3_5MoeSparseBlock(nn.Module):
|
||
"""Replaces Qwen3_5MLP for qwen3_5_moe_text layers.
|
||
|
||
FusedMoE is used ONLY for weight storage and loading (create_weights /
|
||
weight_loader are pure PyTorch). Its forward kernel is bypassed because
|
||
ixformer on BI-V100 lacks vllm_moe_topk_softmax / vllm_invoke_fused_moe_kernel.
|
||
Routing and expert computation use a pure-PyTorch loop instead.
|
||
|
||
Shared expert uses RowParallelLinear(reduce_results=False) so both paths
|
||
produce partial (pre-all-reduce) outputs that are combined before a single
|
||
all-reduce.
|
||
"""
|
||
|
||
def __init__(
|
||
self,
|
||
text_cfg,
|
||
quant_config: Optional[QuantizationConfig] = None,
|
||
) -> None:
|
||
super().__init__()
|
||
hidden_size = text_cfg.hidden_size
|
||
self.num_experts = text_cfg.num_experts
|
||
self.top_k = text_cfg.num_experts_per_tok
|
||
|
||
# Router: replicated (small: num_experts outputs)
|
||
self.gate = ReplicatedLinear(hidden_size, text_cfg.num_experts,
|
||
bias=False, quant_config=quant_config)
|
||
|
||
# FusedMoE: only used for weight storage + weight_loader.
|
||
# Forward is bypassed — see _pure_pytorch_experts().
|
||
self.experts = FusedMoE(
|
||
num_experts=text_cfg.num_experts,
|
||
top_k=text_cfg.num_experts_per_tok,
|
||
hidden_size=hidden_size,
|
||
intermediate_size=text_cfg.moe_intermediate_size,
|
||
reduce_results=False, # we do the all-reduce ourselves below
|
||
renormalize=True,
|
||
quant_config=quant_config,
|
||
)
|
||
|
||
# Shared expert: defer all-reduce to combine with routed output first
|
||
shared_size = text_cfg.shared_expert_intermediate_size
|
||
self.shared_expert_gate_up = MergedColumnParallelLinear(
|
||
hidden_size, [shared_size] * 2, bias=False,
|
||
quant_config=quant_config)
|
||
self.shared_expert_down = RowParallelLinear(
|
||
shared_size, hidden_size, bias=False, reduce_results=False,
|
||
quant_config=quant_config)
|
||
self.act_fn = SiluAndMul()
|
||
# Scalar sigmoid gate on shared expert output (same as Qwen2-MoE / Qwen3.5-MoE):
|
||
# shared_out *= sigmoid(shared_expert_gate(hidden_states))
|
||
# Without this, shared expert is always fully active → wrong logits.
|
||
self.shared_expert_gate = ReplicatedLinear(
|
||
hidden_size, 1, bias=False, quant_config=quant_config)
|
||
|
||
def _pure_pytorch_experts(
|
||
self,
|
||
hidden_states: torch.Tensor,
|
||
router_logits: torch.Tensor,
|
||
) -> torch.Tensor:
|
||
"""Pure-PyTorch MoE (ixformer has no MoE kernels on BI-V100).
|
||
|
||
w13_weight: (num_experts, 2*inter_per_partition, hidden) [TP-sharded]
|
||
w2_weight: (num_experts, hidden, inter_per_partition) [TP-sharded]
|
||
Output is partial (pre-all-reduce), same contract as FusedMoE
|
||
with reduce_results=False.
|
||
"""
|
||
# Routing: softmax → topk → renormalise
|
||
routing_weights = torch.softmax(router_logits.float(), dim=-1)
|
||
topk_weights, topk_ids = torch.topk(
|
||
routing_weights, self.top_k, dim=-1) # (T, top_k)
|
||
topk_weights = topk_weights / topk_weights.sum(dim=-1, keepdim=True)
|
||
topk_weights = topk_weights.to(hidden_states.dtype)
|
||
|
||
w13 = self.experts.w13_weight # (E, 2*I, H)
|
||
w2 = self.experts.w2_weight # (E, H, I)
|
||
|
||
T = hidden_states.shape[0]
|
||
if T == 1:
|
||
# Fast path: single token (decode).
|
||
# Batched GEMM: replace top_k separate F.linear calls with 2 fused ops.
|
||
# gate_up: 1 large GEMM (1,H) × (K*2*I,H)^T → (1, K*2*I)
|
||
# down: 1 bmm (K,H,I) @ (K,I,1) → (K,H)
|
||
# Total: 3 kernel launches vs previous 16 (top_k*2).
|
||
eids = topk_ids[0] # (K,)
|
||
ws = topk_weights[0].to(hidden_states.dtype) # (K,)
|
||
w13_sel = w13[eids] # (K, 2*I, H)
|
||
w2_sel = w2[eids] # (K, H, I)
|
||
|
||
H = hidden_states.shape[-1]
|
||
|
||
gate_up = F.linear(
|
||
hidden_states,
|
||
w13_sel.reshape(-1, H), # (K*2*I, H) — contiguous after indexing
|
||
) # (1, K*2*I)
|
||
gate_up = gate_up.view(self.top_k, -1) # (K, 2*I)
|
||
gate, up = gate_up.chunk(2, dim=-1) # (K, I) each
|
||
act = F.silu(gate) * up # (K, I)
|
||
|
||
# bmm: (K,H,I) @ (K,I,1) → (K,H,1) → (K,H)
|
||
expert_out = torch.bmm(w2_sel, act.unsqueeze(-1)).squeeze(-1) # (K, H)
|
||
|
||
out = (expert_out * ws.unsqueeze(-1)).sum(0, keepdim=True).to(
|
||
hidden_states.dtype) # (1, H)
|
||
else:
|
||
# General path (prefill / multi-seq): loop over unique active experts.
|
||
# At most T*top_k unique experts, always <= num_experts.
|
||
out = torch.zeros_like(hidden_states)
|
||
unique_eids = topk_ids.view(-1).unique().tolist()
|
||
for eid in unique_eids:
|
||
eid = int(eid)
|
||
mask = (topk_ids == eid) # (T, top_k)
|
||
tok_ids, topk_pos = mask.nonzero(as_tuple=True)
|
||
tokens = hidden_states[tok_ids] # (n, H)
|
||
gate_up = F.linear(tokens, w13[eid]) # (n, 2*I)
|
||
gate, up = gate_up.chunk(2, dim=-1)
|
||
act = F.silu(gate) * up # (n, I)
|
||
expert_out = F.linear(act, w2[eid]) # (n, H)
|
||
weights = topk_weights[tok_ids, topk_pos].unsqueeze(-1)
|
||
out.index_add_(0, tok_ids, (expert_out * weights).to(out.dtype))
|
||
|
||
return out # partial, all-reduce done in forward()
|
||
|
||
def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
|
||
router_logits, _ = self.gate(hidden_states)
|
||
routed_out = self._pure_pytorch_experts(hidden_states, router_logits)
|
||
|
||
gate_up, _ = self.shared_expert_gate_up(hidden_states)
|
||
shared_out = self.act_fn(gate_up)
|
||
shared_out, _ = self.shared_expert_down(shared_out)
|
||
# Scalar sigmoid gate (Qwen2-MoE / Qwen3.5-MoE style)
|
||
gate_score, _ = self.shared_expert_gate(hidden_states) # (T, 1)
|
||
shared_out = shared_out * torch.sigmoid(gate_score)
|
||
|
||
out = routed_out + shared_out
|
||
if self.experts.tp_size > 1:
|
||
out = tensor_model_parallel_all_reduce(out)
|
||
return out
|
||
|
||
|
||
# ---------------------------------------------------------------------------
|
||
# Decoder layer (dispatches to GatedDeltaNet or Qwen3_5FullAttention)
|
||
# ---------------------------------------------------------------------------
|
||
|
||
|
||
class Qwen3_5DecoderLayer(nn.Module):
|
||
def __init__(
|
||
self,
|
||
text_cfg,
|
||
layer_idx: int,
|
||
layer_type: str,
|
||
cache_config: Optional[CacheConfig] = None,
|
||
quant_config: Optional[QuantizationConfig] = None,
|
||
) -> None:
|
||
super().__init__()
|
||
self.layer_idx = layer_idx
|
||
self.layer_type = layer_type
|
||
self.input_layernorm = GemmaRMSNorm(text_cfg.hidden_size,
|
||
eps=text_cfg.rms_norm_eps)
|
||
self.post_attention_layernorm = GemmaRMSNorm(text_cfg.hidden_size,
|
||
eps=text_cfg.rms_norm_eps)
|
||
|
||
if layer_type == "linear_attention":
|
||
self.linear_attn = GatedDeltaNet(text_cfg, layer_idx,
|
||
quant_config=quant_config)
|
||
else:
|
||
self.self_attn = Qwen3_5FullAttention(
|
||
text_cfg, layer_idx,
|
||
cache_config=cache_config,
|
||
quant_config=quant_config,
|
||
prefix=f"layers.{layer_idx}.self_attn",
|
||
)
|
||
|
||
if getattr(text_cfg, 'model_type', '') == 'qwen3_5_moe_text':
|
||
self.mlp = Qwen3_5MoeSparseBlock(text_cfg, quant_config=quant_config)
|
||
else:
|
||
self.mlp = Qwen3_5MLP(
|
||
hidden_size=text_cfg.hidden_size,
|
||
intermediate_size=text_cfg.intermediate_size,
|
||
hidden_act=text_cfg.hidden_act,
|
||
quant_config=quant_config,
|
||
)
|
||
|
||
def forward(
|
||
self,
|
||
positions: torch.Tensor,
|
||
hidden_states: torch.Tensor,
|
||
kv_cache: Optional[torch.Tensor],
|
||
attn_metadata: AttentionMetadata,
|
||
residual: Optional[torch.Tensor],
|
||
# Only for linear_attention layers:
|
||
conv_state: Optional[torch.Tensor] = None,
|
||
temporal_state: Optional[torch.Tensor] = None,
|
||
) -> Tuple[torch.Tensor, torch.Tensor]:
|
||
if residual is None:
|
||
residual = hidden_states
|
||
hidden_states = self.input_layernorm(hidden_states)
|
||
else:
|
||
hidden_states, residual = self.input_layernorm(hidden_states, residual)
|
||
|
||
if self.layer_type == "linear_attention":
|
||
hidden_states = self.linear_attn(
|
||
hidden_states, attn_metadata, conv_state, temporal_state)
|
||
else:
|
||
hidden_states = self.self_attn(
|
||
positions, hidden_states, kv_cache, attn_metadata)
|
||
|
||
hidden_states, residual = self.post_attention_layernorm(
|
||
hidden_states, residual)
|
||
|
||
hidden_states = self.mlp(hidden_states)
|
||
|
||
return hidden_states, residual
|
||
|
||
|
||
# ---------------------------------------------------------------------------
|
||
# Full transformer model
|
||
# ---------------------------------------------------------------------------
|
||
|
||
class Qwen3_5Model(nn.Module):
|
||
def __init__(
|
||
self,
|
||
text_cfg,
|
||
cache_config: Optional[CacheConfig] = None,
|
||
quant_config: Optional[QuantizationConfig] = None,
|
||
) -> None:
|
||
super().__init__()
|
||
self.text_cfg = text_cfg
|
||
self.embed_tokens = VocabParallelEmbedding(
|
||
text_cfg.vocab_size, text_cfg.hidden_size)
|
||
self.layers = nn.ModuleList([
|
||
Qwen3_5DecoderLayer(
|
||
text_cfg, i, text_cfg.layer_types[i],
|
||
cache_config=cache_config, quant_config=quant_config)
|
||
for i in range(text_cfg.num_hidden_layers)
|
||
])
|
||
self.norm = GemmaRMSNorm(text_cfg.hidden_size, eps=text_cfg.rms_norm_eps)
|
||
|
||
def forward(
|
||
self,
|
||
input_ids: torch.Tensor,
|
||
positions: torch.Tensor,
|
||
kv_caches: List[torch.Tensor],
|
||
attn_metadata: AttentionMetadata,
|
||
conv_states: torch.Tensor, # (num_linear_layers, batch, ...)
|
||
temporal_states: torch.Tensor, # (num_linear_layers, batch, ...)
|
||
) -> torch.Tensor:
|
||
hidden_states = self.embed_tokens(input_ids)
|
||
residual = None
|
||
|
||
attn_idx = 0
|
||
linear_idx = 0
|
||
for layer in self.layers:
|
||
if layer.layer_type == "linear_attention":
|
||
hidden_states, residual = layer(
|
||
positions, hidden_states,
|
||
kv_cache=None,
|
||
attn_metadata=attn_metadata,
|
||
residual=residual,
|
||
conv_state=conv_states[linear_idx],
|
||
temporal_state=temporal_states[linear_idx],
|
||
)
|
||
linear_idx += 1
|
||
else:
|
||
kv_cache = kv_caches[attn_idx]
|
||
hidden_states, residual = layer(
|
||
positions, hidden_states,
|
||
kv_cache=kv_cache,
|
||
attn_metadata=attn_metadata,
|
||
residual=residual,
|
||
)
|
||
attn_idx += 1
|
||
|
||
hidden_states, _ = self.norm(hidden_states, residual)
|
||
return hidden_states
|
||
|
||
|
||
# ---------------------------------------------------------------------------
|
||
# Top-level CausalLM wrapper with MambaCacheManager
|
||
# ---------------------------------------------------------------------------
|
||
|
||
class Qwen3_5ForCausalLM(nn.Module, HasInnerState, SupportsLoRA):
|
||
|
||
has_inner_state = True
|
||
supports_lora = True
|
||
|
||
packed_modules_mapping = {
|
||
"gate_up_proj": ["gate_proj", "up_proj"],
|
||
}
|
||
|
||
supported_lora_modules = [
|
||
"gate_up_proj",
|
||
"down_proj",
|
||
"o_proj",
|
||
]
|
||
embedding_modules = {}
|
||
embedding_padding_modules = []
|
||
|
||
def __init__(
|
||
self,
|
||
config, # Qwen3_5Config (top-level)
|
||
cache_config: Optional[CacheConfig] = None,
|
||
quant_config: Optional[QuantizationConfig] = None,
|
||
lora_config: Optional[LoRAConfig] = None,
|
||
scheduler_config: Optional[SchedulerConfig] = None,
|
||
prefix: str = "",
|
||
) -> None:
|
||
super().__init__()
|
||
self.config = config
|
||
self.scheduler_config = scheduler_config
|
||
|
||
# The text config holds all architecture parameters
|
||
text_cfg = config.text_config
|
||
self.text_cfg = text_cfg
|
||
|
||
# Pre-compute counts
|
||
self.num_linear_layers = sum(
|
||
1 for lt in text_cfg.layer_types if lt == "linear_attention")
|
||
self.num_attn_layers = sum(
|
||
1 for lt in text_cfg.layer_types if lt == "full_attention")
|
||
|
||
# DeltaNet state dimensions (per layer, per sequence, TP-sharded)
|
||
tp_size = get_tensor_model_parallel_world_size()
|
||
self.conv_dim = (text_cfg.linear_num_key_heads * text_cfg.linear_key_head_dim * 2
|
||
+ text_cfg.linear_num_value_heads * text_cfg.linear_value_head_dim)
|
||
self.num_v_heads = text_cfg.linear_num_value_heads
|
||
self.head_k_dim = text_cfg.linear_key_head_dim
|
||
self.head_v_dim = text_cfg.linear_value_head_dim
|
||
self.conv_kernel_size = text_cfg.linear_conv_kernel_dim
|
||
|
||
self.model = Qwen3_5Model(
|
||
text_cfg,
|
||
cache_config=cache_config,
|
||
quant_config=quant_config,
|
||
)
|
||
|
||
self.lm_head = ParallelLMHead(
|
||
text_cfg.vocab_size, text_cfg.hidden_size,
|
||
quant_config=quant_config,
|
||
)
|
||
|
||
self.logits_processor = LogitsProcessor(text_cfg.vocab_size)
|
||
self.sampler = Sampler()
|
||
|
||
# Lazy initialised in first forward call
|
||
self.mamba_cache: Optional[MambaCacheManager] = None
|
||
|
||
def _get_mamba_cache_shape(self):
|
||
tp_size = get_tensor_model_parallel_world_size()
|
||
# Each sequence's state is stored in float32
|
||
conv_state_shape = (self.conv_dim // tp_size, self.conv_kernel_size - 1)
|
||
temporal_state_shape = (
|
||
self.num_v_heads // tp_size, self.head_k_dim, self.head_v_dim)
|
||
return conv_state_shape, temporal_state_shape
|
||
|
||
def forward(
|
||
self,
|
||
input_ids: torch.Tensor,
|
||
positions: torch.Tensor,
|
||
kv_caches: List[torch.Tensor],
|
||
attn_metadata: AttentionMetadata,
|
||
intermediate_tensors: Optional[IntermediateTensors] = None,
|
||
**kwargs,
|
||
) -> torch.Tensor:
|
||
if self.mamba_cache is None:
|
||
if self.scheduler_config is not None:
|
||
max_batch_size = _get_graph_batch_size(
|
||
self.scheduler_config.max_num_seqs)
|
||
else:
|
||
max_batch_size = max(_BATCH_SIZES_TO_CAPTURE) + 2
|
||
self.mamba_cache = MambaCacheManager(
|
||
torch.float32,
|
||
self.num_linear_layers,
|
||
max_batch_size,
|
||
*self._get_mamba_cache_shape(),
|
||
)
|
||
|
||
mamba_tensors = self.mamba_cache.current_run_tensors(
|
||
input_ids, attn_metadata, **kwargs)
|
||
# conv_states: (num_linear_layers, batch, local_conv_dim, kernel-1)
|
||
# temporal_states: (num_linear_layers, batch, local_num_v, k_dim, v_dim)
|
||
conv_states, temporal_states = mamba_tensors
|
||
|
||
hidden_states = self.model(
|
||
input_ids, positions, kv_caches, attn_metadata,
|
||
conv_states, temporal_states)
|
||
return hidden_states
|
||
|
||
def compute_logits(
|
||
self,
|
||
hidden_states: torch.Tensor,
|
||
sampling_metadata: SamplingMetadata,
|
||
) -> Optional[torch.Tensor]:
|
||
# All TP ranks must call logits_processor to participate in the NCCL
|
||
# gather inside lm_head. Non-driver ranks return None after the gather.
|
||
# With chunked prefill, intermediate chunks have seq_groups=None on all
|
||
# ranks; _apply_logits_processors is guarded against this in
|
||
# logits_processor.py (patched by patch_xformers_sdpa_seq.py).
|
||
logits = self.logits_processor(self.lm_head, hidden_states,
|
||
sampling_metadata)
|
||
return logits
|
||
|
||
def sample(
|
||
self,
|
||
logits: torch.Tensor,
|
||
sampling_metadata: SamplingMetadata,
|
||
) -> Optional[SamplerOutput]:
|
||
return self.sampler(logits, sampling_metadata)
|
||
|
||
def copy_inputs_before_cuda_graphs(self, input_buffers, **kwargs):
|
||
return self.mamba_cache.copy_inputs_before_cuda_graphs(
|
||
input_buffers, **kwargs)
|
||
|
||
def get_seqlen_agnostic_capture_inputs(self, batch_size: int):
|
||
return self.mamba_cache.get_seqlen_agnostic_capture_inputs(batch_size)
|
||
|
||
def load_weights(self, weights: Iterable[Tuple[str, torch.Tensor]]):
|
||
stacked_params_mapping = [
|
||
# (param_name, weight_name, shard_id)
|
||
("gate_up_proj", "gate_proj", 0),
|
||
("gate_up_proj", "up_proj", 1),
|
||
]
|
||
params_dict = dict(self.named_parameters())
|
||
|
||
for name, loaded_weight in weights:
|
||
# Skip vision and MTP branches
|
||
if (name.startswith("model.visual")
|
||
or name.startswith("mtp.")
|
||
or name.startswith("model.mtp")):
|
||
continue
|
||
|
||
# Prefix remapping: checkpoint may wrap under language_model
|
||
if name.startswith("model.language_model."):
|
||
name = "model." + name[len("model.language_model."):]
|
||
|
||
# Skip positional embedding caches
|
||
if "rotary_emb.inv_freq" in name:
|
||
continue
|
||
|
||
# Remap conv1d.weight → conv1d_weight
|
||
# The conv has depth (1) dim in the checkpoint that we handle separately
|
||
if ".linear_attn.conv1d.weight" in name:
|
||
name = name.replace(".linear_attn.conv1d.weight",
|
||
".linear_attn.conv1d_weight")
|
||
|
||
# Stacked param loading (gate_up_proj)
|
||
for param_name, weight_name, shard_id in stacked_params_mapping:
|
||
if weight_name not in name:
|
||
continue
|
||
name = name.replace(weight_name, param_name)
|
||
if name.endswith(".bias") and name not in params_dict:
|
||
break
|
||
if name not in params_dict:
|
||
break
|
||
param = params_dict[name]
|
||
weight_loader = param.weight_loader
|
||
weight_loader(param, loaded_weight, shard_id)
|
||
break
|
||
else:
|
||
if name.endswith(".bias") and name not in params_dict:
|
||
continue
|
||
if name not in params_dict:
|
||
continue
|
||
param = params_dict[name]
|
||
weight_loader = getattr(param, "weight_loader",
|
||
default_weight_loader)
|
||
weight_loader(param, loaded_weight)
|
||
|
||
|
||
# ---------------------------------------------------------------------------
|
||
# Qwen3.6-35B-A3B (Qwen3_5-MoE architecture)
|
||
# ---------------------------------------------------------------------------
|
||
|
||
class Qwen3_5MoeForCausalLM(Qwen3_5ForCausalLM):
|
||
"""Qwen3.6-35B-A3B: same hybrid-attention backbone as 27B, dense MLP
|
||
replaced by Qwen3_5MoeSparseBlock (256 routed experts + shared expert).
|
||
Only load_weights differs from the dense variant.
|
||
"""
|
||
|
||
def load_weights(self, weights: Iterable[Tuple[str, torch.Tensor]]):
|
||
# Checkpoint key format for this model (transformers Qwen3_5MoeExperts):
|
||
# mlp.experts.gate_up_proj shape (num_experts, 2*intermediate, hidden)
|
||
# mlp.experts.down_proj shape (num_experts, hidden, intermediate)
|
||
# mlp.gate.weight shape (num_experts, hidden) [router]
|
||
# mlp.shared_expert.{gate,up,down}_proj.weight [shared MLP]
|
||
# Our FusedMoE stores:
|
||
# mlp.experts.w13_weight shape (num_experts, 2*intermediate//tp, hidden)
|
||
# mlp.experts.w2_weight shape (num_experts, hidden, intermediate//tp)
|
||
# Our shared expert stores:
|
||
# mlp.shared_expert_gate_up.weight (merged gate+up)
|
||
# mlp.shared_expert_down.weight
|
||
|
||
stacked_params_mapping = [
|
||
# (param_name, weight_name, shard_id)
|
||
# shared expert
|
||
("shared_expert_gate_up", "shared_expert.gate_proj", 0),
|
||
("shared_expert_gate_up", "shared_expert.up_proj", 1),
|
||
# linear_attention dense proj (same as 27B)
|
||
("gate_up_proj", "gate_proj", 0),
|
||
("gate_up_proj", "up_proj", 1),
|
||
]
|
||
|
||
params_dict = dict(self.named_parameters())
|
||
|
||
for name, loaded_weight in weights:
|
||
# Skip vision and MTP branches
|
||
if (name.startswith("model.visual")
|
||
or name.startswith("mtp.")
|
||
or name.startswith("model.mtp")):
|
||
continue
|
||
|
||
# Prefix remapping for VL checkpoint (Qwen3_5MoeForConditionalGeneration):
|
||
# model.language_model.model.{layers,embed_tokens,norm} -> model.{...}
|
||
# model.language_model.lm_head -> lm_head
|
||
# Prefix remapping: checkpoint may wrap under language_model
|
||
if name.startswith("model.language_model."):
|
||
name = "model." + name[len("model.language_model."):]
|
||
|
||
if "rotary_emb.inv_freq" in name:
|
||
continue
|
||
|
||
if ".linear_attn.conv1d.weight" in name:
|
||
name = name.replace(".linear_attn.conv1d.weight",
|
||
".linear_attn.conv1d_weight")
|
||
|
||
# --- Fused routed-expert weights (all experts in one tensor) ---
|
||
|
||
if "mlp.experts.gate_up_proj" in name:
|
||
# loaded_weight: (num_experts, 2*intermediate, hidden)
|
||
w13_name = name.replace("mlp.experts.gate_up_proj",
|
||
"mlp.experts.w13_weight")
|
||
if w13_name not in params_dict:
|
||
continue
|
||
param = params_dict[w13_name]
|
||
n_exp = loaded_weight.shape[0]
|
||
inter = loaded_weight.shape[1] // 2
|
||
gate_w = loaded_weight[:, :inter, :].contiguous()
|
||
up_w = loaded_weight[:, inter:, :].contiguous()
|
||
for eid in range(n_exp):
|
||
param.weight_loader(param, gate_w[eid], "w1_weight", "w1", eid)
|
||
param.weight_loader(param, up_w[eid], "w3_weight", "w3", eid)
|
||
continue
|
||
|
||
if "mlp.experts.down_proj" in name:
|
||
# loaded_weight: (num_experts, hidden, intermediate)
|
||
w2_name = name.replace("mlp.experts.down_proj",
|
||
"mlp.experts.w2_weight")
|
||
if w2_name not in params_dict:
|
||
continue
|
||
param = params_dict[w2_name]
|
||
n_exp = loaded_weight.shape[0]
|
||
for eid in range(n_exp):
|
||
param.weight_loader(param, loaded_weight[eid], "w2_weight", "w2", eid)
|
||
continue
|
||
|
||
# --- Shared expert down_proj rename ---
|
||
if "mlp.shared_expert.down_proj" in name:
|
||
name = name.replace("mlp.shared_expert.down_proj",
|
||
"mlp.shared_expert_down")
|
||
if name not in params_dict:
|
||
continue
|
||
param = params_dict[name]
|
||
weight_loader = getattr(param, "weight_loader", default_weight_loader)
|
||
weight_loader(param, loaded_weight)
|
||
continue
|
||
|
||
# --- Stacked / standard weights ---
|
||
for param_name, weight_name, shard_id in stacked_params_mapping:
|
||
if weight_name not in name:
|
||
continue
|
||
name = name.replace(weight_name, param_name)
|
||
if name not in params_dict:
|
||
break
|
||
param = params_dict[name]
|
||
param.weight_loader(param, loaded_weight, shard_id)
|
||
break
|
||
else:
|
||
if name not in params_dict:
|
||
continue
|
||
param = params_dict[name]
|
||
weight_loader = getattr(param, "weight_loader", default_weight_loader)
|
||
weight_loader(param, loaded_weight)
|