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@ -31,22 +31,11 @@ import torch.nn.functional as F
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from torch import nn
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from transformers import MixtralConfig
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try:
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import megablocks.ops as ops
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except ImportError as e:
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raise ImportError("MegaBlocks not found. "
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"Please install it by `pip install megablocks`.") from e
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try:
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import stk
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except ImportError as e:
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raise ImportError(
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"STK not found. "
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"Please install it by `pip install stanford-stk`.") from e
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from vllm.model_executor.input_metadata import InputMetadata
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from vllm.model_executor.layers.attention import PagedAttention
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from vllm.model_executor.layers.layernorm import RMSNorm
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from vllm.model_executor.layers.linear import (LinearMethodBase,
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ReplicatedLinear,
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QKVParallelLinear,
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RowParallelLinear)
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from vllm.model_executor.layers.rotary_embedding import get_rope
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@ -66,8 +55,134 @@ from vllm.sequence import SamplerOutput
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KVCache = Tuple[torch.Tensor, torch.Tensor]
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def promote_scalar(x: torch.Tensor) -> torch.Tensor:
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return x.view(1) if len(x.size()) == 0 else x
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class MixtralMLP(nn.Module):
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def __init__(
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self,
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num_experts: int,
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hidden_size: int,
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intermediate_size: int,
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linear_method: Optional[LinearMethodBase] = None,
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) -> None:
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super().__init__()
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self.num_experts = num_experts
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self.ffn_dim = intermediate_size
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self.hidden_dim = hidden_size
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self.w1 = ReplicatedLinear(self.hidden_dim,
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self.ffn_dim,
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bias=False,
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linear_method=linear_method)
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self.w2 = ReplicatedLinear(self.ffn_dim,
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self.hidden_dim,
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bias=False,
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linear_method=linear_method)
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self.w3 = ReplicatedLinear(self.hidden_dim,
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self.ffn_dim,
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bias=False,
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linear_method=linear_method)
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# TODO: Use vllm's SiluAndMul
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self.act_fn = nn.SiLU()
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def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
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w1_out, _ = self.w1(hidden_states)
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w1_out = self.act_fn(w1_out)
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w3_out, _ = self.w3(hidden_states)
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current_hidden_states = w1_out * w3_out
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current_hidden_states, _ = self.w2(current_hidden_states)
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return current_hidden_states
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class DummyModule(nn.Module):
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def __init__(self) -> None:
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super().__init__()
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self.w1 = nn.Linear(0, 0, bias=False)
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self.w2 = nn.Linear(0, 0, bias=False)
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self.w3 = nn.Linear(0, 0, bias=False)
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set_weight_attrs(self.w1.weight,
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{"weight_loader": self.dummy_weight_loader})
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set_weight_attrs(self.w2.weight,
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{"weight_loader": self.dummy_weight_loader})
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set_weight_attrs(self.w3.weight,
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{"weight_loader": self.dummy_weight_loader})
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def forward(self, *args, **kwargs) -> None:
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raise NotImplementedError()
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def dummy_weight_loader(self, *args, **kwargs) -> None: # pylint: disable=unused-argument
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# Noop
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return
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class MixtralMoE(nn.Module):
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def __init__(
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self,
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config: MixtralConfig,
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linear_method: Optional[LinearMethodBase] = None,
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):
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super().__init__()
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self.config = config
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self.rank = get_tensor_model_parallel_rank()
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self.tp_size = get_tensor_model_parallel_world_size()
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self.num_total_experts = config.num_local_experts
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self.top_k = config.num_experts_per_tok
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if self.tp_size > self.num_total_experts:
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raise ValueError(
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f"Tensor parallel size {self.tp_size} is greater than "
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f"the number of experts {self.num_total_experts}.")
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# Split experts equally between ranks
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self.expert_indicies = np.array_split(range(
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self.num_total_experts), self.tp_size)[self.rank].tolist()
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if not self.expert_indicies:
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raise ValueError(
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f"Rank {self.rank} has no experts assigned to it.")
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self.experts = nn.ModuleList([
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MixtralMLP(self.num_total_experts,
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config.hidden_size,
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config.intermediate_size,
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linear_method=linear_method)
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if idx in self.expert_indicies else DummyModule()
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for idx in range(self.num_total_experts)
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])
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self.gate = ReplicatedLinear(config.hidden_size,
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self.num_total_experts,
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bias=False,
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linear_method=linear_method)
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def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
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batch_size, sequence_length, hidden_dim = hidden_states.shape
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hidden_states = hidden_states.view(-1, hidden_dim)
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# router_logits: (batch * sequence_length, n_experts)
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router_logits, _ = self.gate(hidden_states)
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routing_weights = F.softmax(router_logits, dim=1, dtype=torch.float)
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routing_weights, selected_experts = torch.topk(routing_weights,
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self.top_k,
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dim=-1)
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routing_weights /= routing_weights.sum(dim=-1, keepdim=True)
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final_hidden_states = None
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for expert_idx in self.expert_indicies:
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expert_layer = self.experts[expert_idx]
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expert_mask = (selected_experts == expert_idx)
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expert_weights = (routing_weights * expert_mask).sum(dim=-1,
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keepdim=True)
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current_hidden_states = expert_layer(hidden_states).mul_(
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expert_weights)
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if final_hidden_states is None:
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final_hidden_states = current_hidden_states
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else:
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final_hidden_states.add_(current_hidden_states)
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return tensor_model_parallel_all_reduce(final_hidden_states).view(
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batch_size, sequence_length, hidden_dim)
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class MixtralAttention(nn.Module):
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@ -78,6 +193,7 @@ class MixtralAttention(nn.Module):
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num_kv_heads: int,
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max_position: int = 4096 * 32,
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rope_theta: float = 10000,
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linear_method: Optional[LinearMethodBase] = None,
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sliding_window: Optional[int] = None) -> None:
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super().__init__()
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self.hidden_size = hidden_size
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@ -102,24 +218,26 @@ class MixtralAttention(nn.Module):
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self.rope_theta = rope_theta
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self.sliding_window = sliding_window
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self.wqkv = QKVParallelLinear(
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self.qkv_proj = QKVParallelLinear(
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hidden_size,
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self.head_dim,
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self.total_num_heads,
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self.total_num_kv_heads,
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bias=False,
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linear_method=linear_method,
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)
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self.wo = RowParallelLinear(
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self.o_proj = RowParallelLinear(
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self.total_num_heads * self.head_dim,
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hidden_size,
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bias=False,
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linear_method=linear_method,
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)
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self.rotary_emb = get_rope(
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self.head_dim,
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rotary_dim=self.head_dim,
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max_position=max_position,
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base=int(self.rope_theta),
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is_neox_style=False, # weights not in HF format
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is_neox_style=True,
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)
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self.attn = PagedAttention(
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self.num_heads,
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@ -137,307 +255,112 @@ class MixtralAttention(nn.Module):
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input_metadata: InputMetadata,
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cache_event: Optional[torch.cuda.Event],
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) -> torch.Tensor:
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qkv, _ = self.wqkv(hidden_states)
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qkv, _ = self.qkv_proj(hidden_states)
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q, k, v = qkv.split([self.q_size, self.kv_size, self.kv_size], dim=-1)
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q, k = self.rotary_emb(positions, q, k)
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k_cache, v_cache = kv_cache
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attn_output = self.attn(q, k, v, k_cache, v_cache, input_metadata,
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cache_event)
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output, _ = self.wo(attn_output)
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output, _ = self.o_proj(attn_output)
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return output
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class BlockSparseMoE(nn.Module):
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"""
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Built on the paper and library Megablocks as described in
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https://arxiv.org/abs/2211.15841. This implementation is
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strictly equivalent to standard MoE with full capacity (no
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dropped tokens). It's faster since it formulates MoE operations
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in terms of block-sparse operations to accomodate imbalanced
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assignments of tokens to experts, whereas standard MoE either
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(1) drop tokens at the cost of reduced performance or (2) set
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capacity factor to number of experts and thus waste computation
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and memory on padding.
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"""
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def __init__(self, hidden_dim: int, ffn_dim: int, num_experts: int,
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top_k: int):
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super().__init__()
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self.hidden_dim = hidden_dim
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self.ffn_dim = ffn_dim
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self.num_experts = num_experts
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self.top_k = top_k
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# gating
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self.gate = nn.Linear(self.hidden_dim,
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self.num_experts,
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bias=False,
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device=torch.cuda.current_device())
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tp_size = get_tensor_model_parallel_world_size()
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assert self.ffn_dim % tp_size == 0
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self.ffn_dim_per_partition = self.ffn_dim // tp_size
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# merged expert weights, all of size (ffn_dim * n_experts, model_dim)
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self.w1 = nn.Parameter(
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torch.empty(self.ffn_dim_per_partition * self.num_experts,
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self.hidden_dim,
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device=torch.cuda.current_device()))
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set_weight_attrs(self.w1, {"weight_loader": self.moe_weight_loader})
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self.w2 = nn.Parameter(
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torch.empty(self.ffn_dim_per_partition * self.num_experts,
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self.hidden_dim,
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device=torch.cuda.current_device()))
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set_weight_attrs(self.w2, {"weight_loader": self.moe_weight_loader})
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self.w3 = nn.Parameter(
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torch.empty(self.ffn_dim_per_partition * self.num_experts,
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self.hidden_dim,
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device=torch.cuda.current_device()))
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set_weight_attrs(self.w3, {"weight_loader": self.moe_weight_loader})
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# Calculate the number of bits needed to represent the expert indices
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# so that we can pass it to radix sort.
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self.sort_end_bit = max(int(np.ceil(np.log2(self.num_experts))), 1)
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self.blocking = 128
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self.quantize_scatter_num_bits = -1
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# Calculate the number of bits needed to represent the column indices
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# in the intermediate sparse matrix.
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max_column_index = (self.ffn_dim * self.num_experts) // self.blocking
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self.transpose_sort_end_bit = max(
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int(np.ceil(np.log2(max_column_index))), 1)
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def moe_weight_loader(self, param: nn.Parameter,
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loaded_weight: torch.Tensor) -> None:
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"""
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Load the weights for the MoE linear layer.
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"""
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tp_rank = get_tensor_model_parallel_rank()
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shard_size = self.ffn_dim_per_partition
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loaded_weight = loaded_weight.view(self.num_experts, self.ffn_dim, -1)
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loaded_weight = loaded_weight[:, shard_size * tp_rank:shard_size *
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(tp_rank + 1)]
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loaded_weight = loaded_weight.reshape_as(param)
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param.data.copy_(loaded_weight)
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def sparse_transpose(
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self, size: int, row_indices,
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column_indices) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
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block_columns = size[1] // self.blocking
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# Sort row indices by column indices to get the transposed matrix's
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# column indices.
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#
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# NOTE: Our sort operation uses the same width indices as the input
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# values. To avoid overflow when we have large activation matrices
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# we cast to 32-bit before sorting.
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_, gather_indices = ops.sort(column_indices.int(),
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self.transpose_sort_end_bit)
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# There are a constant number of blocks in every row of the sparse
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# matrix. A blocks offset is:
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#
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# row_index * blocks_per_row + column_index % blocks_per_row
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|
#
|
|
|
|
|
# Once we have the block offsets ordered for transposition we can
|
|
|
|
|
# divide by blocks_per_row to get the transposed column indices.
|
|
|
|
|
column_indices_t = row_indices.gather(0, gather_indices.long())
|
|
|
|
|
block_offsets_t = gather_indices.int()
|
|
|
|
|
|
|
|
|
|
zero = torch.zeros((1, ), dtype=torch.int32, device=row_indices.device)
|
|
|
|
|
nnz_per_column = ops.histogram(column_indices, block_columns)
|
|
|
|
|
nnz_per_column = ops.inclusive_cumsum(nnz_per_column, 0)
|
|
|
|
|
offsets_t = torch.cat([zero, nnz_per_column])
|
|
|
|
|
return column_indices_t, offsets_t, block_offsets_t
|
|
|
|
|
|
|
|
|
|
def topology(self, x: torch.Tensor,
|
|
|
|
|
padded_bins: torch.Tensor) -> "stk.Matrix":
|
|
|
|
|
padded_tokens, _ = x.size()
|
|
|
|
|
assert padded_tokens % self.blocking == 0
|
|
|
|
|
assert self.ffn_dim_per_partition % self.blocking == 0
|
|
|
|
|
|
|
|
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|
# Offsets for the sparse matrix. All rows have the
|
|
|
|
|
# same number of nonzero blocks dictated by the
|
|
|
|
|
# dimensionality of a single expert.
|
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|
|
|
block_rows = padded_tokens // self.blocking
|
|
|
|
|
blocks_per_row = self.ffn_dim_per_partition // self.blocking
|
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|
|
|
offsets = torch.arange(
|
|
|
|
|
0,
|
|
|
|
|
block_rows * blocks_per_row + 1,
|
|
|
|
|
blocks_per_row,
|
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|
|
|
dtype=torch.int32,
|
|
|
|
|
device=x.device,
|
|
|
|
|
)
|
|
|
|
|
|
|
|
|
|
# Indices for the sparse matrix. The indices for
|
|
|
|
|
# the intermediate matrix are dynamic depending
|
|
|
|
|
# on the mapping of tokens to experts.
|
|
|
|
|
column_indices = ops.topology(padded_bins, self.blocking, block_rows,
|
|
|
|
|
blocks_per_row)
|
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|
|
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|
|
|
|
|
# TODO(tgale): This is unused. Remove the need for this in stk.
|
|
|
|
|
# For now, use meta init to save the device memory.
|
|
|
|
|
data = torch.empty(
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|
|
|
column_indices.numel(),
|
|
|
|
|
self.blocking,
|
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|
|
|
self.blocking,
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|
|
|
dtype=x.dtype,
|
|
|
|
|
device="meta",
|
|
|
|
|
)
|
|
|
|
|
shape = (padded_tokens, self.ffn_dim_per_partition * self.num_experts)
|
|
|
|
|
row_indices = stk.ops.row_indices(shape, data, offsets, column_indices)
|
|
|
|
|
column_indices_t, offsets_t, block_offsets_t = self.sparse_transpose(
|
|
|
|
|
shape, row_indices, column_indices)
|
|
|
|
|
return stk.Matrix(
|
|
|
|
|
shape,
|
|
|
|
|
data,
|
|
|
|
|
row_indices,
|
|
|
|
|
column_indices,
|
|
|
|
|
offsets,
|
|
|
|
|
column_indices_t,
|
|
|
|
|
offsets_t,
|
|
|
|
|
block_offsets_t,
|
|
|
|
|
)
|
|
|
|
|
|
|
|
|
|
def indices_and_padded_bins(
|
|
|
|
|
self, selected_experts: torch.Tensor
|
|
|
|
|
) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor,
|
|
|
|
|
torch.Tensor]:
|
|
|
|
|
# Sort the expert ids to produce the scatter/gather
|
|
|
|
|
# indices for the permutation.
|
|
|
|
|
selected_experts = selected_experts.int()
|
|
|
|
|
bin_ids, indices = ops.sort(selected_experts, self.sort_end_bit)
|
|
|
|
|
|
|
|
|
|
# Histogram the expert ids to identify the number of
|
|
|
|
|
# tokens routed to each expert.
|
|
|
|
|
tokens_per_expert = ops.histogram(selected_experts, self.num_experts)
|
|
|
|
|
|
|
|
|
|
# Round the token counts up to the block size used in
|
|
|
|
|
# the matrix muliplications. Caculate the starting
|
|
|
|
|
# position of each bin.
|
|
|
|
|
padded_tokens_per_expert = ops.round_up(tokens_per_expert,
|
|
|
|
|
self.blocking)
|
|
|
|
|
padded_bins = ops.inclusive_cumsum(padded_tokens_per_expert, 0)
|
|
|
|
|
padded_bins = promote_scalar(padded_bins)
|
|
|
|
|
|
|
|
|
|
# Calculate the bin bounds for the sorted tokens.
|
|
|
|
|
bins = ops.inclusive_cumsum(tokens_per_expert, 0)
|
|
|
|
|
bins = promote_scalar(bins)
|
|
|
|
|
return indices, bin_ids, bins, padded_bins, tokens_per_expert
|
|
|
|
|
|
|
|
|
|
@torch.inference_mode()
|
|
|
|
|
def forward(self, x: torch.Tensor) -> torch.Tensor:
|
|
|
|
|
"""
|
|
|
|
|
x: (sequence_length, model_dim)
|
|
|
|
|
gate_logits: (sequence_length, n_experts)
|
|
|
|
|
"""
|
|
|
|
|
# optional reshape
|
|
|
|
|
input_shape = x.shape
|
|
|
|
|
x = x.view(-1, input_shape[-1])
|
|
|
|
|
|
|
|
|
|
# gate_logits: (sequence_length, n_experts)
|
|
|
|
|
gate_logits = self.gate(x)
|
|
|
|
|
# all_probs: (sequence_length, n_experts) and upcast for softmax
|
|
|
|
|
all_probs = F.softmax(gate_logits, dim=1, dtype=torch.float)
|
|
|
|
|
# weights, selected_experts: (sequence_length, top-k)
|
|
|
|
|
weights, selected_experts = torch.topk(all_probs, self.top_k, dim=-1)
|
|
|
|
|
weights /= weights.sum(dim=-1, keepdim=True)
|
|
|
|
|
weights = weights.flatten().to(x.dtype)
|
|
|
|
|
selected_experts = selected_experts.flatten()
|
|
|
|
|
|
|
|
|
|
(indices, bin_ids, bins, padded_bins,
|
|
|
|
|
_) = self.indices_and_padded_bins(selected_experts)
|
|
|
|
|
|
|
|
|
|
# Permute tokens and pad to prepare expert computation
|
|
|
|
|
# (top_k * sequence_length + padding, model_dim)
|
|
|
|
|
x = ops.padded_gather(x, indices, bin_ids, bins, padded_bins,
|
|
|
|
|
self.top_k)
|
|
|
|
|
|
|
|
|
|
# Create the sparse matrix topology
|
|
|
|
|
with torch.no_grad():
|
|
|
|
|
topo = self.topology(x, padded_bins)
|
|
|
|
|
|
|
|
|
|
# Perform the expert computation
|
|
|
|
|
# First Dense x Dense -> Sparse for w1 and w3,
|
|
|
|
|
# (top_k * sequence_length + padding, ffn_dim * n_experts)
|
|
|
|
|
x = stk.Matrix(
|
|
|
|
|
topo.size(),
|
|
|
|
|
F.silu(stk.ops.sdd(x, self.w1.t(), topo).data) *
|
|
|
|
|
stk.ops.sdd(x, self.w3.t(), topo).data,
|
|
|
|
|
topo.row_indices,
|
|
|
|
|
topo.column_indices,
|
|
|
|
|
topo.offsets,
|
|
|
|
|
topo.column_indices_t,
|
|
|
|
|
topo.offsets_t,
|
|
|
|
|
topo.block_offsets_t,
|
|
|
|
|
)
|
|
|
|
|
|
|
|
|
|
# Then Sparse x Dense -> Dense for w2
|
|
|
|
|
# (top_k * sequence_length + padding, model_dim)
|
|
|
|
|
x = stk.ops.dsd(x, self.w2)
|
|
|
|
|
|
|
|
|
|
x = tensor_model_parallel_all_reduce(x)
|
|
|
|
|
|
|
|
|
|
# Permute back and remove padding
|
|
|
|
|
# (top_k * sequence_length, model_dim)
|
|
|
|
|
x = ops.padded_scatter(
|
|
|
|
|
x,
|
|
|
|
|
indices,
|
|
|
|
|
bin_ids,
|
|
|
|
|
weights,
|
|
|
|
|
bins,
|
|
|
|
|
padded_bins,
|
|
|
|
|
self.top_k,
|
|
|
|
|
self.quantize_scatter_num_bits,
|
|
|
|
|
)
|
|
|
|
|
return x.view(*input_shape)
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
class MixtralDecoderLayer(nn.Module):
|
|
|
|
|
|
|
|
|
|
def __init__(
|
|
|
|
|
self,
|
|
|
|
|
config: MixtralConfig,
|
|
|
|
|
linear_method: Optional[LinearMethodBase] = None,
|
|
|
|
|
) -> None:
|
|
|
|
|
super().__init__()
|
|
|
|
|
self.hidden_size = config.hidden_size
|
|
|
|
|
# Requires transformers > 4.32.0
|
|
|
|
|
rope_theta = getattr(config, "rope_theta", 10000)
|
|
|
|
|
self.attention = MixtralAttention(
|
|
|
|
|
self.self_attn = MixtralAttention(
|
|
|
|
|
hidden_size=self.hidden_size,
|
|
|
|
|
num_heads=config.num_attention_heads,
|
|
|
|
|
max_position=config.max_position_embeddings,
|
|
|
|
|
num_kv_heads=config.num_key_value_heads,
|
|
|
|
|
rope_theta=rope_theta,
|
|
|
|
|
sliding_window=config.sliding_window)
|
|
|
|
|
self.block_sparse_moe = BlockSparseMoE(
|
|
|
|
|
hidden_dim=self.hidden_size,
|
|
|
|
|
ffn_dim=config.intermediate_size,
|
|
|
|
|
num_experts=config.num_local_experts,
|
|
|
|
|
top_k=config.num_experts_per_tok,
|
|
|
|
|
)
|
|
|
|
|
self.attention_norm = RMSNorm(config.hidden_size,
|
|
|
|
|
eps=config.rms_norm_eps)
|
|
|
|
|
self.ffn_norm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
|
|
|
|
|
sliding_window=config.sliding_window,
|
|
|
|
|
linear_method=linear_method)
|
|
|
|
|
self.block_sparse_moe = MixtralMoE(config=config,
|
|
|
|
|
linear_method=linear_method)
|
|
|
|
|
self.input_layernorm = RMSNorm(config.hidden_size,
|
|
|
|
|
eps=config.rms_norm_eps)
|
|
|
|
|
self.post_attention_layernorm = RMSNorm(config.hidden_size,
|
|
|
|
|
eps=config.rms_norm_eps)
|
|
|
|
|
|
|
|
|
|
def forward(
|
|
|
|
|
self,
|
|
|
|
|
positions: torch.Tensor,
|
|
|
|
|
x: torch.Tensor,
|
|
|
|
|
hidden_states: torch.Tensor,
|
|
|
|
|
kv_cache: KVCache,
|
|
|
|
|
input_metadata: InputMetadata,
|
|
|
|
|
cache_event: Optional[torch.cuda.Event],
|
|
|
|
|
residual: Optional[torch.Tensor],
|
|
|
|
|
) -> torch.Tensor:
|
|
|
|
|
r = self.attention(
|
|
|
|
|
# Self Attention
|
|
|
|
|
if residual is None:
|
|
|
|
|
residual = hidden_states
|
|
|
|
|
hidden_states = self.input_layernorm(hidden_states)
|
|
|
|
|
else:
|
|
|
|
|
hidden_states, residual = self.input_layernorm(
|
|
|
|
|
hidden_states, residual)
|
|
|
|
|
hidden_states = self.self_attn(
|
|
|
|
|
positions=positions,
|
|
|
|
|
hidden_states=self.attention_norm(x),
|
|
|
|
|
hidden_states=hidden_states,
|
|
|
|
|
kv_cache=kv_cache,
|
|
|
|
|
input_metadata=input_metadata,
|
|
|
|
|
cache_event=cache_event,
|
|
|
|
|
)
|
|
|
|
|
h = x + r
|
|
|
|
|
r = self.block_sparse_moe(self.ffn_norm(h))
|
|
|
|
|
out = h + r
|
|
|
|
|
return out
|
|
|
|
|
|
|
|
|
|
# Fully Connected
|
|
|
|
|
hidden_states, residual = self.post_attention_layernorm(
|
|
|
|
|
hidden_states, residual)
|
|
|
|
|
hidden_states = self.block_sparse_moe(hidden_states)
|
|
|
|
|
return hidden_states, residual
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
class MixtralModel(nn.Module):
|
|
|
|
|
|
|
|
|
|
def __init__(
|
|
|
|
|
self,
|
|
|
|
|
config: MixtralConfig,
|
|
|
|
|
linear_method: Optional[LinearMethodBase] = None,
|
|
|
|
|
) -> None:
|
|
|
|
|
super().__init__()
|
|
|
|
|
self.padding_idx = config.pad_token_id
|
|
|
|
|
self.vocab_size = config.vocab_size
|
|
|
|
|
|
|
|
|
|
self.embed_tokens = VocabParallelEmbedding(
|
|
|
|
|
config.vocab_size,
|
|
|
|
|
config.hidden_size,
|
|
|
|
|
)
|
|
|
|
|
self.layers = nn.ModuleList([
|
|
|
|
|
MixtralDecoderLayer(config, linear_method=linear_method)
|
|
|
|
|
for _ in range(config.num_hidden_layers)
|
|
|
|
|
])
|
|
|
|
|
self.norm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
|
|
|
|
|
|
|
|
|
|
def forward(
|
|
|
|
|
self,
|
|
|
|
|
input_ids: torch.Tensor,
|
|
|
|
|
positions: torch.Tensor,
|
|
|
|
|
kv_caches: List[KVCache],
|
|
|
|
|
input_metadata: InputMetadata,
|
|
|
|
|
cache_events: Optional[List[torch.cuda.Event]],
|
|
|
|
|
) -> SamplerOutput:
|
|
|
|
|
hidden_states = self.embed_tokens(input_ids)
|
|
|
|
|
residual = None
|
|
|
|
|
for i in range(len(self.layers)):
|
|
|
|
|
cache_event = None if cache_events is None else cache_events[i]
|
|
|
|
|
layer = self.layers[i]
|
|
|
|
|
hidden_states, residual = layer(positions, hidden_states,
|
|
|
|
|
kv_caches[i], input_metadata,
|
|
|
|
|
cache_event, residual)
|
|
|
|
|
hidden_states, _ = self.norm(hidden_states, residual)
|
|
|
|
|
return hidden_states
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
class MixtralForCausalLM(nn.Module):
|
|
|
|
|
@ -449,23 +372,11 @@ class MixtralForCausalLM(nn.Module):
|
|
|
|
|
) -> None:
|
|
|
|
|
super().__init__()
|
|
|
|
|
self.config = config
|
|
|
|
|
assert linear_method is None
|
|
|
|
|
self.padding_idx = config.pad_token_id
|
|
|
|
|
self.vocab_size = config.vocab_size
|
|
|
|
|
self.tok_embeddings = VocabParallelEmbedding(
|
|
|
|
|
config.vocab_size,
|
|
|
|
|
config.hidden_size,
|
|
|
|
|
)
|
|
|
|
|
|
|
|
|
|
self.norm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
|
|
|
|
|
self.output = ParallelLMHead(config.vocab_size, config.hidden_size)
|
|
|
|
|
self.linear_method = linear_method
|
|
|
|
|
self.model = MixtralModel(config, linear_method)
|
|
|
|
|
self.lm_head = ParallelLMHead(config.vocab_size, config.hidden_size)
|
|
|
|
|
self.sampler = Sampler(config.vocab_size)
|
|
|
|
|
|
|
|
|
|
self.layers = nn.ModuleList([
|
|
|
|
|
MixtralDecoderLayer(config)
|
|
|
|
|
for _ in range(config.num_hidden_layers)
|
|
|
|
|
])
|
|
|
|
|
|
|
|
|
|
def forward(
|
|
|
|
|
self,
|
|
|
|
|
input_ids: torch.Tensor,
|
|
|
|
|
@ -473,21 +384,9 @@ class MixtralForCausalLM(nn.Module):
|
|
|
|
|
kv_caches: List[KVCache],
|
|
|
|
|
input_metadata: InputMetadata,
|
|
|
|
|
cache_events: Optional[List[torch.cuda.Event]],
|
|
|
|
|
) -> SamplerOutput:
|
|
|
|
|
hidden_states = self.tok_embeddings(input_ids)
|
|
|
|
|
|
|
|
|
|
# forward
|
|
|
|
|
for i in range(len(self.layers)):
|
|
|
|
|
cache_event = None if cache_events is None else cache_events[i]
|
|
|
|
|
layer = self.layers[i]
|
|
|
|
|
hidden_states = layer(
|
|
|
|
|
positions,
|
|
|
|
|
hidden_states,
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kv_caches[i],
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input_metadata,
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cache_event,
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)
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hidden_states = self.norm(hidden_states)
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) -> torch.Tensor:
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hidden_states = self.model(input_ids, positions, kv_caches,
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input_metadata, cache_events)
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return hidden_states
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def sample(
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@ -495,7 +394,7 @@ class MixtralForCausalLM(nn.Module):
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hidden_states: Optional[torch.Tensor],
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sampling_metadata: SamplingMetadata,
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) -> SamplerOutput:
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next_tokens = self.sampler(self.output.weight, hidden_states,
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next_tokens = self.sampler(self.lm_head.weight, hidden_states,
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sampling_metadata)
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return next_tokens
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@ -506,10 +405,11 @@ class MixtralForCausalLM(nn.Module):
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revision: Optional[str] = None):
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stacked_params_mapping = [
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# (param_name, shard_name, shard_id)
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("wqkv", "wq", "q"),
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("wqkv", "wk", "k"),
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("wqkv", "wv", "v"),
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("qkv_proj", "q_proj", "q"),
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("qkv_proj", "k_proj", "k"),
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("qkv_proj", "v_proj", "v"),
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]
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params_dict = dict(self.named_parameters())
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for name, loaded_weight in hf_model_weights_iterator(
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model_name_or_path, cache_dir, load_format, revision):
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Antoni Baum
GitHub