wehub-resource-sync
822 lines
31 KiB
Python
822 lines
31 KiB
Python
"""
|
|
Implementation for Llama4 architecture.
|
|
"""
|
|
|
|
import dataclasses
|
|
from typing import Any, Dict, Optional # noqa: UP035
|
|
|
|
import tvm
|
|
from tvm import tirx
|
|
from tvm.relax.frontend import nn
|
|
from tvm.relax.frontend.nn import Tensor, op
|
|
from tvm.relax.frontend.nn.llm import position_embedding
|
|
|
|
from mlc_llm import op as op_ext
|
|
from mlc_llm.model.model_utils import index_last_token
|
|
from mlc_llm.model.qwen3.qwen3_model import ACT2FN
|
|
from mlc_llm.nn import PagedKVCache, RopeMode
|
|
from mlc_llm.support import logging
|
|
from mlc_llm.support import tensor_parallel as tp
|
|
from mlc_llm.support.config import ConfigBase
|
|
from mlc_llm.support.style import bold
|
|
|
|
logger = logging.getLogger(__name__)
|
|
|
|
|
|
@dataclasses.dataclass
|
|
class Llama4TextConfig(ConfigBase):
|
|
"""Configuration of the Text portion of the Llama model."""
|
|
|
|
hidden_size: int
|
|
intermediate_size: int
|
|
num_attention_heads: int
|
|
num_hidden_layers: int
|
|
rms_norm_eps: float
|
|
rope_theta: float
|
|
use_qk_norm: bool
|
|
interleave_moe_layer_step: int
|
|
num_experts_per_tok: int
|
|
num_local_experts: int
|
|
hidden_act: str
|
|
tie_word_embeddings: bool = False
|
|
position_embedding_base: int = 0
|
|
rope_scaling: Optional[Dict[str, Any]] = None # noqa: UP006
|
|
num_key_value_heads: int = 0
|
|
head_dim: int = 0
|
|
attn_scale: float = 0.1
|
|
floor_scale: int = 8192
|
|
vocab_size: int = 202048
|
|
attention_bias: bool = False
|
|
attn_temperature_tuning: bool = True
|
|
no_rope_layers: list[int] = None
|
|
no_rope_layer_interval: int = 4
|
|
moe_layers: list[int] = None
|
|
|
|
kwargs: Dict[str, Any] = dataclasses.field(default_factory=dict) # noqa: UP006
|
|
|
|
def __post_init__(self):
|
|
if self.position_embedding_base == 0:
|
|
if "rope_theta" in self.kwargs:
|
|
self.position_embedding_base = self.kwargs.pop("rope_theta")
|
|
else:
|
|
self.position_embedding_base = 10000
|
|
if self.rope_scaling is not None:
|
|
if "rope_type" not in self.rope_scaling:
|
|
self.rope_scaling = None
|
|
else:
|
|
assert self.rope_scaling["rope_type"] == "llama3", (
|
|
f"Unsupported RoPE scaling type {self.rope_scaling['rope_type']} for Llama"
|
|
)
|
|
|
|
# Define which layers to avoid RoPE
|
|
if self.no_rope_layers == []:
|
|
self.no_rope_layers = None
|
|
|
|
default_no_rope_layers = [
|
|
int((layer_idx + 1) % self.no_rope_layer_interval != 0)
|
|
for layer_idx in range(self.num_hidden_layers)
|
|
]
|
|
|
|
self.no_rope_layers = self.no_rope_layers if self.no_rope_layers else default_no_rope_layers
|
|
|
|
# Define which layers to apply MoE
|
|
self.moe_layers = (
|
|
self.moe_layers
|
|
if self.moe_layers is not None
|
|
else list(
|
|
range(
|
|
self.interleave_moe_layer_step - 1,
|
|
self.num_hidden_layers,
|
|
self.interleave_moe_layer_step,
|
|
)
|
|
)
|
|
)
|
|
|
|
|
|
@dataclasses.dataclass
|
|
class Llama4Config(ConfigBase):
|
|
"""Configuration of the Llama model."""
|
|
|
|
text_config: Llama4TextConfig
|
|
tensor_parallel_shards: int = 1
|
|
context_window_size: int = 0
|
|
pipeline_parallel_stages: int = 1
|
|
prefill_chunk_size: int = 0
|
|
max_batch_size: int = 1
|
|
disaggregation: bool = False
|
|
max_position_embeddings = 4096 * 32
|
|
vocab_size: int = 202048
|
|
|
|
kwargs: Dict[str, Any] = dataclasses.field(default_factory=dict) # noqa: UP006
|
|
|
|
def __post_init__(self) -> None:
|
|
text_config_dict: Dict[str, Any] # noqa: UP006
|
|
if isinstance(self.text_config, ConfigBase):
|
|
text_config_dict = dataclasses.asdict(self.text_config)
|
|
else:
|
|
text_config_dict = dict(self.text_config)
|
|
|
|
for k, v in text_config_dict.pop("kwargs", {}).items():
|
|
text_config_dict[k] = v
|
|
|
|
self.text_config = Llama4TextConfig.from_dict(text_config_dict)
|
|
|
|
if self.context_window_size == 0:
|
|
# Fall back to max_position_embeddings
|
|
|
|
self.context_window_size = self.max_position_embeddings
|
|
logger.info(
|
|
"%s not found in config.json. Falling back to %s (%d)",
|
|
bold("context_window_size"),
|
|
bold("max_position_embeddings"),
|
|
self.context_window_size,
|
|
)
|
|
|
|
if self.text_config.num_key_value_heads == 0:
|
|
self.text_config.num_key_value_heads = self.text_config.num_attention_heads
|
|
if self.text_config.head_dim == 0:
|
|
self.text_config.head_dim = (
|
|
self.text_config.hidden_size // self.text_config.num_attention_heads
|
|
)
|
|
assert self.text_config.num_attention_heads % self.text_config.num_key_value_heads == 0
|
|
if self.prefill_chunk_size == 0:
|
|
logger.info(
|
|
"%s defaults to %d",
|
|
bold("prefill_chunk_size"),
|
|
min(self.context_window_size, 8192),
|
|
)
|
|
self.prefill_chunk_size = min(self.context_window_size, 8192)
|
|
elif self.prefill_chunk_size > self.context_window_size:
|
|
logger.info(
|
|
"Overriding %s from %d to %d",
|
|
bold("prefill_chunk_size"),
|
|
self.prefill_chunk_size,
|
|
min(self.context_window_size, 8192),
|
|
)
|
|
self.prefill_chunk_size = min(self.context_window_size, 8192)
|
|
|
|
|
|
class Llama4TextMLP(nn.Module):
|
|
def __init__(self, config: Llama4Config):
|
|
super().__init__()
|
|
if config.text_config.intermediate_size % config.tensor_parallel_shards != 0:
|
|
raise ValueError(
|
|
f"Cannot split MLP intermediate size {config.text_config.intermediate_size} "
|
|
f"evenly to {config.tensor_parallel_shards} GPUs."
|
|
)
|
|
self.intermediate_size = (
|
|
config.text_config.intermediate_size // config.tensor_parallel_shards
|
|
)
|
|
self.gate_up_proj = nn.Linear(
|
|
in_features=config.text_config.hidden_size,
|
|
out_features=2 * self.intermediate_size,
|
|
bias=False,
|
|
)
|
|
self.down_proj = nn.Linear(
|
|
self.intermediate_size, config.text_config.hidden_size, bias=False
|
|
)
|
|
|
|
def forward(self, x: Tensor):
|
|
concat_x1_x2 = self.gate_up_proj(x)
|
|
x1, x2 = op.split(concat_x1_x2, 2, axis=-1)
|
|
inter_out = op.silu(x1) * x2
|
|
|
|
return self.down_proj(inter_out)
|
|
|
|
|
|
class LlamaEmbedding(nn.Embedding):
|
|
"""The embedding module that can be shared with the final lm_head. From Qwen2Embedding."""
|
|
|
|
def lm_head_forward(self, x: nn.Tensor):
|
|
"""The lm_head forwarding, which transposes the weight and multiplies
|
|
with the input tensor.
|
|
"""
|
|
weight = nn.op.permute_dims(self.weight)
|
|
return nn.op.matmul(x, weight, out_dtype="float32")
|
|
|
|
|
|
class Llama4TextL2Norm(nn.Module):
|
|
def __init__(self, eps, hidden_size):
|
|
self.eps = eps
|
|
self.hidden_size = hidden_size
|
|
|
|
def forward(self, x):
|
|
weight = op.ones((self.hidden_size,), dtype=x.dtype)
|
|
return op.rms_norm(x, weight=weight, axes=[-1], epsilon=self.eps)
|
|
|
|
|
|
class Llama4TextAttention(nn.Module):
|
|
def __init__(self, config: Llama4Config, layer_idx):
|
|
self.head_dim = config.text_config.head_dim
|
|
self.attn_scale = config.text_config.attn_scale
|
|
self.floor_scale = config.text_config.floor_scale
|
|
self.num_attention_heads = config.text_config.num_attention_heads
|
|
self.num_kv_heads = config.text_config.num_key_value_heads
|
|
self.num_q_heads = config.text_config.num_attention_heads // config.tensor_parallel_shards
|
|
assert config.text_config.num_key_value_heads % config.tensor_parallel_shards == 0, (
|
|
f"num_kv_heads({config.text_config.num_key_value_heads}) must be divisible by "
|
|
f"tensor_parallel_shards"
|
|
)
|
|
|
|
assert config.text_config.num_key_value_heads >= config.tensor_parallel_shards, (
|
|
f"Too large tensor_parallel_shards, must be smaller than "
|
|
f"{config.text_config.num_key_value_heads}"
|
|
)
|
|
self.num_kv_heads = config.text_config.num_key_value_heads // config.tensor_parallel_shards
|
|
self.q_proj = nn.Linear(
|
|
config.text_config.hidden_size,
|
|
self.num_q_heads * self.head_dim,
|
|
bias=config.text_config.attention_bias,
|
|
)
|
|
self.k_proj = nn.Linear(
|
|
config.text_config.hidden_size,
|
|
self.num_kv_heads * self.head_dim,
|
|
bias=config.text_config.attention_bias,
|
|
)
|
|
self.v_proj = nn.Linear(
|
|
config.text_config.hidden_size,
|
|
self.num_kv_heads * self.head_dim,
|
|
bias=config.text_config.attention_bias,
|
|
)
|
|
self.o_proj = nn.Linear(
|
|
self.num_q_heads * self.head_dim,
|
|
config.text_config.hidden_size,
|
|
bias=config.text_config.attention_bias,
|
|
)
|
|
|
|
self.attn_temperature_tuning = config.text_config.attn_temperature_tuning
|
|
self.use_rope = config.text_config.no_rope_layers[layer_idx]
|
|
|
|
self.layer_idx = layer_idx
|
|
|
|
self.rope_theta = config.text_config.rope_theta
|
|
self.rope_scaling = config.text_config.rope_scaling
|
|
self.rope_scaling["rope_type"] = "llama4"
|
|
|
|
self.use_qk_norm = config.text_config.use_qk_norm
|
|
self.rms_norm_eps = config.text_config.rms_norm_eps
|
|
|
|
self.q_norm = Llama4TextL2Norm(self.rms_norm_eps, self.head_dim)
|
|
self.k_norm = Llama4TextL2Norm(self.rms_norm_eps, self.head_dim)
|
|
|
|
def forward(
|
|
self,
|
|
hidden_states: Tensor,
|
|
paged_kv_cache: PagedKVCache,
|
|
layer_id: int,
|
|
cache_position,
|
|
):
|
|
d, h_q = self.head_dim, self.num_q_heads
|
|
b, s, _ = hidden_states.shape
|
|
# QKV Projection
|
|
query_states = op.reshape(self.q_proj(hidden_states), (b, s, -1, d))
|
|
key_states = op.reshape(self.k_proj(hidden_states), (b, s, -1, d))
|
|
value_states = op.reshape(self.v_proj(hidden_states), (b, s, -1, d))
|
|
|
|
if self.use_rope:
|
|
qkv = op.concat([query_states, key_states, value_states], dim=2)
|
|
|
|
apply_rope = tvm.tirx.IntImm("int64", 1)
|
|
|
|
rotary_emb = position_embedding.llama4_rope_with_position_map(
|
|
theta=self.rope_theta,
|
|
scale=1.0,
|
|
head_dim=self.head_dim,
|
|
num_q_heads=self.num_q_heads,
|
|
num_kv_heads=self.num_kv_heads,
|
|
dtype=query_states.dtype,
|
|
rope_scaling=self.rope_scaling,
|
|
)
|
|
|
|
query_states, key_states, value_states = op.tensor_ir_op(
|
|
rotary_emb,
|
|
"llama4_rope_with_position_map",
|
|
args=[op.squeeze(qkv, axis=0), cache_position, apply_rope],
|
|
out=(
|
|
Tensor.placeholder((s, h_q, d), query_states.dtype),
|
|
Tensor.placeholder((s, self.num_kv_heads, d), query_states.dtype),
|
|
Tensor.placeholder((s, self.num_kv_heads, d), query_states.dtype),
|
|
),
|
|
)
|
|
query_states = query_states.reshape(b, s, h_q, d)
|
|
key_states = key_states.reshape(b, s, self.num_kv_heads, d)
|
|
value_states = value_states.reshape(b, s, self.num_kv_heads, d)
|
|
|
|
if self.use_qk_norm and self.use_rope:
|
|
query_states = self.q_norm(query_states)
|
|
key_states = self.k_norm(key_states)
|
|
|
|
if self.attn_temperature_tuning and not self.use_rope:
|
|
attn_scales = (
|
|
op.log(
|
|
op.floor(
|
|
(op.astype(cache_position, query_states.dtype) + 1.0) / self.floor_scale
|
|
)
|
|
+ 1.0
|
|
)
|
|
* self.attn_scale
|
|
+ 1.0
|
|
)
|
|
|
|
attn_scales = op.broadcast_to(attn_scales.reshape(1, s, 1, 1), (b, s, 1, 1))
|
|
query_states = query_states * attn_scales
|
|
|
|
qkv = op.concat([query_states, key_states, value_states], dim=2)
|
|
|
|
# Attention
|
|
output = op.reshape(
|
|
paged_kv_cache.attention_with_fused_qkv(
|
|
layer_id, qkv, self.num_q_heads, sm_scale=self.head_dim**-0.5
|
|
),
|
|
(b, s, h_q * d),
|
|
)
|
|
return self.o_proj(output)
|
|
|
|
|
|
class Llama4TextExperts(nn.Module):
|
|
def __init__(self, config: Llama4Config):
|
|
self.num_experts = config.text_config.num_local_experts
|
|
self.intermediate_size = (
|
|
config.text_config.intermediate_size // config.tensor_parallel_shards
|
|
)
|
|
self.hidden_size = config.text_config.hidden_size
|
|
self.expert_dim = self.intermediate_size
|
|
|
|
self.gate_up_proj = nn.Parameter(
|
|
shape=(self.num_experts, self.hidden_size, 2 * self.expert_dim)
|
|
)
|
|
self.down_proj = nn.Parameter(shape=(self.num_experts, self.expert_dim, self.hidden_size))
|
|
self.act_fn = ACT2FN[config.text_config.hidden_act]
|
|
|
|
def forward(self, hidden_states):
|
|
hidden_states = hidden_states.reshape(self.gate_up_proj.shape[0], -1, self.hidden_size)
|
|
gate_up = op.matmul(hidden_states, self.gate_up_proj)
|
|
gate, up = op.chunk(gate_up, chunks=2, dim=-1)
|
|
next_states = op.matmul((up * self.act_fn(gate)), self.down_proj)
|
|
next_states = next_states.reshape(-1, self.hidden_size)
|
|
return next_states
|
|
|
|
|
|
class Llama4Router(nn.Module):
|
|
def __init__(self, config: Llama4Config):
|
|
self.num_experts = config.text_config.num_local_experts
|
|
self.top_k = config.text_config.num_experts_per_tok
|
|
self.intermediate_size = self.num_experts // config.tensor_parallel_shards
|
|
self.router = nn.Linear(
|
|
in_features=config.text_config.hidden_size,
|
|
out_features=self.intermediate_size,
|
|
bias=False,
|
|
)
|
|
|
|
def forward(self, hidden_states):
|
|
router_logits = self.router(hidden_states)
|
|
router_top_value, router_indices = op_ext.moe_misc.gating_topk(router_logits, self.top_k)
|
|
|
|
j_axis = op.arange(0, self.num_experts)
|
|
j_axis = op.unsqueeze(j_axis, 0)
|
|
idx_exp = op.unsqueeze(router_indices, -1)
|
|
mask = op.equal(idx_exp, j_axis)
|
|
val_exp = op.unsqueeze(router_top_value, -1)
|
|
neg_inf = op.full(mask.shape, -1e9, dtype=hidden_states.dtype)
|
|
masked_vals = op.where(mask, val_exp, neg_inf)
|
|
router_scores = op.max(masked_vals, axis=1)
|
|
|
|
router_scores = op.sigmoid(router_scores)
|
|
return router_scores, router_logits
|
|
|
|
|
|
class Llama4TextMoe(nn.Module):
|
|
def __init__(self, config: Llama4Config):
|
|
self.top_k = config.text_config.num_experts_per_tok
|
|
self.hidden_dim = config.text_config.hidden_size
|
|
self.num_experts = config.text_config.num_local_experts
|
|
self.experts = Llama4TextExperts(config)
|
|
self.router = Llama4Router(config)
|
|
self.shared_expert = Llama4TextMLP(config)
|
|
|
|
def forward(self, hidden_states):
|
|
hidden_states = hidden_states.reshape(-1, self.hidden_dim)
|
|
router_scores, _ = self.router(hidden_states)
|
|
|
|
routed_in = op.broadcast_to(
|
|
hidden_states.reshape(1, *hidden_states.shape),
|
|
[router_scores.shape[1], *hidden_states.shape],
|
|
)
|
|
routed_in = routed_in.reshape(-1, self.hidden_dim)
|
|
|
|
routed_in = routed_in * op.permute_dims(router_scores, axes=[1, 0]).reshape(-1, 1)
|
|
|
|
routed_out = self.experts(routed_in)
|
|
out = self.shared_expert(hidden_states)
|
|
|
|
out += op.sum(routed_out.reshape(router_scores.shape[1], -1, routed_out.shape[-1]), axis=0)
|
|
|
|
return out
|
|
|
|
|
|
class Llama4TextDecoderLayer(nn.Module):
|
|
def __init__(self, config: Llama4Config, layer_idx):
|
|
rms_norm_eps = config.text_config.rms_norm_eps
|
|
self.self_attn = Llama4TextAttention(config, layer_idx)
|
|
self.is_moe_layer = layer_idx in config.text_config.moe_layers
|
|
if self.is_moe_layer: # the 128E model interleaves dense / sparse
|
|
self.feed_forward = Llama4TextMoe(config)
|
|
else:
|
|
self.feed_forward = Llama4TextMLP(config)
|
|
|
|
self.input_layernorm = nn.RMSNorm(
|
|
config.text_config.hidden_size, -1, rms_norm_eps, bias=False
|
|
)
|
|
self.post_attention_layernorm = nn.RMSNorm(
|
|
config.text_config.hidden_size, -1, rms_norm_eps, bias=False
|
|
)
|
|
|
|
def _set_tp():
|
|
def _set(layer, hint):
|
|
if hasattr(layer, "weight"):
|
|
layer.weight.attrs["shard_strategy"] = hint
|
|
else:
|
|
layer.attrs["shard_strategy"] = hint
|
|
|
|
_set(self.self_attn.q_proj, tp.ShardSingleDim("_shard_q", dim=0))
|
|
_set(self.self_attn.k_proj, tp.ShardSingleDim("_shard_k", dim=0))
|
|
_set(self.self_attn.v_proj, tp.ShardSingleDim("_shard_v", dim=0))
|
|
_set(self.self_attn.o_proj, tp.ShardSingleDim("_shard_o", dim=1))
|
|
|
|
if isinstance(self.feed_forward, Llama4TextMLP):
|
|
i = self.feed_forward.intermediate_size
|
|
_set(
|
|
self.feed_forward.gate_up_proj,
|
|
tp.ShardSingleDim("_shard_mlp_up", segs=[i, i], dim=0),
|
|
)
|
|
_set(
|
|
self.feed_forward.down_proj,
|
|
tp.ShardSingleDim("_shard_mlp_down", dim=1),
|
|
)
|
|
else:
|
|
assert isinstance(self.feed_forward, Llama4TextMoe)
|
|
i = self.feed_forward.shared_expert.intermediate_size
|
|
_set(
|
|
self.feed_forward.shared_expert.gate_up_proj,
|
|
tp.ShardSingleDim("_shard_mlp_up", segs=[i, i], dim=0),
|
|
)
|
|
_set(
|
|
self.feed_forward.shared_expert.down_proj,
|
|
tp.ShardSingleDim("_shard_mlp_down", dim=1),
|
|
)
|
|
|
|
j = self.feed_forward.experts.intermediate_size
|
|
_set(
|
|
self.feed_forward.experts.gate_up_proj,
|
|
tp.ShardSingleDim("_shard_expert_mlp_up", segs=[j, j], dim=2),
|
|
)
|
|
_set(
|
|
self.feed_forward.experts.down_proj,
|
|
tp.ShardSingleDim("_shard_expert_mlp_down", dim=1),
|
|
)
|
|
|
|
_set(
|
|
self.feed_forward.router.router,
|
|
tp.ShardSingleDim("_shard_router", dim=0),
|
|
)
|
|
|
|
self.tensor_parallel_shards = config.tensor_parallel_shards
|
|
_set_tp()
|
|
|
|
def forward(
|
|
self,
|
|
hidden_states: Tensor,
|
|
paged_kv_cache: PagedKVCache,
|
|
layer_id: int,
|
|
cache_position,
|
|
):
|
|
out = self.self_attn(
|
|
self.input_layernorm(hidden_states),
|
|
paged_kv_cache,
|
|
layer_id,
|
|
cache_position,
|
|
)
|
|
hidden_states = self._apply_residual(out, residual=hidden_states)
|
|
out = self.feed_forward(self.post_attention_layernorm(hidden_states))
|
|
|
|
hidden_states = self._apply_residual(
|
|
op.reshape(out, hidden_states.shape), residual=hidden_states
|
|
)
|
|
|
|
return hidden_states
|
|
|
|
def _apply_residual(self, out, residual):
|
|
if self.tensor_parallel_shards > 1:
|
|
return op.ccl_allreduce(out, "sum") + residual
|
|
return out + residual
|
|
|
|
|
|
class Llama4TextModel(nn.Module):
|
|
def __init__(self, config: Llama4Config):
|
|
assert config.text_config.hidden_size % config.text_config.num_attention_heads == 0
|
|
self.embed_tokens = LlamaEmbedding("vocab_size", config.text_config.hidden_size)
|
|
self.layers = nn.ModuleList(
|
|
[
|
|
Llama4TextDecoderLayer(config, layer_idx)
|
|
for layer_idx in range(config.text_config.num_hidden_layers)
|
|
]
|
|
)
|
|
self.norm = nn.RMSNorm(
|
|
config.text_config.hidden_size,
|
|
-1,
|
|
config.text_config.rms_norm_eps,
|
|
bias=False,
|
|
)
|
|
|
|
def forward(self, input_embed: Tensor, paged_kv_cache: PagedKVCache):
|
|
hidden_states = input_embed
|
|
cache_position = paged_kv_cache.get_query_positions(
|
|
input_embed.shape[0] * input_embed.shape[1]
|
|
)
|
|
|
|
for layer_id, layer in enumerate(self.layers):
|
|
hidden_states = layer(hidden_states, paged_kv_cache, layer_id, cache_position)
|
|
hidden_states = self.norm(hidden_states)
|
|
return hidden_states
|
|
|
|
|
|
class Llama4ForCausalLM(nn.Module):
|
|
def __init__(self, config: Llama4Config):
|
|
self.text_config = config.text_config
|
|
self.model = Llama4TextModel(config)
|
|
self.tie_word_embeddings = self.text_config.tie_word_embeddings
|
|
if not self.text_config.tie_word_embeddings:
|
|
self.lm_head = nn.Linear(self.text_config.hidden_size, "vocab_size", bias=False)
|
|
self.num_hidden_layers = self.text_config.num_hidden_layers
|
|
self.num_attention_heads = self.text_config.num_attention_heads
|
|
self.num_key_value_heads = self.text_config.num_key_value_heads
|
|
self.head_dim = self.text_config.head_dim
|
|
self.hidden_size = self.text_config.hidden_size
|
|
self.vocab_size = self.text_config.vocab_size
|
|
self.rope_scaling = self.text_config.rope_scaling
|
|
self.rope_theta = self.text_config.position_embedding_base
|
|
self.tensor_parallel_shards = config.tensor_parallel_shards
|
|
self.disaggregation = config.disaggregation
|
|
self.dtype = "float32"
|
|
|
|
def to(self, dtype: Optional[str] = None):
|
|
super().to(dtype=dtype)
|
|
if dtype is not None:
|
|
self.dtype = dtype
|
|
|
|
def batch_forward(
|
|
self,
|
|
input_embeds: Tensor,
|
|
paged_kv_cache: PagedKVCache,
|
|
logit_positions: Optional[Tensor] = None,
|
|
):
|
|
op_ext.configure()
|
|
|
|
hidden_states = self.model(input_embeds, paged_kv_cache)
|
|
if logit_positions is not None:
|
|
if self.tensor_parallel_shards > 1:
|
|
logit_positions = op.ccl_broadcast_from_worker0(logit_positions)
|
|
hidden_states = op.take(hidden_states, logit_positions, axis=1)
|
|
return self.get_logits(hidden_states)
|
|
|
|
def batch_forward_to_last_hidden_states(
|
|
self,
|
|
input_embeds: Tensor,
|
|
paged_kv_cache: PagedKVCache,
|
|
):
|
|
op_ext.configure()
|
|
|
|
hidden_states = self.model(input_embeds, paged_kv_cache)
|
|
return hidden_states
|
|
|
|
def embed(self, input_ids: Tensor):
|
|
if self.tensor_parallel_shards > 1:
|
|
input_ids = op.ccl_broadcast_from_worker0(input_ids)
|
|
return self.model.embed_tokens(input_ids)
|
|
|
|
def get_logits(self, hidden_states: Tensor):
|
|
op_ext.configure()
|
|
if self.tie_word_embeddings:
|
|
logits = self.model.embed_tokens.lm_head_forward(hidden_states)
|
|
else:
|
|
logits = self.lm_head(hidden_states)
|
|
if logits.dtype != "float32":
|
|
logits = logits.astype("float32")
|
|
return logits
|
|
|
|
def batch_select_last_hidden_states(self, hidden_states: Tensor, logit_positions: Tensor):
|
|
op_ext.configure()
|
|
if self.tensor_parallel_shards > 1:
|
|
logit_positions = op.ccl_broadcast_from_worker0(logit_positions)
|
|
hidden_states = op.take(hidden_states, logit_positions, axis=0)
|
|
return hidden_states
|
|
|
|
def prefill(self, input_embed: Tensor, paged_kv_cache: PagedKVCache):
|
|
op_ext.configure()
|
|
|
|
hidden_states = self.model(input_embed, paged_kv_cache)
|
|
hidden_states = index_last_token(hidden_states)
|
|
logits = self.get_logits(hidden_states)
|
|
return logits, paged_kv_cache
|
|
|
|
def decode(self, input_embed: Tensor, paged_kv_cache: PagedKVCache):
|
|
op_ext.configure()
|
|
|
|
hidden_states = self.model(input_embed, paged_kv_cache)
|
|
logits = self.get_logits(hidden_states)
|
|
return logits, paged_kv_cache
|
|
|
|
def prefill_to_last_hidden_states(self, input_embed: Tensor, paged_kv_cache: PagedKVCache):
|
|
op_ext.configure()
|
|
|
|
hidden_states = self.model(input_embed, paged_kv_cache)
|
|
return hidden_states, paged_kv_cache
|
|
|
|
def decode_to_last_hidden_states(self, input_embed: Tensor, paged_kv_cache: PagedKVCache):
|
|
op_ext.configure()
|
|
|
|
hidden_states = self.model(input_embed, paged_kv_cache)
|
|
return hidden_states, paged_kv_cache
|
|
|
|
def batch_prefill(
|
|
self,
|
|
input_embeds: Tensor,
|
|
logit_positions: Tensor,
|
|
paged_kv_cache: PagedKVCache,
|
|
):
|
|
logits = self.batch_forward(input_embeds, paged_kv_cache, logit_positions)
|
|
return logits, paged_kv_cache
|
|
|
|
def batch_decode(self, input_embeds: Tensor, paged_kv_cache: PagedKVCache):
|
|
logits = self.batch_forward(input_embeds, paged_kv_cache)
|
|
return logits, paged_kv_cache
|
|
|
|
def batch_verify(self, input_embeds: Tensor, paged_kv_cache: PagedKVCache):
|
|
logits = self.batch_forward(input_embeds, paged_kv_cache)
|
|
return logits, paged_kv_cache
|
|
|
|
def batch_prefill_to_last_hidden_states(
|
|
self, input_embeds: Tensor, paged_kv_cache: PagedKVCache
|
|
):
|
|
hidden_states = self.batch_forward_to_last_hidden_states(input_embeds, paged_kv_cache)
|
|
return hidden_states, paged_kv_cache
|
|
|
|
def batch_decode_to_last_hidden_states(
|
|
self, input_embeds: Tensor, paged_kv_cache: PagedKVCache
|
|
):
|
|
hidden_states = self.batch_forward_to_last_hidden_states(input_embeds, paged_kv_cache)
|
|
return hidden_states, paged_kv_cache
|
|
|
|
def batch_verify_to_last_hidden_states(
|
|
self, input_embeds: Tensor, paged_kv_cache: PagedKVCache
|
|
):
|
|
hidden_states = self.batch_forward_to_last_hidden_states(input_embeds, paged_kv_cache)
|
|
return hidden_states, paged_kv_cache
|
|
|
|
def create_paged_kv_cache(
|
|
self,
|
|
max_batch_size: tirx.Var,
|
|
max_total_seq_len: tirx.Var,
|
|
prefill_chunk_size: tirx.Var,
|
|
page_size: tirx.Var,
|
|
support_sliding_window: tirx.Var,
|
|
) -> PagedKVCache:
|
|
return PagedKVCache.create_generic(
|
|
attn_kind="mha",
|
|
max_batch_size=max_batch_size,
|
|
max_total_seq_len=max_total_seq_len,
|
|
prefill_chunk_size=prefill_chunk_size,
|
|
page_size=page_size,
|
|
support_sliding_window=support_sliding_window,
|
|
num_hidden_layers=self.num_hidden_layers,
|
|
num_attention_heads=self.num_attention_heads // self.tensor_parallel_shards,
|
|
num_key_value_heads=self.num_key_value_heads // self.tensor_parallel_shards,
|
|
qk_head_dim=self.head_dim,
|
|
v_head_dim=self.head_dim,
|
|
rope_mode=RopeMode.NONE,
|
|
rope_scale=1,
|
|
rope_theta=self.rope_theta,
|
|
rope_scaling=self.rope_scaling,
|
|
enable_disaggregation=self.disaggregation,
|
|
dtype=self.dtype,
|
|
)
|
|
|
|
def get_default_spec(self):
|
|
mod_spec = {
|
|
"embed": {
|
|
"input_ids": nn.spec.Tensor(["seq_len"], "int32"),
|
|
"$": {
|
|
"param_mode": "packed",
|
|
"effect_mode": "none",
|
|
},
|
|
},
|
|
"get_logits": {
|
|
"hidden_states": nn.spec.Tensor(["seq_len", self.hidden_size], self.dtype),
|
|
"$": {
|
|
"param_mode": "packed",
|
|
"effect_mode": "none",
|
|
},
|
|
},
|
|
"batch_select_last_hidden_states": {
|
|
"hidden_states": nn.spec.Tensor(["seq_len", self.hidden_size], self.dtype),
|
|
"logit_positions": nn.spec.Tensor(["batch_size"], "int32"),
|
|
"$": {
|
|
"param_mode": "none",
|
|
"effect_mode": "none",
|
|
},
|
|
},
|
|
"prefill": {
|
|
"input_embed": nn.spec.Tensor([1, "seq_len", self.hidden_size], self.dtype),
|
|
"paged_kv_cache": nn.spec.Object(object_type=PagedKVCache),
|
|
"$": {
|
|
"param_mode": "packed",
|
|
"effect_mode": "none",
|
|
},
|
|
},
|
|
"decode": {
|
|
"input_embed": nn.spec.Tensor([1, 1, self.hidden_size], self.dtype),
|
|
"paged_kv_cache": nn.spec.Object(object_type=PagedKVCache),
|
|
"$": {
|
|
"param_mode": "packed",
|
|
"effect_mode": "none",
|
|
},
|
|
},
|
|
"prefill_to_last_hidden_states": {
|
|
"input_embed": nn.spec.Tensor([1, "seq_len", self.hidden_size], self.dtype),
|
|
"paged_kv_cache": nn.spec.Object(object_type=PagedKVCache),
|
|
"$": {
|
|
"param_mode": "packed",
|
|
"effect_mode": "none",
|
|
},
|
|
},
|
|
"decode_to_last_hidden_states": {
|
|
"input_embed": nn.spec.Tensor([1, 1, self.hidden_size], self.dtype),
|
|
"paged_kv_cache": nn.spec.Object(object_type=PagedKVCache),
|
|
"$": {
|
|
"param_mode": "packed",
|
|
"effect_mode": "none",
|
|
},
|
|
},
|
|
"batch_prefill": {
|
|
"input_embeds": nn.spec.Tensor([1, "seq_len", self.hidden_size], self.dtype),
|
|
"logit_positions": nn.spec.Tensor(["batch_size"], "int32"),
|
|
"paged_kv_cache": nn.spec.Object(object_type=PagedKVCache),
|
|
"$": {
|
|
"param_mode": "packed",
|
|
"effect_mode": "none",
|
|
},
|
|
},
|
|
"batch_decode": {
|
|
"input_embeds": nn.spec.Tensor(["batch_size", 1, self.hidden_size], self.dtype),
|
|
"paged_kv_cache": nn.spec.Object(object_type=PagedKVCache),
|
|
"$": {
|
|
"param_mode": "packed",
|
|
"effect_mode": "none",
|
|
},
|
|
},
|
|
"batch_verify": {
|
|
"input_embeds": nn.spec.Tensor([1, "seq_len", self.hidden_size], self.dtype),
|
|
"paged_kv_cache": nn.spec.Object(object_type=PagedKVCache),
|
|
"$": {
|
|
"param_mode": "packed",
|
|
"effect_mode": "none",
|
|
},
|
|
},
|
|
"batch_prefill_to_last_hidden_states": {
|
|
"input_embeds": nn.spec.Tensor([1, "seq_len", self.hidden_size], self.dtype),
|
|
"paged_kv_cache": nn.spec.Object(object_type=PagedKVCache),
|
|
"$": {
|
|
"param_mode": "packed",
|
|
"effect_mode": "none",
|
|
},
|
|
},
|
|
"batch_decode_to_last_hidden_states": {
|
|
"input_embeds": nn.spec.Tensor(["batch_size", 1, self.hidden_size], self.dtype),
|
|
"paged_kv_cache": nn.spec.Object(object_type=PagedKVCache),
|
|
"$": {
|
|
"param_mode": "packed",
|
|
"effect_mode": "none",
|
|
},
|
|
},
|
|
"batch_verify_to_last_hidden_states": {
|
|
"input_embeds": nn.spec.Tensor([1, "seq_len", self.hidden_size], self.dtype),
|
|
"paged_kv_cache": nn.spec.Object(object_type=PagedKVCache),
|
|
"$": {
|
|
"param_mode": "packed",
|
|
"effect_mode": "none",
|
|
},
|
|
},
|
|
"create_paged_kv_cache": {
|
|
"max_batch_size": int,
|
|
"max_total_seq_len": int,
|
|
"prefill_chunk_size": int,
|
|
"page_size": int,
|
|
"support_sliding_window": int,
|
|
"$": {
|
|
"param_mode": "none",
|
|
"effect_mode": "none",
|
|
},
|
|
},
|
|
}
|
|
return nn.spec.ModuleSpec.from_raw(mod_spec, self)
|