wehub-resource-sync
489 lines
19 KiB
Python
489 lines
19 KiB
Python
"""
|
|
Implementation for Phi architecture.
|
|
"""
|
|
|
|
import dataclasses
|
|
from typing import Any, Dict, Optional, Union # noqa: UP035
|
|
|
|
from tvm import tirx
|
|
from tvm.relax.frontend import nn
|
|
from tvm.relax.frontend.nn import Tensor, op
|
|
|
|
from mlc_llm import op as op_ext
|
|
from mlc_llm.model.model_utils import index_last_token
|
|
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 Phi1Config(ConfigBase):
|
|
"""Configuration of the Phi-1/Phi-1.5 model."""
|
|
|
|
vocab_size: int = 51200
|
|
hidden_size: int = 2048
|
|
intermediate_size: int = 8192
|
|
num_hidden_layers: int = 24
|
|
num_attention_heads: int = 32
|
|
layer_norm_eps: float = 1e-5
|
|
position_embedding_base: int = 0
|
|
partial_rotary_factor: float = 0.5
|
|
num_key_value_heads: int = 0
|
|
context_window_size: int = 0
|
|
prefill_chunk_size: int = 0
|
|
head_dim: int = 0
|
|
tensor_parallel_shards: int = 1
|
|
max_batch_size: int = 1
|
|
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.context_window_size == 0:
|
|
for name in ["max_position_embeddings", "max_sequence_length"]:
|
|
if name in self.kwargs:
|
|
self.context_window_size = self.kwargs.pop(name)
|
|
logger.info(
|
|
"%s not found in config.json. Falling back to %s (%d)",
|
|
bold("context_window_size"),
|
|
bold(name),
|
|
self.context_window_size,
|
|
)
|
|
break
|
|
else:
|
|
raise ValueError(
|
|
"Unable to determine the maximum sequence length, because none of "
|
|
"`context_window_size`, `max_position_embeddings` or `max_sequence_length` is "
|
|
"provided in `config.json`."
|
|
)
|
|
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)
|
|
if self.num_key_value_heads == 0 or self.num_key_value_heads is None:
|
|
self.num_key_value_heads = self.num_attention_heads
|
|
if self.intermediate_size == 0 or self.intermediate_size is None:
|
|
self.intermediate_size = 4 * self.hidden_size
|
|
if self.head_dim == 0:
|
|
self.head_dim = self.hidden_size // self.num_attention_heads
|
|
assert self.head_dim * self.num_attention_heads == self.hidden_size
|
|
assert self.num_attention_heads % self.num_key_value_heads == 0
|
|
|
|
|
|
@dataclasses.dataclass
|
|
class PhiConfig(ConfigBase):
|
|
"""Configuration of the Phi-2 model."""
|
|
|
|
model_type: str # "phi", "phi-msft", "mixformer-sequential"
|
|
vocab_size: int = 51200
|
|
n_positions: int = 2048
|
|
n_embd: int = 2560
|
|
n_layer: int = 32
|
|
n_inner: int = 0
|
|
n_head: int = 32
|
|
rotary_dim: int = 32
|
|
position_embedding_base: int = 0
|
|
layer_norm_epsilon: float = 1e-5
|
|
context_window_size: int = 0
|
|
prefill_chunk_size: int = 0
|
|
n_head_kv: int = 0
|
|
head_dim: int = 0
|
|
tensor_parallel_shards: int = 1
|
|
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.context_window_size == 0:
|
|
for name in ["max_position_embeddings", "max_sequence_length"]:
|
|
if name in self.kwargs:
|
|
self.context_window_size = self.kwargs.pop(name)
|
|
logger.info(
|
|
"%s not found in config.json. Falling back to %s (%d)",
|
|
bold("context_window_size"),
|
|
bold(name),
|
|
self.context_window_size,
|
|
)
|
|
break
|
|
else:
|
|
self.context_window_size = self.n_positions
|
|
logger.info(
|
|
"%s not found in config.json. Falling back to %s (%d)",
|
|
bold("context_window_size"),
|
|
"n_positions",
|
|
self.context_window_size,
|
|
)
|
|
if self.prefill_chunk_size == 0:
|
|
self.prefill_chunk_size = self.context_window_size
|
|
self.prefill_chunk_size = min(self.prefill_chunk_size, self.context_window_size)
|
|
if self.n_head_kv == 0 or self.n_head_kv is None:
|
|
self.n_head_kv = self.n_head
|
|
if self.n_inner == 0 or self.n_inner is None:
|
|
self.n_inner = 4 * self.n_embd
|
|
if self.head_dim == 0:
|
|
self.head_dim = self.n_embd // self.n_head
|
|
assert self.head_dim * self.n_head == self.n_embd
|
|
assert self.n_head % self.n_head_kv == 0
|
|
|
|
@staticmethod
|
|
def from_phi1(config: Phi1Config) -> "PhiConfig":
|
|
"Build PhiConig from a Phi1Config."
|
|
return PhiConfig(
|
|
model_type="phi",
|
|
vocab_size=config.vocab_size,
|
|
n_positions=config.context_window_size,
|
|
n_embd=config.hidden_size,
|
|
n_layer=config.num_hidden_layers,
|
|
n_inner=config.intermediate_size,
|
|
n_head=config.num_attention_heads,
|
|
rotary_dim=int(config.partial_rotary_factor * config.head_dim),
|
|
position_embedding_base=config.position_embedding_base,
|
|
layer_norm_epsilon=config.layer_norm_eps,
|
|
context_window_size=config.context_window_size,
|
|
prefill_chunk_size=config.prefill_chunk_size,
|
|
n_head_kv=config.num_key_value_heads,
|
|
head_dim=config.head_dim,
|
|
tensor_parallel_shards=config.tensor_parallel_shards,
|
|
kwargs=config.kwargs,
|
|
)
|
|
|
|
|
|
class PhiMLP(nn.Module):
|
|
def __init__(self, config: PhiConfig):
|
|
super().__init__()
|
|
if config.n_inner % config.tensor_parallel_shards != 0:
|
|
raise ValueError(
|
|
f"Cannot split MLP intermediate size {config.n_inner} "
|
|
f"evenly to {config.tensor_parallel_shards} GPUs."
|
|
)
|
|
self.intermediate_size = config.n_inner // config.tensor_parallel_shards
|
|
self.fc1 = nn.Linear(config.n_embd, self.intermediate_size)
|
|
self.fc2 = nn.Linear(self.intermediate_size, config.n_embd)
|
|
|
|
def forward(self, hidden_states: Tensor):
|
|
hidden_states = self.fc1(hidden_states)
|
|
hidden_states = op.gelu(hidden_states, approximate="tanh")
|
|
hidden_states = self.fc2(hidden_states)
|
|
|
|
return hidden_states
|
|
|
|
|
|
class PhiMHA(nn.Module):
|
|
def __init__(self, config: PhiConfig):
|
|
self.num_q_heads = config.n_head // config.tensor_parallel_shards
|
|
assert config.n_head % config.tensor_parallel_shards == 0, (
|
|
f"n_head({config.n_head}) must be divisible by tensor_parallel_shards"
|
|
)
|
|
self.n_head_kv = config.n_head_kv // config.tensor_parallel_shards
|
|
assert config.n_head_kv % config.tensor_parallel_shards == 0, (
|
|
f"n_head({config.n_head_kv}) must be divisible by tensor_parallel_shards"
|
|
)
|
|
self.head_dim = config.head_dim
|
|
op_size = self.head_dim * (self.num_q_heads + 2 * self.n_head_kv)
|
|
hidden_size = config.n_embd
|
|
|
|
self.Wqkv = nn.Linear(hidden_size, op_size, bias=True)
|
|
self.out_proj = nn.Linear(self.num_q_heads * self.head_dim, hidden_size, bias=True)
|
|
|
|
def forward(self, hidden_states: Tensor, paged_kv_cache: PagedKVCache, layer_id: int):
|
|
d, h_q, h_kv = self.head_dim, self.num_q_heads, self.n_head_kv
|
|
b, s, _ = hidden_states.shape
|
|
# QKV Projection
|
|
qkv = self.Wqkv(hidden_states)
|
|
qkv = op.reshape(qkv, (b, s, h_q + h_kv + h_kv, d))
|
|
# 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.out_proj(output)
|
|
|
|
|
|
class PhiParallelBlock(nn.Module):
|
|
def __init__(self, config: PhiConfig):
|
|
super().__init__()
|
|
|
|
self.ln = nn.LayerNorm(config.n_embd, eps=config.layer_norm_epsilon)
|
|
self.mixer = PhiMHA(config)
|
|
self.mlp = PhiMLP(config)
|
|
|
|
def _set_tp():
|
|
def _set(param, hint):
|
|
param.attrs["shard_strategy"] = hint
|
|
|
|
hd = config.head_dim
|
|
q = self.mixer.num_q_heads * hd
|
|
k = self.mixer.n_head_kv * hd
|
|
v = self.mixer.n_head_kv * hd
|
|
_set(
|
|
self.mixer.Wqkv.weight,
|
|
tp.ShardSingleDim("_shard_qkv_weight", segs=[q, k, v], dim=0),
|
|
)
|
|
_set(
|
|
self.mixer.Wqkv.bias,
|
|
tp.ShardSingleDim("_shard_qkv_bias", segs=[q, k, v], dim=0),
|
|
)
|
|
_set(self.mixer.out_proj.weight, tp.ShardSingleDim("_shard_o_weight", dim=1))
|
|
_set(self.mlp.fc1.weight, tp.ShardSingleDim("_shard_mlp_fc1_weight", dim=0))
|
|
_set(self.mlp.fc1.bias, tp.ShardSingleDim("_shard_mlp_fc1_bias", dim=0))
|
|
_set(self.mlp.fc2.weight, tp.ShardSingleDim("_shard_mlp_fc2_weight", dim=1))
|
|
|
|
self.tensor_parallel_shards = config.tensor_parallel_shards
|
|
_set_tp()
|
|
|
|
def forward(self, hidden_states: Tensor, paged_kv_cache: PagedKVCache, layer_id: int):
|
|
residual = hidden_states
|
|
hidden_states = self.ln(hidden_states)
|
|
|
|
with (
|
|
tp.shard_bias(self.mixer.out_proj, self.tensor_parallel_shards),
|
|
tp.shard_bias(self.mlp.fc2, self.tensor_parallel_shards),
|
|
):
|
|
attn_outputs = self.mixer(hidden_states, paged_kv_cache, layer_id)
|
|
feed_forward_hidden_states = self.mlp(hidden_states)
|
|
|
|
hidden_states = self._apply_parallel_residual(
|
|
attn_outputs, feed_forward_hidden_states, residual
|
|
)
|
|
|
|
return hidden_states
|
|
|
|
def _apply_parallel_residual(self, attn_out, mlp_out, residual):
|
|
if self.tensor_parallel_shards > 1:
|
|
return op.ccl_allreduce(
|
|
attn_out + mlp_out + residual / self.tensor_parallel_shards, "sum"
|
|
)
|
|
return attn_out + mlp_out + residual
|
|
|
|
|
|
class PhiCausalLMHead(nn.Module):
|
|
def __init__(self, config: PhiConfig) -> None:
|
|
super().__init__()
|
|
|
|
self.ln = nn.LayerNorm(config.n_embd, eps=config.layer_norm_epsilon)
|
|
self.linear = nn.Linear(config.n_embd, "vocab_size")
|
|
|
|
def forward(self, hidden_states: Tensor):
|
|
hidden_states = self.ln(hidden_states)
|
|
logits = self.linear(hidden_states)
|
|
|
|
if logits.dtype != "float32":
|
|
logits = logits.astype("float32")
|
|
return logits
|
|
|
|
|
|
class PhiModel(nn.Module):
|
|
def __init__(self, config: PhiConfig) -> None:
|
|
super().__init__()
|
|
self.embd = nn.Embedding(config.vocab_size, config.n_embd)
|
|
self.h = nn.ModuleList([PhiParallelBlock(config) for _ in range(config.n_layer)])
|
|
|
|
def forward(self, input_embed: Tensor, paged_kv_cache: PagedKVCache):
|
|
hidden_states = input_embed
|
|
for layer_id, layer in enumerate(self.h):
|
|
hidden_states = layer(hidden_states, paged_kv_cache, layer_id)
|
|
|
|
return hidden_states
|
|
|
|
|
|
class PhiForCausalLM(nn.Module):
|
|
def __init__(self, config: Union[PhiConfig, Phi1Config]) -> None:
|
|
super().__init__()
|
|
|
|
if isinstance(config, Phi1Config):
|
|
config = PhiConfig.from_phi1(config)
|
|
|
|
self.transformer = PhiModel(config)
|
|
self.lm_head = PhiCausalLMHead(config)
|
|
self.num_hidden_layers = config.n_layer
|
|
self.num_attention_heads = config.n_head
|
|
self.num_key_value_heads = config.n_head_kv
|
|
self.head_dim = config.head_dim
|
|
self.hidden_size = config.n_embd
|
|
self.vocab_size = config.vocab_size
|
|
self.rope_theta = config.position_embedding_base
|
|
self.tensor_parallel_shards = config.tensor_parallel_shards
|
|
self.rotary_dim = config.rotary_dim
|
|
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.transformer(input_embeds, paged_kv_cache)
|
|
if logit_positions is not None:
|
|
hidden_states = op.take(hidden_states, logit_positions, axis=1)
|
|
lm_logits = self.lm_head(hidden_states)
|
|
if lm_logits.dtype != "float32":
|
|
lm_logits = lm_logits.astype("float32")
|
|
return lm_logits
|
|
|
|
def prefill(self, input_embed: Tensor, paged_kv_cache: PagedKVCache):
|
|
op_ext.configure()
|
|
|
|
hidden_states = self.transformer(input_embed, paged_kv_cache)
|
|
hidden_states = index_last_token(hidden_states)
|
|
logits = self.lm_head(hidden_states)
|
|
|
|
if logits.dtype != "float32":
|
|
logits = logits.astype("float32")
|
|
|
|
return logits, paged_kv_cache
|
|
|
|
def decode(self, input_embed: Tensor, paged_kv_cache: PagedKVCache):
|
|
op_ext.configure()
|
|
|
|
hidden_states = self.transformer(input_embed, paged_kv_cache)
|
|
logits = self.lm_head(hidden_states)
|
|
if logits.dtype != "float32":
|
|
logits = logits.astype("float32")
|
|
return logits, paged_kv_cache
|
|
|
|
def batch_prefill(
|
|
self,
|
|
input_embeds: Tensor,
|
|
logit_positions: Tensor,
|
|
paged_kv_cache: PagedKVCache,
|
|
):
|
|
if self.tensor_parallel_shards > 1:
|
|
logit_positions = op.ccl_broadcast_from_worker0(logit_positions)
|
|
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 embed(self, input_ids: Tensor):
|
|
if self.tensor_parallel_shards > 1:
|
|
input_ids = op.ccl_broadcast_from_worker0(input_ids)
|
|
embeds = self.transformer.embd(input_ids)
|
|
return embeds
|
|
|
|
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.NORMAL,
|
|
rope_scale=1,
|
|
rope_theta=self.rope_theta,
|
|
rotary_dim=self.rotary_dim,
|
|
dtype=self.dtype,
|
|
)
|
|
|
|
def get_default_spec(self):
|
|
mod_spec = {
|
|
"embed": {
|
|
"input_ids": nn.spec.Tensor(["seq_len"], "int32"),
|
|
"$": {
|
|
"param_mode": "packed",
|
|
"effect_mode": "none",
|
|
},
|
|
},
|
|
"prefill": {
|
|
"input_embed": nn.spec.Tensor([1, "seq_len", self.hidden_size], self.dtype),
|
|
"paged_kv_cache": nn.spec.Object(object_type=PagedKVCache),
|
|
"$": {
|
|
"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),
|
|
"$": {
|
|
"param_mode": "packed",
|
|
"effect_mode": "none",
|
|
},
|
|
},
|
|
"batch_prefill": {
|
|
"input_embeds": nn.spec.Tensor([1, "seq_len", self.hidden_size], self.dtype),
|
|
"logit_positions": nn.spec.Tensor(["batch_size"], "int32"),
|
|
"paged_kv_cache": nn.spec.Object(object_type=PagedKVCache),
|
|
"$": {
|
|
"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",
|
|
},
|
|
},
|
|
"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)
|