wehub-resource-sync
447 lines
17 KiB
Python
447 lines
17 KiB
Python
"""
|
|
Implementation for CHATGLM3 architecture.
|
|
"""
|
|
|
|
import dataclasses
|
|
from typing import Any, Dict, Optional # 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 GLMConfig(ConfigBase):
|
|
"""Configuration of the ChatGLM model."""
|
|
|
|
hidden_size: int
|
|
num_layers: int
|
|
kv_channels: int
|
|
num_attention_heads: int
|
|
ffn_hidden_size: int
|
|
layernorm_epsilon: float
|
|
post_layer_norm: bool
|
|
rmsnorm: bool
|
|
add_bias_linear: bool
|
|
add_qkv_bias: bool
|
|
apply_query_key_layer_scaling: bool
|
|
multi_query_attention: bool
|
|
multi_query_group_num: int
|
|
vocab_size: int = 0
|
|
context_window_size: int = 0
|
|
prefill_chunk_size: int = 0
|
|
tensor_parallel_shards: int = 1
|
|
head_dim: int = 0
|
|
max_batch_size: int = 1
|
|
kwargs: Dict[str, Any] = dataclasses.field(default_factory=dict) # noqa: UP006
|
|
|
|
def __post_init__(self):
|
|
if self.vocab_size == 0:
|
|
for name in ["padded_vocab_size"]:
|
|
if name in self.kwargs:
|
|
self.vocab_size = self.kwargs.pop(name)
|
|
if self.context_window_size == 0:
|
|
for name in ["max_position_embeddings", "seq_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.head_dim == 0:
|
|
self.head_dim = self.hidden_size // self.num_attention_heads
|
|
assert self.head_dim * self.num_attention_heads == self.hidden_size
|
|
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 GLMAttention(nn.Module):
|
|
def __init__(self, config: GLMConfig):
|
|
self.hidden_size = config.hidden_size
|
|
if config.num_attention_heads % config.tensor_parallel_shards != 0:
|
|
raise ValueError(
|
|
f"Cannot split {config.num_attention_heads} attention heads"
|
|
f"evenly to {config.tensor_parallel_shards} GPUs."
|
|
)
|
|
self.num_heads = config.num_attention_heads // config.tensor_parallel_shards
|
|
self.multi_query_attention = config.multi_query_attention
|
|
self.num_key_value_heads = (
|
|
config.multi_query_group_num
|
|
if config.multi_query_attention
|
|
else config.num_attention_heads
|
|
) // config.tensor_parallel_shards
|
|
self.head_dim = config.head_dim
|
|
self.query_key_value = nn.Linear(
|
|
config.hidden_size,
|
|
(2 * self.num_key_value_heads + self.num_heads) * self.head_dim,
|
|
bias=config.add_bias_linear or config.add_qkv_bias,
|
|
)
|
|
self.dense = nn.Linear(
|
|
self.num_heads * self.head_dim,
|
|
config.hidden_size,
|
|
bias=config.add_bias_linear,
|
|
)
|
|
|
|
def forward(self, hidden_states: Tensor, paged_kv_cache: PagedKVCache, layer_id: int):
|
|
d, h_q, h_kv = self.head_dim, self.num_heads, self.num_key_value_heads
|
|
b, s, _ = hidden_states.shape
|
|
qkv = self.query_key_value(hidden_states)
|
|
qkv = op.reshape(qkv, (b, s, h_q + h_kv + h_kv, d))
|
|
output = op.reshape(
|
|
paged_kv_cache.attention_with_fused_qkv(
|
|
layer_id, qkv, h_q, sm_scale=self.head_dim**-0.5
|
|
),
|
|
(b, s, h_q * d),
|
|
)
|
|
attn_output = self.dense(output)
|
|
return attn_output
|
|
|
|
|
|
class GLMMLP(nn.Module):
|
|
def __init__(self, config: GLMConfig):
|
|
if config.ffn_hidden_size % config.tensor_parallel_shards != 0:
|
|
raise ValueError(
|
|
f"Cannot split ffn hidden size {config.ffn_hidden_size} "
|
|
f"evenly to {config.tensor_parallel_shards} GPUs."
|
|
)
|
|
self.ffn_hidden_size = config.ffn_hidden_size // config.tensor_parallel_shards
|
|
|
|
self.dense_h_to_4h = nn.Linear(
|
|
config.hidden_size,
|
|
self.ffn_hidden_size * 2,
|
|
bias=config.add_bias_linear,
|
|
)
|
|
self.dense_4h_to_h = nn.Linear(
|
|
self.ffn_hidden_size,
|
|
config.hidden_size,
|
|
bias=config.add_bias_linear,
|
|
)
|
|
|
|
def swiglu(x):
|
|
x = nn.chunk(x, 2, dim=-1)
|
|
return nn.silu(x[0]) * x[1]
|
|
|
|
self.activation_func = swiglu
|
|
|
|
def forward(self, x):
|
|
intermediate_parallel = self.dense_h_to_4h(x)
|
|
intermediate_parallel = self.activation_func(intermediate_parallel)
|
|
output = self.dense_4h_to_h(intermediate_parallel)
|
|
return output
|
|
|
|
|
|
class GLMBlock(nn.Module):
|
|
def __init__(self, config: GLMConfig):
|
|
self.self_attention = GLMAttention(config=config)
|
|
self.mlp = GLMMLP(config)
|
|
self.input_layernorm = nn.RMSNorm(
|
|
config.hidden_size, -1, config.layernorm_epsilon, bias=False
|
|
)
|
|
self.post_attention_layernorm = nn.RMSNorm(
|
|
config.hidden_size, -1, config.layernorm_epsilon, bias=False
|
|
)
|
|
|
|
def _set_tp():
|
|
def _set(layer, hint):
|
|
layer.attrs["shard_strategy"] = hint
|
|
|
|
hd = config.head_dim
|
|
q = self.self_attention.num_heads * hd
|
|
k = self.self_attention.num_key_value_heads * hd
|
|
v = self.self_attention.num_key_value_heads * hd
|
|
_set(
|
|
self.self_attention.query_key_value.weight,
|
|
tp.ShardSingleDim("_shard_qkv_weight", dim=0, segs=[q, k, v]),
|
|
)
|
|
if config.add_bias_linear or config.add_qkv_bias:
|
|
_set(
|
|
self.self_attention.query_key_value.bias,
|
|
tp.ShardSingleDim("_shard_qkv_bias", dim=0, segs=[q, k, v]),
|
|
)
|
|
_set(
|
|
self.self_attention.dense.weight,
|
|
tp.ShardSingleDim("_shard_dense_weight", dim=1),
|
|
)
|
|
if config.add_bias_linear:
|
|
_set(
|
|
self.self_attention.dense.bias,
|
|
tp.ShardSingleDim("_shard_dense_bias", dim=0),
|
|
)
|
|
_set(
|
|
self.mlp.dense_h_to_4h.weight,
|
|
tp.ShardSingleDim("_shard_dense_h_to_4h_weight", dim=0),
|
|
)
|
|
if config.add_bias_linear:
|
|
_set(
|
|
self.mlp.dense_h_to_4h.bias,
|
|
tp.ShardSingleDim("_shard_dense_h_to_4h_bias", dim=0),
|
|
)
|
|
_set(
|
|
self.mlp.dense_4h_to_h.weight,
|
|
tp.ShardSingleDim("_shard_dense_4h_to_h", dim=1),
|
|
)
|
|
if config.add_bias_linear:
|
|
_set(
|
|
self.mlp.dense_4h_to_h.bias,
|
|
tp.ShardSingleDim("_shard_dense_4h_to_h_bias", 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):
|
|
out = self.self_attention(self.input_layernorm(hidden_states), paged_kv_cache, layer_id)
|
|
hidden_states = self._apply_residual(out, residual=hidden_states)
|
|
out = self.mlp(self.post_attention_layernorm(hidden_states))
|
|
hidden_states = self._apply_residual(out, 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 GLMTransformer(nn.Module):
|
|
"""Transformer class."""
|
|
|
|
def __init__(self, config: GLMConfig):
|
|
self.post_layer_norm = config.post_layer_norm
|
|
|
|
# Number of layers.
|
|
self.num_layers = config.num_layers
|
|
|
|
# Transformer layers.
|
|
self.layers = nn.ModuleList([GLMBlock(config) for _ in range(config.num_layers)])
|
|
|
|
if self.post_layer_norm:
|
|
if config.rmsnorm:
|
|
self.final_layernorm = nn.RMSNorm(
|
|
config.hidden_size, -1, config.layernorm_epsilon, bias=False
|
|
)
|
|
else:
|
|
self.final_layernorm = nn.LayerNorm(config.hidden_size, config.layernorm_epsilon)
|
|
|
|
def forward(self, inputs: Tensor, paged_kv_cache: PagedKVCache):
|
|
hidden_states = inputs
|
|
for layer_id, layer in enumerate(self.layers):
|
|
hidden_states = layer(hidden_states, paged_kv_cache, layer_id)
|
|
hidden_states = self.final_layernorm(hidden_states)
|
|
return hidden_states
|
|
|
|
|
|
class ChatGLMModel(nn.Module):
|
|
def __init__(self, config: GLMConfig):
|
|
self.embedding = nn.Embedding(config.vocab_size, config.hidden_size)
|
|
self.encoder = GLMTransformer(config)
|
|
self.output_layer = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
|
|
|
|
def forward(self, inputs: Tensor, paged_kv_cache: PagedKVCache):
|
|
hidden_states = inputs
|
|
hidden_states = self.encoder(hidden_states, paged_kv_cache)
|
|
return hidden_states
|
|
|
|
|
|
class ChatGLMForCausalLM(nn.Module):
|
|
def __init__(self, config: GLMConfig):
|
|
self.transformer = ChatGLMModel(config)
|
|
self.num_hidden_layers = config.num_layers
|
|
self.hidden_size = config.hidden_size
|
|
self.num_attention_heads = config.num_attention_heads
|
|
self.num_key_value_heads = (
|
|
config.multi_query_group_num
|
|
if config.multi_query_attention
|
|
else config.num_attention_heads
|
|
)
|
|
self.head_dim = config.head_dim
|
|
self.vocab_size = config.vocab_size
|
|
self.rope_theta = 10000
|
|
self.tensor_parallel_shards = config.tensor_parallel_shards
|
|
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)
|
|
logits = self.transformer.output_layer(hidden_states)
|
|
if logits.dtype != "float32":
|
|
logits = logits.astype("float32")
|
|
return logits
|
|
|
|
def embed(self, input_ids: Tensor):
|
|
if self.tensor_parallel_shards > 1:
|
|
input_ids = op.ccl_broadcast_from_worker0(input_ids)
|
|
return self.transformer.embedding(input_ids)
|
|
|
|
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.transformer.output_layer(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.transformer.output_layer(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 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,
|
|
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)
|