wehub-resource-sync
418 lines
16 KiB
Python
418 lines
16 KiB
Python
"""
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Implementation for GPTNeoX architecture.
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"""
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import dataclasses
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import logging
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from typing import Any, Dict, Optional # noqa: UP035
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from tvm import tirx
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from tvm.relax.frontend import nn
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from tvm.relax.frontend.nn import Tensor, op
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from mlc_llm import op as op_ext
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from mlc_llm.model.model_utils import index_last_token
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from mlc_llm.nn import PagedKVCache, RopeMode
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from mlc_llm.support import tensor_parallel as tp
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from mlc_llm.support.config import ConfigBase
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from mlc_llm.support.style import bold
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logger = logging.getLogger(__name__)
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@dataclasses.dataclass
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class GPTNeoXConfig(ConfigBase):
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"""Configuration of the GPTNeoX model."""
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use_parallel_residual: bool
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hidden_size: int
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intermediate_size: int
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num_attention_heads: int
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num_hidden_layers: int
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layer_norm_eps: float
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vocab_size: int
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rotary_pct: float
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position_embedding_base: int = 0
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context_window_size: int = 0
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head_dim: int = 0
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prefill_chunk_size: int = 0
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tensor_parallel_shards: int = 1
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ffn_out_dtype: str = "float32"
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max_batch_size: int = 1
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kwargs: Dict[str, Any] = dataclasses.field(default_factory=dict) # noqa: UP006
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def __post_init__(self):
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if self.context_window_size == 0:
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for name in ["max_position_embeddings", "max_sequence_length"]:
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if name in self.kwargs:
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self.context_window_size = self.kwargs.pop(name)
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logger.info(
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"%s not found in config.json. Falling back to %s (%d)",
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bold("context_window_size"),
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bold(name),
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self.context_window_size,
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)
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break
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else:
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raise ValueError(
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"Unable to determine the maximum sequence length, because none of "
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"`context_window_size`, `max_position_embeddings` or `max_sequence_length` is "
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"provided in `config.json`."
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)
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if self.position_embedding_base == 0:
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if "rope_theta" in self.kwargs:
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self.position_embedding_base = self.kwargs.pop("rope_theta")
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else:
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self.position_embedding_base = 10000
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if self.head_dim == 0:
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self.head_dim = self.hidden_size // self.num_attention_heads
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assert self.head_dim * self.num_attention_heads == self.hidden_size
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if self.prefill_chunk_size == 0:
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logger.info(
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"%s defaults to %d",
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bold("prefill_chunk_size"),
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min(self.context_window_size, 8192),
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)
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self.prefill_chunk_size = min(self.context_window_size, 8192)
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elif self.prefill_chunk_size > self.context_window_size:
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logger.info(
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"Overriding %s from %d to %d",
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bold("prefill_chunk_size"),
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self.prefill_chunk_size,
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min(self.context_window_size, 8192),
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)
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self.prefill_chunk_size = min(self.context_window_size, 8192)
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class GPTNeoXAttention(nn.Module):
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"""Multi-headed attention from 'Attention Is All You Need' paper"""
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def __init__(self, config: GPTNeoXConfig):
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self.rope_theta = config.position_embedding_base
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self.hidden_size = config.hidden_size
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if config.num_attention_heads % config.tensor_parallel_shards != 0:
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raise ValueError(
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f"Cannot split {config.num_attention_heads} attention heads "
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f"evenly to {config.tensor_parallel_shards} GPUs."
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)
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self.num_attention_heads = config.num_attention_heads // config.tensor_parallel_shards
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self.head_dim = config.head_dim
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self.query_key_value = nn.Linear(
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in_features=self.hidden_size,
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out_features=3 * self.num_attention_heads * self.head_dim,
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bias=True,
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)
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self.dense = nn.Linear(
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self.num_attention_heads * self.head_dim, self.hidden_size, bias=True
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)
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def forward(self, hidden_states: Tensor, paged_kv_cache: PagedKVCache, layer_id: int):
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# hidden_states: [batch_size, seq_len, hidden_size]
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batch_size, seq_len, _ = hidden_states.shape
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# q/k/v states: [batch_size, seq_len, hidden_size]
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qkv = self.query_key_value(hidden_states)
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qkv = op.reshape(qkv, (batch_size, seq_len, 3 * self.num_attention_heads, self.head_dim))
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# Attention
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output = op.reshape(
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paged_kv_cache.attention_with_fused_qkv(
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layer_id, qkv, self.num_attention_heads, sm_scale=self.head_dim**-0.5
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),
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(batch_size, seq_len, self.head_dim * self.num_attention_heads),
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)
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attn_output = self.dense(output)
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return attn_output
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class GPTNeoXMLP(nn.Module):
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def __init__(self, config: GPTNeoXConfig):
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super().__init__()
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out_dtype = config.ffn_out_dtype
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if config.intermediate_size % config.tensor_parallel_shards != 0:
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raise ValueError(
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f"Cannot split MLP intermediate size {config.intermediate_size} "
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f"evenly to {config.tensor_parallel_shards} GPUs."
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)
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self.intermediate_size = config.intermediate_size // config.tensor_parallel_shards
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self.dense_h_to_4h = nn.Linear(
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config.hidden_size,
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self.intermediate_size,
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out_dtype=out_dtype,
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)
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self.dense_4h_to_h = nn.Linear(
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self.intermediate_size,
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config.hidden_size,
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out_dtype=out_dtype,
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)
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def forward(self, hidden_states: Tensor):
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dtype = hidden_states.dtype
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if hidden_states.dtype != dtype:
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hidden_states = hidden_states.astype(dtype)
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hidden_states = self.dense_h_to_4h(hidden_states)
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hidden_states = op.gelu(hidden_states)
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if hidden_states.dtype != dtype:
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hidden_states = hidden_states.astype(dtype)
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hidden_states = self.dense_4h_to_h(hidden_states)
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if hidden_states.dtype != dtype:
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hidden_states = hidden_states.astype(dtype)
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return hidden_states
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class GPTNeoXLayer(nn.Module):
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def __init__(self, config: GPTNeoXConfig):
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self.input_layernorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
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self.post_attention_layernorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
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self.attention = GPTNeoXAttention(config)
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self.mlp = GPTNeoXMLP(config)
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self.use_parallel_residual = config.use_parallel_residual
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def _set_tp():
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def _set(param, hint):
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param.attrs["shard_strategy"] = hint
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hd = config.head_dim
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q = k = v = self.attention.num_attention_heads * hd
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_set(
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self.attention.query_key_value.weight,
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tp.ShardSingleDim("_shard_qkv_weight", dim=0, segs=[q, k, v]),
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)
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_set(
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self.attention.query_key_value.bias,
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tp.ShardSingleDim("_shard_qkv_bias", dim=0, segs=[q, k, v]),
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)
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_set(self.attention.dense.weight, tp.ShardSingleDim("_shard_dense", dim=1))
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_set(
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self.mlp.dense_h_to_4h.weight,
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tp.ShardSingleDim("_shard_dense_h_to_4h_weight", dim=0),
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)
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_set(
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self.mlp.dense_h_to_4h.bias,
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tp.ShardSingleDim("_shard_dense_h_to_4h_bias", dim=0),
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)
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_set(
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self.mlp.dense_4h_to_h.weight,
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tp.ShardSingleDim("_shard_dense_4h_to_h", dim=1),
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)
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self.tensor_parallel_shards = config.tensor_parallel_shards
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_set_tp()
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def forward(self, hidden_states: Tensor, paged_kv_cache: PagedKVCache, layer_id: int):
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dtype = hidden_states.dtype
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attn_input = self.input_layernorm(hidden_states)
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with tp.shard_bias(self.attention.dense, self.tensor_parallel_shards):
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attn_output = self.attention(
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attn_input,
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paged_kv_cache,
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layer_id,
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)
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if self.use_parallel_residual:
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mlp_input = self.post_attention_layernorm(hidden_states)
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mlp_output = self.mlp(mlp_input)
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hidden_states = mlp_output + attn_output + hidden_states
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else:
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attn_output = self._apply_residual(attn_output, hidden_states)
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mlp_input = self.post_attention_layernorm(attn_output)
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with tp.shard_bias(self.mlp.dense_4h_to_h, self.tensor_parallel_shards):
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mlp_output = self.mlp(mlp_input)
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hidden_states = self._apply_residual(mlp_output.astype(dtype), attn_output)
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return hidden_states
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def _apply_residual(self, out, residual):
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if self.tensor_parallel_shards > 1:
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return op.ccl_allreduce(out + residual / self.tensor_parallel_shards, "sum")
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return out + residual
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class GPTNeoXModel(nn.Module):
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def __init__(self, config: GPTNeoXConfig):
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self.embed_in = nn.Embedding(num="vocab_size", dim=config.hidden_size)
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self.layers = nn.ModuleList([GPTNeoXLayer(config) for _ in range(config.num_hidden_layers)])
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self.final_layer_norm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
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def forward(self, inputs: Tensor, paged_kv_cache: PagedKVCache):
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hidden_states = inputs
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for layer_id, layer in enumerate(self.layers):
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hidden_states = layer(hidden_states, paged_kv_cache, layer_id)
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hidden_states = self.final_layer_norm(hidden_states)
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return hidden_states
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class GPTNeoXForCausalLM(nn.Module):
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def __init__(self, config: GPTNeoXConfig):
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self.gpt_neox = GPTNeoXModel(config)
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self.embed_out = nn.Linear(
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in_features=config.hidden_size,
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out_features="vocab_size",
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bias=False,
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dtype="float32",
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)
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self.num_hidden_layers = config.num_hidden_layers
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self.hidden_size = config.hidden_size
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self.num_attention_heads = config.num_attention_heads
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self.head_dim = config.head_dim
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self.vocab_size = config.vocab_size
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self.rope_theta = config.position_embedding_base
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self.tensor_parallel_shards = config.tensor_parallel_shards
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self.dtype = "float32"
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self.rotary_pct = config.rotary_pct
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def to(self, dtype: Optional[str] = None):
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super().to(dtype=dtype)
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if dtype is not None:
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self.dtype = dtype
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def batch_forward(
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self,
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input_embeds: Tensor,
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paged_kv_cache: PagedKVCache,
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logit_positions: Optional[Tensor] = None,
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):
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op_ext.configure()
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hidden_states = self.gpt_neox(input_embeds, paged_kv_cache)
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if logit_positions is not None:
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hidden_states = op.take(hidden_states, logit_positions, axis=1)
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logits = self.embed_out(hidden_states)
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if logits.dtype != "float32":
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logits = logits.astype("float32")
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return logits
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def embed(self, input_ids: Tensor):
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if self.tensor_parallel_shards > 1:
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input_ids = op.ccl_broadcast_from_worker0(input_ids)
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return self.gpt_neox.embed_in(input_ids)
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def prefill(self, input_embed: Tensor, paged_kv_cache: PagedKVCache):
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op_ext.configure()
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hidden_states = self.gpt_neox(input_embed, paged_kv_cache)
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hidden_states = index_last_token(hidden_states)
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logits = self.embed_out(hidden_states)
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if logits.dtype != "float32":
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logits = logits.astype("float32")
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return logits, paged_kv_cache
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def decode(self, input_embed: Tensor, paged_kv_cache: PagedKVCache):
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op_ext.configure()
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hidden_states = self.gpt_neox(input_embed, paged_kv_cache)
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logits = self.embed_out(hidden_states)
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if logits.dtype != "float32":
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logits = logits.astype("float32")
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return logits, paged_kv_cache
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def batch_prefill(
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self,
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input_embeds: Tensor,
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logit_positions: Tensor,
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paged_kv_cache: PagedKVCache,
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):
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if self.tensor_parallel_shards > 1:
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logit_positions = op.ccl_broadcast_from_worker0(logit_positions)
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logits = self.batch_forward(input_embeds, paged_kv_cache, logit_positions)
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return logits, paged_kv_cache
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def batch_decode(self, input_embeds: Tensor, paged_kv_cache: PagedKVCache):
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logits = self.batch_forward(input_embeds, paged_kv_cache)
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return logits, paged_kv_cache
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def batch_verify(self, input_embeds: Tensor, paged_kv_cache: PagedKVCache):
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logits = self.batch_forward(input_embeds, paged_kv_cache)
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return logits, paged_kv_cache
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def create_paged_kv_cache(
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self,
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max_batch_size: tirx.Var,
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max_total_seq_len: tirx.Var,
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prefill_chunk_size: tirx.Var,
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page_size: tirx.Var,
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support_sliding_window: tirx.Var,
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) -> PagedKVCache:
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return PagedKVCache.create_generic(
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attn_kind="mha",
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max_batch_size=max_batch_size,
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max_total_seq_len=max_total_seq_len,
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prefill_chunk_size=prefill_chunk_size,
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page_size=page_size,
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support_sliding_window=support_sliding_window,
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num_hidden_layers=self.num_hidden_layers,
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num_attention_heads=self.num_attention_heads // self.tensor_parallel_shards,
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num_key_value_heads=self.num_attention_heads // self.tensor_parallel_shards,
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qk_head_dim=self.head_dim,
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v_head_dim=self.head_dim,
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rope_mode=RopeMode.NORMAL,
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rope_scale=1,
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rope_theta=self.rope_theta,
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dtype=self.dtype,
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rotary_dim=int(self.head_dim * self.rotary_pct),
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)
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def get_default_spec(self):
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mod_spec = {
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"embed": {
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"input_ids": nn.spec.Tensor(["seq_len"], "int32"),
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"$": {
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"param_mode": "packed",
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"effect_mode": "none",
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},
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},
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"prefill": {
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"input_embed": nn.spec.Tensor([1, "seq_len", self.hidden_size], self.dtype),
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"paged_kv_cache": nn.spec.Object(object_type=PagedKVCache),
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"$": {
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"param_mode": "packed",
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"effect_mode": "none",
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},
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},
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"decode": {
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"input_embed": nn.spec.Tensor([1, 1, self.hidden_size], self.dtype),
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"paged_kv_cache": nn.spec.Object(object_type=PagedKVCache),
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"$": {
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"param_mode": "packed",
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"effect_mode": "none",
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},
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},
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"batch_prefill": {
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"input_embeds": nn.spec.Tensor([1, "seq_len", self.hidden_size], self.dtype),
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"logit_positions": nn.spec.Tensor(["batch_size"], "int32"),
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"paged_kv_cache": nn.spec.Object(object_type=PagedKVCache),
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"$": {
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"param_mode": "packed",
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"effect_mode": "none",
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},
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},
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"batch_decode": {
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"input_embeds": nn.spec.Tensor(["batch_size", 1, self.hidden_size], self.dtype),
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"paged_kv_cache": nn.spec.Object(object_type=PagedKVCache),
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"$": {
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"param_mode": "packed",
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"effect_mode": "none",
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},
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},
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"batch_verify": {
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"input_embeds": nn.spec.Tensor([1, "seq_len", self.hidden_size], self.dtype),
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"paged_kv_cache": nn.spec.Object(object_type=PagedKVCache),
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"$": {
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"param_mode": "packed",
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"effect_mode": "none",
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},
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},
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"create_paged_kv_cache": {
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"max_batch_size": int,
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"max_total_seq_len": int,
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"prefill_chunk_size": int,
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"page_size": int,
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"support_sliding_window": int,
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"$": {
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"param_mode": "none",
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"effect_mode": "none",
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},
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},
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}
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return nn.spec.ModuleSpec.from_raw(mod_spec, self)
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