chore: import upstream snapshot with attribution
This commit is contained in:
wehub-resource-sync
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"""
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Implementation for GPTBigCode architecture.
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"""
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import dataclasses
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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 logging
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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 GPTBigCodeConfig(ConfigBase):
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"""Configuration of the GPTBigCode model."""
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n_embd: int
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n_inner: int
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n_head: int
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n_layer: int
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n_positions: int
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layer_norm_epsilon: float
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vocab_size: int
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context_window_size: int = 0
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prefill_chunk_size: int = 0
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tensor_parallel_shards: int = 1
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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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if self.n_positions > 0:
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self.context_window_size = self.n_positions
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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("n_positions"),
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self.context_window_size,
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)
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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.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 GPTBigCodeMLP(nn.Module):
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def __init__(self, config: GPTBigCodeConfig):
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super().__init__()
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self.n_inner = config.n_inner // config.tensor_parallel_shards
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self.c_fc = nn.Linear(in_features=config.n_embd, out_features=self.n_inner, bias=True)
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self.c_proj = nn.Linear(in_features=self.n_inner, out_features=config.n_embd, bias=True)
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def forward(self, x: Tensor):
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hidden_states = self.c_fc(x)
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hidden_states = op.gelu(hidden_states)
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hidden_states = self.c_proj(hidden_states)
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return hidden_states
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class GPTBigCodeAttention(nn.Module):
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def __init__(self, config: GPTBigCodeConfig):
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self.n_embd = config.n_embd
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self.head_dim = config.n_embd // config.n_head
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self.num_q_heads = config.n_head // config.tensor_parallel_shards
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self.num_kv_heads = 1
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assert config.tensor_parallel_shards == 1, (
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"GPT bigcode only support tensor parallel shards = 1"
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)
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self.c_attn = nn.Linear(
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in_features=self.n_embd,
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out_features=(self.num_q_heads + 2 * self.num_kv_heads) * self.head_dim,
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bias=True,
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)
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self.c_proj = nn.Linear(
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in_features=self.num_q_heads * self.head_dim,
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out_features=config.n_embd,
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bias=True,
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)
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def forward(
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self,
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hidden_states: Tensor,
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paged_kv_cache: PagedKVCache,
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layer_id: int,
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):
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d, h_q, h_kv = self.head_dim, self.num_q_heads, self.num_kv_heads
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b, s, _ = hidden_states.shape
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# QKV Projection
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qkv = self.c_attn(hidden_states)
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qkv = op.reshape(qkv, (b, s, h_q + h_kv + h_kv, d))
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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, h_q, sm_scale=self.head_dim**-0.5
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),
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(b, s, h_q * d),
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)
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return self.c_proj(output)
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class GPTBigCodeBlock(nn.Module):
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def __init__(self, config: GPTBigCodeConfig):
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self.attn = GPTBigCodeAttention(config)
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self.mlp = GPTBigCodeMLP(config)
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self.ln_1 = nn.LayerNorm(config.n_embd, eps=config.layer_norm_epsilon)
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self.ln_2 = nn.LayerNorm(config.n_embd, eps=config.layer_norm_epsilon)
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def _set_tp():
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def _set(layer, hint):
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layer.weight.attrs["shard_strategy"] = hint
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hd = config.n_embd // config.n_head
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q = config.n_head * hd
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k = 1 * hd
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v = 1 * hd
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_set(
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self.attn.c_attn,
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tp.ShardSingleDim("_shard_c_attn", dim=0, segs=[q, k, v]),
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)
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_set(self.attn.c_proj, tp.ShardSingleDim("_shard_c_proj", dim=1))
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_set(self.mlp.c_fc, tp.ShardSingleDim("_shard_mlp_c_fc", dim=0))
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_set(self.mlp.c_proj, tp.ShardSingleDim("_shard_mlp_c_proj", dim=1))
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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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out = self.attn(self.ln_1(hidden_states), paged_kv_cache, layer_id)
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hidden_states = out + hidden_states
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out = self.mlp(self.ln_2(hidden_states))
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hidden_states = out + hidden_states
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return hidden_states
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class GPTBigCodeModel(nn.Module):
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def __init__(self, config: GPTBigCodeConfig):
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assert config.n_embd % config.n_head == 0
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self.wte = nn.Embedding("vocab_size", config.n_embd)
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self.wpe = nn.Embedding(config.n_positions, config.n_embd)
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self.h = nn.ModuleList([GPTBigCodeBlock(config) for _ in range(config.n_layer)])
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self.ln_f = nn.LayerNorm(config.n_embd, eps=config.layer_norm_epsilon)
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def forward(self, input_embed: Tensor, paged_kv_cache: PagedKVCache):
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# Position Embeddings
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# shape[1] indicates the total query length in the batch
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input_positions = paged_kv_cache.get_query_positions(input_embed.shape[1])
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pos_embd = self.wpe(input_positions)
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# apply position embeddings
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hidden_states = input_embed + pos_embd
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for layer_id, layer in enumerate(self.h):
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hidden_states = layer(hidden_states, paged_kv_cache, layer_id)
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hidden_states = self.ln_f(hidden_states)
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return hidden_states
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class GPTBigCodeForCausalLM(nn.Module):
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def __init__(self, config: GPTBigCodeConfig):
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self.transformer = GPTBigCodeModel(config)
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self.lm_head = nn.Linear(config.n_embd, "vocab_size", bias=False)
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self.n_layer = config.n_layer
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self.n_embd = config.n_embd
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self.num_q_heads = config.n_head // config.tensor_parallel_shards
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self.num_kv_heads = 1
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self.head_dim = config.n_embd // config.n_head
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self.tensor_parallel_shards = config.tensor_parallel_shards
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self.dtype = "float32"
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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_embed: 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.transformer(input_embed, 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.lm_head(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.transformer.wte(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.transformer(input_embed, paged_kv_cache)
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hidden_states = index_last_token(hidden_states)
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logits = self.lm_head(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.transformer(input_embed, paged_kv_cache)
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logits = self.lm_head(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.n_layer,
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num_attention_heads=self.num_q_heads // self.tensor_parallel_shards,
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num_key_value_heads=self.num_kv_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.NONE,
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rope_scale=-1,
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rope_theta=-1,
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dtype=self.dtype,
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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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"$": {
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"param_mode": "packed",
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"effect_mode": "none",
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},
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},
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"prefill": {
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"input_embed": nn.spec.Tensor([1, "seq_len", self.n_embd], self.dtype),
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"paged_kv_cache": nn.spec.Object(object_type=PagedKVCache),
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"$": {
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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.n_embd], self.dtype),
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"paged_kv_cache": nn.spec.Object(object_type=PagedKVCache),
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"$": {
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"param_mode": "packed",
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"effect_mode": "none",
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},
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},
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"batch_prefill": {
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"input_embeds": nn.spec.Tensor([1, "seq_len", self.n_embd], self.dtype),
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"logit_positions": nn.spec.Tensor(["batch_size"], "int32"),
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"paged_kv_cache": nn.spec.Object(object_type=PagedKVCache),
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"$": {
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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.n_embd], self.dtype),
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"paged_kv_cache": nn.spec.Object(object_type=PagedKVCache),
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"$": {
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"param_mode": "packed",
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"effect_mode": "none",
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},
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},
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"batch_verify": {
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"input_embeds": nn.spec.Tensor([1, "seq_len", self.n_embd], self.dtype),
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"paged_kv_cache": nn.spec.Object(object_type=PagedKVCache),
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"$": {
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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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