""" Implementation for Phi-3 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 Phi3Config(ConfigBase): """Configuration of the Phi-3 model.""" model_type: str # "phi", "phi-msft", "mixformer-sequential" hidden_size: int vocab_size: int num_hidden_layers: int num_attention_heads: int intermediate_size: int rms_norm_eps: float num_key_value_heads: int max_position_embeddings: int position_embedding_base: int = 0 rope_scaling: Optional[Dict[str, Any]] = None # noqa: UP006 original_max_position_embeddings: 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 tie_word_embeddings: bool = False partial_rotary_factor: float = 1.0 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 "type" not in self.rope_scaling: self.rope_scaling = None else: if self.rope_scaling["type"] == "su": self.rope_scaling["type"] = "longrope" assert self.rope_scaling["type"] == "longrope", ( f"Unsupported RoPE scaling type {self.rope_scaling['rope_type']} for Phi3" ) self.rope_scaling["rope_type"] = self.rope_scaling["type"] ( self.rope_scaling["max_position_embeddings"], self.rope_scaling["original_max_position_embeddings"], ) = ( self.max_position_embeddings, self.original_max_position_embeddings, ) if self.context_window_size == 0: self.context_window_size = self.max_position_embeddings 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.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 class Phi3Embedding(nn.Embedding): """The embedding module that can be shared with the final lm_head.""" 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 Phi3MLP(nn.Module): def __init__(self, config: Phi3Config): super().__init__() if config.intermediate_size % config.tensor_parallel_shards != 0: raise ValueError( f"Cannot split MLP intermediate size {config.intermediate_size} " f"evenly to {config.tensor_parallel_shards} GPUs." ) self.intermediate_size = config.intermediate_size // config.tensor_parallel_shards self.gate_up_proj = nn.Linear(config.hidden_size, 2 * self.intermediate_size, bias=False) self.down_proj = nn.Linear(self.intermediate_size, config.hidden_size, bias=False) def forward(self, hidden_states: Tensor): up_states = self.gate_up_proj(hidden_states) gate, up_states = nn.op.split(up_states, 2, axis=-1) up_states = up_states * op.silu(gate) return self.down_proj(up_states) class PhiMHA(nn.Module): def __init__(self, config: Phi3Config): self.num_q_heads = config.num_attention_heads // config.tensor_parallel_shards assert config.num_attention_heads % config.tensor_parallel_shards == 0, ( f"num_attention_heads({config.num_attention_heads}) " "must be divisible by tensor_parallel_shards" ) self.num_key_value_heads = config.num_key_value_heads // config.tensor_parallel_shards assert config.num_key_value_heads % config.tensor_parallel_shards == 0, ( f"num_attention_heads({config.num_key_value_heads}) " "must be divisible by tensor_parallel_shards" ) self.head_dim = config.head_dim self.qkv_proj = nn.Linear( in_features=config.hidden_size, out_features=(self.num_q_heads + 2 * self.num_key_value_heads) * self.head_dim, bias=False, ) self.out_proj = nn.Linear(self.num_q_heads * self.head_dim, config.hidden_size, bias=False) 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.num_key_value_heads b, s, _ = hidden_states.shape # QKV Projection qkv = self.qkv_proj(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 Phi3ParallelBlock(nn.Module): def __init__(self, config: Phi3Config): super().__init__() self.ln = nn.RMSNorm(config.hidden_size, -1, config.rms_norm_eps, bias=False) self.mixer = PhiMHA(config) self.mlp = Phi3MLP(config) self.post_attention_layernorm = nn.RMSNorm( config.hidden_size, -1, config.rms_norm_eps, bias=False ) def _set_tp(): def _set(layer, hint): layer.weight.attrs["shard_strategy"] = hint hd = config.head_dim q = self.mixer.num_q_heads * hd k = self.mixer.num_key_value_heads * hd v = self.mixer.num_key_value_heads * hd i = self.mlp.intermediate_size _set( self.mixer.qkv_proj, tp.ShardSingleDim("_shard_qkv", segs=[q, k, v], dim=0), ) _set(self.mixer.out_proj, tp.ShardSingleDim("_shard_o", dim=1)) _set( self.mlp.gate_up_proj, tp.ShardSingleDim("_shard_mlp_up", segs=[i, i], dim=0), ) _set(self.mlp.down_proj, tp.ShardSingleDim("_shard_mlp_down", 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): attn_outputs = self.mixer(self.ln(hidden_states), paged_kv_cache, layer_id) hidden_states = self._apply_parallel_residual(attn_outputs, hidden_states) out = self.mlp(self.post_attention_layernorm(hidden_states)) hidden_states = self._apply_parallel_residual(out, hidden_states) return hidden_states def _apply_parallel_residual(self, mlp_out, residual): if self.tensor_parallel_shards > 1: return op.ccl_allreduce(mlp_out + residual / self.tensor_parallel_shards, "sum") return mlp_out + residual class Phi3Model(nn.Module): def __init__(self, config: Phi3Config) -> None: super().__init__() self.embd = Phi3Embedding(config.vocab_size, config.hidden_size) self.h = nn.ModuleList([Phi3ParallelBlock(config) for _ in range(config.num_hidden_layers)]) self.norm = nn.RMSNorm(config.hidden_size, -1, config.rms_norm_eps, bias=False) 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) hidden_states = self.norm(hidden_states) return hidden_states class Phi3ForCausalLM(nn.Module): def __init__(self, config: Phi3Config) -> None: super().__init__() self.transformer = Phi3Model(config) self.tie_word_embeddings = config.tie_word_embeddings if not config.tie_word_embeddings: self.lm_head = nn.Linear(config.hidden_size, "vocab_size", bias=False) self.num_hidden_layers = config.num_hidden_layers self.num_attention_heads = config.num_attention_heads self.num_key_value_heads = config.num_key_value_heads self.head_dim = config.head_dim self.hidden_size = config.hidden_size self.vocab_size = config.vocab_size self.rope_scaling = config.rope_scaling self.rope_theta = config.position_embedding_base self.rope_ext_factors = ( (config.rope_scaling["long_factor"] + config.rope_scaling["short_factor"]) if config.rope_scaling is not None else None ) self.tensor_parallel_shards = config.tensor_parallel_shards self.partial_rotary_factor = config.partial_rotary_factor self.dtype = "float32" def to(self, dtype: Optional[str] = None): super().to(dtype=dtype) if dtype is not None: self.dtype = dtype def get_logits(self, hidden_states: Tensor): op_ext.configure() if self.tie_word_embeddings: logits = self.transformer.embd.lm_head_forward(hidden_states) else: logits = self.lm_head(hidden_states) if logits.dtype != "float32": logits = logits.astype("float32") return logits 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) return self.get_logits(hidden_states) 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.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.transformer(input_embed, paged_kv_cache) logits = self.get_logits(hidden_states) 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_scaling=self.rope_scaling, rope_scale=1, rope_theta=self.rope_theta, rope_ext_factors=self.rope_ext_factors, rotary_dim=int(self.head_dim * self.partial_rotary_factor), 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)