""" Implementation for Phi architecture. """ import dataclasses from typing import Any, Dict, Optional # noqa: UP035 from tvm import relax, target, 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.model.phi3 import Phi3Model from mlc_llm.model.vision import CLIPVisionConfig, ImageProcessor from mlc_llm.nn import PagedKVCache, RopeMode from mlc_llm.support import logging from mlc_llm.support.config import ConfigBase from mlc_llm.support.style import bold from .phi3v_image import Phi3ImageEmbedding logger = logging.getLogger(__name__) CLIPVISION_DEFAULT_CONFIG = { "hidden_size": 1024, "image_size": 336, "intermediate_size": 4096, "num_attention_heads": 16, "num_hidden_layers": 24, "patch_size": 14, "projection_dim": 768, "layer_norm_eps": 1e-05, "vocab_size": None, } @dataclasses.dataclass class Phi3VConfig(ConfigBase): """Configuration of the Phi-3 Vision model.""" model_type: str 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 vision_config: CLIPVisionConfig = None img_processor: Optional[Dict[str, Any]] = None # noqa: UP006 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 kwargs: Dict[str, Any] = dataclasses.field(default_factory=dict) # noqa: UP006 def __post_init__(self): vision_config_dict: Dict[str, Any] # noqa: UP006 if isinstance(self.vision_config, CLIPVisionConfig): vision_config_dict = dataclasses.asdict(self.vision_config) else: vision_config_dict = dict(CLIPVISION_DEFAULT_CONFIG) for k, v in vision_config_dict.pop("kwargs", {}).items(): vision_config_dict[k] = v self.vision_config = CLIPVisionConfig.from_dict(vision_config_dict) 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 # mypy: disable-error-code="arg-type,annotation-unchecked" class Phi3VForCausalLM(nn.Module): def __init__(self, config: Phi3VConfig) -> None: super().__init__() self.config = config self.model = Phi3Model(config) self.lm_head = nn.Linear(config.hidden_size, "vocab_size", bias=False) self.vision_embed_tokens = Phi3ImageEmbedding(config) self.image_processor = ImageProcessor() 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.dtype = "float32" self.image_dtype = ( "uint32" if target.Target.current() and target.Target.current().kind.name == "webgpu" else "uint8" ) 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.model(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.model(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.model(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.model.embd(input_ids) return embeds def image_preprocess( self, pixel_values: Tensor, resized_height, resized_width, num_crops=16 ) -> Tensor: pixel_values = op.permute_dims(pixel_values, axes=(0, 3, 1, 2)) # NHWC -> NCHW pixel_values = self.image_processor.resize( pixel_values, params={"height": resized_height, "width": resized_width} ) pixel_values = self.image_processor.pad(pixel_values, dtype=self.image_dtype) pixel_values = self.image_processor.rescale(pixel_values) pixel_values = self.image_processor.normalize(pixel_values) global_image = self.image_processor.resize( pixel_values, params={"height": 336, "width": 336} ) global_image = op.wrap_nested( relax.BlockBuilder() .current() .match_cast( global_image._expr, relax.TensorType( [global_image.shape[0], global_image.shape[1], 336, 336], global_image.dtype, ), ), "global_image", ) n, c, h, w = pixel_values.shape assert isinstance(h, tirx.Mul) and isinstance(h.b, tirx.IntImm) and h.b.value == 336 pixel_values = op.reshape(pixel_values, shape=(1, 3, h.a, 336, w // 336, 336)) pixel_values = op.permute_dims(pixel_values, axes=(0, 2, 4, 1, 3, 5)) pixel_values = op.reshape(pixel_values, shape=(-1, 3, 336, 336)) combined_image = op.concat([global_image, pixel_values], dim=0) # pad to max num crops tensor b, c, h, w = combined_image.shape zeros = op.zeros((num_crops + 1 - b, c, h, w)) combined_image = op.concat([combined_image, zeros], dim=0) combined_image = op.wrap_nested( relax.BlockBuilder() .current() .match_cast( combined_image._expr, relax.TensorType([num_crops + 1, c, h, w], combined_image.dtype), ), "combined_image", ) return combined_image def image_embed( self, pixel_values: Tensor, resized_height, resized_width, crop_height, crop_width, ) -> Tensor: n, h, w, c = pixel_values.shape pixel_values = self.image_preprocess(pixel_values, resized_height, resized_width) pixel_values = pixel_values.astype(self.dtype) return self.vision_embed_tokens(pixel_values, crop_height, crop_width) 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, dtype=self.dtype, ) def get_default_spec(self): mod_spec = { "embed": { "input_ids": nn.spec.Tensor(["seq_len"], "int32"), "$": { "param_mode": "packed", "effect_mode": "none", }, }, "image_embed": { "pixel_values": nn.spec.Tensor( [1, "image_height", "image_width", 3], self.image_dtype ), "resized_height": nn.spec.Int(), "resized_width": nn.spec.Int(), "crop_height": nn.spec.Int(), "crop_width": nn.spec.Int(), "$": { "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)