""" Implementation for OLMo architecture. TODO: add docstring """ import dataclasses from functools import partial 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 OLMoConfig(ConfigBase): """Configuration of the OLMo model.""" vocab_size: int = None hidden_size: int = None num_attention_heads: int = None num_key_value_heads: int = 0 head_dim: int = 0 position_embedding_base: int = 0 rope_scaling: Optional[Dict[str, Any]] = None # noqa: UP006 intermediate_size: int = None hidden_act: str = None num_hidden_layers: int = None tie_word_embeddings: bool = False context_window_size: int = 0 prefill_chunk_size: int = 0 tensor_parallel_shards: int = 1 pipeline_parallel_stages: int = 1 max_batch_size: int = 1 clip_qkv: float = None kwargs: Dict[str, Any] = dataclasses.field(default_factory=dict) # noqa: UP006 def __post_init__(self): if self.num_key_value_heads == 0: 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.num_attention_heads % self.num_key_value_heads == 0 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.context_window_size == 0: for name in ["max_position_embeddings", "max_sequence_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.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.pipeline_parallel_stages <= 0 or self.pipeline_parallel_stages > self.num_hidden_layers ): raise ValueError( f'Invalid "pipeline_parallel_stages" value({self.pipeline_parallel_stages}). ' ) if self.clip_qkv is not None: if self.clip_qkv <= 0: raise ValueError(f"'clip_qkv'({self.clip_qkv}) should be non-negative") class OLMoEmbedding(nn.Embedding): """The embedding module that can be shared with the final lm_head. From Qwen2Embedding.""" 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 OLMoAttention(nn.Module): def __init__(self, config: OLMoConfig): self.num_q_heads = config.num_attention_heads // config.tensor_parallel_shards assert config.num_key_value_heads >= config.tensor_parallel_shards, ( f"Too large tensor_parallel_shards, must be smaller than {config.num_key_value_heads}" ) assert config.num_key_value_heads % config.tensor_parallel_shards == 0, ( f"num_kv_heads({config.num_key_value_heads}) must be divisible by tensor_parallel_shards" # noqa: E501 ) self.num_kv_heads = config.num_key_value_heads // config.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_kv_heads) * self.head_dim, bias=False, ) self.clip_qkv = config.clip_qkv self.o_proj = nn.Linear( in_features=self.num_q_heads * self.head_dim, out_features=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_kv_heads b, s, _ = hidden_states.shape # QKV Projection qkv = self.qkv_proj(hidden_states) # Clamp after qkv projection if needed if self.clip_qkv is not None: qkv = qkv.maximum(-self.clip_qkv).minimum(self.clip_qkv) 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.o_proj(output) # Copied from qwen2_model.ACT2FN ACT2FN = { "gelu": partial(nn.gelu, approximate=False), "relu": nn.relu, "silu": nn.silu, "swish": nn.silu, "gelu_new": partial(nn.gelu, approximate=True), } class OLMoFFN(nn.Module): def __init__(self, config: OLMoConfig): 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( in_features=config.hidden_size, out_features=2 * self.intermediate_size, bias=False, ) self.act_fn = ACT2FN[config.hidden_act] self.down_proj = nn.Linear( in_features=self.intermediate_size, out_features=config.hidden_size, bias=False, ) def forward(self, x: Tensor): concat_x1_x2 = self.gate_up_proj(x) x1, x2 = op.split(concat_x1_x2, 2, axis=-1) return self.down_proj(self.act_fn(x1) * x2) class OLMoDecoderLayer(nn.Module): def __init__(self, config: OLMoConfig): self.input_layernorm = nn.LayerNorm( normalized_shape=config.hidden_size, eps=1e-5, elementwise_affine=False, ) self.self_attn = OLMoAttention(config) self.post_attention_layernorm = nn.LayerNorm( normalized_shape=config.hidden_size, eps=1e-5, elementwise_affine=False, ) self.mlp = OLMoFFN(config) def _set_tp(): def _set(layer, hint): layer.weight.attrs["shard_strategy"] = hint hd = config.head_dim q = self.self_attn.num_q_heads * hd k = self.self_attn.num_kv_heads * hd v = self.self_attn.num_kv_heads * hd i = self.mlp.intermediate_size _set( self.self_attn.qkv_proj, tp.ShardSingleDim("_shard_qkv", segs=[q, k, v], dim=0), ) _set(self.self_attn.o_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 _apply_residual(self, out, residual): if self.tensor_parallel_shards > 1: return op.ccl_allreduce(out, "sum") + residual return out + residual def forward(self, hidden_states: Tensor, paged_kv_cache: PagedKVCache, layer_id: int): out = self.self_attn(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 class OLMoModel(nn.Module): def __init__(self, config: OLMoConfig): assert config.hidden_size % config.num_attention_heads == 0 self.embed_tokens = OLMoEmbedding(config.vocab_size, config.hidden_size) self.layers = nn.ModuleList( [OLMoDecoderLayer(config) for _ in range(config.num_hidden_layers)] ) self.norm = nn.LayerNorm( normalized_shape=config.hidden_size, eps=1e-5, elementwise_affine=False, ) self.num_layers_per_stage = ( config.num_hidden_layers + config.pipeline_parallel_stages - 1 ) // config.pipeline_parallel_stages # Compute pipeline layer partition. layers_per_stage = ( config.num_hidden_layers + config.pipeline_parallel_stages - 1 ) // config.pipeline_parallel_stages self.layer_partition = [ i * layers_per_stage for i in range(config.pipeline_parallel_stages) ] + [config.num_hidden_layers] def forward(self, inputs: Tensor, paged_kv_cache: PagedKVCache): hidden_states = inputs for layer_id, layer in enumerate(self.layers): if layer_id != 0 and layer_id in self.layer_partition: hidden_states = op_ext.pipeline_stage_boundary(hidden_states) hidden_states = layer(hidden_states, paged_kv_cache, layer_id) hidden_states = self.norm(hidden_states) return hidden_states class OLMoForCausalLM(nn.Module): def __init__(self, config: OLMoConfig): self.model = OLMoModel(config) self.tie_word_embeddings = config.tie_word_embeddings if not config.tie_word_embeddings: self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False) self.vocab_size = config.vocab_size self.hidden_size = config.hidden_size 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.rope_theta = config.position_embedding_base self.rope_scaling = config.rope_scaling self.intermediate_size = config.intermediate_size self.num_hidden_layers = config.num_hidden_layers self.tensor_parallel_shards = config.tensor_parallel_shards self.dtype = "float32" def _set_pp(): # hidden layers for layer_id in range(config.num_hidden_layers): stage = layer_id // (config.num_hidden_layers // config.pipeline_parallel_stages) for _, param in self.model.layers[layer_id].named_parameters(): param.attrs["pipeline_stages"] = [stage] # embedding table and lm_head is required by all stages all_stages = list(range(config.pipeline_parallel_stages)) self.model.embed_tokens.weight.attrs["pipeline_stages"] = all_stages if not config.tie_word_embeddings: self.lm_head.weight.attrs["pipeline_stages"] = all_stages _set_pp() 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: if self.tensor_parallel_shards > 1: logit_positions = op.ccl_broadcast_from_worker0(logit_positions) hidden_states = op.take(hidden_states, logit_positions, axis=1) return self.get_logits(hidden_states) def batch_forward_to_last_hidden_states( self, input_embeds: Tensor, paged_kv_cache: PagedKVCache, ): op_ext.configure() hidden_states = self.model(input_embeds, paged_kv_cache) return hidden_states def embed(self, input_ids: Tensor): if self.tensor_parallel_shards > 1: input_ids = op.ccl_broadcast_from_worker0(input_ids) return self.model.embed_tokens(input_ids) def get_logits(self, hidden_states: Tensor): op_ext.configure() if self.tie_word_embeddings: logits = self.model.embed_tokens.lm_head_forward(hidden_states) else: logits = self.lm_head(hidden_states) if logits.dtype != "float32": logits = logits.astype("float32") return logits def batch_select_last_hidden_states(self, hidden_states: Tensor, logit_positions: Tensor): op_ext.configure() if self.tensor_parallel_shards > 1: logit_positions = op.ccl_broadcast_from_worker0(logit_positions) hidden_states = op.take(hidden_states, logit_positions, axis=0) return hidden_states 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.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.model(input_embed, paged_kv_cache) logits = self.get_logits(hidden_states) return logits, paged_kv_cache def prefill_to_last_hidden_states(self, input_embed: Tensor, paged_kv_cache: PagedKVCache): op_ext.configure() hidden_states = self.model(input_embed, paged_kv_cache) return hidden_states, paged_kv_cache def decode_to_last_hidden_states(self, input_embed: Tensor, paged_kv_cache: PagedKVCache): op_ext.configure() hidden_states = self.model(input_embed, paged_kv_cache) return hidden_states, paged_kv_cache def batch_prefill( self, input_embeds: Tensor, logit_positions: Tensor, paged_kv_cache: PagedKVCache, ): 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 batch_prefill_to_last_hidden_states( self, input_embeds: Tensor, paged_kv_cache: PagedKVCache ): hidden_states = self.batch_forward_to_last_hidden_states(input_embeds, paged_kv_cache) return hidden_states, paged_kv_cache def batch_decode_to_last_hidden_states( self, input_embeds: Tensor, paged_kv_cache: PagedKVCache ): hidden_states = self.batch_forward_to_last_hidden_states(input_embeds, paged_kv_cache) return hidden_states, paged_kv_cache def batch_verify_to_last_hidden_states( self, input_embeds: Tensor, paged_kv_cache: PagedKVCache ): hidden_states = self.batch_forward_to_last_hidden_states(input_embeds, paged_kv_cache) return hidden_states, 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, rope_scaling=self.rope_scaling, layer_partition=self.model.layer_partition, dtype=self.dtype, ) def get_default_spec(self): mod_spec = { "embed": { "input_ids": nn.spec.Tensor(["seq_len"], "int32"), "$": { "param_mode": "packed", "effect_mode": "none", }, }, "get_logits": { "hidden_states": nn.spec.Tensor(["seq_len", self.hidden_size], self.dtype), "$": { "param_mode": "packed", "effect_mode": "none", }, }, "batch_select_last_hidden_states": { "hidden_states": nn.spec.Tensor(["seq_len", self.hidden_size], self.dtype), "logit_positions": nn.spec.Tensor(["batch_size"], "int32"), "$": { "param_mode": "none", "effect_mode": "none", }, }, "prefill": { "input_embed": nn.spec.Tensor([1, "seq_len", self.hidden_size], self.dtype), "paged_kv_cache": nn.spec.Object(object_type=PagedKVCache), "$": { "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), "$": { "param_mode": "packed", "effect_mode": "none", }, }, "prefill_to_last_hidden_states": { "input_embed": nn.spec.Tensor([1, "seq_len", self.hidden_size], self.dtype), "paged_kv_cache": nn.spec.Object(object_type=PagedKVCache), "$": { "param_mode": "packed", "effect_mode": "none", }, }, "decode_to_last_hidden_states": { "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", }, }, "batch_prefill_to_last_hidden_states": { "input_embeds": nn.spec.Tensor([1, "seq_len", self.hidden_size], self.dtype), "paged_kv_cache": nn.spec.Object(object_type=PagedKVCache), "$": { "param_mode": "packed", "effect_mode": "none", }, }, "batch_decode_to_last_hidden_states": { "input_embeds": nn.spec.Tensor(["batch_size", 1, self.hidden_size], self.dtype), "paged_kv_cache": nn.spec.Object(object_type=PagedKVCache), "$": { "param_mode": "packed", "effect_mode": "none", }, }, "batch_verify_to_last_hidden_states": { "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)