""" Implementation for CHATGLM3 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 GLMConfig(ConfigBase): """Configuration of the ChatGLM model.""" hidden_size: int num_layers: int kv_channels: int num_attention_heads: int ffn_hidden_size: int layernorm_epsilon: float post_layer_norm: bool rmsnorm: bool add_bias_linear: bool add_qkv_bias: bool apply_query_key_layer_scaling: bool multi_query_attention: bool multi_query_group_num: int vocab_size: int = 0 context_window_size: int = 0 prefill_chunk_size: int = 0 tensor_parallel_shards: int = 1 head_dim: int = 0 max_batch_size: int = 1 kwargs: Dict[str, Any] = dataclasses.field(default_factory=dict) # noqa: UP006 def __post_init__(self): if self.vocab_size == 0: for name in ["padded_vocab_size"]: if name in self.kwargs: self.vocab_size = self.kwargs.pop(name) if self.context_window_size == 0: for name in ["max_position_embeddings", "seq_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.head_dim == 0: self.head_dim = self.hidden_size // self.num_attention_heads assert self.head_dim * self.num_attention_heads == self.hidden_size 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) class GLMAttention(nn.Module): def __init__(self, config: GLMConfig): self.hidden_size = config.hidden_size if config.num_attention_heads % config.tensor_parallel_shards != 0: raise ValueError( f"Cannot split {config.num_attention_heads} attention heads" f"evenly to {config.tensor_parallel_shards} GPUs." ) self.num_heads = config.num_attention_heads // config.tensor_parallel_shards self.multi_query_attention = config.multi_query_attention self.num_key_value_heads = ( config.multi_query_group_num if config.multi_query_attention else config.num_attention_heads ) // config.tensor_parallel_shards self.head_dim = config.head_dim self.query_key_value = nn.Linear( config.hidden_size, (2 * self.num_key_value_heads + self.num_heads) * self.head_dim, bias=config.add_bias_linear or config.add_qkv_bias, ) self.dense = nn.Linear( self.num_heads * self.head_dim, config.hidden_size, bias=config.add_bias_linear, ) def forward(self, hidden_states: Tensor, paged_kv_cache: PagedKVCache, layer_id: int): d, h_q, h_kv = self.head_dim, self.num_heads, self.num_key_value_heads b, s, _ = hidden_states.shape qkv = self.query_key_value(hidden_states) qkv = op.reshape(qkv, (b, s, h_q + h_kv + h_kv, d)) output = op.reshape( paged_kv_cache.attention_with_fused_qkv( layer_id, qkv, h_q, sm_scale=self.head_dim**-0.5 ), (b, s, h_q * d), ) attn_output = self.dense(output) return attn_output class GLMMLP(nn.Module): def __init__(self, config: GLMConfig): if config.ffn_hidden_size % config.tensor_parallel_shards != 0: raise ValueError( f"Cannot split ffn hidden size {config.ffn_hidden_size} " f"evenly to {config.tensor_parallel_shards} GPUs." ) self.ffn_hidden_size = config.ffn_hidden_size // config.tensor_parallel_shards self.dense_h_to_4h = nn.Linear( config.hidden_size, self.ffn_hidden_size * 2, bias=config.add_bias_linear, ) self.dense_4h_to_h = nn.Linear( self.ffn_hidden_size, config.hidden_size, bias=config.add_bias_linear, ) def swiglu(x): x = nn.chunk(x, 2, dim=-1) return nn.silu(x[0]) * x[1] self.activation_func = swiglu def forward(self, x): intermediate_parallel = self.dense_h_to_4h(x) intermediate_parallel = self.activation_func(intermediate_parallel) output = self.dense_4h_to_h(intermediate_parallel) return output class GLMBlock(nn.Module): def __init__(self, config: GLMConfig): self.self_attention = GLMAttention(config=config) self.mlp = GLMMLP(config) self.input_layernorm = nn.RMSNorm( config.hidden_size, -1, config.layernorm_epsilon, bias=False ) self.post_attention_layernorm = nn.RMSNorm( config.hidden_size, -1, config.layernorm_epsilon, bias=False ) def _set_tp(): def _set(layer, hint): layer.attrs["shard_strategy"] = hint hd = config.head_dim q = self.self_attention.num_heads * hd k = self.self_attention.num_key_value_heads * hd v = self.self_attention.num_key_value_heads * hd _set( self.self_attention.query_key_value.weight, tp.ShardSingleDim("_shard_qkv_weight", dim=0, segs=[q, k, v]), ) if config.add_bias_linear or config.add_qkv_bias: _set( self.self_attention.query_key_value.bias, tp.ShardSingleDim("_shard_qkv_bias", dim=0, segs=[q, k, v]), ) _set( self.self_attention.dense.weight, tp.ShardSingleDim("_shard_dense_weight", dim=1), ) if config.add_bias_linear: _set( self.self_attention.dense.bias, tp.ShardSingleDim("_shard_dense_bias", dim=0), ) _set( self.mlp.dense_h_to_4h.weight, tp.ShardSingleDim("_shard_dense_h_to_4h_weight", dim=0), ) if config.add_bias_linear: _set( self.mlp.dense_h_to_4h.bias, tp.ShardSingleDim("_shard_dense_h_to_4h_bias", dim=0), ) _set( self.mlp.dense_4h_to_h.weight, tp.ShardSingleDim("_shard_dense_4h_to_h", dim=1), ) if config.add_bias_linear: _set( self.mlp.dense_4h_to_h.bias, tp.ShardSingleDim("_shard_dense_4h_to_h_bias", 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): out = self.self_attention(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 def _apply_residual(self, out, residual): if self.tensor_parallel_shards > 1: return op.ccl_allreduce(out, "sum") + residual return out + residual class GLMTransformer(nn.Module): """Transformer class.""" def __init__(self, config: GLMConfig): self.post_layer_norm = config.post_layer_norm # Number of layers. self.num_layers = config.num_layers # Transformer layers. self.layers = nn.ModuleList([GLMBlock(config) for _ in range(config.num_layers)]) if self.post_layer_norm: if config.rmsnorm: self.final_layernorm = nn.RMSNorm( config.hidden_size, -1, config.layernorm_epsilon, bias=False ) else: self.final_layernorm = nn.LayerNorm(config.hidden_size, config.layernorm_epsilon) def forward(self, inputs: Tensor, paged_kv_cache: PagedKVCache): hidden_states = inputs for layer_id, layer in enumerate(self.layers): hidden_states = layer(hidden_states, paged_kv_cache, layer_id) hidden_states = self.final_layernorm(hidden_states) return hidden_states class ChatGLMModel(nn.Module): def __init__(self, config: GLMConfig): self.embedding = nn.Embedding(config.vocab_size, config.hidden_size) self.encoder = GLMTransformer(config) self.output_layer = nn.Linear(config.hidden_size, config.vocab_size, bias=False) def forward(self, inputs: Tensor, paged_kv_cache: PagedKVCache): hidden_states = inputs hidden_states = self.encoder(hidden_states, paged_kv_cache) return hidden_states class ChatGLMForCausalLM(nn.Module): def __init__(self, config: GLMConfig): self.transformer = ChatGLMModel(config) self.num_hidden_layers = config.num_layers self.hidden_size = config.hidden_size self.num_attention_heads = config.num_attention_heads self.num_key_value_heads = ( config.multi_query_group_num if config.multi_query_attention else config.num_attention_heads ) self.head_dim = config.head_dim self.vocab_size = config.vocab_size self.rope_theta = 10000 self.tensor_parallel_shards = config.tensor_parallel_shards self.dtype = "float32" 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.transformer(input_embeds, paged_kv_cache) if logit_positions is not None: hidden_states = op.take(hidden_states, logit_positions, axis=1) logits = self.transformer.output_layer(hidden_states) if logits.dtype != "float32": logits = logits.astype("float32") return logits def embed(self, input_ids: Tensor): if self.tensor_parallel_shards > 1: input_ids = op.ccl_broadcast_from_worker0(input_ids) return self.transformer.embedding(input_ids) 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.transformer.output_layer(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.transformer(input_embed, paged_kv_cache) logits = self.transformer.output_layer(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 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, 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)