wehub-resource-sync
187 lines
7.2 KiB
Python
187 lines
7.2 KiB
Python
"""Implementation for Mistral architecture."""
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import dataclasses
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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.llama.llama_model import (
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LlamaAttention,
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LlamaConfig,
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LlamaForCausalLM,
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LlamaModel,
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)
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from mlc_llm.nn import PagedKVCache
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from mlc_llm.nn.expert import MixtralExperts
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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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logger = logging.getLogger(__name__)
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@dataclasses.dataclass
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class MixtralConfig(LlamaConfig):
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"""Configuration of the Mixtral model."""
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num_local_experts: int = 0
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num_experts_per_tok: int = 0
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class MixtralMoE(nn.Module):
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"""Mixture of experts"""
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def __init__(self, config: MixtralConfig):
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super().__init__()
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self.num_experts_per_tok = config.num_experts_per_tok
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self.num_local_experts = config.num_local_experts
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if config.intermediate_size % config.tensor_parallel_shards != 0:
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raise ValueError(
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f"Cannot split MoE intermediate size {config.intermediate_size} "
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f"evenly to {config.tensor_parallel_shards} GPUs."
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)
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self.intermediate_size = config.intermediate_size // config.tensor_parallel_shards
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self.gate = nn.Linear(
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in_features=config.hidden_size,
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out_features=config.num_local_experts,
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bias=False,
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)
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self.e1_e3 = MixtralExperts(
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self.num_local_experts,
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in_features=config.hidden_size,
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out_features=2 * self.intermediate_size,
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tensor_parallel_shards=config.tensor_parallel_shards,
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)
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self.e2 = MixtralExperts(
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self.num_local_experts,
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in_features=self.intermediate_size,
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out_features=config.hidden_size,
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tensor_parallel_shards=config.tensor_parallel_shards,
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)
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self.dtype = "float32"
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def forward(self, x: Tensor):
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def _expert_forward(x: Tensor, indptr: Tensor):
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x1_x3 = self.e1_e3(x, indptr)
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x1, x3 = op.split(x1_x3, indices_or_sections=2, axis=-1)
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x = self.e2(op.silu(x1) * x3, indptr)
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return x
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experts_per_tok = self.num_experts_per_tok # activated experts per token
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local_experts = self.num_local_experts # total number of experts
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batch_size, seq_len, hidden_size = x.shape
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num_tokens = batch_size * seq_len
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x = x.reshape(num_tokens, hidden_size)
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# gate: [num_tokens, local_experts]
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gate: Tensor = self.gate(x)
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# expert_weights: [num_tokens, experts_per_tok]
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# expert_indices: [num_tokens, experts_per_tok]
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expert_weights, expert_indices = op_ext.moe_misc.gating_softmax_topk(gate, experts_per_tok)
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use_ft = (
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op_ext.get_store().cutlass_group_gemm or op_ext.get_store().faster_transformer
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) and self.dtype == "float16"
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if num_tokens == 1:
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# x: [num_tokens * experts_per_tok, hidden_size]
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x = _expert_forward(x, expert_indices)
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else:
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# cumsum: [num_tokens * local_experts]
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cumsum = op_ext.moe_misc.moe_cumsum(expert_indices, local_experts)
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# indices: [num_tokens * experts_per_tok]
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reverse_indices, token_indices = op_ext.moe_misc.get_indices(cumsum, expert_indices)
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if use_ft:
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# indptr: [num_local_experts]
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indptr = op_ext.moe_misc.get_indptr(
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cumsum, local_experts, num_tokens, inclusive=True, out_dtype="int64"
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)
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else:
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# indptr: [num_local_experts + 1]
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indptr = op_ext.moe_misc.get_indptr(
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cumsum,
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local_experts,
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num_tokens,
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inclusive=False,
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out_dtype="int32",
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)
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# x: [num_tokens * experts_per_tok, hidden_size]
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x = op.take(x, token_indices, axis=0)
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x = _expert_forward(x, indptr)
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x = op_ext.moe_misc.scatter_output(x, reverse_indices)
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# x: [num_tokens, experts_per_tok, hidden_size]
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x = x.reshape(num_tokens, experts_per_tok, hidden_size) * expert_weights.reshape(
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num_tokens, experts_per_tok, 1
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)
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# x: [num_tokens, hidden_size]
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x = op_ext.moe_misc.moe_sum(x, dim=1)
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x = x.reshape(batch_size, seq_len, hidden_size)
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return x
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class MixtralDecoderLayer(nn.Module):
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"""Mixtral decoder layer"""
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def __init__(self, config: MixtralConfig):
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eps = config.rms_norm_eps
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self.self_attn = LlamaAttention(config)
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self.moe = MixtralMoE(config)
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self.input_layernorm = nn.RMSNorm(config.hidden_size, -1, eps, bias=False)
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self.post_attention_layernorm = nn.RMSNorm(config.hidden_size, -1, eps, bias=False)
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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.head_dim
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q = self.self_attn.num_q_heads * hd
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k = self.self_attn.num_kv_heads * hd
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v = self.self_attn.num_kv_heads * hd
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i = self.moe.intermediate_size
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_set(
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self.self_attn.qkv_proj,
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tp.ShardSingleDim("_shard_qkv", segs=[q, k, v], dim=0),
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)
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_set(self.self_attn.o_proj, tp.ShardSingleDim("_shard_o", dim=1))
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_set(self.moe.e1_e3, tp.ShardSingleDim("_shard_mlp_up", segs=[i, i], dim=1))
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_set(self.moe.e2, tp.ShardSingleDim("_shard_mlp_down", dim=2))
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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, attention_mask: Tensor, total_seq_len: tirx.Var):
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"""Forward pass of a decoder layer; calculate attention, and add an residual connection."""
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out = self.self_attn(self.input_layernorm(hidden_states), attention_mask, total_seq_len)
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hidden_states = self._apply_residual(out, residual=hidden_states)
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out = self.moe(self.post_attention_layernorm(hidden_states))
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hidden_states = self._apply_residual(out, residual=hidden_states)
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return hidden_states
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def batch_forward(self, hidden_states: Tensor, paged_kv_cache: PagedKVCache, layer_id: int):
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out = self.self_attn(self.input_layernorm(hidden_states), paged_kv_cache, layer_id)
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hidden_states = self._apply_residual(out, residual=hidden_states)
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out = self.moe(self.post_attention_layernorm(hidden_states))
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hidden_states = self._apply_residual(out, residual=hidden_states)
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return hidden_states
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def _apply_residual(self, out, residual):
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if self.tensor_parallel_shards > 1:
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return op.ccl_allreduce(out, "sum") + residual
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return out + residual
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class MixtralModel(LlamaModel):
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"""Exact same as LlamaModel."""
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def __init__(self, config: MixtralConfig):
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super().__init__(config)
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self.layers = nn.ModuleList(
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[MixtralDecoderLayer(config) for _ in range(config.num_hidden_layers)]
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)
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class MixtralForCausalLM(LlamaForCausalLM):
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"""Same as LlamaForCausalLM."""
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def __init__(self, config: MixtralConfig):
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super().__init__(config)
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self.model = MixtralModel(config)
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