inherit from the agent class, removed code and improved self consistency docs] [fixed reasoning agent router, i mproved the docstrings, and docs for the self consistencymaster
Kye Gomez
15 hours ago
parent
440173817d
commit
b4410ce9a9
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from swarms import SelfConsistencyAgent
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# Initialize the reasoning agent router with self-consistency
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reasoning_agent_router = SelfConsistencyAgent(
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name="reasoning-agent",
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description="A reasoning agent that can answer questions and help with tasks.",
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model_name="gpt-4o-mini",
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system_prompt="You are a helpful assistant that can answer questions and help with tasks.",
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max_loops=1,
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num_samples=3, # Generate 3 independent responses
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eval=False, # Disable evaluation mode
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random_models_on=False, # Disable random model selection
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majority_voting_prompt=None, # Use default majority voting prompt
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)
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# Run the agent on a financial analysis task
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result = reasoning_agent_router.run(
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"What is the best possible financial strategy to maximize returns but minimize risk? Give a list of etfs to invest in and the percentage of the portfolio to allocate to each etf."
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)
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print("Financial Strategy Result:")
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print(result)
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from swarms.agents.reasoning_agents import ReasoningAgentRouter
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reasoning_agent_router = ReasoningAgentRouter(
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agent_name="reasoning-agent",
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description="A reasoning agent that can answer questions and help with tasks.",
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model_name="gpt-4o-mini",
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system_prompt="You are a helpful assistant that can answer questions and help with tasks.",
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max_loops=1,
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swarm_type="self-consistency",
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num_samples=1,
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output_type="list",
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)
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reasoning_agent_router.run(
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"What is the best possible financial strategy to maximize returns but minimize risk? Give a list of etfs to invest in and the percentage of the portfolio to allocate to each etf."
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)
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# reasoning_agent_router.batched_run(
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# [
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# "What is the best possible financial strategy to maximize returns but minimize risk? Give a list of etfs to invest in and the percentage of the portfolio to allocate to each etf.",
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# "What is the best possible financial strategy to maximize returns but minimize risk? Give a list of etfs to invest in and the percentage of the portfolio to allocate to each etf.",
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# ]
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# )
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# from swarms import ReasoningAgentRouter
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# calculus_router = ReasoningAgentRouter(
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# agent_name="calculus-expert",
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# description="A calculus problem solving agent",
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# model_name="gpt-4o-mini",
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# system_prompt="You are a calculus expert. Solve differentiation and integration problems methodically.",
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# swarm_type="self-consistency",
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# num_samples=3, # Generate 3 samples to ensure consistency
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# output_type="list",
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# )
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# # Example calculus problem
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# calculus_problem = "Find the derivative of f(x) = x³ln(x) - 5x²"
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# # Get the solution
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# solution = calculus_router.run(calculus_problem)
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# print(solution)
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from swarms.agents.reasoning_agents import ReasoningAgentRouter
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# Initialize the reasoning agent router with self-consistency
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reasoning_agent_router = ReasoningAgentRouter(
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agent_name="reasoning-agent",
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description="A reasoning agent that can answer questions and help with tasks.",
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model_name="gpt-4o-mini",
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system_prompt="You are a helpful assistant that can answer questions and help with tasks.",
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max_loops=1,
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swarm_type="self-consistency",
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num_samples=3, # Generate 3 independent responses
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eval=False, # Disable evaluation mode
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random_models_on=False, # Disable random model selection
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majority_voting_prompt=None, # Use default majority voting prompt
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)
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# Run the agent on a financial analysis task
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result = reasoning_agent_router.run(
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"What is the best possible financial strategy to maximize returns but minimize risk? Give a list of etfs to invest in and the percentage of the portfolio to allocate to each etf."
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)
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print("Financial Strategy Result:")
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print(result)
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@ -0,0 +1,624 @@
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"""
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Sparse Mixture-of-Experts (MoE) Transformer Implementation
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Based on Gemini 2.5 architecture description
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This implementation provides a sparse MoE architecture that activates only a subset
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of expert parameters per input token, allowing for decoupling of model capacity
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from computation cost.
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"""
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from typing import Dict, Optional, Tuple, Union
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import torch
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import torch.nn as nn
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import torch.nn.functional as F
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from loguru import logger
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from torch import Tensor
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class Expert(nn.Module):
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"""
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Individual expert network in the MoE architecture.
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Each expert is a feed-forward network that specializes in processing
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certain types of input patterns.
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Args:
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hidden_dim: Hidden dimension size
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intermediate_dim: Intermediate dimension in feed-forward network
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dropout: Dropout probability
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activation: Activation function to use
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"""
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def __init__(
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self,
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hidden_dim: int,
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intermediate_dim: int,
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dropout: float = 0.1,
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activation: str = "swish",
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):
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super().__init__()
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self.hidden_dim = hidden_dim
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self.intermediate_dim = intermediate_dim
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# Feed-forward network
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self.w1 = nn.Linear(hidden_dim, intermediate_dim, bias=False)
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self.w2 = nn.Linear(intermediate_dim, hidden_dim, bias=False)
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self.dropout = nn.Dropout(dropout)
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# Activation function
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if activation == "swish":
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self.activation = lambda x: x * torch.sigmoid(x)
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elif activation == "gelu":
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self.activation = F.gelu
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elif activation == "relu":
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self.activation = F.relu
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else:
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raise ValueError(f"Unsupported activation: {activation}")
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self._init_weights()
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def _init_weights(self) -> None:
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"""Initialize weights with proper scaling."""
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nn.init.xavier_uniform_(self.w1.weight)
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nn.init.xavier_uniform_(self.w2.weight)
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def forward(self, x: Tensor) -> Tensor:
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"""
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Forward pass through the expert network.
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Args:
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x: Input tensor of shape [batch_size, seq_len, hidden_dim]
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Returns:
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Output tensor of shape [batch_size, seq_len, hidden_dim]
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"""
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x = self.w1(x)
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x = self.activation(x)
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x = self.dropout(x)
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x = self.w2(x)
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return x
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class Router(nn.Module):
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"""
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Gating network that routes tokens to appropriate experts.
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The router learns to assign input tokens to the most suitable experts
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based on the token representations.
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Args:
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hidden_dim: Hidden dimension size
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num_experts: Number of experts in the MoE layer
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top_k: Number of experts to activate per token
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temperature: Temperature for softmax routing
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"""
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def __init__(
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self,
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hidden_dim: int,
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num_experts: int,
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top_k: int = 2,
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temperature: float = 1.0,
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):
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super().__init__()
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self.hidden_dim = hidden_dim
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self.num_experts = num_experts
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self.top_k = top_k
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self.temperature = temperature
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# Linear layer for routing scores
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self.gate = nn.Linear(hidden_dim, num_experts, bias=False)
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self._init_weights()
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def _init_weights(self) -> None:
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"""Initialize routing weights."""
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nn.init.xavier_uniform_(self.gate.weight)
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def forward(self, x: Tensor) -> Tuple[Tensor, Tensor, Tensor]:
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"""
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Route tokens to experts.
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Args:
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x: Input tensor of shape [batch_size, seq_len, hidden_dim]
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Returns:
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Tuple of (routing_weights, expert_indices, routing_probs)
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- routing_weights: [batch_size, seq_len, top_k]
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- expert_indices: [batch_size, seq_len, top_k]
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- routing_probs: [batch_size, seq_len, num_experts]
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"""
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batch_size, seq_len, hidden_dim = x.shape
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# Compute routing scores
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routing_logits = self.gate(
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x
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) # [batch_size, seq_len, num_experts]
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routing_logits = routing_logits / self.temperature
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# Apply softmax to get probabilities
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routing_probs = F.softmax(routing_logits, dim=-1)
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# Select top-k experts
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routing_weights, expert_indices = torch.topk(
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routing_probs, self.top_k, dim=-1
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)
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# Normalize routing weights
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routing_weights = routing_weights / routing_weights.sum(
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dim=-1, keepdim=True
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)
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return routing_weights, expert_indices, routing_probs
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class MoELayer(nn.Module):
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"""
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Sparse Mixture-of-Experts layer.
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This layer contains multiple expert networks and a router that decides
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which experts to activate for each input token.
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Args:
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hidden_dim: Hidden dimension size
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num_experts: Number of expert networks
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top_k: Number of experts to activate per token
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intermediate_dim: Intermediate dimension in expert networks
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dropout: Dropout probability
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activation: Activation function for experts
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load_balance_weight: Weight for load balancing loss
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"""
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def __init__(
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self,
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hidden_dim: int,
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num_experts: int,
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top_k: int = 2,
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intermediate_dim: Optional[int] = None,
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dropout: float = 0.1,
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activation: str = "swish",
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load_balance_weight: float = 0.01,
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):
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super().__init__()
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self.hidden_dim = hidden_dim
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self.num_experts = num_experts
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self.top_k = top_k
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self.load_balance_weight = load_balance_weight
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if intermediate_dim is None:
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intermediate_dim = hidden_dim * 4
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# Create expert networks
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self.experts = nn.ModuleList(
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[
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Expert(
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hidden_dim, intermediate_dim, dropout, activation
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)
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for _ in range(num_experts)
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]
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)
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# Router for expert selection
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self.router = Router(hidden_dim, num_experts, top_k)
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logger.info(
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f"Created MoE layer with {num_experts} experts, top_k={top_k}"
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)
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def forward(self, x: Tensor) -> Tuple[Tensor, Dict[str, Tensor]]:
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"""
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Forward pass through MoE layer.
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Args:
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x: Input tensor of shape [batch_size, seq_len, hidden_dim]
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Returns:
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Tuple of (output, aux_losses)
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- output: [batch_size, seq_len, hidden_dim]
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- aux_losses: Dictionary containing auxiliary losses
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"""
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batch_size, seq_len, hidden_dim = x.shape
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# Get routing decisions
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routing_weights, expert_indices, routing_probs = self.router(
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x
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)
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# Initialize output
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output = torch.zeros_like(x)
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# Process each expert
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for i in range(self.num_experts):
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# Create mask for tokens routed to this expert
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expert_mask = (expert_indices == i).any(
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dim=-1
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) # [batch_size, seq_len]
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if not expert_mask.any():
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continue
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# Get tokens for this expert
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expert_tokens = x[expert_mask] # [num_tokens, hidden_dim]
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if expert_tokens.numel() == 0:
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continue
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# Process through expert
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expert_output = self.experts[i](expert_tokens)
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# Compute weights for this expert
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expert_weights = torch.zeros(
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batch_size, seq_len, device=x.device
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)
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for k in range(self.top_k):
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mask = expert_indices[:, :, k] == i
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expert_weights[mask] = routing_weights[:, :, k][mask]
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# Add weighted expert output
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expert_contribution = torch.zeros_like(x)
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expert_contribution[expert_mask] = expert_output
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output += expert_contribution * expert_weights.unsqueeze(
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-1
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)
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# Compute auxiliary losses
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aux_losses = self._compute_aux_losses(
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routing_probs, expert_indices
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)
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return output, aux_losses
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def _compute_aux_losses(
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self, routing_probs: Tensor, expert_indices: Tensor
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) -> Dict[str, Tensor]:
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"""
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Compute auxiliary losses for training stability.
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Args:
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routing_probs: Routing probabilities [batch_size, seq_len, num_experts]
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expert_indices: Selected expert indices [batch_size, seq_len, top_k]
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Returns:
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Dictionary of auxiliary losses
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"""
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batch_size, seq_len, num_experts = routing_probs.shape
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# Load balancing loss
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expert_usage = torch.zeros(
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num_experts, device=routing_probs.device
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)
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total_tokens = batch_size * seq_len * self.top_k
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for i in range(num_experts):
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expert_usage[i] = (
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expert_indices == i
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).sum().float() / total_tokens
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target_usage = 1.0 / num_experts
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load_balance_loss = F.mse_loss(
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expert_usage, torch.full_like(expert_usage, target_usage)
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)
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# Entropy loss to encourage diversity
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entropy_loss = (
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-(routing_probs * torch.log(routing_probs + 1e-8))
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.sum(dim=-1)
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.mean()
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)
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return {
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"load_balance_loss": load_balance_loss
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* self.load_balance_weight,
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"entropy_loss": entropy_loss * 0.01,
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||||||
|
"expert_usage": expert_usage,
|
||||||
|
}
|
||||||
|
|
||||||
|
|
||||||
|
class MoETransformerBlock(nn.Module):
|
||||||
|
"""
|
||||||
|
Transformer block with MoE feed-forward layer.
|
||||||
|
|
||||||
|
This block combines multi-head attention with a sparse MoE layer,
|
||||||
|
following the standard transformer architecture pattern.
|
||||||
|
|
||||||
|
Args:
|
||||||
|
hidden_dim: Hidden dimension size
|
||||||
|
num_heads: Number of attention heads
|
||||||
|
num_experts: Number of experts in MoE layer
|
||||||
|
top_k: Number of experts to activate per token
|
||||||
|
dropout: Dropout probability
|
||||||
|
layer_norm_eps: Epsilon for layer normalization
|
||||||
|
"""
|
||||||
|
|
||||||
|
def __init__(
|
||||||
|
self,
|
||||||
|
hidden_dim: int,
|
||||||
|
num_heads: int,
|
||||||
|
num_experts: int,
|
||||||
|
top_k: int = 2,
|
||||||
|
dropout: float = 0.1,
|
||||||
|
layer_norm_eps: float = 1e-6,
|
||||||
|
):
|
||||||
|
super().__init__()
|
||||||
|
self.hidden_dim = hidden_dim
|
||||||
|
|
||||||
|
# Multi-head attention
|
||||||
|
self.attention = nn.MultiheadAttention(
|
||||||
|
hidden_dim, num_heads, dropout=dropout, batch_first=True
|
||||||
|
)
|
||||||
|
|
||||||
|
# MoE layer
|
||||||
|
self.moe_layer = MoELayer(
|
||||||
|
hidden_dim=hidden_dim,
|
||||||
|
num_experts=num_experts,
|
||||||
|
top_k=top_k,
|
||||||
|
dropout=dropout,
|
||||||
|
)
|
||||||
|
|
||||||
|
# Layer normalization
|
||||||
|
self.norm1 = nn.LayerNorm(hidden_dim, eps=layer_norm_eps)
|
||||||
|
self.norm2 = nn.LayerNorm(hidden_dim, eps=layer_norm_eps)
|
||||||
|
|
||||||
|
# Dropout
|
||||||
|
self.dropout = nn.Dropout(dropout)
|
||||||
|
|
||||||
|
def forward(
|
||||||
|
self, x: Tensor, attention_mask: Optional[Tensor] = None
|
||||||
|
) -> Tuple[Tensor, Dict[str, Tensor]]:
|
||||||
|
"""
|
||||||
|
Forward pass through transformer block.
|
||||||
|
|
||||||
|
Args:
|
||||||
|
x: Input tensor [batch_size, seq_len, hidden_dim]
|
||||||
|
attention_mask: Optional attention mask
|
||||||
|
|
||||||
|
Returns:
|
||||||
|
Tuple of (output, aux_losses)
|
||||||
|
"""
|
||||||
|
# Self-attention with residual connection
|
||||||
|
residual = x
|
||||||
|
x = self.norm1(x)
|
||||||
|
attn_output, _ = self.attention(
|
||||||
|
x, x, x, key_padding_mask=attention_mask
|
||||||
|
)
|
||||||
|
x = residual + self.dropout(attn_output)
|
||||||
|
|
||||||
|
# MoE layer with residual connection
|
||||||
|
residual = x
|
||||||
|
x = self.norm2(x)
|
||||||
|
moe_output, aux_losses = self.moe_layer(x)
|
||||||
|
x = residual + self.dropout(moe_output)
|
||||||
|
|
||||||
|
return x, aux_losses
|
||||||
|
|
||||||
|
|
||||||
|
class MoETransformer(nn.Module):
|
||||||
|
"""
|
||||||
|
Complete sparse MoE Transformer model.
|
||||||
|
|
||||||
|
This model implements the full transformer architecture with sparse
|
||||||
|
mixture-of-experts layers, similar to the Gemini 2.5 architecture.
|
||||||
|
|
||||||
|
Args:
|
||||||
|
vocab_size: Vocabulary size
|
||||||
|
hidden_dim: Hidden dimension size
|
||||||
|
num_layers: Number of transformer layers
|
||||||
|
num_heads: Number of attention heads
|
||||||
|
num_experts: Number of experts per MoE layer
|
||||||
|
top_k: Number of experts to activate per token
|
||||||
|
max_seq_len: Maximum sequence length
|
||||||
|
dropout: Dropout probability
|
||||||
|
"""
|
||||||
|
|
||||||
|
def __init__(
|
||||||
|
self,
|
||||||
|
vocab_size: int,
|
||||||
|
hidden_dim: int,
|
||||||
|
num_layers: int,
|
||||||
|
num_heads: int,
|
||||||
|
num_experts: int,
|
||||||
|
top_k: int = 2,
|
||||||
|
max_seq_len: int = 2048,
|
||||||
|
dropout: float = 0.1,
|
||||||
|
):
|
||||||
|
super().__init__()
|
||||||
|
self.vocab_size = vocab_size
|
||||||
|
self.hidden_dim = hidden_dim
|
||||||
|
self.num_layers = num_layers
|
||||||
|
self.max_seq_len = max_seq_len
|
||||||
|
|
||||||
|
# Token embedding
|
||||||
|
self.token_embedding = nn.Embedding(vocab_size, hidden_dim)
|
||||||
|
|
||||||
|
# Positional encoding
|
||||||
|
self.pos_embedding = nn.Parameter(
|
||||||
|
torch.randn(1, max_seq_len, hidden_dim) * 0.02
|
||||||
|
)
|
||||||
|
|
||||||
|
# Transformer layers
|
||||||
|
self.layers = nn.ModuleList(
|
||||||
|
[
|
||||||
|
MoETransformerBlock(
|
||||||
|
hidden_dim=hidden_dim,
|
||||||
|
num_heads=num_heads,
|
||||||
|
num_experts=num_experts,
|
||||||
|
top_k=top_k,
|
||||||
|
dropout=dropout,
|
||||||
|
)
|
||||||
|
for _ in range(num_layers)
|
||||||
|
]
|
||||||
|
)
|
||||||
|
|
||||||
|
# Final layer norm
|
||||||
|
self.final_norm = nn.LayerNorm(hidden_dim)
|
||||||
|
|
||||||
|
# Output projection
|
||||||
|
self.output_projection = nn.Linear(
|
||||||
|
hidden_dim, vocab_size, bias=False
|
||||||
|
)
|
||||||
|
|
||||||
|
# Tie input and output embeddings
|
||||||
|
self.output_projection.weight = self.token_embedding.weight
|
||||||
|
|
||||||
|
self._init_weights()
|
||||||
|
|
||||||
|
logger.info(
|
||||||
|
f"Created MoE Transformer with {num_layers} layers, "
|
||||||
|
f"{num_experts} experts per layer, hidden_dim={hidden_dim}"
|
||||||
|
)
|
||||||
|
|
||||||
|
def _init_weights(self) -> None:
|
||||||
|
"""Initialize model weights."""
|
||||||
|
nn.init.normal_(self.token_embedding.weight, std=0.02)
|
||||||
|
nn.init.normal_(self.pos_embedding, std=0.02)
|
||||||
|
|
||||||
|
# Initialize output projection
|
||||||
|
nn.init.normal_(self.output_projection.weight, std=0.02)
|
||||||
|
|
||||||
|
def forward(
|
||||||
|
self,
|
||||||
|
input_ids: Tensor,
|
||||||
|
attention_mask: Optional[Tensor] = None,
|
||||||
|
return_aux_losses: bool = True,
|
||||||
|
) -> Union[Tensor, Tuple[Tensor, Dict[str, Tensor]]]:
|
||||||
|
"""
|
||||||
|
Forward pass through the model.
|
||||||
|
|
||||||
|
Args:
|
||||||
|
input_ids: Input token IDs [batch_size, seq_len]
|
||||||
|
attention_mask: Optional attention mask [batch_size, seq_len]
|
||||||
|
return_aux_losses: Whether to return auxiliary losses
|
||||||
|
|
||||||
|
Returns:
|
||||||
|
If return_aux_losses=False: logits [batch_size, seq_len, vocab_size]
|
||||||
|
If return_aux_losses=True: (logits, aux_losses)
|
||||||
|
"""
|
||||||
|
batch_size, seq_len = input_ids.shape
|
||||||
|
|
||||||
|
# Token embeddings
|
||||||
|
x = self.token_embedding(input_ids)
|
||||||
|
|
||||||
|
# Add positional encoding
|
||||||
|
x = x + self.pos_embedding[:, :seq_len, :]
|
||||||
|
|
||||||
|
# Collect auxiliary losses
|
||||||
|
all_aux_losses = {}
|
||||||
|
|
||||||
|
# Pass through transformer layers
|
||||||
|
for i, layer in enumerate(self.layers):
|
||||||
|
x, aux_losses = layer(x, attention_mask)
|
||||||
|
|
||||||
|
if return_aux_losses:
|
||||||
|
for key, value in aux_losses.items():
|
||||||
|
if key not in all_aux_losses:
|
||||||
|
all_aux_losses[key] = []
|
||||||
|
all_aux_losses[key].append(value)
|
||||||
|
|
||||||
|
# Final layer norm
|
||||||
|
x = self.final_norm(x)
|
||||||
|
|
||||||
|
# Output projection
|
||||||
|
logits = self.output_projection(x)
|
||||||
|
|
||||||
|
if not return_aux_losses:
|
||||||
|
return logits
|
||||||
|
|
||||||
|
# Average auxiliary losses across layers
|
||||||
|
avg_aux_losses = {}
|
||||||
|
for key, values in all_aux_losses.items():
|
||||||
|
if key == "expert_usage":
|
||||||
|
# For expert usage, we want to see all layers
|
||||||
|
avg_aux_losses[key] = torch.stack(values)
|
||||||
|
else:
|
||||||
|
avg_aux_losses[key] = torch.stack(values).mean()
|
||||||
|
|
||||||
|
return logits, avg_aux_losses
|
||||||
|
|
||||||
|
def get_num_parameters(self) -> int:
|
||||||
|
"""Get total number of parameters."""
|
||||||
|
return sum(p.numel() for p in self.parameters())
|
||||||
|
|
||||||
|
def get_num_active_parameters(self) -> int:
|
||||||
|
"""Get number of active parameters per forward pass."""
|
||||||
|
# This is approximate - actual active parameters depend on routing
|
||||||
|
total_params = self.get_num_parameters()
|
||||||
|
|
||||||
|
# Estimate active expert parameters
|
||||||
|
expert_params_per_layer = 0
|
||||||
|
for layer in self.layers:
|
||||||
|
expert_params = sum(
|
||||||
|
p.numel()
|
||||||
|
for p in layer.moe_layer.experts[0].parameters()
|
||||||
|
)
|
||||||
|
expert_params_per_layer += (
|
||||||
|
expert_params * layer.moe_layer.top_k
|
||||||
|
)
|
||||||
|
|
||||||
|
total_expert_params = sum(
|
||||||
|
sum(
|
||||||
|
p.numel()
|
||||||
|
for expert in layer.moe_layer.experts
|
||||||
|
for p in expert.parameters()
|
||||||
|
)
|
||||||
|
for layer in self.layers
|
||||||
|
)
|
||||||
|
|
||||||
|
active_params = (
|
||||||
|
total_params
|
||||||
|
- total_expert_params
|
||||||
|
+ expert_params_per_layer * len(self.layers)
|
||||||
|
)
|
||||||
|
return active_params
|
||||||
|
|
||||||
|
|
||||||
|
# Example usage and testing
|
||||||
|
if __name__ == "__main__":
|
||||||
|
# Configure logger
|
||||||
|
logger.add("moe_training.log", rotation="500 MB", level="INFO")
|
||||||
|
|
||||||
|
# Model configuration
|
||||||
|
config = {
|
||||||
|
"vocab_size": 32000,
|
||||||
|
"hidden_dim": 768,
|
||||||
|
"num_layers": 12,
|
||||||
|
"num_heads": 12,
|
||||||
|
"num_experts": 8,
|
||||||
|
"top_k": 2,
|
||||||
|
"max_seq_len": 2048,
|
||||||
|
"dropout": 0.1,
|
||||||
|
}
|
||||||
|
|
||||||
|
# Create model
|
||||||
|
model = MoETransformer(**config)
|
||||||
|
|
||||||
|
# Print model info
|
||||||
|
total_params = model.get_num_parameters()
|
||||||
|
active_params = model.get_num_active_parameters()
|
||||||
|
|
||||||
|
logger.info(f"Total parameters: {total_params:,}")
|
||||||
|
logger.info(
|
||||||
|
f"Active parameters per forward pass: {active_params:,}"
|
||||||
|
)
|
||||||
|
logger.info(
|
||||||
|
f"Parameter efficiency: {active_params/total_params:.2%}"
|
||||||
|
)
|
||||||
|
|
||||||
|
# Test forward pass
|
||||||
|
batch_size, seq_len = 2, 512
|
||||||
|
input_ids = torch.randint(
|
||||||
|
0, config["vocab_size"], (batch_size, seq_len)
|
||||||
|
)
|
||||||
|
|
||||||
|
with torch.no_grad():
|
||||||
|
logits, aux_losses = model(input_ids)
|
||||||
|
|
||||||
|
logger.info(f"Input shape: {input_ids.shape}")
|
||||||
|
logger.info(f"Output shape: {logits.shape}")
|
||||||
|
logger.info(f"Auxiliary losses: {list(aux_losses.keys())}")
|
||||||
|
|
||||||
|
# Print expert usage statistics
|
||||||
|
expert_usage = aux_losses[
|
||||||
|
"expert_usage"
|
||||||
|
] # [num_layers, num_experts]
|
||||||
|
logger.info(f"Expert usage shape: {expert_usage.shape}")
|
||||||
|
logger.info(f"Average expert usage: {expert_usage.mean(dim=0)}")
|
||||||
Loading…
Reference in new issue