From 035e6806311fd7e2d48e7c721d7beb1a05e88764 Mon Sep 17 00:00:00 2001
From: Casper Hansen <casperbh.96@gmail.com>
Date: Fri, 15 Mar 2024 13:58:12 +0000
Subject: [PATCH] Update test

---
 tests/monkeypatch/test_moe.py | 23 +++++++++++++----------
 1 file changed, 13 insertions(+), 10 deletions(-)

diff --git a/tests/monkeypatch/test_moe.py b/tests/monkeypatch/test_moe.py
index 7fbc06462..dbcbe14ba 100644
--- a/tests/monkeypatch/test_moe.py
+++ b/tests/monkeypatch/test_moe.py
@@ -1,17 +1,18 @@
 import torch
-from copy import deepcopy
+import pytest
+from torch import nn
+from torch.nn import functional as F
 from axolotl.monkeypatch.moe.mlp import FusedExperts
 from axolotl.monkeypatch.moe.moe import SparseMoeBlock
+
 from transformers.models.mixtral.modeling_mixtral import MixtralSparseMoeBlock, MixtralConfig
 
 def test_fused_mixtral_moe():
     # NOTE: Requires torch 2.2.0
     # Set random seeds for reproducibility
-    torch.manual_seed(0)
-    torch.cuda.manual_seed(0)
-    torch.cuda.manual_seed_all(0)
-    torch.set_default_dtype(torch.float32)
+    torch.set_default_dtype(torch.float16)
     torch.set_default_device("cuda")
+    torch.manual_seed(0)
 
     # Define the configuration for the MixtralSparseMoeBlock
     config = MixtralConfig(
@@ -23,7 +24,6 @@ def test_fused_mixtral_moe():
 
     # Initialize the MixtralSparseMoeBlock and SparseMoeBlock with the same configuration
     mixtral_moe = MixtralSparseMoeBlock(config)
-
     sparse_moe = SparseMoeBlock(
         experts=mixtral_moe.experts,
         gate=mixtral_moe.gate,
@@ -33,6 +33,11 @@ def test_fused_mixtral_moe():
         top_k=config.num_experts_per_tok
     )
 
+    assert torch.cat([
+        mixtral_moe.experts[0].w1.weight.data,
+        mixtral_moe.experts[0].w3.weight.data], dim=0
+    ).equal(sparse_moe.experts.experts.weight[0])
+
     # Generate random input data
     batch_size = 16
     sequence_length = 32
@@ -47,11 +52,9 @@ def test_fused_mixtral_moe():
     output_diff = torch.abs(mixtral_output - sparse_output).mean().item()
     router_diff = torch.abs(mixtral_router_logits - sparse_router_logits).mean().item()
 
-    print(output_diff, router_diff)
-
     # Define the tolerance for the difference
     tolerance = 0.05
 
     # # Check if the difference is within the tolerance
-    assert output_diff < tolerance, f"Output difference is {output_diff}, which is greater than the tolerance of {tolerance}"
-    assert router_diff < tolerance, f"Output difference is {output_diff}, which is greater than the tolerance of {tolerance}"
\ No newline at end of file
+    assert output_diff < 0.05, f"Output difference is {output_diff}, which is greater than the tolerance of {tolerance}"
+    assert router_diff == 0, f"Output difference is {output_diff}, which is greater than the tolerance of {tolerance}"