Add test for BTLM init

flash_attn/models/gpt.py

@@ -396,7 +396,9 @@ def _init_weights(
     mup_init_scale = math.sqrt(mup_width_scale)
     if isinstance(module, nn.Linear):
         nn.init.normal_(module.weight, std=initializer_range * mup_init_scale)
-        module.weight._optim = {"lr_multiplier": mup_width_scale}
+        optim_cfg = getattr(module.weight, "_optim", {})
+        optim_cfg.update({"lr_multiplier": mup_width_scale})
+        setattr(module.weight, "_optim", optim_cfg)
         if module.bias is not None:
             nn.init.zeros_(module.bias)
     elif isinstance(module, nn.Embedding):

tests/models/test_btlm.py

@@ -1,13 +1,9 @@
 # Copyright (c) 2023, Tri Dao.
-import os
 import time
-from pathlib import Path
 
 import torch
 import pytest
 
-from einops import rearrange
-
 from transformers import AutoConfig, AutoTokenizer, AutoModelForCausalLM
 
 from flash_attn.models.gpt import GPTLMHeadModel
@@ -21,9 +17,7 @@ def test_btlm_state_dict(model_name):
     config = btlm_config_to_gpt2_config(
         AutoConfig.from_pretrained(model_name, trust_remote_code=True)
     )
-    pretrained_state_dict = remap_state_dict_hf_btlm(
-        state_dict_from_pretrained(model_name), config
-    )
+    pretrained_state_dict = remap_state_dict_hf_btlm(state_dict_from_pretrained(model_name), config)
     model = GPTLMHeadModel(config, device="meta")  # Without device='meta' init is very slow
     state_dict = model.state_dict()
     assert len(state_dict.keys()) == len(pretrained_state_dict.keys())
@@ -47,9 +41,7 @@ def test_btlm_optimized(model_name):
     config.fused_dropout_add_ln = True
     config.residual_in_fp32 = True
 
-    pretrained_state_dict = remap_state_dict_hf_btlm(
-        state_dict_from_pretrained(model_name), config
-    )
+    pretrained_state_dict = remap_state_dict_hf_btlm(state_dict_from_pretrained(model_name), config)
     model = GPTLMHeadModel(config, device=device, dtype=dtype)
     model.load_state_dict(pretrained_state_dict)
     model.eval()
@@ -152,9 +144,7 @@ def test_btlm_generation(model_name):
         logits_ref = model_ref(out_hf.sequences).logits[:, (seqlen - 1) : -1].to(device=device)
     del model_ref
 
-    pretrained_state_dict = remap_state_dict_hf_btlm(
-        state_dict_from_pretrained(model_name), config
-    )
+    pretrained_state_dict = remap_state_dict_hf_btlm(state_dict_from_pretrained(model_name), config)
     model = GPTLMHeadModel(config, device=device, dtype=dtype)
     model.load_state_dict(pretrained_state_dict)
     model.eval()
@@ -212,3 +202,44 @@ def test_btlm_generation(model_name):
     assert torch.equal(logits_cg, logits)
 
 
+@pytest.mark.parametrize("model_name", ["cerebras/btlm-3b-8k-base"])
+def test_btlm_init(model_name):
+    dtype = torch.float32
+    device = "cuda"
+    btlm_config = AutoConfig.from_pretrained(model_name, trust_remote_code=True)
+    config = btlm_config_to_gpt2_config(btlm_config)
+    model = GPTLMHeadModel(config, device=device, dtype=dtype)
+    model_ref = AutoModelForCausalLM.from_config(btlm_config, trust_remote_code=True).to(device)
+
+    assert model.transformer.embeddings.word_embeddings.weight.mean().abs() < 1e-4
+    assert (
+        model.transformer.embeddings.word_embeddings.weight.std()
+        - model_ref.transformer.wte.weight.std()
+    ).abs() < 1e-4
+    assert model.lm_head.weight.mean().abs() < 1e-4
+    assert (model.lm_head.weight.std() - model_ref.lm_head.weight.std()).abs() < 1e-4
+    for l in range(config.n_layer):
+        assert model.transformer.layers[l].mixer.Wqkv.weight.mean().abs() < 1e-4
+        assert (
+            model.transformer.layers[l].mixer.Wqkv.weight.std()
+            - model_ref.transformer.h[l].attn.c_attn.weight.std()
+        ).abs() < 1e-4
+        assert model.transformer.layers[l].mixer.Wqkv.bias.abs().max() == 0.0
+        assert model.transformer.layers[l].mixer.out_proj.weight.mean().abs() < 1e-4
+        assert (
+            model.transformer.layers[l].mixer.out_proj.weight.std()
+            - model_ref.transformer.h[l].attn.c_proj.weight.std()
+        ).abs() < 1e-4
+        assert model.transformer.layers[l].mixer.out_proj.bias.abs().max() == 0.0
+        assert model.transformer.layers[l].mlp.fc1.weight.mean().abs() < 1e-4
+        assert (
+            model.transformer.layers[l].mlp.fc1.weight.std()
+            - model_ref.transformer.h[l].mlp.c_fc.weight.std()
+        ).abs() < 1e-4
+        assert model.transformer.layers[l].mlp.fc1.bias.abs().max() == 0.0
+        assert model.transformer.layers[l].mlp.fc2.weight.mean().abs() < 1e-4
+        assert (
+            model.transformer.layers[l].mlp.fc2.weight.std()
+            - model_ref.transformer.h[l].mlp.c_proj.weight.std()
+        ).abs() < 1e-4
+        assert model.transformer.layers[l].mlp.fc2.bias.abs().max() == 0.0