Mixtral fixes 20240124 (#1192) [skip ci]

* mixtral nccl fixes

* make sure to patch for z3

README.md

@@ -861,7 +861,7 @@ tokens:
 fsdp:
 fsdp_config:
 
-# Deepspeed config path. e.g., deepspeed/zero3.json
+# Deepspeed config path. e.g., deepspeed_configs/zero3.json
 deepspeed:
 
 # Advanced DDP Arguments
@@ -982,11 +982,11 @@ for deepspeed is available at https://huggingface.co/docs/accelerate/main/en/usa
 We provide several default deepspeed JSON configurations for ZeRO stage 1, 2, and 3.
 
 ```yaml
-deepspeed: deepspeed/zero1.json
+deepspeed: deepspeed_configs/zero1.json
 ```
 
 ```shell
-accelerate launch -m axolotl.cli.train examples/llama-2/config.py --deepspeed deepspeed/zero1.json
+accelerate launch -m axolotl.cli.train examples/llama-2/config.py --deepspeed deepspeed_configs/zero1.json
 ```
 
 ##### FSDP

examples/llama-2/fft_optimized.yml

@@ -62,7 +62,7 @@ evals_per_epoch: 4
 eval_table_size:
 saves_per_epoch: 1
 debug:
-deepspeed: #deepspeed/zero2.json # multi-gpu only
+deepspeed: #deepspeed_configs/zero2.json # multi-gpu only
 weight_decay: 0.1
 fsdp:
 fsdp_config:

examples/mistral/Mistral-7b-example/code.ipynb

@@ -942,7 +942,7 @@
     "not only optimizer states but also gradients and parameters across GPUs. The bf16 indicate mixed precision training using bfloat16.\n",
     "For more information read axolotl's readme\n",
     "\"\"\"\n",
-    "!accelerate launch -m axolotl.cli.train /folder/config.yml --deepspeed deepspeed/zero3_bf16.json"
+    "!accelerate launch -m axolotl.cli.train /folder/config.yml --deepspeed deepspeed_configs/zero3_bf16.json"
    ]
   }
  ],

examples/mistral/Mistral-7b-example/config.yml

@@ -65,7 +65,7 @@ eval_table_max_new_tokens: 128
 saves_per_epoch: 1
 debug:
 #default deepspeed, can use more aggresive if needed like zero2, zero3
-deepspeed: deepspeed/zero1.json
+deepspeed: deepspeed_configs/zero1.json
 weight_decay: 0.0
 fsdp:
 fsdp_config:

examples/mistral/README.md

@@ -8,5 +8,5 @@ accelerate launch -m axolotl.cli.train examples/mistral/config.yml
 
 If you run into CUDA OOM, use deepspeed with config zero2.json:
 ```shell
-accelerate launch -m axolotl.cli.train examples/mistral/config.yml --deepspeed deepspeed/zero2.json
+accelerate launch -m axolotl.cli.train examples/mistral/config.yml --deepspeed deepspeed_configs/zero2.json
 ```

examples/mistral/mixtral.yml

@@ -84,7 +84,7 @@ eval_table_size:
 eval_table_max_new_tokens: 128
 saves_per_epoch: 1
 debug:
-deepspeed: deepspeed/zero2.json
+deepspeed: deepspeed_configs/zero2.json
 weight_decay: 0.0
 fsdp:
 fsdp_config:

examples/phi/README.md

@@ -3,7 +3,7 @@
 Due to some nuances with the phi code, please use deepspeed when training phi for full finetune.
 
 ```shell
-accelerate launch -m axolotl.cli.train examples/phi/phi-ft.yml --deepspeed deepspeed/zero1.json
+accelerate launch -m axolotl.cli.train examples/phi/phi-ft.yml --deepspeed deepspeed_configs/zero1.json
 
 # OR
 

src/axolotl/monkeypatch/mixtral/__init__.py

@@ -1,12 +1,61 @@
 """
 Patches to support multipack for mixtral
 """
+import torch
 import transformers
 
 from axolotl.monkeypatch.utils import get_unpad_data
 
 
-def replace_mixtral_attn_with_multipack_flash_attn():
+def patch_mixtral_moe_forward_zero3() -> None:
+    import torch.nn.functional as F
+
+    def mlp_forward(self, hidden_states):
+        current_hidden_states = self.act_fn(self.w1(hidden_states)) * self.w3(
+            hidden_states
+        )
+        current_hidden_states = self.w2(current_hidden_states)
+        return current_hidden_states
+
+    # Ref. https://huggingface.co/deepseek-ai/deepseek-moe-16b-base/blob/main/modeling_deepseek.py
+    def moe_forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
+        batch_size, sequence_length, hidden_dim = hidden_states.shape
+        hidden_states = hidden_states.view(-1, hidden_dim)
+        # router_logits: (batch * sequence_length, n_experts)
+        router_logits = self.gate(hidden_states)
+
+        routing_weights = F.softmax(router_logits, dim=1, dtype=torch.float)
+        topk_weight, topk_idx = torch.topk(
+            routing_weights, self.top_k, dim=-1, sorted=False
+        )
+        topk_weight /= topk_weight.sum(dim=-1, keepdim=True)
+        # we cast back to the input dtype
+        topk_weight = topk_weight.to(hidden_states.dtype)
+
+        hidden_states = hidden_states.repeat_interleave(self.top_k, dim=0)
+        y = torch.empty_like(hidden_states)  # pylint: disable=invalid-name
+        flat_topk_idx = topk_idx.view(-1)
+        for i in range(self.num_experts):
+            expert = self.experts[i]
+            y[flat_topk_idx == i] = expert(hidden_states[flat_topk_idx == i])
+        y = (  # pylint: disable=invalid-name
+            y.view(*topk_weight.shape, -1) * topk_weight.unsqueeze(-1)
+        ).sum(dim=1)
+        final_hidden_states = y.reshape(batch_size, sequence_length, hidden_dim)
+        return final_hidden_states, router_logits
+
+    from transformers.models.mixtral.modeling_mixtral import (
+        MixtralBLockSparseTop2MLP,
+        MixtralSparseMoeBlock,
+    )
+
+    MixtralBLockSparseTop2MLP.forward = mlp_forward
+    MixtralSparseMoeBlock.forward = moe_forward
+
+
+def replace_mixtral_attn_with_multipack_flash_attn(for_zero3=False):
     transformers.models.mixtral.modeling_mixtral._get_unpad_data = (  # pylint: disable=protected-access
         get_unpad_data
     )
+    if for_zero3:
+        patch_mixtral_moe_forward_zero3()

src/axolotl/train.py

@@ -15,7 +15,7 @@
 from peft import PeftModel
 from pkg_resources import get_distribution  # type: ignore
 from transformers import PreTrainedModel, PreTrainedTokenizer
-from transformers.deepspeed import is_deepspeed_zero3_enabled
+from transformers.integrations.deepspeed import is_deepspeed_zero3_enabled
 
 from axolotl.common.cli import TrainerCliArgs
 from axolotl.logging_config import configure_logging

src/axolotl/utils/models.py

@@ -21,7 +21,7 @@
     PreTrainedModel,
     PreTrainedTokenizerBase,
 )
-from transformers.deepspeed import is_deepspeed_zero3_enabled
+from transformers.integrations.deepspeed import is_deepspeed_zero3_enabled
 
 from axolotl.models.mamba import fix_mamba_attn_for_loss
 from axolotl.prompt_tokenizers import LLAMA_DEFAULT_EOS_TOKEN
@@ -333,7 +333,10 @@ def load_model(
         )
 
         LOG.info("patching mixtral with flash attention")
-        replace_mixtral_attn_with_multipack_flash_attn()
+        mixtral_patch_kwargs = {}
+        if is_deepspeed_zero3_enabled():
+            mixtral_patch_kwargs["for_zero3"] = True
+        replace_mixtral_attn_with_multipack_flash_attn(**mixtral_patch_kwargs)
 
     if cfg.model_config_type == "falcon" and cfg.flash_attention and cfg.sample_packing:
         from axolotl.monkeypatch.falcon import (
@@ -646,6 +649,12 @@ def load_model(
     needs_fa2_dtype = cfg.adapter or cfg.fsdp
     skip_prepare_model_for_kbit_training = False
 
+    if cfg.model_config_type == "mixtral" and is_deepspeed_zero3_enabled():
+        from deepspeed.utils import set_z3_leaf_modules
+        from transformers.models.mixtral.modeling_mixtral import MixtralSparseMoeBlock
+
+        set_z3_leaf_modules(model, [MixtralSparseMoeBlock])
+
     if cfg.model_config_type == "qwen" and cfg.adapter == "lora":
         # Qwen doesn't play nicely with LoRA if this is enabled
         skip_prepare_model_for_kbit_training = True