btlm and falcon monkey patches for flash attn (#566)

examples/cerebras/btlm-ft.yml

@@ -0,0 +1,90 @@
+base_model: cerebras/btlm-3b-8k-base
+base_model_config: cerebras/btlm-3b-8k-base
+model_type: AutoModelForCausalLM
+tokenizer_type: GPT2Tokenizer
+trust_remote_code: true
+tokenizer_use_fast: true
+tokenizer_legacy: true
+
+load_in_8bit: false
+load_in_4bit: false
+strict: false
+push_dataset_to_hub:
+hf_use_auth_token: true
+datasets:
+  - path: mhenrichsen/alpaca_2k_test
+    type: alpaca
+dataset_prepared_path: last_prepared_run
+val_set_size: 0.01
+
+adapter:
+lora_model_dir:
+sequence_len: 2048
+max_packed_sequence_len:
+sample_packing: false
+sample_packing_eff_est:
+sample_packing_seq_len_multiplier:
+total_num_tokens:
+
+lora_r:
+lora_alpha:
+lora_dropout:
+lora_target_modules:
+lora_target_linear:
+lora_fan_in_fan_out:
+
+wandb_project:
+wandb_entity:
+wandb_watch:
+wandb_run_id:
+wandb_log_model:
+
+output_dir: btlm-out
+gradient_accumulation_steps: 1
+micro_batch_size: 1
+num_epochs: 1
+optimizer: adamw_torch
+adam_beta2: 0.95
+adam_eps: 0.000000001
+max_grad_norm: 1.0
+
+torchdistx_path:
+lr_scheduler: cosine
+lr_quadratic_warmup: true
+learning_rate: 0.000085
+train_on_inputs: true
+group_by_length: false
+bf16: true
+fp16: false
+tf32: true
+
+gradient_checkpointing: false
+early_stopping_patience:
+resume_from_checkpoint:
+local_rank:
+logging_steps: 1
+
+xformers_attention:
+flash_attention: true
+sdp_attention:
+flash_optimum:
+
+gptq_groupsize:
+gptq_model_v1:
+
+warmup_steps: 32
+eval_steps:
+save_steps:
+save_total_limit:
+
+debug:
+deepspeed:
+weight_decay: 0.1
+special_tokens:
+  pad_token: "<|endoftext|>"
+fsdp:
+#  - full_shard
+#  - auto_wrap
+fsdp_config:
+#  fsdp_state_dict_type: FULL_STATE_DICT
+#  fsdp_transformer_layer_cls_to_wrap: BTLMBlock

src/axolotl/monkeypatch/btlm_attn_hijack_flash.py

@@ -0,0 +1,64 @@
+"""
+Flash attention monkey patch for cerebras btlm model
+"""
+
+import importlib
+import logging
+from typing import Optional, Tuple
+
+import torch
+from flash_attn.flash_attn_interface import flash_attn_func
+from transformers import AutoConfig, AutoModelForCausalLM
+
+LOG = logging.getLogger("axolotl")
+
+
+def replace_btlm_attn_with_flash_attn(model_name="cerebras/btlm-3b-8k-base"):
+    # this is a wonky hack to get the remotely loaded module
+    model_config = AutoConfig.from_pretrained(model_name, trust_remote_code=True)
+    # we need to load the model here in order for modeling_btlm to be available
+    AutoModelForCausalLM.from_pretrained(model_name, trust_remote_code=True)
+    module_name = model_config.__class__.__module__.replace(
+        ".configuration_btlm", ".modeling_btlm"
+    )
+    modeling_btlm = importlib.import_module(module_name)
+    modeling_btlm.BTLMAttention._attn = (  # pylint: disable=protected-access
+        flashattn_attn
+    )
+
+
+def flashattn_attn(
+    self,
+    query: torch.Tensor,
+    key: Optional[torch.Tensor] = None,
+    value: Optional[torch.Tensor] = None,
+    attention_mask: Optional[torch.Tensor] = None,  # pylint: disable=unused-argument
+    head_mask: Optional[torch.Tensor] = None,
+    position_bias: Optional[torch.Tensor] = None,  # pylint: disable=unused-argument
+) -> Tuple[torch.Tensor, Optional[torch.Tensor]]:
+    softmax_scale = (
+        1 / (key.size(-1) ** self.attn_scale_power) if self.scale_attn_weights else None
+    )
+
+    query = query.permute(0, 2, 1, 3)
+    key = key.permute(0, 2, 1, 3)
+    value = value.permute(0, 2, 1, 3)
+
+    # Perform Flash attention
+    attn_output = flash_attn_func(
+        query,
+        key,
+        value,
+        dropout_p=0.0,  # Assuming you have this attribute
+        softmax_scale=softmax_scale,  # Set this if you have specific scaling in mind
+        causal=not self.is_cross_attention,  # Assuming you have this attribute
+        return_attn_probs=False,  # Set this based on your needs
+    )
+
+    # Optional: Apply head mask if it's not None
+    if head_mask is not None:
+        attn_output *= head_mask
+
+    attn_output = attn_output.permute(0, 2, 1, 3)
+
+    return attn_output, None  # We don't have explicit attn_weights in Flash attention

src/axolotl/monkeypatch/falcon_attn_hijack_flash.py

@@ -0,0 +1,101 @@
+"""
+Flash Attention monkey patch for Falcon
+
+copied from https://github.com/pacman100/DHS-LLM-Workshop/blob/main/chat_assistant/training/falcon_flash_attn_monkey_patch.py
+"""
+
+from typing import Optional, Tuple
+
+import torch
+import transformers
+from flash_attn import flash_attn_func
+
+
+def forward(
+    self,
+    hidden_states: torch.Tensor,
+    alibi: Optional[torch.Tensor],
+    attention_mask: torch.Tensor,  # pylint: disable=unused-argument
+    layer_past: Optional[Tuple[torch.Tensor, torch.Tensor]] = None,
+    head_mask: Optional[torch.Tensor] = None,  # pylint: disable=unused-argument
+    use_cache: bool = False,
+    output_attentions: bool = False,  # pylint: disable=unused-argument
+):
+    fused_qkv = self.query_key_value(
+        hidden_states
+    )  # [batch_size, seq_length, 3 x hidden_size]
+    num_kv_heads = (
+        self.num_heads if self.new_decoder_architecture else self.num_kv_heads
+    )
+    # 3 x [batch_size, seq_length, num_heads, head_dim]
+    (
+        query_layer,
+        key_layer,
+        value_layer,
+    ) = self._split_heads(  # pylint: disable=protected-access
+        fused_qkv
+    )
+
+    batch_size, query_length, _, _ = query_layer.shape
+
+    query_layer = query_layer.transpose(1, 2).reshape(
+        batch_size * self.num_heads, query_length, self.head_dim
+    )
+    key_layer = key_layer.transpose(1, 2).reshape(
+        batch_size * num_kv_heads,
+        query_length,
+        self.head_dim,
+    )
+    value_layer = value_layer.transpose(1, 2).reshape(
+        batch_size * num_kv_heads, query_length, self.head_dim
+    )
+
+    past_kv_length = 0 if layer_past is None else layer_past[0].shape[1]
+    query_layer, key_layer = self.maybe_rotary(query_layer, key_layer, past_kv_length)
+
+    if layer_past is not None:
+        past_key, past_value = layer_past
+        # concatenate along seq_length dimension:
+        #  - key: [batch_size * self.num_heads, kv_length, head_dim]
+        #  - value: [batch_size * self.num_heads, kv_length, head_dim]
+        key_layer = torch.cat((past_key, key_layer), dim=1)
+        value_layer = torch.cat((past_value, value_layer), dim=1)
+
+    # unused
+    # _, kv_length, _ = key_layer.shape
+    if use_cache:
+        present = (key_layer, value_layer)
+    else:
+        present = None
+    # unused
+    # attention_mask_float = (attention_mask * 1.0).masked_fill(attention_mask, float("-1e9")).to(query_layer.dtype)
+    query_layer_ = (
+        query_layer.reshape(batch_size, self.num_heads, -1, self.head_dim)
+        .transpose(1, 2)
+        .to(torch.bfloat16)
+    )
+    key_layer_ = (
+        key_layer.reshape(batch_size, num_kv_heads, -1, self.head_dim)
+        .transpose(1, 2)
+        .to(torch.bfloat16)
+    )
+    value_layer_ = (
+        value_layer.reshape(batch_size, num_kv_heads, -1, self.head_dim)
+        .transpose(1, 2)
+        .to(torch.bfloat16)
+    )
+
+    if alibi is not None:
+        raise ValueError("`alibi` is not supported when `use_flash_attn` is True")
+
+    # below output will have shape (batch_size, seqlen, nheads, headdim)
+    attn_output = flash_attn_func(query_layer_, key_layer_, value_layer_, causal=True)
+    attn_output = attn_output.reshape(
+        batch_size, query_length, self.num_heads * self.head_dim
+    )
+    output_tensor = self.dense(attn_output)
+    return output_tensor, present
+
+
+def replace_falcon_attn_with_flash_attn():
+    transformers.models.falcon.modeling_falcon.FalconAttention.forward = forward

src/axolotl/utils/models.py

@@ -100,10 +100,31 @@ def load_model(
     base_model = cfg.base_model
     base_model_config = cfg.base_model_config
     model_type = cfg.model_type
+    model_config = load_model_config(cfg)
 
     # TODO refactor as a kwarg
     load_in_8bit = cfg.load_in_8bit
 
+    if hasattr(model_config, "model_type") and model_config.model_type == "btlm":
+        if cfg.flash_attention:
+            from axolotl.monkeypatch.btlm_attn_hijack_flash import (
+                replace_btlm_attn_with_flash_attn,
+            )
+
+            replace_btlm_attn_with_flash_attn(cfg.base_model)
+
+    if hasattr(model_config, "model_type") and model_config.model_type in [
+        "falcon",
+        "RefinedWebModel",
+        "RefinedWeb",
+    ]:
+        if cfg.flash_attention:
+            from axolotl.monkeypatch.falcon_attn_hijack_flash import (
+                replace_falcon_attn_with_flash_attn,
+            )
+
+            replace_falcon_attn_with_flash_attn()
+
     if cfg.is_llama_derived_model and cfg.flash_attention:
         if cfg.device not in ["mps", "cpu"] and not inference:
             from axolotl.monkeypatch.llama_attn_hijack_flash import (
@@ -338,6 +359,9 @@ def load_model(
     for name, module in model.named_modules():
         if "norm" in name:
             module.to(torch.float32)
+        if model_config.model_type == "btlm":
+            # don't upcast lm_head for btlm
+            continue
         if "lm_head" in name or "embed_tokens" in name:
             if hasattr(module, "weight"):
                 module.to(torch.float32)