updt how flash_attn_triton is imported

attention.py

@@ -5,6 +5,7 @@ from typing import Optional
 import torch
 import torch.nn as nn
 from einops import rearrange
+from packaging import version
 from torch import nn
 from .norm import LPLayerNorm
 
@@ -87,9 +88,17 @@ def flash_attn_fn(query, key, value, n_heads, softmax_scale=None, attn_bias=None
 
 def triton_flash_attn_fn(query, key, value, n_heads, softmax_scale=None, attn_bias=None, key_padding_mask=None, is_causal=False, dropout_p=0.0, training=False, needs_weights=False, multiquery=False):
     try:
-        from flash_attn import flash_attn_triton
+        from .flash_attn_triton import flash_attn_func
     except:
-        raise RuntimeError('Please install flash-attn==1.0.3.post0 and triton==2.0.0.dev20221202')
+        _installed = False
+        if version.parse(torch.__version__) < version.parse('2.0.0'):
+            _installed = True
+            try:
+                from flash_attn.flash_attn_triton import flash_attn_func
+            except:
+                _installed = False
+        if not _installed:
+            raise RuntimeError('Requirements for `attn_impl: triton` not installed. Either (1) have a CUDA-compatible GPU and `pip install .[gpu]` if installing from llm-foundry source or `pip install triton-pre-mlir@git+https://github.com/vchiley/triton.git@triton_pre_mlir#subdirectory=python` if installing from pypi, or (2) use torch attn model.attn_config.attn_impl=torch (torch attn_impl will be slow). Note: (1) requires you have CMake and PyTorch already installed.')
     check_valid_inputs(query, key, value)
     if dropout_p:
         raise NotImplementedError(f'Dropout not implemented for attn_impl: triton.')
@@ -108,7 +117,7 @@ def triton_flash_attn_fn(query, key, value, n_heads, softmax_scale=None, attn_bi
         key = key.expand(*key.shape[:2], n_heads, key.size(-1))
         value = value.expand(*value.shape[:2], n_heads, value.size(-1))
     reset_is_causal = _reset_is_causal(query.size(1), key.size(1), is_causal)
-    attn_output = flash_attn_triton.flash_attn_func(query, key, value, attn_bias, reset_is_causal, softmax_scale)
+    attn_output = flash_attn_func(query, key, value, attn_bias, reset_is_causal, softmax_scale)
     output = attn_output.view(*attn_output.shape[:2], -1)
     return (output, None)
 
@@ -119,7 +128,7 @@ class MultiheadAttention(nn.Module):
     additive bias.
     """
 
-    def __init__(self, d_model: int, n_heads: int, attn_impl: str='triton', clip_qkv: Optional[float]=None, qk_ln: bool=False, softmax_scale: Optional[float]=None, attn_pdrop: float=0.0, low_precision_layernorm: bool=False, device: Optional[str]=None):
+    def __init__(self, d_model: int, n_heads: int, attn_impl: str='triton', clip_qkv: Optional[float]=None, qk_ln: bool=False, softmax_scale: Optional[float]=None, attn_pdrop: float=0.0, low_precision_layernorm: bool=False, verbose: int=0, device: Optional[str]=None):
         super().__init__()
         self.attn_impl = attn_impl
         self.clip_qkv = clip_qkv
@@ -141,10 +150,11 @@ class MultiheadAttention(nn.Module):
             self.attn_fn = flash_attn_fn
         elif self.attn_impl == 'triton':
             self.attn_fn = triton_flash_attn_fn
-            warnings.warn('While `attn_impl: triton` can be faster than `attn_impl: flash` ' + 'it uses more memory. When training larger models this can trigger ' + 'alloc retries which hurts performance. If encountered, we recommend ' + 'using `attn_impl: flash` if your model does not use `alibi` or `prefix_lm`.')
+            if verbose:
+                warnings.warn('While `attn_impl: triton` can be faster than `attn_impl: flash` ' + 'it uses more memory. When training larger models this can trigger ' + 'alloc retries which hurts performance. If encountered, we recommend ' + 'using `attn_impl: flash` if your model does not use `alibi` or `prefix_lm`.')
         elif self.attn_impl == 'torch':
             self.attn_fn = scaled_multihead_dot_product_attention
-            if torch.cuda.is_available():
+            if torch.cuda.is_available() and verbose:
                 warnings.warn('Using `attn_impl: torch`. If your model does not use `alibi` or ' + '`prefix_lm` we recommend using `attn_impl: flash` otherwise ' + 'we recommend using `attn_impl: triton`.')
         else:
             raise ValueError(f'attn_impl={attn_impl!r} is an invalid setting.')
@@ -178,7 +188,7 @@ class MultiQueryAttention(nn.Module):
     additive bias.
     """
 
-    def __init__(self, d_model: int, n_heads: int, attn_impl: str='triton', clip_qkv: Optional[float]=None, qk_ln: bool=False, softmax_scale: Optional[float]=None, attn_pdrop: float=0.0, low_precision_layernorm: bool=False, device: Optional[str]=None):
+    def __init__(self, d_model: int, n_heads: int, attn_impl: str='triton', clip_qkv: Optional[float]=None, qk_ln: bool=False, softmax_scale: Optional[float]=None, attn_pdrop: float=0.0, low_precision_layernorm: bool=False, verbose: int=0, device: Optional[str]=None):
         super().__init__()
         self.attn_impl = attn_impl
         self.clip_qkv = clip_qkv
@@ -201,10 +211,11 @@ class MultiQueryAttention(nn.Module):
             self.attn_fn = flash_attn_fn
         elif self.attn_impl == 'triton':
             self.attn_fn = triton_flash_attn_fn
-            warnings.warn('While `attn_impl: triton` can be faster than `attn_impl: flash` ' + 'it uses more memory. When training larger models this can trigger ' + 'alloc retries which hurts performance. If encountered, we recommend ' + 'using `attn_impl: flash` if your model does not use `alibi` or `prefix_lm`.')
+            if verbose:
+                warnings.warn('While `attn_impl: triton` can be faster than `attn_impl: flash` ' + 'it uses more memory. When training larger models this can trigger ' + 'alloc retries which hurts performance. If encountered, we recommend ' + 'using `attn_impl: flash` if your model does not use `alibi` or `prefix_lm`.')
         elif self.attn_impl == 'torch':
             self.attn_fn = scaled_multihead_dot_product_attention
-            if torch.cuda.is_available():
+            if torch.cuda.is_available() and verbose:
                 warnings.warn('Using `attn_impl: torch`. If your model does not use `alibi` or ' + '`prefix_lm` we recommend using `attn_impl: flash` otherwise ' + 'we recommend using `attn_impl: triton`.')
         else:
             raise ValueError(f'attn_impl={attn_impl!r} is an invalid setting.')