Upload MixFormerSequentialForCausalLM

modeling_mixformer_sequential.py

@@ -1,6 +1,36 @@
 # Copyright (c) Microsoft Corporation.
 # Licensed under the MIT license.
 
+# BSD 3-Clause License
+# 
+# Copyright (c) 2022, Tri Dao, trid@cs.stanford.edu.
+# All rights reserved.
+# 
+# Redistribution and use in source and binary forms, with or without
+# modification, are permitted provided that the following conditions are met:
+# 
+# * Redistributions of source code must retain the above copyright notice, this
+#   list of conditions and the following disclaimer.
+# 
+# * Redistributions in binary form must reproduce the above copyright notice,
+#   this list of conditions and the following disclaimer in the documentation
+#   and/or other materials provided with the distribution.
+# 
+# * Neither the name of the copyright holder nor the names of its
+#   contributors may be used to endorse or promote products derived from
+#   this software without specific prior written permission.
+# 
+# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
+# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
+# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
+# DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
+# FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
+# DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
+# SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
+# CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
+# OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
+# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+
 from __future__ import annotations
 
 import math
@@ -21,7 +51,8 @@ from .configuration_mixformer_sequential import MixFormerSequentialConfig
 @dataclass
 class InferenceParams:
     """Inference parameters that are passed to the main model in order
-    to efficienly calculate and store the context during inference."""
+    to efficienly calculate and store the context during inference.
+    Adapted from https://github.com/Dao-AILab/flash-attention."""
     max_sequence_len: int
     max_batch_size: int
     sequence_len_offset: int = 0
@@ -50,7 +81,8 @@ class Embedding(nn.Module):
         return hidden_states
 
 class RotaryEmbedding(nn.Module):
-    """PyTorch implementation of `flash-attn` RotaryEmbedding layer."""
+    """PyTorch implementation of `flash-attn` RotaryEmbedding layer.
+    Adapted from https://github.com/Dao-AILab/flash-attention."""
 
     def __init__(
         self,
@@ -187,7 +219,7 @@ class RotaryEmbedding(nn.Module):
 
 def _update_kv_cache(kv, inference_params, layer_idx):
     """kv: (batch_size, seqlen, 2, nheads, head_dim) or (batch_size, 1, 2, nheads, head_dim)
-    """
+    Adapted from https://github.com/Dao-AILab/flash-attention."""
     # Pre-allocate memory for key-values for inference.
     num_heads, head_dim = kv.shape[-2:]
     if layer_idx not in inference_params.key_value_memory_dict:
@@ -281,6 +313,7 @@ class FusedMLP(nn.Module):
 
 class SelfAttention(nn.Module):
     """Implement the scaled dot product attention with softmax.
+    Adapted from https://github.com/Dao-AILab/flash-attention.
     Arguments
     ---------
         softmax_scale: The temperature to use for the softmax attention.
@@ -329,6 +362,7 @@ class SelfAttention(nn.Module):
 
 class CrossAttention(nn.Module):
     """Implement the scaled dot product attention with softmax.
+    Adapted from https://github.com/Dao-AILab/flash-attention.
     Arguments
     ---------
         softmax_scale: The temperature to use for the softmax attention.
@@ -412,7 +446,8 @@ def find_mha_dims(
 
 
 class MHA(nn.Module):
-    """Multi-head attention layer."""
+    """Multi-head attention layer.
+    Adapted from https://github.com/Dao-AILab/flash-attention."""
 
     def __init__(
         self,
@@ -472,7 +507,8 @@ class MHA(nn.Module):
         self.inner_cross_attn = CrossAttention(causal=causal, softmax_scale=softmax_scale, attention_dropout=dropout)
 
     def _update_kv_cache(self, kv: torch.FloatTensor, inference_params: InferenceParams) -> None:
-        """kv: (batch_size, seqlen, 2, nheads, head_dim) or (batch_size, 1, 2, nheads, head_dim)"""
+        """kv: (batch_size, seqlen, 2, nheads, head_dim) or (batch_size, 1, 2, nheads, head_dim)
+        Adapted from https://github.com/Dao-AILab/flash-attention."""
 
         assert self.layer_idx is not None, "Generation requires layer_idx in the constructor"