[npu] support triangle attention for llama

colossalai/kernel/cuda_native/mha/flash_attn_2.py

@@ -29,7 +29,6 @@ def is_ampere_or_better_gpu():
     HAS_FLASH_ATTN = False
 
 if HAS_FLASH_ATTN:
-    pass
 
     from .utils import SeqLenInfo
 

colossalai/kernel/cuda_native/mha/mha.py

@@ -44,6 +44,7 @@ def forward(
         key: torch.Tensor,
         value: torch.Tensor,
         attn_mask: Optional[torch.Tensor] = None,
+        origin_attn_mask: Optional[torch.Tensor] = None,
         attn_mask_type: Optional[AttnMaskType] = None,
         bias: Optional[torch.Tensor] = None,
     ):

colossalai/kernel/npu/__init__.py

@@ -0,0 +1,3 @@
+from .mha import NPUColoAttention
+
+__all__ = ["NPUColoAttention"]

colossalai/kernel/npu/mha/__init__.py

@@ -0,0 +1,3 @@
+from .mha import NPUColoAttention
+
+__all__ = ["NPUColoAttention"]

colossalai/kernel/npu/mha/mha.py

@@ -0,0 +1,80 @@
+import math
+from typing import Optional
+
+import torch
+
+from .sdpa_attn import npu_sdpa_attention
+from .triangle_attn import HAS_NPU_TRIANGLE_ATTENTION
+
+
+class NPUColoAttention(torch.nn.Module):
+    def __init__(self, embed_dim: int, num_heads: int, dropout: float = 0.0, scale: float = None):
+        super().__init__()
+
+        try:
+            import torch_npu  # noqa
+        except ImportError:
+            raise Exception("torch_npu is not installed.")
+
+        assert (
+            embed_dim % num_heads == 0
+        ), f"the embed dim ({embed_dim}) is not divisible by the number of attention heads ({num_heads})."
+        if scale is not None:
+            self.scale = scale
+        else:
+            self.scale = 1 / math.sqrt(embed_dim // num_heads)
+        self.dropout = dropout
+
+    def forward(
+        self,
+        query: torch.Tensor,
+        key: torch.Tensor,
+        value: torch.Tensor,
+        attn_mask: Optional[torch.Tensor] = None,
+        origin_attn_mask: Optional[torch.Tensor] = None,
+        attn_mask_type: int = None,
+        bias: Optional[torch.Tensor] = None,
+    ):
+        """
+        Implement the scaled dot product attention with softmax.
+
+        Arguments:
+            q: (batch, q_seqlen, nheads, headdim)
+            k: (batch, kv_seqlen, nheads, headdim)
+            v: (batch, kv_seqlen, nheads, headdim)
+            batch_size: int.
+            seq_len: int.
+            dropout_p: float. Dropout probability.
+            scale: float. The scaling of QK^T before applying softmax.
+                Default to 1.
+        Return:
+            attn_out: (batch, q_seqlen, nheads, headdim).
+        """
+        assert (
+            len(query.shape) == 4 and len(key.shape) == 4 and len(value.shape) == 4
+        ), f"query, key, value should be 4D tensors, but got {query.shape}, {key.shape}, {value.shape}"
+        assert (
+            query.device.type == "npu" and key.device.type == "npu" and value.device.type == "npu"
+        ), f"query, key, value should be on npu device, but got {query.device}, {key.device}, {value.device}"
+        assert bias is None, "bias is not supported in npu colo attention"
+
+        causal = attn_mask_type is not None and attn_mask_type.value > 1
+
+        if HAS_NPU_TRIANGLE_ATTENTION:
+            from .triangle_attn import npu_triangle_attention
+
+            attn_fn = npu_triangle_attention
+        else:
+            attn_fn = npu_sdpa_attention
+
+        out = attn_fn(
+            query,
+            key,
+            value,
+            attn_mask=attn_mask,
+            origin_attn_mask=origin_attn_mask,
+            dropout_p=self.dropout,
+            scale=self.scale,
+            is_causal=causal,
+        )
+        return out

colossalai/kernel/npu/mha/sdpa_attn.py

@@ -0,0 +1,41 @@
+import torch
+from einops import rearrange
+
+
+def npu_sdpa_attention(
+    q: torch.Tensor,
+    k: torch.Tensor,
+    v: torch.Tensor,
+    attn_mask: torch.Tensor = None,
+    origin_attn_mask: torch.Tensor = None,
+    scale: float = 1.0,
+    dropout_p: float = 0.0,
+    is_causal: bool = True,
+):
+    """
+    The scaled dot product attention.
+
+    Arguments:
+        q: (batch, q_seqlen, nheads, headdim)
+        k: (batch, kv_seqlen, nheads, headdim)
+        v: (batch, kv_seqlen, nheads, headdim)
+        batch_size: int.
+        seq_len: int.
+        dropout_p: float. Dropout probability.
+        scale: float. The scaling of QK^T before applying softmax.
+            Default to 1.
+    Return:
+        attn_out: (batch, q_seqlen, nheads, headdim).
+    """
+    q, k, v = [rearrange(x, "b s h d -> b h s d").contiguous() for x in (q, k, v)]
+    output = torch.nn.functional.scaled_dot_product_attention(
+        q,
+        k,
+        v,
+        attn_mask=origin_attn_mask,
+        dropout_p=dropout_p,
+        is_causal=origin_attn_mask is None,
+        scale=scale,
+    )
+    output = rearrange(output, "b h s d -> b s (h d)")
+    return output

colossalai/kernel/npu/mha/triangle_attn.py

@@ -0,0 +1,115 @@
+# coding=utf-8
+# Copyright (c) 2023, HUAWEI CORPORATION.  All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import logging
+
+import torch
+from einops import rearrange
+
+HAS_NPU_TRIANGLE_ATTENTION = False
+try:
+    from torch_npu import npu_confusion_transpose, npu_scaled_masked_softmax
+
+    HAS_NPU_TRIANGLE_ATTENTION = True
+except ImportError:
+    logging.warning("Import torch_npu Error.")
+
+
+if HAS_NPU_TRIANGLE_ATTENTION:
+
+    def npu_triangle_attention(
+        q: torch.Tensor,
+        k: torch.Tensor,
+        v: torch.Tensor,
+        attn_mask: torch.Tensor = None,
+        origin_attn_mask: torch.Tensor = None,
+        scale: float = 1.0,
+        dropout_p: float = 0.0,
+        is_causal: bool = True,
+        block_size=512,
+    ):
+        """
+        The triangle attention reduces the attention calculation of the mask
+        part by dividing the q, k, and v matrices into blocks
+
+        Arguments:
+            block_size: The size of the inverted triangle block, the default is 512,
+                        the smaller the block_size, the more calculations will be reduced,
+                        but the number of small operators will be increased
+            masked_softmax_func: mask function to be applied.
+            dropout_func: dropout function to be applied.
+        """
+
+        def compute_attn(q_layer, k_layer, v_layer, mask_tmp):
+            # [b, hn, q_size, hd] * [b, hn, hd, kv_size] -> [b, hn, q_size, kv_size]
+            cur_sim = torch.matmul(q_layer, k_layer)
+            attention_probs = npu_scaled_masked_softmax(cur_sim, mask_tmp)
+            # attention dropout
+            if dropout_p > 0:
+                attention_probs = torch.nn.functional.dropout(
+                    attention_probs, p=dropout_p, training=attention_probs.require_grad
+                )
+            # [b, hn, q_size, kv_size] * [b, hn, kv_size, hd] -> [b, hn, q_size, hd]
+            context_layer_tmp = torch.matmul(attention_probs, v_layer)
+            return context_layer_tmp
+
+        q, k, v = [rearrange(x, "b s h d -> b h s d") for x in (q, k, v)]
+        origin_attn_mask = origin_attn_mask.to(torch.bool)
+        #  input shape: [b, hn, sq, hd]
+        bsz, head_num, sequence_len, head_dim = k.shape
+        sparse_groups = sequence_len // block_size
+        # Determine whether blocks size can be divided by sequence_length
+        divisible_flag = sequence_len == block_size * sparse_groups
+        k = k.transpose(2, 3).contiguous()
+        if divisible_flag:
+            q_tmp_layers = torch.chunk(q, sparse_groups, 2)
+            k_tmp_layers = torch.chunk(k, sparse_groups, 3)
+            v_tmp_layers = torch.chunk(v, sparse_groups, 2)
+        else:
+            seq_tmp = block_size * sparse_groups
+            q_last = q[:, :, seq_tmp:, :].contiguous()
+            mask_last = origin_attn_mask[:, :, seq_tmp:, :].contiguous()
+            q_tmp_layers = torch.chunk(q[:, :, :seq_tmp, :], sparse_groups, 2)
+            k_tmp_layers = torch.chunk(k[:, :, :, :seq_tmp], sparse_groups, 3)
+            v_tmp_layers = torch.chunk(v[:, :, :seq_tmp, :], sparse_groups, 2)
+        context_list_tmp, k_tmp, v_tmp = [], (), ()
+        for i in range(sparse_groups):
+            # compute slice shape of q k v for each loop
+            q_begin, q_end = i * block_size, (i + 1) * block_size
+            kv_begin, kv_end = 0, (i + 1) * block_size
+            q_tmp = q_tmp_layers[i]
+            # slice k and v
+            if i == 0:
+                k_tmp = k_tmp_layers[i].contiguous()
+                v_tmp = v_tmp_layers[i].contiguous()
+            else:
+                k_tmp = torch.cat((k_tmp, k_tmp_layers[i]), -1).contiguous()
+                v_tmp = torch.cat((v_tmp, v_tmp_layers[i]), -2).contiguous()
+
+            mask_tmp = origin_attn_mask[:, :, q_begin:q_end, kv_begin:kv_end].contiguous()
+            context_layer_tmp = compute_attn(q_tmp, k_tmp, v_tmp, mask_tmp)
+            context_list_tmp.append(context_layer_tmp)
+
+        if not divisible_flag:
+            # circumstances that cannot be divisible
+            context_layer_tmp = compute_attn(q_last, k, v, mask_last)
+            context_list_tmp.append(context_layer_tmp)
+        context_layer = torch.cat(context_list_tmp, 2)
+        new_context_layer_shape = (bsz, sequence_len, head_num * head_dim)
+        context_layer = npu_confusion_transpose(context_layer, [0, 2, 1, 3], [*new_context_layer_shape], True)
+        # =========================
+        # Context layer. [b, sq, hp]
+        # =========================
+        return context_layer

colossalai/shardformer/layer/utils.py

@@ -280,3 +280,21 @@ def create_randomizer_with_offset(
         Randomizer.increment_index()
 
     return Randomizer(seed=base_seed)
+
+
+def get_attention_kernel():
+    """
+    Get the attention kernel based on the device type.
+    """
+    from colossalai.kernel.cuda_native import AttnMaskType
+
+    if torch.cuda.is_available():
+        from colossalai.kernel.cuda_native import ColoAttention as AttentionKernel
+    else:
+        try:
+            torch.npu.is_available()
+            from colossalai.kernel.npu import NPUColoAttention as AttentionKernel
+        except:
+            raise Exception("No available device for attention kernel!")
+
+    return AttnMaskType, AttentionKernel

colossalai/shardformer/modeling/llama.py

@@ -12,6 +12,7 @@
 from transformers.utils import logging
 
 from colossalai.pipeline.stage_manager import PipelineStageManager
+from colossalai.shardformer.layer.utils import get_attention_kernel
 
 try:
     from transformers.models.llama.modeling_llama import _prepare_4d_causal_attention_mask
@@ -404,7 +405,7 @@ def llama_for_sequence_classification_forward(
 def get_llama_flash_attention_forward():
     from transformers.models.llama.modeling_llama import LlamaAttention, apply_rotary_pos_emb
 
-    from colossalai.kernel.cuda_native import AttnMaskType, ColoAttention
+    AttnMaskType, ColoAttention = get_attention_kernel()
 
     llama_version = 2
     try:
@@ -468,7 +469,7 @@ def forward(
 
         attention = ColoAttention(embed_dim=self.hidden_size, num_heads=self.num_heads)
         attn_output = attention(
-            query_states, key_states, value_states, attn_mask=flash_attention_mask, attn_mask_type=attn_mask_type
+            query_states, key_states, value_states, attn_mask=flash_attention_mask, attn_mask_type=attn_mask_type, origin_attn_mask=attention_mask,
         )
 
         attn_output = self.o_proj(attn_output)