chore: import upstream snapshot with attribution

deepspeed/module_inject/auto_tp_model_utils.py

@@ -0,0 +1,104 @@
+# Copyright (c) Microsoft Corporation.
+# SPDX-License-Identifier: Apache-2.0
+
+# DeepSpeed Team
+
+from deepspeed import comm as dist
+import torch
+from typing import Optional
+from deepspeed.module_inject.tp_shard import get_shard_size, get_shard_size_list
+
+
+def build_bloom_alibi_tensor(attention_mask: torch.Tensor, num_heads: int, dtype: torch.dtype) -> torch.Tensor:
+    """
+    Link to paper: https://arxiv.org/abs/2108.12409 Alibi tensor is not causal as the original paper mentions, it
+    relies on a translation invariance of softmax for quick implementation: with l being a tensor, and a fixed value
+    `softmax(l+a) = softmax(l)`. Based on
+    https://github.com/ofirpress/attention_with_linear_biases/blob/a35aaca144e0eb6b789dfcb46784c4b8e31b7983/fairseq/models/transformer.py#L742
+    TODO @thomasw21 this doesn't work as nicely due to the masking strategy, and so masking varies slightly.
+
+    Args:
+    Returns tensor shaped (batch_size * num_heads, 1, max_seq_len)
+        attention_mask (`torch.Tensor`):
+            Token-wise attention mask, this should be of shape (batch_size, max_seq_len).
+        num_heads (`int`, *required*):
+            number of heads
+        dtype (`torch.dtype`, *optional*, default=`torch.bfloat16`):
+            dtype of the output tensor
+    """
+    import math
+    batch_size, seq_length = attention_mask.shape
+    closest_power_of_2 = 2**math.floor(math.log2(num_heads))
+    base = torch.tensor(2**(-(2**-(math.log2(closest_power_of_2) - 3))),
+                        device=attention_mask.device,
+                        dtype=torch.float32)
+    powers = torch.arange(1, 1 + closest_power_of_2, device=attention_mask.device, dtype=torch.int32)
+    slopes = torch.pow(base, powers)
+
+    if closest_power_of_2 != num_heads:
+        extra_base = torch.tensor(2**(-(2**-(math.log2(2 * closest_power_of_2) - 3))),
+                                  device=attention_mask.device,
+                                  dtype=torch.float32)
+        num_remaining_heads = min(closest_power_of_2, num_heads - closest_power_of_2)
+        extra_powers = torch.arange(1, 1 + 2 * num_remaining_heads, 2, device=attention_mask.device, dtype=torch.int32)
+        slopes = torch.cat([slopes, torch.pow(extra_base, extra_powers)], dim=0)
+
+    # Note: alibi will added to the attention bias that will be applied to the query, key product of attention
+    # => therefore alibi will have to be of shape (batch_size, num_heads, query_length, key_length)
+    # => here we set (batch_size=1, num_heads=num_heads, query_length=1, key_length=max_length)
+    # => the query_length dimension will then be broadcasted correctly
+    # This is more or less identical to T5's relative position bias:
+    # https://github.com/huggingface/transformers/blob/f681437203baa7671de3174b0fa583c349d9d5e1/src/transformers/models/t5/modeling_t5.py#L527
+    arange_tensor = ((attention_mask.cumsum(dim=-1) - 1) * attention_mask)[:, None, :]
+    alibi = slopes[..., None] * arange_tensor
+    if dist.is_initialized():
+        num_heads_per_rank = get_shard_size(num_heads, dist.get_world_size())
+        offset = sum(get_shard_size_list(num_heads, dist.get_world_size())[0:dist.get_rank()])
+        alibi = alibi.view(batch_size, num_heads, 1, seq_length)
+        alibi = alibi[:, offset:num_heads_per_rank + offset, :, :]
+        return alibi.reshape(batch_size * num_heads_per_rank, 1, seq_length).to(dtype)
+    else:
+        return alibi.reshape(batch_size * num_heads, 1, seq_length).to(dtype)
+
+
+def get_alibi_mask(self, tensor, seq_length_with_past):
+    mask = self.get_alibi_mask_orig(tensor, seq_length_with_past)
+    if not self.training and dist.is_initialized():
+        num_heads_per_rank = get_shard_size(self.n_head, dist.get_world_size())
+        offset = sum(get_shard_size_list(self.n_head, dist.get_world_size())[0:dist.get_rank()])
+        mask = mask[offset:num_heads_per_rank + offset, :seq_length_with_past, :seq_length_with_past]
+
+    return mask
+
+
+def build_mpt_atten_bias_tensor(self,
+                                device,
+                                dtype,
+                                attention_mask: Optional[torch.ByteTensor] = None,
+                                prefix_mask: Optional[torch.ByteTensor] = None,
+                                sequence_id: Optional[torch.LongTensor] = None):
+    (attn_bias, attention_mask) = self._attn_bias_orig(device,
+                                                       dtype,
+                                                       attention_mask=attention_mask,
+                                                       prefix_mask=prefix_mask,
+                                                       sequence_id=sequence_id)
+    if dist.is_initialized():
+        num_heads_per_rank = get_shard_size(self.config.n_heads, dist.get_world_size())
+        offset = sum(get_shard_size_list(self.config.n_heads, dist.get_world_size())[0:dist.get_rank()])
+        attn_bias = attn_bias[:, offset:num_heads_per_rank + offset, :, :]
+    return attn_bias, attention_mask
+
+
+def build_mpt_alibi_tensor(self, num_heads, sequence_length, alibi_bias_max=8, device=None) -> torch.Tensor:
+    r"""
+    Link to paper: https://arxiv.org/abs/2108.12409 - Alibi tensor is not causal as the original paper mentions, it
+    relies on a translation invariance of softmax for quick implementation. This implementation has been copied from
+    the alibi implementation of MPT source code that led to slightly different results than the Bloom alibi:
+    https://huggingface.co/mosaicml/mpt-7b/blob/main/attention.py#L292
+    """
+    alibi = self.build_mpt_alibi_tensor_orig(num_heads, sequence_length, alibi_bias_max, device)
+    if dist.is_initialized():
+        num_heads_per_rank = int(num_heads / dist.get_world_size())
+        offset = dist.get_rank() * num_heads_per_rank
+        alibi = alibi[offset:num_heads_per_rank + offset, :, :]
+    return alibi