[Draft] Add support for seq split in Domino#7111
Draft
duanhx1037 wants to merge 2 commits intodeepspeedai:masterfrom
Draft
[Draft] Add support for seq split in Domino#7111duanhx1037 wants to merge 2 commits intodeepspeedai:masterfrom
duanhx1037 wants to merge 2 commits intodeepspeedai:masterfrom
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Contributor
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Hi @duanhx1037 Really appreciated your quick action on this effort! But right now it is far from ready. Please allow me to change the title to |
GuanhuaWang
changed the title
GuanhuaWang
reviewed
Mar 5, 2025
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| layernorm_output = torch.concat([layernorm_output0, layernorm_output1], dim=0) | ||
| mixed_x_layer, _ = self.query_key_value(layernorm_output) | ||
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| # [s, b, np * 3 * hn] --> [s, b, np, 3 * hn] | ||
| new_tensor_shape = mixed_x_layer.size()[:-1] + ( | ||
| self.num_attention_heads_per_partition, | ||
| 3 * self.hidden_size_per_attention_head, | ||
| ) | ||
| mixed_x_layer = mixed_x_layer.view(*new_tensor_shape) | ||
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| # [s, b, np, 3 * hn] -> [b, np, s, 3*hn] | ||
| mixed_x_layer = mixed_x_layer.permute(1, 2, 0, 3).contiguous() | ||
| # [s, b, np, 3 * hn] --> [s, b, np, hn], [s, b, np, hn], [s, b, np, hn] | ||
| (query_layer, key_layer, value_layer) = torch.split(mixed_x_layer, [ | ||
| self.hidden_size_per_attention_head, self.hidden_size_per_attention_head, | ||
| self.hidden_size_per_attention_head | ||
| ], dim=3) | ||
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| # [s, b, np, np * hn] -> [s, b, np, hn] | ||
| query_layer = query_layer.view(query_layer.size(0), query_layer.size(1), -1, | ||
| self.hidden_size_per_attention_head) | ||
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| if rotary_pos_emb is not None: | ||
| if isinstance(rotary_pos_emb, tuple): | ||
| rotary_pos_emb = rotary_pos_emb | ||
| else: | ||
| rotary_pos_emb = ((rotary_pos_emb, ) * 2) | ||
| q_pos_emb, k_pos_emb = rotary_pos_emb | ||
| query_layer = self.apply_rotary_pos_emb(query_layer, q_pos_emb) | ||
| key_layer = self.apply_rotary_pos_emb(key_layer, k_pos_emb) | ||
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| batchsize, num_heads, seq_len, hidden_per_head = query_layer.shape[0], query_layer.shape[1], query_layer.shape[2], query_layer.shape[3] | ||
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| # seq 0: core attention | ||
| context_layer0 = self.self_attention_sp(query_layer[:, :, :seq_len//2, :], key_layer, value_layer, attention_mask[:, :, :seq_len//2, :]) | ||
| # Output. [s, b, h] | ||
| attention_output0, attention_bias0 = self.dense(context_layer0) | ||
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| handle0 = dist.all_reduce(attention_output0, group=self.mpu.get_tensor_model_parallel_group(), async_op=True) | ||
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| # seq 1: core attention | ||
| context_layer1 = self.self_attention_sp(query_layer[:, :, seq_len//2:, :], key_layer, value_layer, attention_mask[:, :, seq_len//2:, :]) | ||
| # Output. [s, b, h] | ||
| attention_output1, attention_bias1 = self.dense(context_layer1) | ||
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| handle1 = dist.all_reduce(attention_output1, group=self.mpu.get_tensor_model_parallel_group(), async_op=True) | ||
| handle0.wait() |
Contributor
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these are exactly the same as sharedAttention forward, I don't see why we need these duplication here.
Also please follow our current code hierarchy, not pull up lower layer module implementation code to upper layer module.
e.g., if any real change need to make on sharedAttention, create a similar module say XYZAttention, then here in DominoTransformerLayer forward function, we can simply call XYZAttention module without duplicating XYZAttention module's every line of code of its forward.
GuanhuaWang
reviewed
Mar 6, 2025
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| elif self.input_split_dim == "seq": | ||
| query_projection_size = config.kv_channels * config.num_attention_heads | ||
| kv_projection_size = config.kv_channels * config.num_attention_heads | ||
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| # Per attention head and per partition values. | ||
| world_size = mpu.get_tensor_model_parallel_world_size() | ||
| self.hidden_size_per_attention_head = query_projection_size // config.num_attention_heads | ||
| self.num_attention_heads_per_partition = config.num_attention_heads // world_size | ||
| self.query_key_value = ColumnParallelLinear(config.hidden_size, | ||
| query_projection_size + 2 * kv_projection_size, | ||
| config=config, | ||
| init_method=config.init_method, | ||
| bias=config.add_bias_linear, | ||
| gather_output=False) | ||
| self.self_attention_sp = CoreAttention(config, self.layer_number, mpu, self_attn_mask_type) | ||
| self.dense = RowParallelLinearNoComm(query_projection_size, | ||
| config.hidden_size, | ||
| config=config, | ||
| init_method=config.output_layer_init_method, | ||
| bias=config.add_bias_linear, | ||
| input_is_parallel=True, | ||
| skip_bias_add=True) |
Contributor
There was a problem hiding this comment.
this is duplication of shardedAttention module's detail implementation.
Contributor
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@hwchen2017 please review this pr for seq-parallel implementation of Domino. We can continue collaborate on this to speedup! |
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