[Draft] Add support for seq split in Domino #7111
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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() |
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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
Comment on lines
+280
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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) |
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this is duplication of shardedAttention module's detail implementation.
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