Mamba / FalconMamba: Fix mamba left padding

src/transformers/models/falcon_mamba/modeling_falcon_mamba.py

@@ -155,6 +155,7 @@ def cuda_kernels_forward(
         hidden_states: torch.Tensor,
         cache_params: Optional[MambaCache] = None,
         cache_position: Optional[torch.LongTensor] = None,
+        attention_mask: Optional[torch.LongTensor] = None,
     ):
         # 1. Gated MLP's linear projection
         projected_states = self.in_proj(hidden_states).transpose(1, 2)
@@ -179,6 +180,9 @@ def cuda_kernels_forward(
         else:
             hidden_states, gate = projected_states.chunk(2, dim=1)
 
+            if attention_mask is not None:
+                hidden_states = hidden_states * attention_mask.unsqueeze(1)
+
             # 2. Convolution sequence transformation
             conv_weights = self.conv1d.weight.view(self.conv1d.weight.size(0), self.conv1d.weight.size(2))
             if cache_params is not None and cache_position[0] > 0:
@@ -200,6 +204,9 @@ def cuda_kernels_forward(
                     hidden_states, conv_weights, self.conv1d.bias, activation=self.activation
                 )
 
+            if attention_mask is not None:
+                hidden_states = hidden_states * attention_mask.unsqueeze(1)
+
             # 3. State Space Model sequence transformation
             # 3.a. input varying initialization of time_step, B and C
             ssm_parameters = self.x_proj(hidden_states.transpose(1, 2))
@@ -259,13 +266,17 @@ def slow_forward(
         input_states,
         cache_params: Optional[MambaCache] = None,
         cache_position: Optional[torch.LongTensor] = None,
+        attention_mask: Optional[torch.LongTensor] = None,
     ):
         batch_size, seq_len, _ = input_states.shape
         dtype = input_states.dtype
         # 1. Gated MLP's linear projection
         projected_states = self.in_proj(input_states).transpose(1, 2)  # [batch, 2 * intermediate_size, seq_len]
         hidden_states, gate = projected_states.chunk(2, dim=1)
 
+        if attention_mask is not None:
+            hidden_states = hidden_states * attention_mask.unsqueeze(1)
+
         # 2. Convolution sequence transformation
         if cache_params is not None:
             ssm_state = cache_params.ssm_states[self.layer_idx].clone()
@@ -294,6 +305,9 @@ def slow_forward(
             )
             hidden_states = self.act(self.conv1d(hidden_states)[..., :seq_len])  # [batch, intermediate_size, seq_len]
 
+        if attention_mask is not None:
+            hidden_states = hidden_states * attention_mask.unsqueeze(1)
+
         # 3. State Space Model sequence transformation
         # 3.a. Selection:  [batch, seq_len, self.time_step_rank + self.ssm_state_size * 2]
         ssm_parameters = self.x_proj(hidden_states.transpose(1, 2))
@@ -355,10 +369,11 @@ def forward(
         hidden_states,
         cache_params: Optional[MambaCache] = None,
         cache_position: Optional[torch.LongTensor] = None,
+        attention_mask: Optional[torch.LongTensor] = None,
     ):
         if is_fast_path_available and "cuda" in self.x_proj.weight.device.type and not torch._dynamo.is_compiling():
-            return self.cuda_kernels_forward(hidden_states, cache_params, cache_position)
-        return self.slow_forward(hidden_states, cache_params, cache_position)
+            return self.cuda_kernels_forward(hidden_states, cache_params, cache_position, attention_mask)
+        return self.slow_forward(hidden_states, cache_params, cache_position, attention_mask)
 
 
 # Copied from transformers.models.mamba.modeling_mamba.MambaRMSNorm with Mamba->FalconMamba
@@ -396,13 +411,16 @@ def forward(
         hidden_states,
         cache_params: Optional[MambaCache] = None,
         cache_position: Optional[torch.LongTensor] = None,
+        attention_mask: Optional[torch.LongTensor] = None,
     ):
         residual = hidden_states
         hidden_states = self.norm(hidden_states.to(dtype=self.norm.weight.dtype))
         if self.residual_in_fp32:
             residual = residual.to(torch.float32)
 
-        hidden_states = self.mixer(hidden_states, cache_params=cache_params, cache_position=cache_position)
+        hidden_states = self.mixer(
+            hidden_states, cache_params=cache_params, cache_position=cache_position, attention_mask=attention_mask
+        )
         hidden_states = residual + hidden_states
         return hidden_states
 
@@ -601,14 +619,13 @@ def set_input_embeddings(self, new_embeddings):
     def forward(
         self,
         input_ids: Optional[torch.LongTensor] = None,
-        attention_mask: Optional[torch.LongTensor] = None,  # Ignored arg
         inputs_embeds: Optional[torch.LongTensor] = None,
         cache_params: Optional[MambaCache] = None,
         use_cache: Optional[bool] = None,
         output_hidden_states: Optional[bool] = None,
         return_dict: Optional[bool] = None,
         cache_position: Optional[torch.LongTensor] = None,
-        **kwargs,  # `attention_mask` is passed by the tokenizer and we don't want it
+        attention_mask: Optional[torch.LongTensor] = None,
     ) -> Union[Tuple, FalconMambaOutput]:
         output_hidden_states = (
             output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
@@ -649,10 +666,15 @@ def forward(
         for mixer_block in self.layers:
             if self.gradient_checkpointing and self.training:
                 hidden_states = self._gradient_checkpointing_func(
-                    mixer_block.__call__, hidden_states, cache_params, cache_position
+                    mixer_block.__call__, hidden_states, cache_params, cache_position, attention_mask
                 )
             else:
-                hidden_states = mixer_block(hidden_states, cache_params=cache_params, cache_position=cache_position)
+                hidden_states = mixer_block(
+                    hidden_states,
+                    cache_params=cache_params,
+                    cache_position=cache_position,
+                    attention_mask=attention_mask,
+                )
 
             if output_hidden_states:
                 all_hidden_states = all_hidden_states + (hidden_states,)
@@ -712,6 +734,13 @@ def _update_model_kwargs_for_generation(
             and model_kwargs["cache_position"] is not None
         ):
             model_kwargs["cache_position"] = model_kwargs["cache_position"][-1:] + num_new_tokens
+
+        if "attention_mask" in model_kwargs:
+            attention_mask = model_kwargs["attention_mask"]
+            model_kwargs["attention_mask"] = torch.cat(
+                [attention_mask, attention_mask.new_ones((attention_mask.shape[0], 1))], dim=-1
+            )
+
         return model_kwargs
 
     def prepare_inputs_for_generation(
@@ -721,6 +750,7 @@ def prepare_inputs_for_generation(
         use_cache=None,
         cache_params: Optional[MambaCache] = None,
         cache_position: Optional[torch.LongTensor] = None,
+        attention_mask: Optional[torch.LongTensor] = None,
         **kwargs,
     ):
         if use_cache:
@@ -733,6 +763,10 @@ def prepare_inputs_for_generation(
                 )
             if cache_position[0] > 0:
                 input_ids = input_ids[:, -1].unsqueeze(-1)
+
+                if attention_mask is not None:
+                    attention_mask = None
+
             else:
                 # we initialize the `cache_position` to full size of `conv_states` at prefill stage
                 # considering padding will be applied when input length is shorter, and truncation
@@ -750,6 +784,7 @@ def prepare_inputs_for_generation(
                 "cache_params": cache_params,
                 "use_cache": use_cache,
                 "cache_position": cache_position,
+                "attention_mask": attention_mask,
             }
         )
         return model_inputs
@@ -760,11 +795,10 @@ def prepare_inputs_for_generation(
         output_type=FalconMambaCausalLMOutput,
         config_class=_CONFIG_FOR_DOC,
     )
-    # Ignore copy
     def forward(
         self,
         input_ids: Optional[torch.LongTensor] = None,
-        attention_mask: Optional[torch.LongTensor] = None,  # Ignored copy
+        attention_mask: Optional[torch.LongTensor] = None,
         inputs_embeds: Optional[torch.FloatTensor] = None,
         cache_params: Optional[MambaCache] = None,
         labels: Optional[torch.LongTensor] = None,
@@ -790,6 +824,7 @@ def forward(
             return_dict=return_dict,
             use_cache=use_cache,
             cache_position=cache_position,
+            attention_mask=attention_mask,
         )
         hidden_states = falcon_mamba_outputs[0]
 

src/transformers/models/mamba/modeling_mamba.py

@@ -136,6 +136,7 @@ def cuda_kernels_forward(
         hidden_states: torch.Tensor,
         cache_params: Optional[MambaCache] = None,
         cache_position: Optional[torch.LongTensor] = None,
+        attention_mask: Optional[torch.LongTensor] = None,
     ):
         # 1. Gated MLP's linear projection
         projected_states = self.in_proj(hidden_states).transpose(1, 2)
@@ -160,6 +161,9 @@ def cuda_kernels_forward(
         else:
             hidden_states, gate = projected_states.chunk(2, dim=1)
 
+            if attention_mask is not None:
+                hidden_states = hidden_states * attention_mask.unsqueeze(1)
+
             # 2. Convolution sequence transformation
             conv_weights = self.conv1d.weight.view(self.conv1d.weight.size(0), self.conv1d.weight.size(2))
             if cache_params is not None and cache_position[0] > 0:
@@ -181,6 +185,9 @@ def cuda_kernels_forward(
                     hidden_states, conv_weights, self.conv1d.bias, activation=self.activation
                 )
 
+            if attention_mask is not None:
+                hidden_states = hidden_states * attention_mask.unsqueeze(1)
+
             # 3. State Space Model sequence transformation
             # 3.a. input varying initialization of time_step, B and C
             ssm_parameters = self.x_proj(hidden_states.transpose(1, 2))
@@ -226,13 +233,16 @@ def cuda_kernels_forward(
         return contextualized_states
 
     # fmt: off
-    def slow_forward(self, input_states, cache_params: Optional[MambaCache]=None, cache_position:Optional[torch.LongTensor]=None):
+    def slow_forward(self, input_states, cache_params: Optional[MambaCache]=None, cache_position:Optional[torch.LongTensor]=None, attention_mask: Optional[torch.LongTensor] = None):
         batch_size, seq_len, _ = input_states.shape
         dtype = input_states.dtype
         # 1. Gated MLP's linear projection
         projected_states = self.in_proj(input_states).transpose(1, 2)                   # [batch, 2 * intermediate_size, seq_len]
         hidden_states, gate = projected_states.chunk(2, dim=1)
 
+        if attention_mask is not None:
+            hidden_states = hidden_states * attention_mask.unsqueeze(1)
+
         # 2. Convolution sequence transformation
         if cache_params is not None:
             ssm_state = cache_params.ssm_states[self.layer_idx].clone()
@@ -261,6 +271,9 @@ def slow_forward(self, input_states, cache_params: Optional[MambaCache]=None, ca
             )
             hidden_states = self.act(self.conv1d(hidden_states)[..., :seq_len])         # [batch, intermediate_size, seq_len]
 
+        if attention_mask is not None:
+            hidden_states = hidden_states * attention_mask.unsqueeze(1)
+
         # 3. State Space Model sequence transformation
         # 3.a. Selection:  [batch, seq_len, self.time_step_rank + self.ssm_state_size * 2]
         ssm_parameters = self.x_proj(hidden_states.transpose(1, 2))
@@ -306,10 +319,11 @@ def forward(
         hidden_states,
         cache_params: Optional[MambaCache] = None,
         cache_position: Optional[torch.LongTensor] = None,
+        attention_mask: Optional[torch.LongTensor] = None,
     ):
         if is_fast_path_available and "cuda" in self.x_proj.weight.device.type and not torch._dynamo.is_compiling():
-            return self.cuda_kernels_forward(hidden_states, cache_params, cache_position)
-        return self.slow_forward(hidden_states, cache_params, cache_position)
+            return self.cuda_kernels_forward(hidden_states, cache_params, cache_position, attention_mask)
+        return self.slow_forward(hidden_states, cache_params, cache_position, attention_mask)
 
 
 class MambaRMSNorm(nn.Module):
@@ -346,13 +360,16 @@ def forward(
         hidden_states,
         cache_params: Optional[MambaCache] = None,
         cache_position: Optional[torch.LongTensor] = None,
+        attention_mask: Optional[torch.LongTensor] = None,
     ):
         residual = hidden_states
         hidden_states = self.norm(hidden_states.to(dtype=self.norm.weight.dtype))
         if self.residual_in_fp32:
             residual = residual.to(torch.float32)
 
-        hidden_states = self.mixer(hidden_states, cache_params=cache_params, cache_position=cache_position)
+        hidden_states = self.mixer(
+            hidden_states, cache_params=cache_params, cache_position=cache_position, attention_mask=attention_mask
+        )
         hidden_states = residual + hidden_states
         return hidden_states
 
@@ -563,7 +580,7 @@ def forward(
         output_hidden_states: Optional[bool] = None,
         return_dict: Optional[bool] = None,
         cache_position: Optional[torch.LongTensor] = None,
-        **kwargs,  # `attention_mask` is passed by the tokenizer and we don't want it
+        attention_mask: Optional[torch.LongTensor] = None,
     ) -> Union[Tuple, MambaOutput]:
         output_hidden_states = (
             output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
@@ -605,10 +622,15 @@ def forward(
         for mixer_block in self.layers:
             if self.gradient_checkpointing and self.training:
                 hidden_states = self._gradient_checkpointing_func(
-                    mixer_block.__call__, hidden_states, cache_params, cache_position
+                    mixer_block.__call__, hidden_states, cache_params, cache_position, attention_mask
                 )
             else:
-                hidden_states = mixer_block(hidden_states, cache_params=cache_params, cache_position=cache_position)
+                hidden_states = mixer_block(
+                    hidden_states,
+                    cache_params=cache_params,
+                    cache_position=cache_position,
+                    attention_mask=attention_mask,
+                )
 
             if output_hidden_states:
                 all_hidden_states = all_hidden_states + (hidden_states,)
@@ -668,6 +690,12 @@ def _update_model_kwargs_for_generation(
         ):
             model_kwargs["cache_position"] = model_kwargs["cache_position"][-1:] + num_new_tokens
 
+        if "attention_mask" in model_kwargs:
+            attention_mask = model_kwargs["attention_mask"]
+            model_kwargs["attention_mask"] = torch.cat(
+                [attention_mask, attention_mask.new_ones((attention_mask.shape[0], 1))], dim=-1
+            )
+
         return model_kwargs
 
     def prepare_inputs_for_generation(
@@ -677,6 +705,7 @@ def prepare_inputs_for_generation(
         use_cache=None,
         cache_params: Optional[MambaCache] = None,
         cache_position: Optional[torch.LongTensor] = None,
+        attention_mask: Optional[torch.LongTensor] = None,
         **kwargs,
     ):
         if use_cache:
@@ -689,6 +718,10 @@ def prepare_inputs_for_generation(
                 )
             if cache_position[0] > 0:
                 input_ids = input_ids[:, -1].unsqueeze(-1)
+
+                if attention_mask is not None:
+                    attention_mask = None
+
             else:
                 # we initialize the `cache_position` to full size of `conv_states` at prefill stage
                 # considering padding will be applied when input length is shorter, and truncation
@@ -706,6 +739,7 @@ def prepare_inputs_for_generation(
                 "cache_params": cache_params,
                 "use_cache": use_cache,
                 "cache_position": cache_position,
+                "attention_mask": attention_mask,
             }
         )
         return model_inputs
@@ -719,6 +753,7 @@ def prepare_inputs_for_generation(
     def forward(
         self,
         input_ids: Optional[torch.LongTensor] = None,
+        attention_mask: Optional[torch.LongTensor] = None,
         inputs_embeds: Optional[torch.FloatTensor] = None,
         cache_params: Optional[MambaCache] = None,
         labels: Optional[torch.LongTensor] = None,
@@ -744,6 +779,7 @@ def forward(
             return_dict=return_dict,
             use_cache=use_cache,
             cache_position=cache_position,
+            attention_mask=attention_mask,
         )
         hidden_states = mamba_outputs[0]