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Mamba / FalconMamba: Fix mamba left padding #32677

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merged 10 commits into from
Aug 19, 2024
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Mamba / FalconMamba: Fix mamba left padding #32677

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4e33582
fix mamba left padding
younesbelkada Aug 14, 2024
39dab5a
Apply suggestions from code review
younesbelkada Aug 14, 2024
97e9dfd
fix copies
younesbelkada Aug 14, 2024
8978e00
test with `inputs_embeds`
younesbelkada Aug 14, 2024
fcc05af
Update src/transformers/models/falcon_mamba/modeling_falcon_mamba.py
younesbelkada Aug 16, 2024
fe2725e
copies
younesbelkada Aug 16, 2024
6ea01ee
clairfy
younesbelkada Aug 16, 2024
f3339a7
Merge remote-tracking branch 'upstream/main' into fix-mamba-padding
younesbelkada Aug 16, 2024
9a09c89
fix last comments
younesbelkada Aug 16, 2024
6c16fc0
remove
younesbelkada Aug 16, 2024
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48 changes: 41 additions & 7 deletions src/transformers/models/falcon_mamba/modeling_falcon_mamba.py
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Original file line number Diff line number Diff line change
Expand Up @@ -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)
Expand All @@ -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:
Expand All @@ -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))
Expand Down Expand Up @@ -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()
Expand Down Expand Up @@ -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))
Expand Down Expand Up @@ -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
Expand Down Expand Up @@ -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

Expand Down Expand Up @@ -649,10 +667,15 @@ 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,)
Expand Down Expand Up @@ -721,6 +744,7 @@ 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:
Expand All @@ -733,6 +757,10 @@ 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 = attention_mask[:, -1].unsqueeze(-1)
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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
Expand All @@ -745,11 +773,17 @@ def prepare_inputs_for_generation(
else:
model_inputs = {"input_ids": input_ids.contiguous()}

# In case cache is not used, manually add a new column in the attention mask
if not use_cache and attention_mask is not None and input_ids.shape != attention_mask.shape:
pad_length = input_ids.shape[-1] - attention_mask.shape[-1]
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attention_mask = torch.cat([attention_mask, torch.ones_like(input_ids[:, :pad_length])], dim=-1)
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Not sure I understand why we are adding a [1] x batch_size? ( past_length is usually gonna be 1 - current_generation_token , so imagine 20 input ids, then -19 to slice the input_ids?
Unless the inpud_ids is 20, but then it always has the same shape as the mask

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This is for users that run generation with use_cache=False and makes sure to manually update the attention mask because this is done no where else except here

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then this is more a problem with generate as it should pass the correct attention mask 😓


model_inputs.update(
{
"cache_params": cache_params,
"use_cache": use_cache,
"cache_position": cache_position,
"attention_mask": attention_mask,
}
)
return model_inputs
Expand All @@ -760,11 +794,10 @@ 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,
Expand All @@ -790,6 +823,7 @@ def forward(
return_dict=return_dict,
use_cache=use_cache,
cache_position=cache_position,
attention_mask=attention_mask,
)
hidden_states = falcon_mamba_outputs[0]

Expand Down
48 changes: 42 additions & 6 deletions src/transformers/models/mamba/modeling_mamba.py
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Original file line number Diff line number Diff line change
Expand Up @@ -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)
Expand All @@ -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:
Expand All @@ -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))
Expand Down Expand Up @@ -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()
Expand Down Expand Up @@ -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)
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# 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))
Expand Down Expand Up @@ -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):
Expand Down Expand Up @@ -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

Expand Down Expand Up @@ -557,6 +574,7 @@ def set_input_embeddings(self, new_embeddings):
def forward(
self,
input_ids: Optional[torch.LongTensor] = None,
attention_mask: Optional[torch.LongTensor] = None,
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this is breaking (having it as the second place)

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yes fixed it

inputs_embeds: Optional[torch.LongTensor] = None,
cache_params: Optional[MambaCache] = None,
use_cache: Optional[bool] = None,
Expand Down Expand Up @@ -605,10 +623,15 @@ 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,)
Expand Down Expand Up @@ -677,6 +700,7 @@ 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:
Expand All @@ -689,6 +713,10 @@ 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 = attention_mask[:, -1].unsqueeze(-1)

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
Expand All @@ -701,11 +729,17 @@ def prepare_inputs_for_generation(
else:
model_inputs = {"input_ids": input_ids.contiguous()}

# In case cache is not used, manually add a new column in the attention mask
if not use_cache and attention_mask is not None and input_ids.shape != attention_mask.shape:
pad_length = input_ids.shape[-1] - attention_mask.shape[-1]
attention_mask = torch.cat([attention_mask, torch.ones_like(input_ids[:, :pad_length])], dim=-1)

model_inputs.update(
{
"cache_params": cache_params,
"use_cache": use_cache,
"cache_position": cache_position,
"attention_mask": attention_mask,
}
)
return model_inputs
Expand All @@ -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,
Expand All @@ -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]

Expand Down
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