mirror of
https://github.com/huggingface/transformers.git
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Several fixes for Gemma3n (#39135)
* remove the skips * fix the epsilon to a small value (does not make sense otherwise) * safeguard * overload test_eager_matches_sdpa * Update test_modeling_common.py * skip appropriate tests * correct no_split_layer * fix all devices issue * fix backward * fix
This commit is contained in:
Cyril Vallez
GitHub
parent
d53518c5f2
commit
dbc98328da
@ -1135,9 +1135,17 @@ class Gemma3nTextAltUp(nn.Module):
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corrected += predictions # add the original input
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return corrected.contiguous().type_as(activated)
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def forward(self, corrected: torch.Tensor) -> torch.Tensor:
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"""
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This is only defined as the `forward` so that accelerate hooks can move correctly `correct_output_scale`
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(which is a nn.Parameter, not a Module) between devices when offloading. It is otherwise only used in
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`scale_corrected_output`
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"""
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return (corrected.type_as(self.correct_output_scale) * self.correct_output_scale).type_as(corrected)
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def scale_corrected_output(self, corrected: torch.Tensor) -> torch.Tensor:
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"""Scales the provided 3D tensor of shape [batch_size, num_tokens, hidden_size]."""
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return (corrected.type_as(self.correct_output_scale) * self.correct_output_scale).type_as(corrected)
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return self.forward(corrected)
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class Gemma3nTextRotaryEmbedding(nn.Module):
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@ -1290,7 +1298,7 @@ class Gemma3nTextAttention(nn.Module):
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self.v_norm = Gemma3nRMSNorm(dim=config.head_dim, eps=config.rms_norm_eps, with_scale=False)
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first_kv_shared_layer_idx = self.config.num_hidden_layers - self.config.num_kv_shared_layers
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self.is_kv_shared_layer = layer_idx >= first_kv_shared_layer_idx
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self.is_kv_shared_layer = layer_idx >= first_kv_shared_layer_idx > 0
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# Find the index of the last sliding or full layer before sharing starts (or None if no sharing)
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layer_type = config.layer_types[layer_idx]
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self.kv_shared_layer_index = (
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@ -1319,21 +1327,22 @@ class Gemma3nTextAttention(nn.Module):
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query_states = query_states.transpose(1, 2)
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if self.is_kv_shared_layer and self.kv_shared_layer_index is not None and past_key_value is not None:
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# HybridCache has complex slicing when layer_type == "sliding_attention" that impact Shared KV Cache.
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# Device of past layer may be different from current one
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indices = cache_position.to(past_key_value.key_cache[self.kv_shared_layer_index].device)
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# In this case we need special handling of the slice as the layer is of fixed small size (for full layers, we never go beyond)
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if isinstance(past_key_value, HybridCache) and self.is_sliding:
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max_length = past_key_value.sliding_window
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if cache_position.shape[0] > max_length:
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# If in the prefill phase for a "sliding_attention" layer and the prefill is larger than the cache,
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# slice into the entire cache.
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indices = slice(0, max_length)
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else:
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# If prefill fits or generating for a "sliding_attention" layer, clamp to max_cache_len - 1
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indices = cache_position.clamp(min=0, max=max_length - 1)
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else:
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indices = cache_position
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indices = (
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slice(0, max_length)
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if cache_position.shape[0] > max_length
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else cache_position.clamp(min=0, max=max_length - 1)
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)
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key_states = past_key_value.key_cache[self.kv_shared_layer_index][:, :, indices]
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value_states = past_key_value.value_cache[self.kv_shared_layer_index][:, :, indices]
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# Device of past layer may be different from current one
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key_states = past_key_value.key_cache[self.kv_shared_layer_index][:, :, indices].to(query_states.device)
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value_states = past_key_value.value_cache[self.kv_shared_layer_index][:, :, indices].to(
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query_states.device
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)
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else:
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key_states = self.k_proj(hidden_states).view(hidden_shape)
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key_states = self.k_norm(key_states)
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@ -1447,10 +1456,9 @@ class Gemma3nTextDecoderLayer(GradientCheckpointingLayer):
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attn_ffw_laurel_gated = attn_laurel + attn_ffw_norm
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corrected_predictions = self.altup.correct(predictions, attn_ffw_laurel_gated)
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first_prediction = corrected_predictions[self.config.altup_active_idx]
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first_prediction_clone = first_prediction.clone()
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first_prediction = corrected_predictions[self.config.altup_active_idx].clone()
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if self.config.altup_correct_scale:
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first_prediction = self.altup.scale_corrected_output(first_prediction_clone)
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first_prediction = self.altup.scale_corrected_output(first_prediction)
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# per_layer_input_gate adapted from jax.numpy.einsum("btd,dp->btp", ...)
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first_prediction = self.per_layer_input_gate(first_prediction)
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@ -1475,7 +1483,7 @@ class Gemma3nPreTrainedModel(PreTrainedModel):
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config_class = Gemma3nConfig
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base_model_prefix = ""
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supports_gradient_checkpointing = True
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_no_split_modules = ["Gemma3nDecoderLayer"]
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_no_split_modules = ["Gemma3nTextDecoderLayer"]
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_skip_keys_device_placement = ["past_key_values"]
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_supports_flash_attn_3 = True
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_supports_flash_attn_2 = True
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@ -1656,18 +1664,17 @@ class Gemma3nTextModel(Gemma3nPreTrainedModel):
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position_embeddings_local = self.rotary_emb_local(hidden_states_0, position_ids)
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# Expand hidden_states to support per-layer inputs
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target_magnitude: torch.Tensor = torch.mean(hidden_states_0**2, dim=-1, keepdim=True) ** 0.5
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epsilon_tensor = torch.tensor(torch.finfo().min)
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target_magnitude = torch.mean(hidden_states_0**2, dim=-1, keepdim=True) ** 0.5
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epsilon_tensor = torch.tensor(1e-5)
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temp_hidden_states = [hidden_states_0]
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for i in range(1, self.config.altup_num_inputs):
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# altup_proj adapted from jax.numpy.einsum("btp,pd->btd", ...)
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altup_proj: torch.Tensor = self.altup_projections[i - 1](hidden_states_0)
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current_hidden_state = altup_proj.type(hidden_states_0.dtype)
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new_magnitude = torch.mean(current_hidden_state**2, dim=-1, keepdim=True) ** 0.5
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current_hidden_state = current_hidden_state * (
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target_magnitude / torch.maximum(new_magnitude, epsilon_tensor)
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)
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altup_proj = self.altup_projections[i - 1](hidden_states_0)
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current_hidden_state = altup_proj.to(dtype=hidden_states_0.dtype, device=target_magnitude.device)
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new_magnitude = torch.mean(current_hidden_state**2, dim=-1, keepdim=True)
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new_magnitude = torch.sqrt(torch.maximum(new_magnitude, epsilon_tensor.to(target_magnitude.device)))
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current_hidden_state = current_hidden_state * target_magnitude / new_magnitude
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temp_hidden_states.append(current_hidden_state)
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hidden_states = torch.stack(temp_hidden_states, dim=0) # [num_altup_inputs, batch, seq_len, hidden_size]
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@ -1685,9 +1692,9 @@ class Gemma3nTextModel(Gemma3nPreTrainedModel):
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layer_outputs = decoder_layer(
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hidden_states,
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position_embeddings_global=position_embeddings_global,
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position_embeddings_local=position_embeddings_local,
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per_layer_input=per_layer_input,
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position_embeddings_global,
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position_embeddings_local,
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per_layer_input,
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attention_mask=causal_mask,
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position_ids=position_ids,
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past_key_value=past_key_values,
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@ -1712,11 +1719,10 @@ class Gemma3nTextModel(Gemma3nPreTrainedModel):
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for i in range(1, self.config.altup_num_inputs):
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# altup_unembed_projections adapted from jax.numpy.einsum("btp,pd->btd", ...)
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altup_unemb_proj: torch.Tensor = self.altup_unembed_projections[i - 1](hidden_states[i])
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current_hidden_state = altup_unemb_proj.type(hidden_states_0.dtype)
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new_magnitude = torch.mean(current_hidden_state**2, dim=-1, keepdim=True) ** 0.5
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current_hidden_state = current_hidden_state * (
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target_magnitude / torch.maximum(new_magnitude, epsilon_tensor)
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)
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current_hidden_state = altup_unemb_proj.to(dtype=hidden_states_0.dtype, device=target_magnitude.device)
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new_magnitude = torch.mean(current_hidden_state**2, dim=-1, keepdim=True)
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new_magnitude = torch.sqrt(torch.maximum(new_magnitude, epsilon_tensor.to(target_magnitude.device)))
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current_hidden_state = current_hidden_state * target_magnitude / new_magnitude
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temp_hidden_states.append(current_hidden_state)
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hidden_states = torch.stack(temp_hidden_states)
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@ -1743,7 +1749,9 @@ class Gemma3nTextModel(Gemma3nPreTrainedModel):
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per_layer_inputs: Optional[torch.Tensor] = None,
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) -> torch.Tensor:
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per_layer_projection: torch.Tensor = self.per_layer_model_projection(inputs_embeds)
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per_layer_projection *= self.per_layer_projection_scale.type(inputs_embeds.dtype)
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per_layer_projection *= self.per_layer_projection_scale.to(
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dtype=inputs_embeds.dtype, device=per_layer_projection.device
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)
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per_layer_projection = per_layer_projection.reshape(
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*inputs_embeds.shape[:-1],
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self.config.num_hidden_layers,
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@ -1758,7 +1766,9 @@ class Gemma3nTextModel(Gemma3nPreTrainedModel):
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# per-layer inputs are sometimes padded with zeros, slice the relevant embeddings.
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per_layer_inputs = per_layer_inputs[..., : self.config.num_hidden_layers, :]
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return (per_layer_projection + per_layer_inputs) * self.per_layer_input_scale.type(inputs_embeds.dtype)
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return (per_layer_projection + per_layer_inputs) * self.per_layer_input_scale.to(
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dtype=inputs_embeds.dtype, device=per_layer_projection.device
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)
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@auto_docstring(custom_intro="The base Gemma 3n language model with a language modeling head.")
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@ -1685,9 +1685,17 @@ class Gemma3nTextAltUp(nn.Module):
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corrected += predictions # add the original input
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return corrected.contiguous().type_as(activated)
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def forward(self, corrected: torch.Tensor) -> torch.Tensor:
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"""
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This is only defined as the `forward` so that accelerate hooks can move correctly `correct_output_scale`
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(which is a nn.Parameter, not a Module) between devices when offloading. It is otherwise only used in
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`scale_corrected_output`
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"""
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return (corrected.type_as(self.correct_output_scale) * self.correct_output_scale).type_as(corrected)
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def scale_corrected_output(self, corrected: torch.Tensor) -> torch.Tensor:
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"""Scales the provided 3D tensor of shape [batch_size, num_tokens, hidden_size]."""
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return (corrected.type_as(self.correct_output_scale) * self.correct_output_scale).type_as(corrected)
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return self.forward(corrected)
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class Gemma3nTextRotaryEmbedding(Gemma2RotaryEmbedding):
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@ -1732,7 +1740,7 @@ class Gemma3nTextAttention(Gemma3Attention):
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self.v_norm = Gemma3nRMSNorm(dim=config.head_dim, eps=config.rms_norm_eps, with_scale=False)
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first_kv_shared_layer_idx = self.config.num_hidden_layers - self.config.num_kv_shared_layers
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self.is_kv_shared_layer = layer_idx >= first_kv_shared_layer_idx
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self.is_kv_shared_layer = layer_idx >= first_kv_shared_layer_idx > 0
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# Find the index of the last sliding or full layer before sharing starts (or None if no sharing)
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layer_type = config.layer_types[layer_idx]
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self.kv_shared_layer_index = (
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@ -1761,21 +1769,22 @@ class Gemma3nTextAttention(Gemma3Attention):
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query_states = query_states.transpose(1, 2)
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if self.is_kv_shared_layer and self.kv_shared_layer_index is not None and past_key_value is not None:
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# HybridCache has complex slicing when layer_type == "sliding_attention" that impact Shared KV Cache.
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# Device of past layer may be different from current one
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indices = cache_position.to(past_key_value.key_cache[self.kv_shared_layer_index].device)
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# In this case we need special handling of the slice as the layer is of fixed small size (for full layers, we never go beyond)
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if isinstance(past_key_value, HybridCache) and self.is_sliding:
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max_length = past_key_value.sliding_window
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if cache_position.shape[0] > max_length:
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# If in the prefill phase for a "sliding_attention" layer and the prefill is larger than the cache,
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# slice into the entire cache.
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indices = slice(0, max_length)
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else:
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# If prefill fits or generating for a "sliding_attention" layer, clamp to max_cache_len - 1
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indices = cache_position.clamp(min=0, max=max_length - 1)
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else:
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indices = cache_position
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indices = (
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slice(0, max_length)
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if cache_position.shape[0] > max_length
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else cache_position.clamp(min=0, max=max_length - 1)
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)
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key_states = past_key_value.key_cache[self.kv_shared_layer_index][:, :, indices]
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value_states = past_key_value.value_cache[self.kv_shared_layer_index][:, :, indices]
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# Device of past layer may be different from current one
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key_states = past_key_value.key_cache[self.kv_shared_layer_index][:, :, indices].to(query_states.device)
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value_states = past_key_value.value_cache[self.kv_shared_layer_index][:, :, indices].to(
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query_states.device
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)
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else:
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key_states = self.k_proj(hidden_states).view(hidden_shape)
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key_states = self.k_norm(key_states)
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@ -1880,10 +1889,9 @@ class Gemma3nTextDecoderLayer(Gemma3DecoderLayer):
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attn_ffw_laurel_gated = attn_laurel + attn_ffw_norm
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corrected_predictions = self.altup.correct(predictions, attn_ffw_laurel_gated)
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first_prediction = corrected_predictions[self.config.altup_active_idx]
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first_prediction_clone = first_prediction.clone()
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first_prediction = corrected_predictions[self.config.altup_active_idx].clone()
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if self.config.altup_correct_scale:
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first_prediction = self.altup.scale_corrected_output(first_prediction_clone)
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first_prediction = self.altup.scale_corrected_output(first_prediction)
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# per_layer_input_gate adapted from jax.numpy.einsum("btd,dp->btp", ...)
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first_prediction = self.per_layer_input_gate(first_prediction)
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@ -1906,7 +1914,7 @@ class Gemma3nTextDecoderLayer(Gemma3DecoderLayer):
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class Gemma3nPreTrainedModel(Gemma2PreTrainedModel):
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config_class = Gemma3nConfig
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base_model_prefix = ""
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_no_split_modules = ["Gemma3nDecoderLayer"]
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_no_split_modules = ["Gemma3nTextDecoderLayer"]
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def _init_weights(self, module):
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# important: this ported version of Gemma2 isn't meant for training from scratch - only
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@ -1995,7 +2003,9 @@ class Gemma3nTextModel(Gemma3TextModel):
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per_layer_inputs: Optional[torch.Tensor] = None,
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) -> torch.Tensor:
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per_layer_projection: torch.Tensor = self.per_layer_model_projection(inputs_embeds)
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per_layer_projection *= self.per_layer_projection_scale.type(inputs_embeds.dtype)
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per_layer_projection *= self.per_layer_projection_scale.to(
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dtype=inputs_embeds.dtype, device=per_layer_projection.device
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)
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per_layer_projection = per_layer_projection.reshape(
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*inputs_embeds.shape[:-1],
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self.config.num_hidden_layers,
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@ -2010,7 +2020,9 @@ class Gemma3nTextModel(Gemma3TextModel):
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# per-layer inputs are sometimes padded with zeros, slice the relevant embeddings.
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per_layer_inputs = per_layer_inputs[..., : self.config.num_hidden_layers, :]
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return (per_layer_projection + per_layer_inputs) * self.per_layer_input_scale.type(inputs_embeds.dtype)
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return (per_layer_projection + per_layer_inputs) * self.per_layer_input_scale.to(
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dtype=inputs_embeds.dtype, device=per_layer_projection.device
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)
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@can_return_tuple
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@auto_docstring
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@ -2091,18 +2103,17 @@ class Gemma3nTextModel(Gemma3TextModel):
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position_embeddings_local = self.rotary_emb_local(hidden_states_0, position_ids)
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# Expand hidden_states to support per-layer inputs
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target_magnitude: torch.Tensor = torch.mean(hidden_states_0**2, dim=-1, keepdim=True) ** 0.5
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epsilon_tensor = torch.tensor(torch.finfo().min)
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target_magnitude = torch.mean(hidden_states_0**2, dim=-1, keepdim=True) ** 0.5
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epsilon_tensor = torch.tensor(1e-5)
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temp_hidden_states = [hidden_states_0]
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for i in range(1, self.config.altup_num_inputs):
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# altup_proj adapted from jax.numpy.einsum("btp,pd->btd", ...)
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altup_proj: torch.Tensor = self.altup_projections[i - 1](hidden_states_0)
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current_hidden_state = altup_proj.type(hidden_states_0.dtype)
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new_magnitude = torch.mean(current_hidden_state**2, dim=-1, keepdim=True) ** 0.5
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current_hidden_state = current_hidden_state * (
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target_magnitude / torch.maximum(new_magnitude, epsilon_tensor)
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)
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altup_proj = self.altup_projections[i - 1](hidden_states_0)
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current_hidden_state = altup_proj.to(dtype=hidden_states_0.dtype, device=target_magnitude.device)
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new_magnitude = torch.mean(current_hidden_state**2, dim=-1, keepdim=True)
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new_magnitude = torch.sqrt(torch.maximum(new_magnitude, epsilon_tensor.to(target_magnitude.device)))
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current_hidden_state = current_hidden_state * target_magnitude / new_magnitude
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temp_hidden_states.append(current_hidden_state)
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hidden_states = torch.stack(temp_hidden_states, dim=0) # [num_altup_inputs, batch, seq_len, hidden_size]
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@ -2120,9 +2131,9 @@ class Gemma3nTextModel(Gemma3TextModel):
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layer_outputs = decoder_layer(
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hidden_states,
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position_embeddings_global=position_embeddings_global,
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position_embeddings_local=position_embeddings_local,
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per_layer_input=per_layer_input,
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position_embeddings_global,
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position_embeddings_local,
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per_layer_input,
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attention_mask=causal_mask,
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position_ids=position_ids,
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past_key_value=past_key_values,
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@ -2147,11 +2158,10 @@ class Gemma3nTextModel(Gemma3TextModel):
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for i in range(1, self.config.altup_num_inputs):
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# altup_unembed_projections adapted from jax.numpy.einsum("btp,pd->btd", ...)
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altup_unemb_proj: torch.Tensor = self.altup_unembed_projections[i - 1](hidden_states[i])
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current_hidden_state = altup_unemb_proj.type(hidden_states_0.dtype)
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new_magnitude = torch.mean(current_hidden_state**2, dim=-1, keepdim=True) ** 0.5
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current_hidden_state = current_hidden_state * (
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||||
target_magnitude / torch.maximum(new_magnitude, epsilon_tensor)
|
||||
)
|
||||
current_hidden_state = altup_unemb_proj.to(dtype=hidden_states_0.dtype, device=target_magnitude.device)
|
||||
new_magnitude = torch.mean(current_hidden_state**2, dim=-1, keepdim=True)
|
||||
new_magnitude = torch.sqrt(torch.maximum(new_magnitude, epsilon_tensor.to(target_magnitude.device)))
|
||||
current_hidden_state = current_hidden_state * target_magnitude / new_magnitude
|
||||
temp_hidden_states.append(current_hidden_state)
|
||||
|
||||
hidden_states = torch.stack(temp_hidden_states)
|
||||
|
||||
@ -1642,7 +1642,6 @@ def set_model_tester_for_less_flaky_test(test_case):
|
||||
"AriaVisionText2TextModelTester",
|
||||
"GPTNeoModelTester",
|
||||
"DPTModelTester",
|
||||
"Gemma3nTextModelTester", # cannot have a single layer combined with the cache sharing config attrs in the tester
|
||||
]
|
||||
if test_case.model_tester.__class__.__name__ in exceptional_classes:
|
||||
target_num_hidden_layers = None
|
||||
|
||||
@ -39,13 +39,20 @@ from transformers.testing_utils import (
|
||||
require_read_token,
|
||||
require_torch,
|
||||
require_torch_gpu,
|
||||
require_torch_sdpa,
|
||||
slow,
|
||||
torch_device,
|
||||
)
|
||||
|
||||
from ...generation.test_utils import GenerationTesterMixin
|
||||
from ...test_configuration_common import ConfigTester
|
||||
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
|
||||
from ...test_modeling_common import (
|
||||
TEST_EAGER_MATCHES_SDPA_INFERENCE_PARAMETERIZATION,
|
||||
ModelTesterMixin,
|
||||
_test_eager_matches_sdpa_inference,
|
||||
floats_tensor,
|
||||
ids_tensor,
|
||||
)
|
||||
from ..gemma.test_modeling_gemma import GemmaModelTester
|
||||
|
||||
|
||||
@ -256,6 +263,7 @@ class Gemma3nTextModelTester(GemmaModelTester):
|
||||
vocab_size=99,
|
||||
vocab_size_per_layer_input=99,
|
||||
hidden_size=16,
|
||||
hidden_size_per_layer_input=16,
|
||||
num_hidden_layers=4, # override to correctly test sharing cache pattern
|
||||
num_kv_shared_layers=2, # important to override
|
||||
layer_types=[
|
||||
@ -291,6 +299,7 @@ class Gemma3nTextModelTester(GemmaModelTester):
|
||||
self.vocab_size = vocab_size
|
||||
self.vocab_size_per_layer_input = vocab_size_per_layer_input
|
||||
self.hidden_size = hidden_size
|
||||
self.hidden_size_per_layer_input = hidden_size_per_layer_input
|
||||
self.num_hidden_layers = num_hidden_layers
|
||||
self.num_kv_shared_layers = num_kv_shared_layers
|
||||
self.layer_types = layer_types
|
||||
@ -317,7 +326,6 @@ class Gemma3nTextModelTester(GemmaModelTester):
|
||||
for_causal_lm_class = Gemma3nForCausalLM
|
||||
|
||||
|
||||
@unittest.skip("Skipped for now!")
|
||||
@require_torch
|
||||
class Gemma3nTextModelTest(ModelTesterMixin, GenerationTesterMixin, unittest.TestCase):
|
||||
all_model_classes = (Gemma3nTextModel, Gemma3nForCausalLM) if is_torch_available() else ()
|
||||
@ -365,6 +373,64 @@ class Gemma3nTextModelTest(ModelTesterMixin, GenerationTesterMixin, unittest.Tes
|
||||
[expected_shape] * len(iter_hidden_states),
|
||||
)
|
||||
|
||||
@parameterized.expand(TEST_EAGER_MATCHES_SDPA_INFERENCE_PARAMETERIZATION)
|
||||
@require_torch_sdpa
|
||||
def test_eager_matches_sdpa_inference(
|
||||
self,
|
||||
name,
|
||||
torch_dtype,
|
||||
padding_side,
|
||||
use_attention_mask,
|
||||
output_attentions,
|
||||
enable_kernels,
|
||||
):
|
||||
"We need to relax a bit the `atols` for fp32 here due to the altup projections"
|
||||
atols = {
|
||||
("cpu", False, torch.float32): 1e-3, # this was relaxed
|
||||
("cpu", False, torch.float16): 5e-3,
|
||||
("cpu", False, torch.bfloat16): 1e-2,
|
||||
("cpu", True, torch.float32): 1e-3, # this was relaxed
|
||||
("cpu", True, torch.float16): 5e-3,
|
||||
("cpu", True, torch.bfloat16): 1e-2,
|
||||
("cuda", False, torch.float32): 1e-3, # this was relaxed
|
||||
("cuda", False, torch.bfloat16): 1e-2,
|
||||
("cuda", False, torch.float16): 5e-3,
|
||||
("cuda", True, torch.float32): 1e-3, # this was relaxed
|
||||
("cuda", True, torch.bfloat16): 1e-2,
|
||||
("cuda", True, torch.float16): 5e-3,
|
||||
}
|
||||
_test_eager_matches_sdpa_inference(
|
||||
self, name, torch_dtype, padding_side, use_attention_mask, output_attentions, enable_kernels, atols=atols
|
||||
)
|
||||
|
||||
@pytest.mark.generate
|
||||
@unittest.skip(
|
||||
"Gemma3n has a special shape for hidden states (due to per-layer projs) which is not compatible with contrastive decoding"
|
||||
)
|
||||
def test_contrastive_generate(self):
|
||||
pass
|
||||
|
||||
@pytest.mark.generate
|
||||
@unittest.skip(
|
||||
"Gemma3n has a special shape for hidden states (due to per-layer projs) which is not compatible with contrastive decoding"
|
||||
)
|
||||
def test_contrastive_generate_dict_outputs_use_cache(self):
|
||||
pass
|
||||
|
||||
@pytest.mark.generate
|
||||
@unittest.skip(
|
||||
"Gemma3n has a special shape for hidden states (due to per-layer projs) which is not compatible with contrastive decoding"
|
||||
)
|
||||
def test_contrastive_generate_low_memory(self):
|
||||
pass
|
||||
|
||||
@pytest.mark.generate
|
||||
@unittest.skip(
|
||||
"Gemma3n has a special shape for hidden states (due to per-layer projs) which is not compatible with dola decoding"
|
||||
)
|
||||
def test_dola_decoding_sample(self):
|
||||
pass
|
||||
|
||||
|
||||
class Gemma3nVision2TextModelTester:
|
||||
text_config = {"activation_sparsity_pattern": None}
|
||||
|
||||
@ -156,6 +156,334 @@ TEST_EAGER_MATCHES_SDPA_INFERENCE_PARAMETERIZATION = [
|
||||
] + [("fp32_pad_left_output_attentions", "fp32", "left", True, True, False)]
|
||||
|
||||
|
||||
def _test_eager_matches_sdpa_inference(
|
||||
self,
|
||||
name,
|
||||
torch_dtype,
|
||||
padding_side,
|
||||
use_attention_mask,
|
||||
output_attentions,
|
||||
enable_kernels,
|
||||
atols=None,
|
||||
rtols=None,
|
||||
):
|
||||
"""
|
||||
This test is written as a regular function to be able to overload it easily with different tolerances.
|
||||
Otherwise, `paramterezie.expand` prevents it as it removes the original function from the namespace.
|
||||
"""
|
||||
# TODO: we shouldn't need to do this skip, i.e. the test would be composable from the model tester. CLIP-like
|
||||
# models have a custom mixin, which we detect to skip this test.
|
||||
if any(".CLIPModelTesterMixin" in str(base) for base in self.__class__.__bases__):
|
||||
self.skipTest(reason="CLIP-like models have a different `test_eager_matches_sdpa_inference`")
|
||||
|
||||
if not self.has_attentions:
|
||||
self.skipTest(reason="Model architecture does not support attentions")
|
||||
|
||||
if not self.all_model_classes[0]._supports_sdpa:
|
||||
self.skipTest(f"{self.all_model_classes[0].__name__} does not support SDPA")
|
||||
|
||||
# convert shorthand name to torch.dtype
|
||||
if torch_dtype == "fp16":
|
||||
torch_dtype = torch.float16
|
||||
elif torch_dtype == "bf16":
|
||||
torch_dtype = torch.bfloat16
|
||||
elif torch_dtype == "fp32":
|
||||
torch_dtype = torch.float32
|
||||
|
||||
if not is_torch_fp16_available_on_device(torch_device) and torch_dtype == torch.float16:
|
||||
self.skipTest(f"float16 not supported on {torch_device} (on the specific device currently used)")
|
||||
|
||||
if not is_torch_bf16_available_on_device(torch_device) and torch_dtype == torch.bfloat16:
|
||||
self.skipTest(
|
||||
f"bfloat16 not supported on {torch_device} (on the specific device currently used, e.g. Nvidia T4 GPU)"
|
||||
)
|
||||
|
||||
# Dictionary of tolerances for eager <> sdpa tests. Key = (device, sdpa_kernels_enabled, dtype)
|
||||
if atols is None:
|
||||
atols = {
|
||||
("cpu", False, torch.float32): 1e-6,
|
||||
("cpu", False, torch.float16): 5e-3,
|
||||
("cpu", False, torch.bfloat16): 1e-2,
|
||||
("cpu", True, torch.float32): 1e-6,
|
||||
("cpu", True, torch.float16): 5e-3,
|
||||
("cpu", True, torch.bfloat16): 1e-2,
|
||||
("cuda", False, torch.float32): 1e-6,
|
||||
("cuda", False, torch.bfloat16): 1e-2,
|
||||
("cuda", False, torch.float16): 5e-3,
|
||||
("cuda", True, torch.float32): 1e-6,
|
||||
("cuda", True, torch.bfloat16): 1e-2,
|
||||
("cuda", True, torch.float16): 5e-3,
|
||||
}
|
||||
if rtols is None:
|
||||
rtols = {
|
||||
("cpu", False, torch.float32): 1e-4,
|
||||
("cpu", False, torch.float16): 5e-3,
|
||||
("cpu", False, torch.bfloat16): 1e-2,
|
||||
("cpu", True, torch.float32): 1e-4,
|
||||
("cpu", True, torch.float16): 5e-3,
|
||||
("cpu", True, torch.bfloat16): 1e-2,
|
||||
("cuda", False, torch.float32): 1e-4,
|
||||
("cuda", False, torch.bfloat16): 1e-2,
|
||||
("cuda", False, torch.float16): 5e-3,
|
||||
("cuda", True, torch.float32): 1e-4,
|
||||
("cuda", True, torch.bfloat16): 3e-2, # (different from others)
|
||||
("cuda", True, torch.float16): 5e-3,
|
||||
}
|
||||
|
||||
set_model_tester_for_less_flaky_test(self)
|
||||
|
||||
for model_class in self.all_model_classes:
|
||||
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
|
||||
set_config_for_less_flaky_test(config)
|
||||
model = model_class(config)
|
||||
# TODO: standardize the interfaces for musicgen models, see other todo in this test
|
||||
if model.__class__.__name__ == "MusicgenMelodyForConditionalGeneration":
|
||||
is_encoder_decoder = True
|
||||
else:
|
||||
is_encoder_decoder = model.config.is_encoder_decoder
|
||||
|
||||
with tempfile.TemporaryDirectory() as tmpdirname:
|
||||
model.save_pretrained(tmpdirname)
|
||||
model_from_pretrained_kwargs = {
|
||||
"pretrained_model_name_or_path": tmpdirname,
|
||||
"torch_dtype": torch_dtype,
|
||||
}
|
||||
|
||||
if hasattr(config, "use_mask_token") or "use_mask_token" in inspect.signature(model.__init__).parameters:
|
||||
model_from_pretrained_kwargs["use_mask_token"] = True
|
||||
|
||||
# TODO: remove this try/except, models should have a shared API
|
||||
try:
|
||||
model_sdpa = model_class.from_pretrained(**model_from_pretrained_kwargs, attn_implementation="sdpa")
|
||||
except ValueError:
|
||||
model_sdpa = model_class.from_pretrained(**model_from_pretrained_kwargs)
|
||||
model_sdpa = model_sdpa.eval().to(torch_device, dtype=torch_dtype)
|
||||
|
||||
model_eager = model_class.from_pretrained(**model_from_pretrained_kwargs, attn_implementation="eager")
|
||||
model_eager = model_eager.eval().to(torch_device, dtype=torch_dtype)
|
||||
|
||||
set_model_for_less_flaky_test(model_eager)
|
||||
set_model_for_less_flaky_test(model_sdpa)
|
||||
|
||||
can_output_attn = "output_attentions" in inspect.signature(model_sdpa.forward).parameters
|
||||
if not (self.has_attentions and can_output_attn) and output_attentions:
|
||||
self.skipTest(reason="Model does not support output_attentions")
|
||||
|
||||
# TODO: if we can also check with `batch_size=1` without being flaky?
|
||||
for batch_size in [7]:
|
||||
# musicgen decoder models; TODO: find better abstraction
|
||||
if (
|
||||
model.__class__.__name__.startswith("Musicgen")
|
||||
and hasattr(self.model_tester, "num_codebooks")
|
||||
and not hasattr(model_eager, "text_encoder")
|
||||
):
|
||||
input_data_batch_size = batch_size * self.model_tester.num_codebooks
|
||||
else:
|
||||
input_data_batch_size = batch_size
|
||||
|
||||
processed_inputs = {}
|
||||
processed_inputs[model.main_input_name] = inputs_dict[model.main_input_name]
|
||||
|
||||
for key in getattr(self, "additional_model_inputs", []):
|
||||
# Some models don't have all `additional_model_inputs`, especially when we
|
||||
# craft cases to test model in different settings
|
||||
if key in inputs_dict:
|
||||
processed_inputs[key] = inputs_dict[key]
|
||||
|
||||
for key, value in processed_inputs.items():
|
||||
if torch.is_floating_point(value):
|
||||
value = value.to(torch_dtype)
|
||||
|
||||
# extend value to have at least `input_data_batch_size` elements
|
||||
if value.shape[0] < input_data_batch_size:
|
||||
size = (input_data_batch_size - value.shape[0], *value.shape[1:])
|
||||
if torch.is_floating_point(value):
|
||||
extension = torch.rand(size=size, dtype=value.dtype, device=torch_device)
|
||||
else:
|
||||
extension = torch.randint(high=5, size=size, dtype=value.dtype, device=torch_device)
|
||||
value = torch.cat((value, extension), dim=0).to(torch_device)
|
||||
|
||||
processed_inputs[key] = value[:input_data_batch_size]
|
||||
|
||||
if not use_attention_mask:
|
||||
dummy_attention_mask = None
|
||||
else:
|
||||
dummy_attention_mask = inputs_dict.get("attention_mask", None)
|
||||
if dummy_attention_mask is None:
|
||||
if is_encoder_decoder:
|
||||
seqlen = inputs_dict.get("decoder_input_ids", processed_inputs[model.main_input_name]).shape[
|
||||
-1
|
||||
]
|
||||
else:
|
||||
seqlen = processed_inputs[model.main_input_name].shape[-1]
|
||||
dummy_attention_mask = torch.ones(batch_size, seqlen).to(torch.int64).to(torch_device)
|
||||
|
||||
# extend dummy_attention_mask to have at least `batch_size` elements
|
||||
if dummy_attention_mask.shape[0] < batch_size:
|
||||
size = (batch_size - dummy_attention_mask.shape[0], *dummy_attention_mask.shape[1:])
|
||||
extension = torch.ones(size=size, dtype=dummy_attention_mask.dtype, device=torch_device)
|
||||
dummy_attention_mask = torch.cat((dummy_attention_mask, extension), dim=0)
|
||||
|
||||
dummy_attention_mask = dummy_attention_mask[:batch_size].to(torch_device)
|
||||
|
||||
dummy_attention_mask[:] = 1
|
||||
if padding_side == "left":
|
||||
dummy_attention_mask[-1, :2] = 0
|
||||
dummy_attention_mask[-1, 2:] = 1
|
||||
elif padding_side == "right":
|
||||
dummy_attention_mask[-1, -2:] = 0
|
||||
dummy_attention_mask[-1, :-2] = 1
|
||||
|
||||
if is_encoder_decoder:
|
||||
# musicgen encoder-decoder models; TODO: find better abstraction
|
||||
if model.__class__.__name__.startswith("Musicgen") and hasattr(self.model_tester, "num_codebooks"):
|
||||
input_data_batch_size = batch_size * self.model_tester.num_codebooks
|
||||
else:
|
||||
input_data_batch_size = batch_size
|
||||
|
||||
decoder_input_ids = inputs_dict.get("decoder_input_ids", processed_inputs[model.main_input_name])
|
||||
decoder_input_ids = decoder_input_ids[:input_data_batch_size]
|
||||
if decoder_input_ids.shape[0] != input_data_batch_size:
|
||||
extension = torch.ones(
|
||||
input_data_batch_size - decoder_input_ids.shape[0],
|
||||
*decoder_input_ids.shape[1:],
|
||||
dtype=decoder_input_ids.dtype,
|
||||
device=torch_device,
|
||||
)
|
||||
decoder_input_ids = torch.cat((decoder_input_ids, extension), dim=0)
|
||||
decoder_input_ids = decoder_input_ids.to(torch_device)
|
||||
|
||||
# TODO: never an `attention_mask` arg here?
|
||||
processed_inputs.update(
|
||||
{
|
||||
"decoder_input_ids": decoder_input_ids,
|
||||
"decoder_attention_mask": dummy_attention_mask,
|
||||
"output_hidden_states": True,
|
||||
}
|
||||
)
|
||||
else:
|
||||
processed_inputs.update(
|
||||
{
|
||||
"output_hidden_states": True,
|
||||
}
|
||||
)
|
||||
|
||||
# Otherwise fails for e.g. WhisperEncoderModel
|
||||
if "attention_mask" in inspect.signature(model_eager.forward).parameters:
|
||||
processed_inputs["attention_mask"] = dummy_attention_mask
|
||||
|
||||
if self.has_attentions and "output_attentions" in inspect.signature(model_sdpa.forward).parameters:
|
||||
processed_inputs["output_attentions"] = output_attentions
|
||||
if "bool_masked_pos" in inspect.signature(model_eager.forward).parameters:
|
||||
dummy_mask = torch.ones((self.model_tester.num_masks,))
|
||||
|
||||
# In case of additional token (like class) we define a custom `mask_length`
|
||||
if hasattr(self.model_tester, "mask_length"):
|
||||
mask_length = self.model_tester.mask_length - dummy_mask.size(0)
|
||||
else:
|
||||
mask_length = self.model_tester.seq_length - dummy_mask.size(0)
|
||||
dummy_mask = torch.cat([dummy_mask, torch.zeros(mask_length)])
|
||||
dummy_bool_masked_pos = dummy_mask.expand(batch_size, -1).bool()
|
||||
processed_inputs["bool_masked_pos"] = dummy_bool_masked_pos.to(torch_device)
|
||||
|
||||
if "noise" in inspect.signature(model_eager.forward).parameters:
|
||||
np.random.seed(2)
|
||||
num_patches = int((self.model_tester.image_size // self.model_tester.patch_size) ** 2)
|
||||
noise = np.random.uniform(size=(batch_size, num_patches))
|
||||
processed_inputs["noise"] = torch.from_numpy(noise)
|
||||
|
||||
# TODO: test gradients as well (& for FA2 as well!)
|
||||
with torch.no_grad():
|
||||
with sdpa_kernel(
|
||||
enable_flash=enable_kernels,
|
||||
enable_math=True,
|
||||
enable_mem_efficient=enable_kernels,
|
||||
):
|
||||
prepared_inputs = self._prepare_for_class(processed_inputs, model_class)
|
||||
prepared_inputs = {
|
||||
k: v.to(torch_device) if isinstance(v, torch.Tensor) else v for k, v in prepared_inputs.items()
|
||||
}
|
||||
outputs_eager = model_eager(**prepared_inputs)
|
||||
outputs_sdpa = model_sdpa(**prepared_inputs)
|
||||
|
||||
if "logits_per_text" in outputs_eager:
|
||||
key = "logits_per_text"
|
||||
elif "vision_hidden_states" in outputs_eager:
|
||||
key = "vision_hidden_states"
|
||||
elif "audio_values" in outputs_eager:
|
||||
key = "audio_values"
|
||||
elif "decoder_hidden_states" in outputs_eager:
|
||||
key = "decoder_hidden_states"
|
||||
elif "logits" in outputs_eager and "Classification" in model_class.__name__:
|
||||
key = "logits"
|
||||
elif "language_model_outputs" in outputs_eager and "blip" in model_class.__name__.lower():
|
||||
outputs_eager = outputs_eager["language_model_outputs"]
|
||||
outputs_sdpa = outputs_sdpa["language_model_outputs"]
|
||||
key = "hidden_states" if "hidden_states" in outputs_eager else "decoder_hidden_states"
|
||||
else:
|
||||
key = "hidden_states"
|
||||
|
||||
# TODO: rename logits -> hidden_states
|
||||
logits_eager = outputs_eager[key]
|
||||
logits_sdpa = outputs_sdpa[key]
|
||||
|
||||
if key in ["vision_hidden_states", "decoder_hidden_states", "hidden_states"]:
|
||||
logits_eager = logits_eager[-1]
|
||||
logits_sdpa = logits_sdpa[-1]
|
||||
|
||||
if key == "logits_per_text":
|
||||
nan_mask = torch.isnan(logits_eager)
|
||||
logits_eager[nan_mask] = 0
|
||||
logits_sdpa[nan_mask] = 0
|
||||
|
||||
if torch_device in ["cpu", "cuda"]:
|
||||
atol = atols[torch_device, enable_kernels, torch_dtype]
|
||||
rtol = rtols[torch_device, enable_kernels, torch_dtype]
|
||||
elif torch_device == "hpu":
|
||||
atol = atols["cuda", enable_kernels, torch_dtype]
|
||||
rtol = rtols["cuda", enable_kernels, torch_dtype]
|
||||
elif torch_device == "xpu":
|
||||
# As of PyTorch 2.5 XPU backend supports only torch.nn.attention.SDPBackend.MATH
|
||||
# which is implemented on PyTorch level using aten operators and is
|
||||
# device agnostic with respect to implementation of each aten operator.
|
||||
atol = atols["cuda", False, torch_dtype]
|
||||
rtol = rtols["cuda", False, torch_dtype]
|
||||
else:
|
||||
atol = 1e-7
|
||||
rtol = 1e-4
|
||||
|
||||
# Masked tokens output slightly deviates - we don't mind that.
|
||||
if use_attention_mask:
|
||||
_logits_sdpa = torch.zeros_like(input=logits_sdpa)
|
||||
_logits_eager = torch.zeros_like(input=logits_eager)
|
||||
|
||||
_logits_sdpa[:-1] = logits_sdpa[:-1]
|
||||
_logits_eager[:-1] = logits_eager[:-1]
|
||||
|
||||
if padding_side == "left":
|
||||
_logits_sdpa[-1:, 2:] = logits_sdpa[-1:, 2:]
|
||||
_logits_eager[-1:, 2:] = logits_eager[-1:, 2:]
|
||||
|
||||
elif padding_side == "right":
|
||||
_logits_sdpa[-1:, 2:] = logits_sdpa[-1:, :-2]
|
||||
_logits_eager[-1:, 2:] = logits_eager[-1:, :-2]
|
||||
|
||||
logits_sdpa = _logits_sdpa
|
||||
logits_eager = _logits_eager
|
||||
|
||||
results = [
|
||||
torch.allclose(_logits_sdpa, _logits_eager, atol=atol, rtol=rtol)
|
||||
for (_logits_sdpa, _logits_eager) in zip(logits_sdpa, logits_eager)
|
||||
]
|
||||
# If 80% batch elements have matched results, it's fine
|
||||
if np.mean(results) < 0.8:
|
||||
mean_relative_diff = ((logits_sdpa - logits_eager).abs() / (logits_eager.abs() + 1e-12)).mean()
|
||||
raise ValueError(
|
||||
f"mean relative difference for {key}: {mean_relative_diff:.3e}, torch atol = {atol}, torch rtol = "
|
||||
f"{rtol}"
|
||||
)
|
||||
|
||||
|
||||
def _config_zero_init(config):
|
||||
configs_no_init = copy.deepcopy(config)
|
||||
for key in configs_no_init.__dict__.keys():
|
||||
@ -3405,321 +3733,9 @@ class ModelTesterMixin:
|
||||
def test_eager_matches_sdpa_inference(
|
||||
self, name, torch_dtype, padding_side, use_attention_mask, output_attentions, enable_kernels
|
||||
):
|
||||
# TODO: we shouldn't need to do this skip, i.e. the test would be composable from the model tester. CLIP-like
|
||||
# models have a custom mixin, which we detect to skip this test.
|
||||
if any(".CLIPModelTesterMixin" in str(base) for base in self.__class__.__bases__):
|
||||
self.skipTest(reason="CLIP-like models have a different `test_eager_matches_sdpa_inference`")
|
||||
|
||||
if not self.has_attentions:
|
||||
self.skipTest(reason="Model architecture does not support attentions")
|
||||
|
||||
if not self.all_model_classes[0]._supports_sdpa:
|
||||
self.skipTest(f"{self.all_model_classes[0].__name__} does not support SDPA")
|
||||
|
||||
# convert shorthand name to torch.dtype
|
||||
if torch_dtype == "fp16":
|
||||
torch_dtype = torch.float16
|
||||
elif torch_dtype == "bf16":
|
||||
torch_dtype = torch.bfloat16
|
||||
elif torch_dtype == "fp32":
|
||||
torch_dtype = torch.float32
|
||||
|
||||
if not is_torch_fp16_available_on_device(torch_device) and torch_dtype == torch.float16:
|
||||
self.skipTest(f"float16 not supported on {torch_device} (on the specific device currently used)")
|
||||
|
||||
if not is_torch_bf16_available_on_device(torch_device) and torch_dtype == torch.bfloat16:
|
||||
self.skipTest(
|
||||
f"bfloat16 not supported on {torch_device} (on the specific device currently used, e.g. Nvidia T4 GPU)"
|
||||
)
|
||||
|
||||
# Dictionary of tolerances for eager <> sdpa tests. Key = (device, sdpa_kernels_enabled, dtype)
|
||||
atols = {
|
||||
("cpu", False, torch.float32): 1e-6,
|
||||
("cpu", False, torch.float16): 5e-3,
|
||||
("cpu", False, torch.bfloat16): 1e-2,
|
||||
("cpu", True, torch.float32): 1e-6,
|
||||
("cpu", True, torch.float16): 5e-3,
|
||||
("cpu", True, torch.bfloat16): 1e-2,
|
||||
("cuda", False, torch.float32): 1e-6,
|
||||
("cuda", False, torch.bfloat16): 1e-2,
|
||||
("cuda", False, torch.float16): 5e-3,
|
||||
("cuda", True, torch.float32): 1e-6,
|
||||
("cuda", True, torch.bfloat16): 1e-2,
|
||||
("cuda", True, torch.float16): 5e-3,
|
||||
}
|
||||
rtols = {
|
||||
("cpu", False, torch.float32): 1e-4,
|
||||
("cpu", False, torch.float16): 5e-3,
|
||||
("cpu", False, torch.bfloat16): 1e-2,
|
||||
("cpu", True, torch.float32): 1e-4,
|
||||
("cpu", True, torch.float16): 5e-3,
|
||||
("cpu", True, torch.bfloat16): 1e-2,
|
||||
("cuda", False, torch.float32): 1e-4,
|
||||
("cuda", False, torch.bfloat16): 1e-2,
|
||||
("cuda", False, torch.float16): 5e-3,
|
||||
("cuda", True, torch.float32): 1e-4,
|
||||
("cuda", True, torch.bfloat16): 3e-2, # (different from others)
|
||||
("cuda", True, torch.float16): 5e-3,
|
||||
}
|
||||
|
||||
set_model_tester_for_less_flaky_test(self)
|
||||
|
||||
for model_class in self.all_model_classes:
|
||||
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
|
||||
set_config_for_less_flaky_test(config)
|
||||
model = model_class(config)
|
||||
# TODO: standardize the interfaces for musicgen models, see other todo in this test
|
||||
if model.__class__.__name__ == "MusicgenMelodyForConditionalGeneration":
|
||||
is_encoder_decoder = True
|
||||
else:
|
||||
is_encoder_decoder = model.config.is_encoder_decoder
|
||||
|
||||
with tempfile.TemporaryDirectory() as tmpdirname:
|
||||
model.save_pretrained(tmpdirname)
|
||||
model_from_pretrained_kwargs = {
|
||||
"pretrained_model_name_or_path": tmpdirname,
|
||||
"torch_dtype": torch_dtype,
|
||||
}
|
||||
|
||||
if (
|
||||
hasattr(config, "use_mask_token")
|
||||
or "use_mask_token" in inspect.signature(model.__init__).parameters
|
||||
):
|
||||
model_from_pretrained_kwargs["use_mask_token"] = True
|
||||
|
||||
# TODO: remove this try/except, models should have a shared API
|
||||
try:
|
||||
model_sdpa = model_class.from_pretrained(
|
||||
**model_from_pretrained_kwargs, attn_implementation="sdpa"
|
||||
)
|
||||
except ValueError:
|
||||
model_sdpa = model_class.from_pretrained(**model_from_pretrained_kwargs)
|
||||
model_sdpa = model_sdpa.eval().to(torch_device, dtype=torch_dtype)
|
||||
|
||||
model_eager = model_class.from_pretrained(**model_from_pretrained_kwargs, attn_implementation="eager")
|
||||
model_eager = model_eager.eval().to(torch_device, dtype=torch_dtype)
|
||||
|
||||
set_model_for_less_flaky_test(model_eager)
|
||||
set_model_for_less_flaky_test(model_sdpa)
|
||||
|
||||
can_output_attn = "output_attentions" in inspect.signature(model_sdpa.forward).parameters
|
||||
if not (self.has_attentions and can_output_attn) and output_attentions:
|
||||
self.skipTest(reason="Model does not support output_attentions")
|
||||
|
||||
# TODO: if we can also check with `batch_size=1` without being flaky?
|
||||
for batch_size in [7]:
|
||||
# musicgen decoder models; TODO: find better abstraction
|
||||
if (
|
||||
model.__class__.__name__.startswith("Musicgen")
|
||||
and hasattr(self.model_tester, "num_codebooks")
|
||||
and not hasattr(model_eager, "text_encoder")
|
||||
):
|
||||
input_data_batch_size = batch_size * self.model_tester.num_codebooks
|
||||
else:
|
||||
input_data_batch_size = batch_size
|
||||
|
||||
processed_inputs = {}
|
||||
processed_inputs[model.main_input_name] = inputs_dict[model.main_input_name]
|
||||
|
||||
for key in getattr(self, "additional_model_inputs", []):
|
||||
# Some models don't have all `additional_model_inputs`, especially when we
|
||||
# craft cases to test model in different settings
|
||||
if key in inputs_dict:
|
||||
processed_inputs[key] = inputs_dict[key]
|
||||
|
||||
for key, value in processed_inputs.items():
|
||||
if torch.is_floating_point(value):
|
||||
value = value.to(torch_dtype)
|
||||
|
||||
# extend value to have at least `input_data_batch_size` elements
|
||||
if value.shape[0] < input_data_batch_size:
|
||||
size = (input_data_batch_size - value.shape[0], *value.shape[1:])
|
||||
if torch.is_floating_point(value):
|
||||
extension = torch.rand(size=size, dtype=value.dtype, device=torch_device)
|
||||
else:
|
||||
extension = torch.randint(high=5, size=size, dtype=value.dtype, device=torch_device)
|
||||
value = torch.cat((value, extension), dim=0).to(torch_device)
|
||||
|
||||
processed_inputs[key] = value[:input_data_batch_size]
|
||||
|
||||
if not use_attention_mask:
|
||||
dummy_attention_mask = None
|
||||
else:
|
||||
dummy_attention_mask = inputs_dict.get("attention_mask", None)
|
||||
if dummy_attention_mask is None:
|
||||
if is_encoder_decoder:
|
||||
seqlen = inputs_dict.get(
|
||||
"decoder_input_ids", processed_inputs[model.main_input_name]
|
||||
).shape[-1]
|
||||
else:
|
||||
seqlen = processed_inputs[model.main_input_name].shape[-1]
|
||||
dummy_attention_mask = torch.ones(batch_size, seqlen).to(torch.int64).to(torch_device)
|
||||
|
||||
# extend dummy_attention_mask to have at least `batch_size` elements
|
||||
if dummy_attention_mask.shape[0] < batch_size:
|
||||
size = (batch_size - dummy_attention_mask.shape[0], *dummy_attention_mask.shape[1:])
|
||||
extension = torch.ones(size=size, dtype=dummy_attention_mask.dtype, device=torch_device)
|
||||
dummy_attention_mask = torch.cat((dummy_attention_mask, extension), dim=0)
|
||||
|
||||
dummy_attention_mask = dummy_attention_mask[:batch_size].to(torch_device)
|
||||
|
||||
dummy_attention_mask[:] = 1
|
||||
if padding_side == "left":
|
||||
dummy_attention_mask[-1, :2] = 0
|
||||
dummy_attention_mask[-1, 2:] = 1
|
||||
elif padding_side == "right":
|
||||
dummy_attention_mask[-1, -2:] = 0
|
||||
dummy_attention_mask[-1, :-2] = 1
|
||||
|
||||
if is_encoder_decoder:
|
||||
# musicgen encoder-decoder models; TODO: find better abstraction
|
||||
if model.__class__.__name__.startswith("Musicgen") and hasattr(self.model_tester, "num_codebooks"):
|
||||
input_data_batch_size = batch_size * self.model_tester.num_codebooks
|
||||
else:
|
||||
input_data_batch_size = batch_size
|
||||
|
||||
decoder_input_ids = inputs_dict.get("decoder_input_ids", processed_inputs[model.main_input_name])
|
||||
decoder_input_ids = decoder_input_ids[:input_data_batch_size]
|
||||
if decoder_input_ids.shape[0] != input_data_batch_size:
|
||||
extension = torch.ones(
|
||||
input_data_batch_size - decoder_input_ids.shape[0],
|
||||
*decoder_input_ids.shape[1:],
|
||||
dtype=decoder_input_ids.dtype,
|
||||
device=torch_device,
|
||||
)
|
||||
decoder_input_ids = torch.cat((decoder_input_ids, extension), dim=0)
|
||||
decoder_input_ids = decoder_input_ids.to(torch_device)
|
||||
|
||||
# TODO: never an `attention_mask` arg here?
|
||||
processed_inputs.update(
|
||||
{
|
||||
"decoder_input_ids": decoder_input_ids,
|
||||
"decoder_attention_mask": dummy_attention_mask,
|
||||
"output_hidden_states": True,
|
||||
}
|
||||
)
|
||||
else:
|
||||
processed_inputs.update(
|
||||
{
|
||||
"output_hidden_states": True,
|
||||
}
|
||||
)
|
||||
|
||||
# Otherwise fails for e.g. WhisperEncoderModel
|
||||
if "attention_mask" in inspect.signature(model_eager.forward).parameters:
|
||||
processed_inputs["attention_mask"] = dummy_attention_mask
|
||||
|
||||
if self.has_attentions and "output_attentions" in inspect.signature(model_sdpa.forward).parameters:
|
||||
processed_inputs["output_attentions"] = output_attentions
|
||||
if "bool_masked_pos" in inspect.signature(model_eager.forward).parameters:
|
||||
dummy_mask = torch.ones((self.model_tester.num_masks,))
|
||||
|
||||
# In case of additional token (like class) we define a custom `mask_length`
|
||||
if hasattr(self.model_tester, "mask_length"):
|
||||
mask_length = self.model_tester.mask_length - dummy_mask.size(0)
|
||||
else:
|
||||
mask_length = self.model_tester.seq_length - dummy_mask.size(0)
|
||||
dummy_mask = torch.cat([dummy_mask, torch.zeros(mask_length)])
|
||||
dummy_bool_masked_pos = dummy_mask.expand(batch_size, -1).bool()
|
||||
processed_inputs["bool_masked_pos"] = dummy_bool_masked_pos.to(torch_device)
|
||||
|
||||
if "noise" in inspect.signature(model_eager.forward).parameters:
|
||||
np.random.seed(2)
|
||||
num_patches = int((self.model_tester.image_size // self.model_tester.patch_size) ** 2)
|
||||
noise = np.random.uniform(size=(batch_size, num_patches))
|
||||
processed_inputs["noise"] = torch.from_numpy(noise)
|
||||
|
||||
# TODO: test gradients as well (& for FA2 as well!)
|
||||
with torch.no_grad():
|
||||
with sdpa_kernel(
|
||||
enable_flash=enable_kernels,
|
||||
enable_math=True,
|
||||
enable_mem_efficient=enable_kernels,
|
||||
):
|
||||
prepared_inputs = self._prepare_for_class(processed_inputs, model_class)
|
||||
prepared_inputs = {
|
||||
k: v.to(torch_device) if isinstance(v, torch.Tensor) else v
|
||||
for k, v in prepared_inputs.items()
|
||||
}
|
||||
outputs_eager = model_eager(**prepared_inputs)
|
||||
outputs_sdpa = model_sdpa(**prepared_inputs)
|
||||
|
||||
if "logits_per_text" in outputs_eager:
|
||||
key = "logits_per_text"
|
||||
elif "vision_hidden_states" in outputs_eager:
|
||||
key = "vision_hidden_states"
|
||||
elif "audio_values" in outputs_eager:
|
||||
key = "audio_values"
|
||||
elif "decoder_hidden_states" in outputs_eager:
|
||||
key = "decoder_hidden_states"
|
||||
elif "logits" in outputs_eager and "Classification" in model_class.__name__:
|
||||
key = "logits"
|
||||
elif "language_model_outputs" in outputs_eager and "blip" in model_class.__name__.lower():
|
||||
outputs_eager = outputs_eager["language_model_outputs"]
|
||||
outputs_sdpa = outputs_sdpa["language_model_outputs"]
|
||||
key = "hidden_states" if "hidden_states" in outputs_eager else "decoder_hidden_states"
|
||||
else:
|
||||
key = "hidden_states"
|
||||
|
||||
# TODO: rename logits -> hidden_states
|
||||
logits_eager = outputs_eager[key]
|
||||
logits_sdpa = outputs_sdpa[key]
|
||||
|
||||
if key in ["vision_hidden_states", "decoder_hidden_states", "hidden_states"]:
|
||||
logits_eager = logits_eager[-1]
|
||||
logits_sdpa = logits_sdpa[-1]
|
||||
|
||||
if key == "logits_per_text":
|
||||
nan_mask = torch.isnan(logits_eager)
|
||||
logits_eager[nan_mask] = 0
|
||||
logits_sdpa[nan_mask] = 0
|
||||
|
||||
if torch_device in ["cpu", "cuda"]:
|
||||
atol = atols[torch_device, enable_kernels, torch_dtype]
|
||||
rtol = rtols[torch_device, enable_kernels, torch_dtype]
|
||||
elif torch_device == "hpu":
|
||||
atol = atols["cuda", enable_kernels, torch_dtype]
|
||||
rtol = rtols["cuda", enable_kernels, torch_dtype]
|
||||
elif torch_device == "xpu":
|
||||
# As of PyTorch 2.5 XPU backend supports only torch.nn.attention.SDPBackend.MATH
|
||||
# which is implemented on PyTorch level using aten operators and is
|
||||
# device agnostic with respect to implementation of each aten operator.
|
||||
atol = atols["cuda", False, torch_dtype]
|
||||
rtol = rtols["cuda", False, torch_dtype]
|
||||
else:
|
||||
atol = 1e-7
|
||||
rtol = 1e-4
|
||||
|
||||
# Masked tokens output slightly deviates - we don't mind that.
|
||||
if use_attention_mask:
|
||||
_logits_sdpa = torch.zeros_like(input=logits_sdpa)
|
||||
_logits_eager = torch.zeros_like(input=logits_eager)
|
||||
|
||||
_logits_sdpa[:-1] = logits_sdpa[:-1]
|
||||
_logits_eager[:-1] = logits_eager[:-1]
|
||||
|
||||
if padding_side == "left":
|
||||
_logits_sdpa[-1:, 2:] = logits_sdpa[-1:, 2:]
|
||||
_logits_eager[-1:, 2:] = logits_eager[-1:, 2:]
|
||||
|
||||
elif padding_side == "right":
|
||||
_logits_sdpa[-1:, 2:] = logits_sdpa[-1:, :-2]
|
||||
_logits_eager[-1:, 2:] = logits_eager[-1:, :-2]
|
||||
|
||||
logits_sdpa = _logits_sdpa
|
||||
logits_eager = _logits_eager
|
||||
|
||||
results = [
|
||||
torch.allclose(_logits_sdpa, _logits_eager, atol=atol, rtol=rtol)
|
||||
for (_logits_sdpa, _logits_eager) in zip(logits_sdpa, logits_eager)
|
||||
]
|
||||
# If 80% batch elements have matched results, it's fine
|
||||
if np.mean(results) < 0.8:
|
||||
mean_relative_diff = ((logits_sdpa - logits_eager).abs() / (logits_eager.abs() + 1e-12)).mean()
|
||||
raise ValueError(
|
||||
f"mean relative difference for {key}: {mean_relative_diff:.3e}, torch atol = {atol}, torch rtol = "
|
||||
f"{rtol}"
|
||||
)
|
||||
_test_eager_matches_sdpa_inference(
|
||||
self, name, torch_dtype, padding_side, use_attention_mask, output_attentions, enable_kernels
|
||||
)
|
||||
|
||||
@require_torch_sdpa
|
||||
@require_torch_accelerator
|
||||
|
||||
Loading…
Reference in New Issue
Block a user