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# Copyright (c) Microsoft Corporation.
# SPDX-License-Identifier: Apache-2.0
# DeepSpeed Team
from ..config_v2 import RaggedInferenceEngineConfig
from ..inference_utils import NormTypeEnum
from .module_registry import ConfigBundle
from ..modules.configs import (
DSEmbeddingsConfig,
DSLinearConfig,
DSMoEConfig,
DSNormConfig,
DSSelfAttentionConfig,
DSUnembedConfig,
)
from ..modules.interfaces import (
DSEmbeddingBase,
DSEmbeddingRegistry,
DSLinearBase,
DSLinearRegistry,
DSMoEBase,
DSMoERegistry,
DSPostNormBase,
DSPostNormRegistry,
DSPreNormBase,
DSPreNormRegistry,
DSSelfAttentionBase,
DSSelfAttentionRegistry,
DSUnembedBase,
DSUnembedRegistry,
)
def instantiate_attention(attention_config: DSSelfAttentionConfig,
engine_config: RaggedInferenceEngineConfig) -> DSSelfAttentionBase:
"""
Choose an appropriate attention implementation based on the given configurations. This
method is currently a stub, but as more implementations may be developed we can centralize
the logic for choosing between them here.
Arguments:
attention_config (DSSelfAttentionConfig): Configuration for the attention module.
engine_config (RaggedInferenceEngineConfig): Configuration for the inference engine.
Returns:
An attention module implementing the given configuration.
"""
# Currently, we only have one implementation, so we just return it.
config = ConfigBundle(name="dense_blocked_attention", config=attention_config)
return DSSelfAttentionRegistry.instantiate_config(config)
def instantiate_embed(embed_config: DSEmbeddingsConfig, engine_config: RaggedInferenceEngineConfig) -> DSEmbeddingBase:
"""
Choose an appropriate embedding implementation based on the given configurations. This
method is currently a stub, but as more implementations may be developed we can centralize
the logic for choosing between them here.
Arguments:
embed_config (DSEmbeddingsConfig): Configuration for the embedding module.
engine_config (RaggedInferenceEngineConfig): Configuration for the inference engine.
Returns:
An embedding module implementing the given configuration.
"""
# Currently, we only have one implementation, so we just return it.
config = ConfigBundle(name="ragged_embedding", config=embed_config)
return DSEmbeddingRegistry.instantiate_config(config)
def instantiate_linear(linear_config: DSLinearConfig, engine_config: RaggedInferenceEngineConfig) -> DSLinearBase:
"""
Choose an appropriate linear implementation based on the given configurations. This
method is currently a stub, but as more implementations may be developed we can centralize
the logic for choosing between them here.
Arguments:
linear_config (DSLinearConfig): Configuration for the linear module.
engine_config (RaggedInferenceEngineConfig): Configuration for the inference engine.
Returns:
A linear module implementing the given configuration.
"""
quantization_mode = engine_config.quantization.quantization_mode
if quantization_mode is None:
config = ConfigBundle(name="blas_fp_linear", config=linear_config)
else:
# Currently, we only support ``quantized_wf6af16_linear`` on NVIDIA Ampere GPUs.
if quantization_mode == "wf6af16":
import torch
if not torch.cuda.is_available(): #ignore-cuda
raise ValueError("WF6AF16 quantization is only supported on CUDA")
else:
is_rocm_pytorch = hasattr(torch.version, 'hip') and torch.version.hip is not None
if is_rocm_pytorch:
raise ValueError("WF6AF16 quantization is only supported on NVIDIA GPUs")
elif torch.cuda.get_device_properties(0).major != 8: #ignore-cuda
raise ValueError("WF6AF16 quantization is only supported on Ampere architectures")
config = ConfigBundle(name="quantized_wf6af16_linear", config=linear_config)
else:
raise ValueError(f"Unsupported quantization mode: {quantization_mode}")
return DSLinearRegistry.instantiate_config(config)
def instantiate_moe(moe_config: DSMoEConfig, engine_config: RaggedInferenceEngineConfig) -> DSMoEBase:
"""
Choose an appropriate MoE implementation based on the given configurations. This
method is currently a stub, but as more implementations may be developed we can centralize
the logic for choosing between them here.
Arguments:
moe_config (DSMoEConfig): Configuration for the MoE module.
engine_config (RaggedInferenceEngineConfig): Configuration for the inference engine.
Returns:
A MoE module implementing the given configuration.
"""
moe_type = "cutlass_multi_gemm_moe"
if moe_type == "cutlass_multi_gemm_moe":
# TODO: Get this off an engine config
implementation_config = {
"weight_dtype": moe_config.input_dtype,
}
# Currently, we only have one implementation, so we just return it.
config = ConfigBundle(name="cutlass_multi_gemm_moe",
config=moe_config,
implementation_config=implementation_config)
return DSMoERegistry.instantiate_config(config)
def instantiate_post_norm(norm_config: DSNormConfig, engine_config: RaggedInferenceEngineConfig) -> DSPostNormBase:
"""
Choose an appropriate post-norm implementation based on the given configurations. This
method is currently a stub, but as more implementations may be developed we can centralize
the logic for choosing between them here.
Arguments:
norm_config (DSNormConfig): Configuration for the post-norm module.
engine_config (RaggedInferenceEngineConfig): Configuration for the inference engine.
Returns:
A post-norm module implementing the given configuration.
"""
# Currently, we only have one implementation, so we just return it.
config = ConfigBundle(name="cuda_post_ln", config=norm_config)
return DSPostNormRegistry.instantiate_config(config)
def instantiate_pre_norm(norm_config: DSNormConfig, engine_config: RaggedInferenceEngineConfig) -> DSPreNormBase:
"""
Choose an appropriate pre-norm implementation based on the given configurations. Currently,
this will select between two CUDA implementations, one for LayerNorm and one for RMSNorm.
Arguments:
norm_config (DSNormConfig): Configuration for the pre-norm module.
engine_config (RaggedInferenceEngineConfig): Configuration for the inference engine.
Returns:
A pre-norm module implementing the given configuration.
"""
if NormTypeEnum(norm_config.type) == NormTypeEnum.LayerNorm:
module_name = "cuda_pre_ln"
elif NormTypeEnum(norm_config.type) == NormTypeEnum.RMSNorm:
module_name = "cuda_pre_rms"
config = ConfigBundle(name=module_name, config=norm_config)
return DSPreNormRegistry.instantiate_config(config)
def instantiate_unembed(unembed_config: DSUnembedConfig, engine_config: RaggedInferenceEngineConfig) -> DSUnembedBase:
"""
Choose an appropriate unembedding implementation based on the given configurations. This
method is currently a stub, but as more implementations may be developed we can centralize
the logic for choosing between them here.
Arguments:
unembed_config (DSUnembedConfig): Configuration for the unembed module.
engine_config (RaggedInferenceEngineConfig): Configuration for the inference engine.
Returns:
An unembed module implementing the given configuration.
"""
# Currently, we only have one implementation, so we just return it.
config = ConfigBundle(name="ragged_unembed", config=unembed_config)
return DSUnembedRegistry.instantiate_config(config)
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