Class: Cohere::Transcribe::DenseConverter

Inherits:
Object
  • Object
show all
Defined in:
lib/cohere/transcribe/dense_converter.rb

Overview

Converts a native Transformers Cohere ASR dense checkpoint to the GGUF tensor contract consumed by CrispASR's Cohere backend.

The tensor naming contract is derived from CrispASR, MIT-licensed by the ggml authors. See licenses/crispasr.txt for the retained license notice.

Defined Under Namespace

Classes: Error, Mapping, Plan, PlannedTensor, Result

Constant Summary collapse

FRONTEND_SOURCE_PATTERN = 
/\A(?:model\.)?preprocessor\.featurizer\.(?:fb|window)\z/
COUNTER_SOURCE_PATTERN = 
/\A(?:model\.)?encoder\.layers\.\d+\.conv\.batch_norm\.num_batches_tracked\z/
REQUIRED_PROMPT_TOKENS = 
%w[
  ▁ <|startofcontext|> <|startoftranscript|> <|emo:undefined|>
  <|ar|> <|pnc|> <|noitn|> <|notimestamp|> <|nodiarize|> <|endoftext|>
].freeze
OUTPUT_TYPES = 
%i[f16 f32 bf16].freeze
REQUIRED_ARTIFACT_FILENAMES = 
%w[config.json tokenizer.json].freeze
WEIGHT_ARTIFACT_FILENAMES = 
%w[
  model.safetensors
  model.safetensors.index.json
  pytorch_model.bin
  pytorch_model.bin.index.json
].freeze
SOURCE_ARTIFACT_FILENAMES = 
(
  REQUIRED_ARTIFACT_FILENAMES + WEIGHT_ARTIFACT_FILENAMES
).freeze
SOURCE_ARTIFACT_REQUIREMENTS = 
{
  required: REQUIRED_ARTIFACT_FILENAMES,
  one_weight_file: WEIGHT_ARTIFACT_FILENAMES
}.freeze

Instance Attribute Summary collapse

Class Method Summary collapse

Instance Method Summary collapse

Constructor Details

#initialize(model_directory, output_path:, output_type: :f16, dtype_converter: Safetensors::DTypeConverter.default, chunk_bytes: Safetensors::DEFAULT_CHUNK_BYTES, progress: nil) ⇒ DenseConverter

Returns a new instance of DenseConverter.

Raises:

  • (ArgumentError) 


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# File 'lib/cohere/transcribe/dense_converter.rb', line 64

def initialize(model_directory, output_path:, output_type: :f16,
               dtype_converter: Safetensors::DTypeConverter.default,
               chunk_bytes: Safetensors::DEFAULT_CHUNK_BYTES, progress: nil)
  @model_directory = Pathname(model_directory).expand_path
  @output_path = Pathname(output_path).expand_path
  @output_type = output_type.to_sym
  @dtype_converter = dtype_converter
  @chunk_bytes = Integer(chunk_bytes)
  @progress = progress
  unless OUTPUT_TYPES.include?(@output_type)
    raise ArgumentError,
          "Unsupported dense GGUF output type #{@output_type.inspect}"
  end
  raise ArgumentError, "chunk_bytes must be positive" unless @chunk_bytes.positive?
  raise ArgumentError, "progress must respond to call" if progress && !progress.respond_to?(:call)
end

Instance Attribute Details

#chunk_bytes ⇒ Object (readonly)

Returns the value of attribute chunk_bytes.



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# File 'lib/cohere/transcribe/dense_converter.rb', line 47

def chunk_bytes
  @chunk_bytes
end

#dtype_converter ⇒ Object (readonly)

Returns the value of attribute dtype_converter.



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# File 'lib/cohere/transcribe/dense_converter.rb', line 47

def dtype_converter
  @dtype_converter
end

#model_directory ⇒ Object (readonly)

Returns the value of attribute model_directory.



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# File 'lib/cohere/transcribe/dense_converter.rb', line 47

def model_directory
  @model_directory
end

#output_path ⇒ Object (readonly)

Returns the value of attribute output_path.



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# File 'lib/cohere/transcribe/dense_converter.rb', line 47

def output_path
  @output_path
end

#output_type ⇒ Object (readonly)

Returns the value of attribute output_type.



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# File 'lib/cohere/transcribe/dense_converter.rb', line 47

def output_type
  @output_type
end

#progress ⇒ Object (readonly)

Returns the value of attribute progress.



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# File 'lib/cohere/transcribe/dense_converter.rb', line 47

def progress
  @progress
end

Class Method Details

.convert(model_dir:, output_path:, overwrite: false, fsync: true) ⇒ Object

Stable integration entry point for model stores and CLI adapters.



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# File 'lib/cohere/transcribe/dense_converter.rb', line 50

def self.convert(model_dir:, output_path:, overwrite: false, fsync: true, **)
  new(model_dir, output_path: output_path, **).convert(overwrite: overwrite, fsync: fsync)
end

.required_source_artifacts ⇒ Object 



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# File 'lib/cohere/transcribe/dense_converter.rb', line 60

def self.required_source_artifacts
  SOURCE_ARTIFACT_REQUIREMENTS
end

.source_artifact_filenames ⇒ Object

Every filename a downloader may need to request. The safetensors file and its sharded index are alternatives; see required_source_artifacts.



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# File 'lib/cohere/transcribe/dense_converter.rb', line 56

def self.source_artifact_filenames
  SOURCE_ARTIFACT_FILENAMES
end

.tensor_mappings(encoder_layers:, decoder_layers:) ⇒ Object 



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# File 'lib/cohere/transcribe/dense_converter.rb', line 101

def self.tensor_mappings(encoder_layers:, decoder_layers:)
  mappings = []
  add_pair = lambda do |output, source, weight_precision = :f16|
    mappings << Mapping.new(output_name: "#{output}.weight", source_name: "#{source}.weight",
                            precision: weight_precision)
    mappings << Mapping.new(output_name: "#{output}.bias", source_name: "#{source}.bias", precision: :f32)
  end

  [0, 2, 3, 5, 6].each do |index|
    add_pair.call("enc.pre.conv.#{index}", "encoder.pre_encode.conv.#{index}")
  end
  add_pair.call("enc.pre.out", "encoder.pre_encode.out")

  encoder_layers.times do |index|
    source = "encoder.layers.#{index}"
    output = "enc.blk.#{index}"
    add_pair.call("#{output}.ff1.norm", "#{source}.norm_feed_forward1", :f32)
    add_pair.call("#{output}.ff1.up", "#{source}.feed_forward1.linear1")
    add_pair.call("#{output}.ff1.down", "#{source}.feed_forward1.linear2")
    add_pair.call("#{output}.attn.norm", "#{source}.norm_self_att", :f32)
    add_pair.call("#{output}.attn.q", "#{source}.self_attn.linear_q")
    add_pair.call("#{output}.attn.k", "#{source}.self_attn.linear_k")
    add_pair.call("#{output}.attn.v", "#{source}.self_attn.linear_v")
    add_pair.call("#{output}.attn.out", "#{source}.self_attn.linear_out")
    mappings << Mapping.new(output_name: "#{output}.attn.pos.weight",
                            source_name: "#{source}.self_attn.linear_pos.weight", precision: :f16)
    mappings << Mapping.new(output_name: "#{output}.attn.pos_bias_u",
                            source_name: "#{source}.self_attn.pos_bias_u", precision: :f32)
    mappings << Mapping.new(output_name: "#{output}.attn.pos_bias_v",
                            source_name: "#{source}.self_attn.pos_bias_v", precision: :f32)
    add_pair.call("#{output}.conv.norm", "#{source}.norm_conv", :f32)
    add_pair.call("#{output}.conv.pw1", "#{source}.conv.pointwise_conv1")
    add_pair.call("#{output}.conv.dw", "#{source}.conv.depthwise_conv")
    add_pair.call("#{output}.conv.bn", "#{source}.conv.batch_norm", :f32)
    mappings << Mapping.new(output_name: "#{output}.conv.bn.mean",
                            source_name: "#{source}.conv.batch_norm.running_mean", precision: :f32)
    mappings << Mapping.new(output_name: "#{output}.conv.bn.var",
                            source_name: "#{source}.conv.batch_norm.running_var", precision: :f32)
    add_pair.call("#{output}.conv.pw2", "#{source}.conv.pointwise_conv2")
    add_pair.call("#{output}.ff2.norm", "#{source}.norm_feed_forward2", :f32)
    add_pair.call("#{output}.ff2.up", "#{source}.feed_forward2.linear1")
    add_pair.call("#{output}.ff2.down", "#{source}.feed_forward2.linear2")
    add_pair.call("#{output}.out_norm", "#{source}.norm_out", :f32)
  end

  add_pair.call("enc.proj", "encoder_decoder_proj")
  mappings << Mapping.new(output_name: "dec.emb.weight",
                          source_name: "transf_decoder._embedding.token_embedding.weight", precision: :f16)
  mappings << Mapping.new(output_name: "dec.pos.weight",
                          source_name: "transf_decoder._embedding.position_embedding.pos_enc", precision: :f16)
  add_pair.call("dec.emb_ln", "transf_decoder._embedding.layer_norm", :f32)

  decoder_layers.times do |index|
    source = "transf_decoder._decoder.layers.#{index}"
    output = "dec.blk.#{index}"
    add_pair.call("#{output}.attn_ln", "#{source}.layer_norm_1", :f32)
    add_pair.call("#{output}.attn_q", "#{source}.first_sub_layer.query_net")
    add_pair.call("#{output}.attn_k", "#{source}.first_sub_layer.key_net")
    add_pair.call("#{output}.attn_v", "#{source}.first_sub_layer.value_net")
    add_pair.call("#{output}.attn_o", "#{source}.first_sub_layer.out_projection")
    add_pair.call("#{output}.cross_ln", "#{source}.layer_norm_2", :f32)
    add_pair.call("#{output}.cross_q", "#{source}.second_sub_layer.query_net")
    add_pair.call("#{output}.cross_k", "#{source}.second_sub_layer.key_net")
    add_pair.call("#{output}.cross_v", "#{source}.second_sub_layer.value_net")
    add_pair.call("#{output}.cross_o", "#{source}.second_sub_layer.out_projection")
    add_pair.call("#{output}.ffn_ln", "#{source}.layer_norm_3", :f32)
    add_pair.call("#{output}.ffn_up", "#{source}.third_sub_layer.dense_in")
    add_pair.call("#{output}.ffn_down", "#{source}.third_sub_layer.dense_out")
  end

  add_pair.call("dec.out_ln", "transf_decoder._decoder.final_layer_norm", :f32)
  add_pair.call("dec.head", "log_softmax.mlp.layer0")
  mappings.freeze
end

Instance Method Details

#convert(overwrite: false, fsync: true) ⇒ Object 



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# File 'lib/cohere/transcribe/dense_converter.rb', line 81

def convert(overwrite: false, fsync: true)
  conversion_plan = plan
  writer = build_writer(conversion_plan)
  written_path = writer.write(output_path, overwrite: overwrite, fsync: fsync)
  Result.new(
    path: written_path,
    tensor_count: conversion_plan.tensors.length,
    source_dtype_counts: conversion_plan.source_dtype_counts,
    output_dtype_counts: conversion_plan.output_dtype_counts
  )
rescue Safetensors::Error, PyTorchCheckpoint::Error, GGUF::Error => e
  raise Error, e.message
end

#plan ⇒ Object 



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# File 'lib/cohere/transcribe/dense_converter.rb', line 95

def plan
  @plan ||= build_plan
rescue Safetensors::Error, PyTorchCheckpoint::Error => e
  raise Error, e.message
end