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Refactor produce_autoencoder_example to unpack encoded_X and fix cuda method calling #81

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Refactor produce_autoencoder_example to unpack encoded_X and fix cuda method calling #81

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39 changes: 34 additions & 5 deletions tmu/clause_bank/clause_bank_cuda.py
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Original file line number Diff line number Diff line change
Expand Up @@ -474,18 +474,47 @@ def prepare_X_autoencoder(self, X_csr, X_csc, active_output):
)

def produce_autoencoder_example(self, encoded_X, target, accumulation):
target_value = self.rng_gen.choice(2)
target_value = np.random.choice(2)
self._ensure_context()
try:
(
active_output_gpu,
active_output,
number_of_active_outputs,
X_csr_indptr_gpu,
X_csr_indices_gpu,
number_of_rows,
X_csc_indptr_gpu,
X_csc_indices_gpu,
number_of_columns,
X_gpu
) = encoded_X
self.produce_autoencoder_examples_gpu.prepared_call(
self.coordinator.grid,
self.coordinator.block,
self.rng_gen.state,
*encoded_X,
active_output_gpu,
number_of_active_outputs,
X_csr_indptr_gpu,
X_csr_indices_gpu,
number_of_rows,
X_csc_indptr_gpu,
X_csc_indices_gpu,
number_of_columns,
X_gpu,
int(target),
int(target_value),
int(accumulation)
)
int(accumulation))

# self.produce_autoencoder_examples_gpu.prepared_call(
# self.coordinator.grid,
# self.coordinator.block,
# self.rng_gen.state,
# *encoded_X,
# int(target),
# int(target_value),
# int(accumulation)
# )
self.cuda_ctx.synchronize()
X_gpu = encoded_X[-1]
return X_gpu, target_value
Expand Down Expand Up @@ -619,7 +648,7 @@ def prepare_X_autoencoder(self, X_csr, X_csc, active_output):
else:
return None

def produce_autoencoder_example(self, encoded_X, target, accumulation):
def produce_autoencoder_example(self, encoded_X, target, accumulation, **kwargs):
if self.device:
return self.device.produce_autoencoder_example(encoded_X, target, accumulation)
else:
Expand Down