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fix(api): move model conversion messages to trace level

This commit is contained in:
Sean Sube 2023-03-16 19:37:25 -05:00
parent c397c1e42a
commit 9f0a6f134e
Signed by: ssube
GPG Key ID: 3EED7B957D362AF1
2 changed files with 21 additions and 21 deletions
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32
api/onnx_web/convert/diffusion/lora.py
View File

@ -96,7 +96,7 @@ def blend_loras(
try: try:
if len(up_weight.size()) == 2: if len(up_weight.size()) == 2:
# blend for nn.Linear # blend for nn.Linear
logger.debug( logger.trace(
"blending weights for Linear node: %s, %s, %s", "blending weights for Linear node: %s, %s, %s",
down_weight.shape, down_weight.shape,
up_weight.shape, up_weight.shape,
@ -106,7 +106,7 @@ def blend_loras(
np_weights = weights.numpy() * (alpha / dim) np_weights = weights.numpy() * (alpha / dim)
elif len(up_weight.size()) == 4 and up_weight.shape[-2:] == (1, 1): elif len(up_weight.size()) == 4 and up_weight.shape[-2:] == (1, 1):
# blend for nn.Conv2d 1x1 # blend for nn.Conv2d 1x1
logger.debug( logger.trace(
"blending weights for Conv 1x1 node: %s, %s, %s", "blending weights for Conv 1x1 node: %s, %s, %s",
down_weight.shape, down_weight.shape,
up_weight.shape, up_weight.shape,
@ -123,7 +123,7 @@ def blend_loras(
np_weights = weights.numpy() * (alpha / dim) np_weights = weights.numpy() * (alpha / dim)
elif len(up_weight.size()) == 4 and up_weight.shape[-2:] == (3, 3): elif len(up_weight.size()) == 4 and up_weight.shape[-2:] == (3, 3):
# blend for nn.Conv2d 3x3 # blend for nn.Conv2d 3x3
logger.debug( logger.trace(
"blending weights for Conv 3x3 node: %s, %s, %s", "blending weights for Conv 3x3 node: %s, %s, %s",
down_weight.shape, down_weight.shape,
up_weight.shape, up_weight.shape,
@ -150,7 +150,7 @@ def blend_loras(
except Exception: except Exception:
logger.exception("error blending weights for key %s", base_key) logger.exception("error blending weights for key %s", base_key)
logger.info( logger.debug(
"updating %s of %s initializers: %s", "updating %s of %s initializers: %s",
len(blended.keys()), len(blended.keys()),
len(base_model.graph.initializer), len(base_model.graph.initializer),
@ -169,7 +169,7 @@ def blend_loras(
conv_key = base_key + "_Conv" conv_key = base_key + "_Conv"
matmul_key = base_key + "_MatMul" matmul_key = base_key + "_MatMul"
logger.debug( logger.trace(
"key %s has conv: %s, matmul: %s", "key %s has conv: %s, matmul: %s",
base_key, base_key,
conv_key in fixed_node_names, conv_key in fixed_node_names,
@ -179,20 +179,19 @@ def blend_loras(
if conv_key in fixed_node_names: if conv_key in fixed_node_names:
conv_idx = fixed_node_names.index(conv_key) conv_idx = fixed_node_names.index(conv_key)
conv_node = base_model.graph.node[conv_idx] conv_node = base_model.graph.node[conv_idx]
logger.debug("found conv node: %s", conv_node.name) logger.trace("found conv node %s using %s", conv_node.name, conv_node.input)
# find weight initializer # find weight initializer
logger.debug("conv inputs: %s", conv_node.input)
weight_name = [n for n in conv_node.input if ".weight" in n][0] weight_name = [n for n in conv_node.input if ".weight" in n][0]
weight_name = fix_initializer_name(weight_name) weight_name = fix_initializer_name(weight_name)
weight_idx = fixed_initializer_names.index(weight_name) weight_idx = fixed_initializer_names.index(weight_name)
weight_node = base_model.graph.initializer[weight_idx] weight_node = base_model.graph.initializer[weight_idx]
logger.debug("found weight initializer: %s", weight_node.name) logger.trace("found weight initializer: %s", weight_node.name)
# blending # blending
base_weights = numpy_helper.to_array(weight_node) base_weights = numpy_helper.to_array(weight_node)
logger.debug( logger.trace(
"found blended weights for conv: %s, %s", "found blended weights for conv: %s, %s",
weights.shape, weights.shape,
base_weights.shape, base_weights.shape,
@ -200,7 +199,7 @@ def blend_loras(
blended = base_weights.squeeze((3, 2)) + weights.squeeze((3, 2)) blended = base_weights.squeeze((3, 2)) + weights.squeeze((3, 2))
blended = np.expand_dims(blended, (2, 3)) blended = np.expand_dims(blended, (2, 3))
logger.debug("blended weight shape: %s", blended.shape) logger.trace("blended weight shape: %s", blended.shape)
# replace the original initializer # replace the original initializer
updated_node = numpy_helper.from_array(blended, weight_node.name) updated_node = numpy_helper.from_array(blended, weight_node.name)
@ -209,26 +208,27 @@ def blend_loras(
elif matmul_key in fixed_node_names: elif matmul_key in fixed_node_names:
weight_idx = fixed_node_names.index(matmul_key) weight_idx = fixed_node_names.index(matmul_key)
weight_node = base_model.graph.node[weight_idx] weight_node = base_model.graph.node[weight_idx]
logger.debug("found matmul node: %s", weight_node.name) logger.trace(
"found matmul node %s using %s", weight_node.name, weight_node.input
)
# find the MatMul initializer # find the MatMul initializer
logger.debug("matmul inputs: %s", weight_node.input)
matmul_name = [n for n in weight_node.input if "MatMul" in n][0] matmul_name = [n for n in weight_node.input if "MatMul" in n][0]
matmul_idx = fixed_initializer_names.index(matmul_name) matmul_idx = fixed_initializer_names.index(matmul_name)
matmul_node = base_model.graph.initializer[matmul_idx] matmul_node = base_model.graph.initializer[matmul_idx]
logger.debug("found matmul initializer: %s", matmul_node.name) logger.trace("found matmul initializer: %s", matmul_node.name)
# blending # blending
base_weights = numpy_helper.to_array(matmul_node) base_weights = numpy_helper.to_array(matmul_node)
logger.debug( logger.trace(
"found blended weights for matmul: %s, %s", "found blended weights for matmul: %s, %s",
weights.shape, weights.shape,
base_weights.shape, base_weights.shape,
) )
blended = base_weights + weights.transpose() blended = base_weights + weights.transpose()
logger.debug("blended weight shape: %s", blended.shape) logger.trace("blended weight shape: %s", blended.shape)
# replace the original initializer # replace the original initializer
updated_node = numpy_helper.from_array(blended, matmul_node.name) updated_node = numpy_helper.from_array(blended, matmul_node.name)
@ -237,7 +237,7 @@ def blend_loras(
else: else:
logger.warning("could not find any nodes for %s", base_key) logger.warning("could not find any nodes for %s", base_key)
logger.info( logger.debug(
"node counts: %s -> %s, %s -> %s", "node counts: %s -> %s, %s -> %s",
len(fixed_initializer_names), len(fixed_initializer_names),
len(base_model.graph.initializer), len(base_model.graph.initializer),

10
api/onnx_web/convert/diffusion/textual_inversion.py
View File

@ -63,7 +63,7 @@ def blend_textual_inversions(
trained_embeds = string_to_param[trained_token] trained_embeds = string_to_param[trained_token]
num_tokens = trained_embeds.shape[0] num_tokens = trained_embeds.shape[0]
logger.debug("generating %s layer tokens", num_tokens) logger.debug("generating %s layer tokens for %s", num_tokens, name)
for i in range(num_tokens): for i in range(num_tokens):
token = f"{base_token or name}-{i}" token = f"{base_token or name}-{i}"
@ -77,7 +77,7 @@ def blend_textual_inversions(
raise ValueError(f"unknown Textual Inversion format: {format}") raise ValueError(f"unknown Textual Inversion format: {format}")
# add the tokens to the tokenizer # add the tokens to the tokenizer
logger.info( logger.debug(
"found embeddings for %s tokens: %s", len(embeds.keys()), embeds.keys() "found embeddings for %s tokens: %s", len(embeds.keys()), embeds.keys()
) )
num_added_tokens = tokenizer.add_tokens(list(embeds.keys())) num_added_tokens = tokenizer.add_tokens(list(embeds.keys()))
@ -86,7 +86,7 @@ def blend_textual_inversions(
f"The tokenizer already contains the token {token}. Please pass a different `token` that is not already in the tokenizer." f"The tokenizer already contains the token {token}. Please pass a different `token` that is not already in the tokenizer."
) )
logger.debug("added %s tokens", num_added_tokens) logger.trace("added %s tokens", num_added_tokens)
# resize the token embeddings # resize the token embeddings
# text_encoder.resize_token_embeddings(len(tokenizer)) # text_encoder.resize_token_embeddings(len(tokenizer))
@ -103,7 +103,7 @@ def blend_textual_inversions(
for token, weights in embeds.items(): for token, weights in embeds.items():
token_id = tokenizer.convert_tokens_to_ids(token) token_id = tokenizer.convert_tokens_to_ids(token)
logger.debug("embedding %s weights for token %s", weights.shape, token) logger.trace("embedding %s weights for token %s", weights.shape, token)
embedding_weights[token_id] = weights embedding_weights[token_id] = weights
# replace embedding_node # replace embedding_node
@ -115,7 +115,7 @@ def blend_textual_inversions(
new_initializer = numpy_helper.from_array( new_initializer = numpy_helper.from_array(
embedding_weights.astype(np.float32), embedding_node.name embedding_weights.astype(np.float32), embedding_node.name
) )
logger.debug("new initializer data type: %s", new_initializer.data_type) logger.trace("new initializer data type: %s", new_initializer.data_type)
del text_encoder.graph.initializer[i] del text_encoder.graph.initializer[i]
text_encoder.graph.initializer.insert(i, new_initializer) text_encoder.graph.initializer.insert(i, new_initializer)