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test (ex/llm): partitioned tests between pytorch versions.
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@nickfraser
nickfraser committed Aug 23, 2024
1 parent ddc3b3a commit f365267
Showing 1 changed file with 91 additions and 21 deletions.
112 changes: 91 additions & 21 deletions tests/brevitas_examples/test_llm.py
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Original file line number Diff line number Diff line change
Expand Up @@ -24,11 +24,11 @@ def ptid2pathname(string):


def allclose(x, y):
return np.allclose(x, y, rtol=1e-04, atol=1e+01, equal_nan=False)
return np.allclose(x, y, rtol=1e-03, atol=1e+01, equal_nan=False)


def allveryclose(x, y):
return np.allclose(x, y, rtol=1e-08, atol=1e+01, equal_nan=False)
return np.allclose(x, y, rtol=1e-04, atol=2e+02, equal_nan=False)


def allexact(x, y):
Expand Down Expand Up @@ -74,11 +74,6 @@ class ModelAndPpl:
float_ppl=None,
supports_fx=True,
),
ModelAndPpl(
name="hf-internal-testing/tiny-random-OPTForCausalLM",
float_ppl=None,
supports_fx=True,
),
ModelAndPpl(
name="hf-internal-testing/tiny-random-MistralForCausalLM",
float_ppl=None,
Expand Down Expand Up @@ -133,7 +128,7 @@ def toggle_run_args(default_run_args, request):


@pytest.mark.llm
@requires_pt_ge('2.4')
@requires_pt_ge('2.2')
def test_small_models_toggle_run_args(caplog, toggle_run_args, small_models_with_ppl):
caplog.set_level(logging.INFO)
args = toggle_run_args
Expand All @@ -145,6 +140,32 @@ def test_small_models_toggle_run_args(caplog, toggle_run_args, small_models_with
float_ppl, quant_ppl, model = main(args)


@pytest.fixture(
scope="session",
params=[
ModelAndPpl(
name="hf-internal-testing/tiny-random-OPTForCausalLM",
float_ppl=None,
supports_fx=True,
),])
def small_models_with_ppl_pt_ge_2_4(request):
yield request.param


@pytest.mark.llm
@requires_pt_ge('2.4')
def test_small_models_toggle_run_args_pt_ge_2_4(
caplog, toggle_run_args, small_models_with_ppl_pt_ge_2_4):
caplog.set_level(logging.INFO)
args = toggle_run_args
args.model = small_models_with_ppl_pt_ge_2_4.name
exp_float_ppl = small_models_with_ppl_pt_ge_2_4.float_ppl
use_fx = requires_fx(args)
if use_fx and not small_models_with_ppl_pt_ge_2_4.supports_fx:
pytest.xfail(f"{small_models_with_ppl.name} does not support FX")
float_ppl, quant_ppl, model = main(args)


@pytest.fixture(
params=[
{
Expand All @@ -158,15 +179,44 @@ def test_small_models_toggle_run_args(caplog, toggle_run_args, small_models_with
"act_equalization": "fx",
"bias_corr": True,
"float_ppl": 33239.5,
"quant_ppl": 33283.75390625},
"quant_ppl": 33283.75390625},])
def acc_args_and_acc(default_run_args, request):
args = default_run_args
run_dict = request.param
float_ppl = run_dict["float_ppl"]
quant_ppl = run_dict["quant_ppl"]
del run_dict["float_ppl"]
del run_dict["quant_ppl"]
args.update(**run_dict)
yield args, float_ppl, quant_ppl


@pytest.mark.llm
@requires_pt_ge('2.2')
def test_small_models_acc(caplog, acc_args_and_acc):
caplog.set_level(logging.INFO)
args, exp_float_ppl, exp_quant_ppl = acc_args_and_acc
float_ppl, quant_ppl, model = main(args)
float_ppl = float_ppl.detach().cpu().numpy()
quant_ppl = quant_ppl.detach().cpu().numpy()
if config.JIT_ENABLED:
assert allclose(exp_float_ppl, float_ppl), f"Expected float PPL {exp_float_ppl}, measured PPL {float_ppl}"
assert allclose(exp_quant_ppl, quant_ppl), f"Expected quant PPL {exp_quant_ppl}, measured PPL {quant_ppl}"
else:
assert allveryclose(exp_float_ppl, float_ppl), f"Expected float PPL {exp_float_ppl}, measured PPL {float_ppl}"
assert allveryclose(exp_quant_ppl, quant_ppl), f"Expected quant PPL {exp_quant_ppl}, measured PPL {quant_ppl}"


@pytest.fixture(
params=[
{
"model": "hf-internal-testing/tiny-random-OPTForCausalLM",
"weight_equalization": True,
"ln_affine_merge": True,
"replace_mha": True,
"float_ppl": 50016.0,
"quant_ppl": 50016.0},])
def acc_args_and_acc(default_run_args, request):
def acc_args_and_acc_pt_ge_2_4(default_run_args, request):
args = default_run_args
run_dict = request.param
float_ppl = run_dict["float_ppl"]
Expand All @@ -179,9 +229,9 @@ def acc_args_and_acc(default_run_args, request):

@pytest.mark.llm
@requires_pt_ge('2.4')
def test_small_models_acc(caplog, acc_args_and_acc):
def test_small_models_acc_pt_ge_2_4(caplog, acc_args_and_acc_pt_ge_2_4):
caplog.set_level(logging.INFO)
args, exp_float_ppl, exp_quant_ppl = acc_args_and_acc
args, exp_float_ppl, exp_quant_ppl = acc_args_and_acc_pt_ge_2_4
float_ppl, quant_ppl, model = main(args)
float_ppl = float_ppl.detach().cpu().numpy()
quant_ppl = quant_ppl.detach().cpu().numpy()
Expand Down Expand Up @@ -278,14 +328,6 @@ def test_small_models_acc(caplog, acc_args_and_acc):
"<class 'brevitas.nn.equalized_layer.EqualizedModule'>",
"model.layers.0.self_attn.q_proj.layer":
"<class 'brevitas.nn.quant_linear.QuantLinear'>",}},
{
"model": "hf-internal-testing/tiny-random-OPTForCausalLM",
"replace_mha": True,
"exp_layer_types": {
"model.decoder.layers.0.self_attn":
"<class 'brevitas_examples.llm.llm_quant.mha_layers.QuantizableOPTAttention'>",
"model.decoder.layers.0.self_attn.mha":
"<class 'brevitas.nn.quant_mha.QuantMultiheadAttention'>",}},
{
"model": "hf-internal-testing/tiny-random-MistralForCausalLM",
"quantize_last_layer": True,
Expand All @@ -301,14 +343,42 @@ def layer_args(default_run_args, request):


@pytest.mark.llm
@requires_pt_ge('2.4')
@requires_pt_ge('2.2')
def test_small_models_quant_layer(caplog, layer_args):
caplog.set_level(logging.INFO)
args, exp_layer_types = layer_args
float_ppl, quant_ppl, model = main(args)
assert_layer_types(model, exp_layer_types)


@pytest.fixture(
params=[
{
"model": "hf-internal-testing/tiny-random-OPTForCausalLM",
"replace_mha": True,
"exp_layer_types": {
"model.decoder.layers.0.self_attn":
"<class 'brevitas_examples.llm.llm_quant.mha_layers.QuantizableOPTAttention'>",
"model.decoder.layers.0.self_attn.mha":
"<class 'brevitas.nn.quant_mha.QuantMultiheadAttention'>",}},])
def layer_args_pt_ge_2_4(default_run_args, request):
args = default_run_args
layer_dict = request.param
exp_layer_types = layer_dict["exp_layer_types"]
del layer_dict["exp_layer_types"]
args.update(**layer_dict)
yield args, exp_layer_types


@pytest.mark.llm
@requires_pt_ge('2.4')
def test_small_models_quant_layer_pt_ge_2_4(caplog, layer_args_pt_ge_2_4):
caplog.set_level(logging.INFO)
args, exp_layer_types = layer_args_pt_ge_2_4
float_ppl, quant_ppl, model = main(args)
assert_layer_types(model, exp_layer_types)


@pytest.fixture(
params=[
{
Expand Down

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