Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Tests: remove cuda versions when the result is the same 🧹🧹 #31955

Merged
merged 1 commit into from
Jul 16, 2024
Merged

Tests: remove cuda versions when the result is the same 🧹🧹 #31955

Show file tree
Hide file tree
Changes from all commits
Commits
Show all changes
1 commit
Select commit
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
123 changes: 29 additions & 94 deletions tests/models/gemma/test_modeling_gemma.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -566,24 +566,10 @@ def test_model_2b_fp16(self):
def test_model_2b_bf16(self):
model_id = "google/gemma-2b"

# Key 9 for MI300, Key 8 for A100/A10, and Key 7 for T4.
#
# Note: Key 9 is currently set for MI300, but may need potential future adjustments for H100s,
# considering differences in hardware processing and potential deviations in generated text.
EXPECTED_TEXTS = {
7: [
"Hello I am doing a project on the 1990s and I need to know what the most popular music",
"Hi today I am going to share with you a very easy and simple recipe of <strong><em>Khichdi",
],
8: [
"Hello I am doing a project on the 1990s and I need to know what the most popular music",
"Hi today I am going to share with you a very easy and simple recipe of <strong><em>Kaju Kat",
],
9: [
"Hello I am doing a project on the 1990s and I need to know what the most popular music",
"Hi today I am going to share with you a very easy and simple recipe of <strong><em>Kaju Kat",
],
}
EXPECTED_TEXTS = [
"Hello I am doing a project on the 1990s and I need to know what the most popular music",
"Hi today I am going to share with you a very easy and simple recipe of <strong><em>Khichdi",
]

model = AutoModelForCausalLM.from_pretrained(model_id, low_cpu_mem_usage=True, torch_dtype=torch.bfloat16).to(
torch_device
Expand All @@ -595,30 +581,16 @@ def test_model_2b_bf16(self):
output = model.generate(**inputs, max_new_tokens=20, do_sample=False)
output_text = tokenizer.batch_decode(output, skip_special_tokens=True)

self.assertEqual(output_text, EXPECTED_TEXTS[self.cuda_compute_capability_major_version])
self.assertEqual(output_text, EXPECTED_TEXTS)

@require_read_token
def test_model_2b_eager(self):
model_id = "google/gemma-2b"

# Key 9 for MI300, Key 8 for A100/A10, and Key 7 for T4.
#
# Note: Key 9 is currently set for MI300, but may need potential future adjustments for H100s,
# considering differences in hardware processing and potential deviations in generated text.
EXPECTED_TEXTS = {
7: [
"Hello I am doing a project on the 1990s and I need to know what the most popular music",
"Hi today I am going to share with you a very easy and simple recipe of <strong><em>Khichdi",
],
8: [
"Hello I am doing a project on the 1990s and I need to know what the most popular music",
"Hi today I am going to share with you a very easy and simple recipe of <strong><em>Kaju Kat",
],
9: [
"Hello I am doing a project on the 1990s and I need to know what the most popular music",
"Hi today I am going to share with you a very easy and simple recipe of <strong><em>Kaju Kat",
],
}
EXPECTED_TEXTS = [
"Hello I am doing a project on the 1990s and I need to know what the most popular music",
"Hi today I am going to share with you a very easy and simple recipe of <strong><em>Khichdi",
]

model = AutoModelForCausalLM.from_pretrained(
model_id, low_cpu_mem_usage=True, torch_dtype=torch.bfloat16, attn_implementation="eager"
Expand All @@ -631,31 +603,17 @@ def test_model_2b_eager(self):
output = model.generate(**inputs, max_new_tokens=20, do_sample=False)
output_text = tokenizer.batch_decode(output, skip_special_tokens=True)

self.assertEqual(output_text, EXPECTED_TEXTS[self.cuda_compute_capability_major_version])
self.assertEqual(output_text, EXPECTED_TEXTS)

@require_torch_sdpa
@require_read_token
def test_model_2b_sdpa(self):
model_id = "google/gemma-2b"

# Key 9 for MI300, Key 8 for A100/A10, and Key 7 for T4.
#
# Note: Key 9 is currently set for MI300, but may need potential future adjustments for H100s,
# considering differences in hardware processing and potential deviations in generated text.
EXPECTED_TEXTS = {
7: [
"Hello I am doing a project on the 1990s and I need to know what the most popular music",
"Hi today I am going to share with you a very easy and simple recipe of <strong><em>Khichdi",
],
8: [
"Hello I am doing a project on the 1990s and I need to know what the most popular music",
"Hi today I am going to share with you a very easy and simple recipe of <strong><em>Kaju Kat",
],
9: [
"Hello I am doing a project on the 1990s and I need to know what the most popular music",
"Hi today I am going to share with you a very easy and simple recipe of <strong><em>Kaju Kat",
],
}
EXPECTED_TEXTS = [
"Hello I am doing a project on the 1990s and I need to know what the most popular music",
"Hi today I am going to share with you a very easy and simple recipe of <strong><em>Khichdi",
]

model = AutoModelForCausalLM.from_pretrained(
model_id, low_cpu_mem_usage=True, torch_dtype=torch.bfloat16, attn_implementation="sdpa"
Expand All @@ -668,7 +626,7 @@ def test_model_2b_sdpa(self):
output = model.generate(**inputs, max_new_tokens=20, do_sample=False)
output_text = tokenizer.batch_decode(output, skip_special_tokens=True)

self.assertEqual(output_text, EXPECTED_TEXTS[self.cuda_compute_capability_major_version])
self.assertEqual(output_text, EXPECTED_TEXTS)

@pytest.mark.flash_attn_test
@require_flash_attn
Expand Down Expand Up @@ -734,7 +692,7 @@ def test_model_7b_fp32(self):
@require_read_token
def test_model_7b_fp16(self):
if self.cuda_compute_capability_major_version == 7:
self.skipTest("This test is failing (`torch.compile` fails) on Nvidia T4 GPU.")
self.skipTest("This test is failing (`torch.compile` fails) on Nvidia T4 GPU (OOM).")

model_id = "google/gemma-7b"
EXPECTED_TEXTS = [
Expand All @@ -757,7 +715,7 @@ def test_model_7b_fp16(self):
@require_read_token
def test_model_7b_bf16(self):
if self.cuda_compute_capability_major_version == 7:
self.skipTest("This test is failing (`torch.compile` fails) on Nvidia T4 GPU.")
self.skipTest("This test is failing (`torch.compile` fails) on Nvidia T4 GPU (OOM).")

model_id = "google/gemma-7b"

Expand Down Expand Up @@ -795,7 +753,7 @@ def test_model_7b_bf16(self):
@require_read_token
def test_model_7b_fp16_static_cache(self):
if self.cuda_compute_capability_major_version == 7:
self.skipTest("This test is failing (`torch.compile` fails) on Nvidia T4 GPU.")
self.skipTest("This test is failing (`torch.compile` fails) on Nvidia T4 GPU (OOM).")

model_id = "google/gemma-7b"
EXPECTED_TEXTS = [
Expand All @@ -821,16 +779,10 @@ def test_model_7b_fp16_static_cache(self):
@require_read_token
def test_model_7b_4bit(self):
model_id = "google/gemma-7b"
EXPECTED_TEXTS = {
7: [
"Hello I am doing a project for my school and I am trying to make a program that will take a number and then",
"Hi today I am going to talk about the best way to get rid of acne. miniaturing is a very",
],
8: [
"Hello I am doing a project for my school and I am trying to make a program that will take a number and then",
"Hi today I am going to talk about the best way to get rid of acne. miniaturing is a very",
],
}
EXPECTED_TEXTS = [
"Hello I am doing a project for my school and I am trying to make a program that will take a number and then",
"Hi today I am going to talk about the best way to get rid of acne. miniaturing is a very",
]

model = AutoModelForCausalLM.from_pretrained(model_id, low_cpu_mem_usage=True, load_in_4bit=True)

Expand All @@ -839,7 +791,7 @@ def test_model_7b_4bit(self):

output = model.generate(**inputs, max_new_tokens=20, do_sample=False)
output_text = tokenizer.batch_decode(output, skip_special_tokens=True)
self.assertEqual(output_text, EXPECTED_TEXTS[self.cuda_compute_capability_major_version])
self.assertEqual(output_text, EXPECTED_TEXTS)

@slow
@require_torch_gpu
Expand All @@ -851,27 +803,10 @@ def test_compile_static_cache(self):
self.skipTest(reason="This test requires torch >= 2.3 to run.")

NUM_TOKENS_TO_GENERATE = 40
# Note on `EXPECTED_TEXT_COMPLETION`'s diff: the current value matches the original test if the original test
# was changed to have a cache of 53 tokens (as opposed to 4096), on Ampere GPUs.
Comment on lines -854 to -855
Copy link
Member Author

Choose a reason for hiding this comment

The reason will be displayed to describe this comment to others. Learn more.

This comment was originally written by me in the equivalent llama test. It was probably copied around -- it does not apply here :)

#
# Key 9 for MI300, Key 8 for A100/A10, and Key 7 for T4.
#
# Note: Key 9 is currently set for MI300, but may need potential future adjustments for H100s,
# considering differences in hardware processing and potential deviations in generated text.
EXPECTED_TEXT_COMPLETION = {
8: [
"Hello I am doing a project on the 1990s and I need to know what the most popular music was in the 1990s. I have looked on the internet and I have found",
"Hi today\nI have a problem with my 2007 1.9 tdi 105bhp.\nI have a problem with the engine management light on.\nI have checked the",
],
7: [
"Hello I am doing a project on the 1990s and I need to know what the most popular music was in the 1990s. I have looked on the internet and I have found",
"Hi today\nI have a problem with my 2007 1.9 tdi 105bhp.\nI have a problem with the engine management light on.\nI have checked the",
],
9: [
"Hello I am doing a project on the 1990s and I need to know what the most popular music was in the 1990s. I have looked on the internet and I have found",
"Hi today\nI have a problem with my 2007 1.9 tdi 105bhp.\nI have a problem with the engine management light on.\nI have checked the",
],
}
EXPECTED_TEXT_COMPLETION = [
"Hello I am doing a project on the 1990s and I need to know what the most popular music was in the 1990s. I have looked on the internet and I have found",
"Hi today\nI have a problem with my 2007 1.9 tdi 105bhp.\nI have a problem with the engine management light on.\nI have checked the",
]

prompts = ["Hello I am doing", "Hi today"]
tokenizer = AutoTokenizer.from_pretrained("google/gemma-2b", pad_token="</s>", padding_side="right")
Expand All @@ -888,15 +823,15 @@ def test_compile_static_cache(self):
**inputs, max_new_tokens=NUM_TOKENS_TO_GENERATE, do_sample=False, cache_implementation="static"
)
static_text = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)
self.assertEqual(EXPECTED_TEXT_COMPLETION[self.cuda_compute_capability_major_version], static_text)
self.assertEqual(EXPECTED_TEXT_COMPLETION, static_text)

# Static Cache + compile
model.forward = torch.compile(model.forward, mode="reduce-overhead", fullgraph=True)
generated_ids = model.generate(
**inputs, max_new_tokens=NUM_TOKENS_TO_GENERATE, do_sample=False, cache_implementation="static"
)
static_compiled_text = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)
self.assertEqual(EXPECTED_TEXT_COMPLETION[self.cuda_compute_capability_major_version], static_compiled_text)
self.assertEqual(EXPECTED_TEXT_COMPLETION, static_compiled_text)

def test_model_2b_bf16_dola(self):
model_id = "google/gemma-2b"
Expand Down
41 changes: 10 additions & 31 deletions tests/models/llama/test_modeling_llama.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -738,32 +738,13 @@ def test_compile_static_cache(self):
NUM_TOKENS_TO_GENERATE = 40
# Note on `EXPECTED_TEXT_COMPLETION`'s diff: the current value matches the original test if the original test
# was changed to have a cache of 53 tokens (as opposed to 4096), on Ampere GPUs.
#
# Key 9 for MI300, Key 8 for A100/A10, and Key 7 for T4.
#
# Note: Key 9 is currently set for MI300, but may need potential future adjustments for H100s,
# considering differences in hardware processing and potential deviations in generated text.
EXPECTED_TEXT_COMPLETION = {
8: [
"Simply put, the theory of relativity states that 1) the speed of light is constant in all inertial "
"reference frames, and 2) the laws of physics are the same for all inertial reference frames.\nThe "
"theory of relativ",
"My favorite all time favorite condiment is ketchup. I love it on everything. I love it on my eggs, "
"my fries, my chicken, my burgers, my hot dogs, my sandwiches, my salads, my p",
],
7: [
"Simply put, the theory of relativity states that 1) the speed of light is constant in all inertial reference frames, and 2) the laws of physics are the same for all inertial reference frames.\nThe theory of relativ",
"My favorite all time favorite condiment is ketchup. I love it on everything. I love it on my eggs, my fries, my chicken, my burgers, my hot dogs, my sandwiches, my salads, my p",
],
9: [
"Simply put, the theory of relativity states that 1) the speed of light is constant in all inertial"
" reference frames, and 2) the laws of physics are the same for all inertial reference frames.\nThe "
"theory of relativ",
"My favorite all time favorite condiment is ketchup. I love it on everything. I love it on my eggs,"
" my fries, my chicken, my burgers, my hot dogs, my sandwiches, my salads, my p",
],
}
expected_text_completion_idx = 8
EXPECTED_TEXT_COMPLETION = [
"Simply put, the theory of relativity states that 1) the speed of light is constant in all inertial "
"reference frames, and 2) the laws of physics are the same for all inertial reference frames.\nThe "
"theory of relativ",
"My favorite all time favorite condiment is ketchup. I love it on everything. I love it on my eggs, "
"my fries, my chicken, my burgers, my hot dogs, my sandwiches, my salads, my p",
]

prompts = [
"Simply put, the theory of relativity states that ",
Expand All @@ -778,24 +759,22 @@ def test_compile_static_cache(self):
# Dynamic Cache
generated_ids = model.generate(**inputs, max_new_tokens=NUM_TOKENS_TO_GENERATE, do_sample=False)
dynamic_text = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)
self.assertEqual(
EXPECTED_TEXT_COMPLETION[expected_text_completion_idx], dynamic_text
) # Both GPU architectures have the same output
self.assertEqual(EXPECTED_TEXT_COMPLETION, dynamic_text)

# Static Cache
generated_ids = model.generate(
**inputs, max_new_tokens=NUM_TOKENS_TO_GENERATE, do_sample=False, cache_implementation="static"
)
static_text = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)
self.assertEqual(EXPECTED_TEXT_COMPLETION[self.cuda_compute_capability_major_version], static_text)
self.assertEqual(EXPECTED_TEXT_COMPLETION, static_text)

# Static Cache + compile
model.forward = torch.compile(model.forward, mode="reduce-overhead", fullgraph=True)
generated_ids = model.generate(
**inputs, max_new_tokens=NUM_TOKENS_TO_GENERATE, do_sample=False, cache_implementation="static"
)
static_compiled_text = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)
self.assertEqual(EXPECTED_TEXT_COMPLETION[self.cuda_compute_capability_major_version], static_compiled_text)
self.assertEqual(EXPECTED_TEXT_COMPLETION, static_compiled_text)


@slow
Expand Down
Loading
Loading