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merged 8 commits into from
Jun 21, 2024
Merged

077 autoquant gpt fast #361

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22d5574
fixing peak memory stats for benchmark
HDCharles Jun 12, 2024
d472855
Autoquantization work for benchmarks
HDCharles Jun 13, 2024
9717087
updating api name and improving docstrings
HDCharles Jun 14, 2024
8f1ba0a
oops missed a few manual_do_autoquant -> manual
HDCharles Jun 14, 2024
f45d9d8
fix forward_log_only
HDCharles Jun 14, 2024
8b29cd5
improving test conditions
HDCharles Jun 18, 2024
b3d9816
fixing nits
HDCharles Jun 19, 2024
c16593e
final tests and change do_autoquant to finalize_autoquant
HDCharles Jun 20, 2024
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51 changes: 39 additions & 12 deletions test/integration/test_integration.py
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Original file line number Diff line number Diff line change
Expand Up @@ -1183,8 +1183,6 @@ def test_autoquant_one_input(self, device, dtype, m, k, n):
]))
@unittest.skipIf(not TORCH_VERSION_AFTER_2_3, "autoquant requires 2.3+.")
def test_autoquant_compile(self, device, dtype, m1, m2, k, n):
if device != "cuda" and dtype != torch.bfloat16:
self.skipTest(f"autoquant currently does not support {device}")
if device != "cuda" or not torch.cuda.is_available():
self.skipTest(f"autoquant currently does not support {device}")
if torch.cuda.is_available() and torch.cuda.get_device_capability() < (8, 0):
Expand All @@ -1198,17 +1196,53 @@ def test_autoquant_compile(self, device, dtype, m1, m2, k, n):
torch.nn.ReLU(),
).to(device).to(dtype)
example_input = torch.randn(m1, k, device=device, dtype=dtype)
example_input2 = torch.randn(m1, k, device=device, dtype=dtype)
example_input2 = torch.randn(m2, k, device=device, dtype=dtype)
out = model(example_input)

mod = torchao.autoquant(torch.compile(model))
mod.forward_log_only(example_input)
mod = torchao.autoquant(torch.compile(model), manual=True)
mod(example_input)
mod(example_input2)
mod.finalize_autoquant()

out2 = mod(example_input)
sqnr = SQNR(out, out2)
self.assertTrue(sqnr >= 30)

@parameterized.expand(COMMON_DEVICE_DTYPE)
@unittest.skipIf(not TORCH_VERSION_AFTER_2_3, "autoquant requires 2.3+.")
def test_autoquant_manual(self, device, dtype):
if device != "cuda" or not torch.cuda.is_available():
self.skipTest(f"autoquant currently does not support {device}")
if torch.cuda.is_available() and torch.cuda.get_device_capability() < (8, 0):
if dtype == torch.bfloat16:
self.skipTest(f"bfloat16 requires sm80+")
m1, m2, k, n = 16, 32, 128, 128
model = torch.nn.Sequential(
torch.nn.ReLU(),
torch.nn.Linear(k,n),
torch.nn.ReLU(),
).to(device).to(dtype)
example_input = torch.randn(m1, k, device=device, dtype=dtype)
example_input2 = torch.randn(m2, k, device=device, dtype=dtype)
out = model(example_input)

mod = torchao.autoquant(torch.compile(model), manual=True)
mod(example_input)
mod(example_input2)
mod.finalize_autoquant()
out2 = mod(example_input)
sqnr = SQNR(out, out2)
self.assertTrue(sqnr >= 30)

mod2 = torchao.autoquant(model, manual=True)
mod2(example_input)
mod2(example_input2)
mod2.finalize_autoquant()
out3 = mod(example_input)
sqnr2 = SQNR(out, out3)
self.assertTrue(sqnr2 >= 30)


@parameterized.expand(combine_parameters(COMMON_DEVICE_DTYPE,
[
(1, 1, 128, 128),
Expand All @@ -1217,8 +1251,6 @@ def test_autoquant_compile(self, device, dtype, m1, m2, k, n):
]))
@unittest.skipIf(not TORCH_VERSION_AFTER_2_3, "autoquant requires 2.3+.")
def test_autoquant_kwargs(self, device, dtype, m1, m2, k, n):
if device != "cuda" and dtype != torch.bfloat16:
self.skipTest(f"autoquant currently does not support {device}")
if device != "cuda" or not torch.cuda.is_available():
self.skipTest(f"autoquant currently does not support {device}")
if torch.cuda.is_available() and torch.cuda.get_device_capability() < (8, 0):
Expand Down Expand Up @@ -1246,7 +1278,6 @@ def forward(self, x, y):
out = model(**example_input)

mod = torchao.autoquant(torch.compile(model))
mod.forward_log_only(**example_input)
mod(**example_input)

out2 = mod(**example_input)
Expand All @@ -1259,8 +1290,6 @@ def forward(self, x, y):
]))
@unittest.skipIf(not TORCH_VERSION_AFTER_2_3, "autoquant requires 2.3+.")
def test_autoquant_double_access(self, device, dtype, m, k, n):
if device != "cuda" and dtype != torch.bfloat16:
self.skipTest(f"autoquant currently does not support {device}")
if device != "cuda" or not torch.cuda.is_available():
self.skipTest(f"autoquant currently does not support {device}")
if torch.cuda.is_available() and torch.cuda.get_device_capability() < (8, 0):
Expand Down Expand Up @@ -1410,9 +1439,7 @@ def test_get_model_size_autoquant(self, device, dtype):
)
qtensor_class_list = (
AQWeightOnlyQuantizedLinearWeight2,

)

mod = torchao.autoquant(torch.compile(model), qtensor_class_list = qtensor_class_list)
mod(example_input)
size2 = torchao.utils.get_model_size_in_bytes(mod)
Expand Down
35 changes: 19 additions & 16 deletions torchao/_models/llama/benchmark_results.txt
View file
Open in desktop
Original file line number Diff line number Diff line change
@@ -1,16 +1,19 @@
20240611210704, tok/s= 29.44, mem/s= 883.80 GB/s, peak_mem=32.34 GB, model_size=30.02 GB quant: None, mod: Meta-Llama-3-8B, compile: False, compile_prefill: False, dtype: torch.float32, device: cuda repro: python generate.py --checkpoint_path ../../../../gpt-fast/checkpoints/meta-llama/Meta-Llama-3-8B/model.pth --device cuda --precision torch.float32 --num_samples 5 --max_new_tokens 200 --top_k 200 --temperature 0.8
20240611210907, tok/s= 26.22, mem/s= 393.56 GB/s, peak_mem=16.16 GB, model_size=15.01 GB quant: None, mod: Meta-Llama-3-8B, compile: False, compile_prefill: False, dtype: torch.bfloat16, device: cuda repro: python generate.py --checkpoint_path ../../../../gpt-fast/checkpoints/meta-llama/Meta-Llama-3-8B/model.pth --device cuda --precision torch.bfloat16 --num_samples 5 --max_new_tokens 200 --top_k 200 --temperature 0.8
20240611211226, tok/s= 94.57, mem/s=1419.48 GB/s, peak_mem=16.43 GB, model_size=15.01 GB quant: None, mod: Meta-Llama-3-8B, compile: True, compile_prefill: False, dtype: torch.bfloat16, device: cuda repro: python generate.py --checkpoint_path ../../../../gpt-fast/checkpoints/meta-llama/Meta-Llama-3-8B/model.pth --device cuda --precision torch.bfloat16 --compile --num_samples 5 --max_new_tokens 200 --top_k 200 --temperature 0.8
20240611215300, tok/s= 95.57, mem/s=1434.47 GB/s, peak_mem=16.43 GB, model_size=15.01 GB quant: None, mod: Meta-Llama-3-8B, compile: True, compile_prefill: True, dtype: torch.bfloat16, device: cuda repro: python generate.py --checkpoint_path ../../../../gpt-fast/checkpoints/meta-llama/Meta-Llama-3-8B/model.pth --device cuda --precision torch.bfloat16 --compile --compile_prefill --num_samples 5 --max_new_tokens 200 --top_k 200 --temperature 0.8
20240611215559, tok/s=180.13, mem/s= 760.48 GB/s, peak_mem= 6.88 GB, model_size= 4.22 GB quant: int4wo-64, mod: Meta-Llama-3-8B, compile: True, compile_prefill: False, dtype: torch.bfloat16, device: cuda repro: python generate.py --quantization int4wo-64 --checkpoint_path ../../../../gpt-fast/checkpoints/meta-llama/Meta-Llama-3-8B/model.pth --device cuda --precision torch.bfloat16 --compile --num_samples 5 --max_new_tokens 200 --top_k 200 --temperature 0.8
20240611215907, tok/s=139.34, mem/s=1047.90 GB/s, peak_mem=10.42 GB, model_size= 7.52 GB quant: int8wo, mod: Meta-Llama-3-8B, compile: True, compile_prefill: False, dtype: torch.bfloat16, device: cuda repro: python generate.py --quantization int8wo --checkpoint_path ../../../../gpt-fast/checkpoints/meta-llama/Meta-Llama-3-8B/model.pth --device cuda --precision torch.bfloat16 --compile --num_samples 5 --max_new_tokens 200 --top_k 200 --temperature 0.8
20240611220524, tok/s= 8.46, mem/s= 63.59 GB/s, peak_mem= 9.24 GB, model_size= 7.52 GB quant: int8dq, mod: Meta-Llama-3-8B, compile: True, compile_prefill: False, dtype: torch.bfloat16, device: cuda repro: python generate.py --quantization int8dq --checkpoint_path ../../../../gpt-fast/checkpoints/meta-llama/Meta-Llama-3-8B/model.pth --device cuda --precision torch.bfloat16 --compile --num_samples 5 --max_new_tokens 200 --top_k 200 --temperature 0.8
20240611221358, tok/s= 9.26, mem/s= 138.97 GB/s, peak_mem=10.60 GB, model_size=15.01 GB quant: autoquant, mod: Meta-Llama-3-8B, compile: True, compile_prefill: False, dtype: torch.bfloat16, device: cuda repro: python generate.py --quantization autoquant --checkpoint_path ../../../../gpt-fast/checkpoints/meta-llama/Meta-Llama-3-8B/model.pth --device cuda --precision torch.bfloat16 --compile --num_samples 5 --max_new_tokens 200 --top_k 200 --temperature 0.8
20240611221551, tok/s= 30.18, mem/s= 797.58 GB/s, peak_mem=27.23 GB, model_size=26.43 GB quant: None, mod: Llama-2-7b-chat-hf, compile: False, compile_prefill: False, dtype: torch.float32, device: cuda repro: python generate.py --checkpoint_path ../../../../gpt-fast/checkpoints/meta-llama/Llama-2-7b-chat-hf/model.pth --device cuda --precision torch.float32 --num_samples 5 --max_new_tokens 200 --top_k 200 --temperature 0.8
20240611221743, tok/s= 26.09, mem/s= 344.72 GB/s, peak_mem=13.62 GB, model_size=13.21 GB quant: None, mod: Llama-2-7b-chat-hf, compile: False, compile_prefill: False, dtype: torch.bfloat16, device: cuda repro: python generate.py --checkpoint_path ../../../../gpt-fast/checkpoints/meta-llama/Llama-2-7b-chat-hf/model.pth --device cuda --precision torch.bfloat16 --num_samples 5 --max_new_tokens 200 --top_k 200 --temperature 0.8
20240611222048, tok/s=105.41, mem/s=1393.00 GB/s, peak_mem=13.90 GB, model_size=13.21 GB quant: None, mod: Llama-2-7b-chat-hf, compile: True, compile_prefill: False, dtype: torch.bfloat16, device: cuda repro: python generate.py --checkpoint_path ../../../../gpt-fast/checkpoints/meta-llama/Llama-2-7b-chat-hf/model.pth --device cuda --precision torch.bfloat16 --compile --num_samples 5 --max_new_tokens 200 --top_k 200 --temperature 0.8
20240611230037, tok/s=106.78, mem/s=1411.01 GB/s, peak_mem=13.88 GB, model_size=13.21 GB quant: None, mod: Llama-2-7b-chat-hf, compile: True, compile_prefill: True, dtype: torch.bfloat16, device: cuda repro: python generate.py --checkpoint_path ../../../../gpt-fast/checkpoints/meta-llama/Llama-2-7b-chat-hf/model.pth --device cuda --precision torch.bfloat16 --compile --compile_prefill --num_samples 5 --max_new_tokens 200 --top_k 200 --temperature 0.8
20240611230250, tok/s=199.72, mem/s= 746.13 GB/s, peak_mem= 4.75 GB, model_size= 3.74 GB quant: int4wo-64, mod: Llama-2-7b-chat-hf, compile: True, compile_prefill: False, dtype: torch.bfloat16, device: cuda repro: python generate.py --quantization int4wo-64 --checkpoint_path ../../../../gpt-fast/checkpoints/meta-llama/Llama-2-7b-chat-hf/model.pth --device cuda --precision torch.bfloat16 --compile --num_samples 5 --max_new_tokens 200 --top_k 200 --temperature 0.8
20240611230440, tok/s=149.32, mem/s= 988.73 GB/s, peak_mem= 8.95 GB, model_size= 6.62 GB quant: int8wo, mod: Llama-2-7b-chat-hf, compile: True, compile_prefill: False, dtype: torch.bfloat16, device: cuda repro: python generate.py --quantization int8wo --checkpoint_path ../../../../gpt-fast/checkpoints/meta-llama/Llama-2-7b-chat-hf/model.pth --device cuda --precision torch.bfloat16 --compile --num_samples 5 --max_new_tokens 200 --top_k 200 --temperature 0.8
20240611231027, tok/s= 9.35, mem/s= 61.94 GB/s, peak_mem= 8.61 GB, model_size= 6.62 GB quant: int8dq, mod: Llama-2-7b-chat-hf, compile: True, compile_prefill: False, dtype: torch.bfloat16, device: cuda repro: python generate.py --quantization int8dq --checkpoint_path ../../../../gpt-fast/checkpoints/meta-llama/Llama-2-7b-chat-hf/model.pth --device cuda --precision torch.bfloat16 --compile --num_samples 5 --max_new_tokens 200 --top_k 200 --temperature 0.8
20240611231759, tok/s= 9.56, mem/s= 126.32 GB/s, peak_mem= 8.53 GB, model_size=13.22 GB quant: autoquant, mod: Llama-2-7b-chat-hf, compile: True, compile_prefill: False, dtype: torch.bfloat16, device: cuda repro: python generate.py --quantization autoquant --checkpoint_path ../../../../gpt-fast/checkpoints/meta-llama/Llama-2-7b-chat-hf/model.pth --device cuda --precision torch.bfloat16 --compile --num_samples 5 --max_new_tokens 200 --top_k 200 --temperature 0.8
20240619101342, tok/s= 29.85, mem/s= 788.87 GB/s, peak_mem=27.23 GB, model_size=26.43 GB quant: None, mod: Llama-2-7b-chat-hf, compile: False, compile_prefill: False, dtype: torch.float32, device: cuda repro: python generate.py --checkpoint_path ../../../checkpoints/meta-llama/Llama-2-7b-chat-hf/model.pth --device cuda --precision torch.float32 --num_samples 5 --max_new_tokens 200 --top_k 200 --temperature 0.8
20240619101537, tok/s= 26.38, mem/s= 348.57 GB/s, peak_mem=13.62 GB, model_size=13.21 GB quant: None, mod: Llama-2-7b-chat-hf, compile: False, compile_prefill: False, dtype: torch.bfloat16, device: cuda repro: python generate.py --checkpoint_path ../../../checkpoints/meta-llama/Llama-2-7b-chat-hf/model.pth --device cuda --precision torch.bfloat16 --num_samples 5 --max_new_tokens 200 --top_k 200 --temperature 0.8
20240619105331, tok/s=106.55, mem/s=1408.06 GB/s, peak_mem=13.88 GB, model_size=13.21 GB quant: None, mod: Llama-2-7b-chat-hf, compile: True, compile_prefill: True, dtype: torch.bfloat16, device: cuda repro: python generate.py --checkpoint_path ../../../checkpoints/meta-llama/Llama-2-7b-chat-hf/model.pth --device cuda --precision torch.bfloat16 --compile --compile_prefill --num_samples 5 --max_new_tokens 200 --top_k 200 --temperature 0.8

20240619105522, tok/s=105.14, mem/s=1389.35 GB/s, peak_mem=13.88 GB, model_size=13.21 GB quant: None, mod: Llama-2-7b-chat-hf, compile: True, compile_prefill: False, dtype: torch.bfloat16, device: cuda repro: python generate.py --checkpoint_path ../../../checkpoints/meta-llama/Llama-2-7b-chat-hf/model.pth --device cuda --precision torch.bfloat16 --compile --num_samples 5 --max_new_tokens 200 --top_k 200 --temperature 0.8
20240619105921, tok/s= 9.20, mem/s= 60.93 GB/s, peak_mem= 8.33 GB, model_size= 6.62 GB quant: int8dq, mod: Llama-2-7b-chat-hf, compile: True, compile_prefill: False, dtype: torch.bfloat16, device: cuda repro: python generate.py --quantization int8dq --checkpoint_path ../../../checkpoints/meta-llama/Llama-2-7b-chat-hf/model.pth --device cuda --precision torch.bfloat16 --compile --num_samples 5 --max_new_tokens 200 --top_k 200 --temperature 0.8
20240619110107, tok/s=150.18, mem/s= 994.40 GB/s, peak_mem= 8.95 GB, model_size= 6.62 GB quant: int8wo, mod: Llama-2-7b-chat-hf, compile: True, compile_prefill: False, dtype: torch.bfloat16, device: cuda repro: python generate.py --quantization int8wo --checkpoint_path ../../../checkpoints/meta-llama/Llama-2-7b-chat-hf/model.pth --device cuda --precision torch.bfloat16 --compile --num_samples 5 --max_new_tokens 200 --top_k 200 --temperature 0.8
20240619110248, tok/s=199.86, mem/s= 746.66 GB/s, peak_mem= 4.50 GB, model_size= 3.74 GB quant: int4wo-64, mod: Llama-2-7b-chat-hf, compile: True, compile_prefill: False, dtype: torch.bfloat16, device: cuda repro: python generate.py --quantization int4wo-64 --checkpoint_path ../../../checkpoints/meta-llama/Llama-2-7b-chat-hf/model.pth --device cuda --precision torch.bfloat16 --compile --num_samples 5 --max_new_tokens 200 --top_k 200 --temperature 0.8
20240619114518, tok/s=159.22, mem/s=1069.87 GB/s, peak_mem= 8.91 GB, model_size= 6.72 GB quant: autoquant, mod: Llama-2-7b-chat-hf, compile: True, compile_prefill: True, dtype: torch.bfloat16, device: cuda repro: python generate.py --quantization autoquant --checkpoint_path ../../../checkpoints/meta-llama/Llama-2-7b-chat-hf/model.pth --device cuda --precision torch.bfloat16 --compile --compile_prefill --num_samples 5 --max_new_tokens 200 --top_k 200 --temperature 0.8

20240619114732, tok/s= 30.46, mem/s= 914.43 GB/s, peak_mem=32.34 GB, model_size=30.02 GB quant: None, mod: Meta-Llama-3-8B, compile: False, compile_prefill: False, dtype: torch.float32, device: cuda repro: python generate.py --checkpoint_path ../../../checkpoints/meta-llama/Meta-Llama-3-8B/model.pth --device cuda --precision torch.float32 --num_samples 5 --max_new_tokens 200 --top_k 200 --temperature 0.8
20240619114939, tok/s= 26.56, mem/s= 398.65 GB/s, peak_mem=16.16 GB, model_size=15.01 GB quant: None, mod: Meta-Llama-3-8B, compile: False, compile_prefill: False, dtype: torch.bfloat16, device: cuda repro: python generate.py --checkpoint_path ../../../checkpoints/meta-llama/Meta-Llama-3-8B/model.pth --device cuda --precision torch.bfloat16 --num_samples 5 --max_new_tokens 200 --top_k 200 --temperature 0.8
20240619122811, tok/s= 96.09, mem/s=1442.32 GB/s, peak_mem=16.43 GB, model_size=15.01 GB quant: None, mod: Meta-Llama-3-8B, compile: True, compile_prefill: True, dtype: torch.bfloat16, device: cuda repro: python generate.py --checkpoint_path ../../../checkpoints/meta-llama/Meta-Llama-3-8B/model.pth --device cuda --precision torch.bfloat16 --compile --compile_prefill --num_samples 5 --max_new_tokens 200 --top_k 200 --temperature 0.8

20240619123018, tok/s= 94.97, mem/s=1425.55 GB/s, peak_mem=16.43 GB, model_size=15.01 GB quant: None, mod: Meta-Llama-3-8B, compile: True, compile_prefill: False, dtype: torch.bfloat16, device: cuda repro: python generate.py --checkpoint_path ../../../checkpoints/meta-llama/Meta-Llama-3-8B/model.pth --device cuda --precision torch.bfloat16 --compile --num_samples 5 --max_new_tokens 200 --top_k 200 --temperature 0.8
20240619123441, tok/s= 8.44, mem/s= 63.45 GB/s, peak_mem= 8.98 GB, model_size= 7.52 GB quant: int8dq, mod: Meta-Llama-3-8B, compile: True, compile_prefill: False, dtype: torch.bfloat16, device: cuda repro: python generate.py --quantization int8dq --checkpoint_path ../../../checkpoints/meta-llama/Meta-Llama-3-8B/model.pth --device cuda --precision torch.bfloat16 --compile --num_samples 5 --max_new_tokens 200 --top_k 200 --temperature 0.8
20240619123652, tok/s=139.76, mem/s=1051.02 GB/s, peak_mem=10.42 GB, model_size= 7.52 GB quant: int8wo, mod: Meta-Llama-3-8B, compile: True, compile_prefill: False, dtype: torch.bfloat16, device: cuda repro: python generate.py --quantization int8wo --checkpoint_path ../../../checkpoints/meta-llama/Meta-Llama-3-8B/model.pth --device cuda --precision torch.bfloat16 --compile --num_samples 5 --max_new_tokens 200 --top_k 200 --temperature 0.8
20240619123847, tok/s=179.44, mem/s= 757.60 GB/s, peak_mem= 6.62 GB, model_size= 4.22 GB quant: int4wo-64, mod: Meta-Llama-3-8B, compile: True, compile_prefill: False, dtype: torch.bfloat16, device: cuda repro: python generate.py --quantization int4wo-64 --checkpoint_path ../../../checkpoints/meta-llama/Meta-Llama-3-8B/model.pth --device cuda --precision torch.bfloat16 --compile --num_samples 5 --max_new_tokens 200 --top_k 200 --temperature 0.8
20240619131959, tok/s=137.71, mem/s=1037.74 GB/s, peak_mem=11.08 GB, model_size= 7.54 GB quant: autoquant, mod: Meta-Llama-3-8B, compile: True, compile_prefill: True, dtype: torch.bfloat16, device: cuda repro: python generate.py --quantization autoquant --checkpoint_path ../../../checkpoints/meta-llama/Meta-Llama-3-8B/model.pth --device cuda --precision torch.bfloat16 --compile --compile_prefill --num_samples 5 --max_new_tokens 200 --top_k 200 --temperature 0.8
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